PK!U<<susy_cross_section/__init__.py"""Module to handle CSV-like data of SUSY cross section.""" PK!Ca#susy_cross_section/base/__init__.py"""Table of values with asymmetric uncertainties. `BaseTable` carries data and the table-level annotation `TableInfo`. The `TableInfo` class contains the other three classes as sub-information. ========================= ============================================= `base.table.BaseTable` contains data and `TableInfo` `base.info.TableInfo` has table-wide properties and `ColumnInfo`, `ParameterInfo`, and `ValueInfo` `base.info.ColumnInfo` has properties of each column `base.info.ParameterInfo` annotates a column as a parameter `base.info.ValueInfo` defines a value from columns ========================= ============================================= """ PK!q1M1Msusy_cross_section/base/info.py"""Classes to describe annotations of general-purpose tables. This module provides annotation classes for CSV-like table data. The data is a two-dimensional table and represents functions over a parameter space. Some columns represent parameters and others do values. Each row represents a single data point and corresponding value. Two structural annotations and two semantic annotations are defined. `TableInfo` and `ColumnInfo` are structural, which respectively annotate the whole table and each columns. For semantics, `ParameterInfo` collects the information of parameters, each of which is a column, and `ValueInfo` is for a value. A value may be given by multiple columns if, for example, the value has uncertainties or the value is given by the average of two columns. """ from __future__ import absolute_import, division, print_function # py2 import itertools import json import logging import pathlib # noqa: F401 import sys from typing import Any, List, Mapping, MutableMapping, Optional, 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. Instead of the :typ:`int` identifier `!index`, we use `!name` as the principal identifier for readability. We also annotate a column by `!unit`, which is :typ:`str` that is passed to `Unit()`. Attributes ---------- index : int The zero-based index of column. The columns of a table should have valid `!index`, i.e., no overlap, no gap, and starting from zero. name : str The human-readable and machine-readable name of the column. As it is used as the identifier, it should be unique in one table. unit : str The unit of column, or empty string if the column has no unit. The default value is an empty str ``''``, which means the column has no unit. Internally this is passed to `Unit()`. Note ---- As for now, `!unit` is restricted as a str object, but in future a float should be allowed to describe "x1000" etc. """ 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 @classmethod def from_json(cls, json_obj): # type: (Any)->ColumnInfo """Initialize an instance from valid json data. Parameters ---------- json_obj: Any a valid json object. Returns ------- ColumnInfo Constructed instance. Raises ------ ValueError If :ar:`json_obj` has invalid data. """ try: obj = cls(index=json_obj['index'], name=json_obj['name'], unit=json_obj.get('unit', '')) except (TypeError, AttributeError) as e: logger.error('ColumnInfo.from_json: %s', e) raise ValueError('Invalid data passed to ColumnInfo.from_json: %s') except KeyError as e: logger.error('ColumnInfo.from_json: %s', e) raise ValueError('ColumnInfo data missing: %s', e) for k in json_obj.keys(): if k not in ['index', 'name', 'unit']: logger.warn('Unknown data for ColumnInfo.from_json: %s', k) obj.validate() return obj def to_json(self): # type: ()->MutableMapping[str, Union[str, int]] """Serialize the object to a json data. Returns ------- dict(str, str or int) The json data describing the object. """ json_obj = {'index': self.index, 'name': self.name} # type: MutableMapping[str, Union[str, int]] if self.unit: json_obj['unit'] = self.unit return json_obj def validate(self): # type: ()->None """Validate the content. Raises ------ TypeError If any attributes are invalid type of instance. ValueError If any attributes have invalid content. """ 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 a parameter. A parameter set defines a data point for the functions described by the table. A parameter set has one or more parameters, each of which corresponds to a column of the table. The `!column` attribute has :attr:`ColumnInfo.name` of the column. Since the parameter value is read from an ASCII file, :typ:`float` values might have round-off errors, which might cause grid misalignments in grid- based interpolations. To have the same :typ:`float` expression on the numbers that should be on the same grid, `!granularity` should be provided. Attributes ---------- column: str Name of the column that stores this parameter. granularity: int or float, optional Assumed presicion of the parameter. This is used to round the parameter so that a data point should be exactly on the grid. Internally, a parameter is rounded to:: round(value / granularity) * granularity For example, for a grid ``[10, 20, 30, 50, 70]``, it should be set to 10 (or 5, 1, 0.1, etc.), while for ``[33.3, 50, 90]``, it should be 0.01. """ def __init__(self, column='', granularity=None): # type: (str, float)->None self.column = column # type: str self.granularity = granularity or None # type: Optional[float] @classmethod def from_json(cls, json_obj): # type: (Any)->ParameterInfo """Initialize an instance from valid json data. Parameters ---------- json_obj: Any a valid json object. Returns ------- ParameterInfo Constructed instance. Raises ------ ValueError If :ar:`json_obj` has invalid data. """ try: obj = cls(column=json_obj['column'], granularity=json_obj.get('granularity')) except (TypeError, AttributeError) as e: logger.error('ParameterInfo.from_json: %s', e) raise ValueError('Invalid data passed to ParameterInfo.from_json: %s') except KeyError as e: logger.error('ParameterInfo.from_json: %s', e) raise ValueError('ColumnInfo data missing: %s', e) for k in json_obj.keys(): if k not in ['column', 'granularity']: logger.warn('Unknown data for ParameterInfo.from_json: %s', k) obj.validate() return obj def to_json(self): # type: ()->MutableMapping[str, Union[str, float]] """Serialize the object to a json data. Returns ------- dict(str, str or float) The json data describing the object. """ json_obj = {'column': self.column} # type: MutableMapping[str, Union[str, float]] if self.granularity: json_obj['unit'] = self.granularity return json_obj def validate(self): # type: ()->None """Validate the content. Raises ------ TypeError If any attributes are invalid type of instance. ValueError If any attributes have invalid content. """ 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) class ValueInfo(object): """Stores information of value accompanied by uncertainties. A value is generally composed from several columns. In current implementation, the central value must be given by one column, whose name is specified by :attr:`column`. The positive- and negative-direction uncertainties are specified by `!unc_p` and `!unc_m`, respectively, which are :typ:`dict(str, str)`. Attributes ---------- column: str Name of the column that stores this value. This must be match one of the :attr:`ColumnInfo.name` in the table. unc_p : dict (str, str) The sources of "plus" uncertainties. Multiple uncertainty sources can be specified. Each key corresponds :attr:`ColumnInfo.name` of the source column, and each value denotes the "type" of the source. Currently, two types are implementend: - ``"relative"`` for relative uncertainty, where the unit of the column must be dimension-less. - ``"absolute"`` for absolute uncertainty, where the unit of the column must be the same as that of the value column up to a factor. The unit of the uncertainty column should be consistent with the unit of the value column. unc_m : dict(str, str) The sources of "minus" uncertainties. Details are the same as `!unc_p`. """ _valid_uncertainty_types = ['relative', 'absolute'] # type: List[str] 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.""" 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, v in unc.items(): if not isinstance(k, str): raise TypeError('Value %s: %s has invalid column name: %s', self.column, title, k) if v not in self._valid_uncertainty_types: raise ValueError('Value %s: %s has wrong value: %s', self.column, title, v) @classmethod def from_json(cls, json_obj): # type: (Any)->ValueInfo """Initialize an instance from valid json data. Parameters ---------- json_obj: typing.Any a valid json object. Returns ------- ValueInfo Constructed instance. Raises ------ ValueError If :ar:`json_obj` has invalid data. """ if not isinstance(json_obj, Mapping): raise TypeError('Entry of "values" must be a dict: %s', json_obj) if 'column' not in json_obj: raise KeyError('Entry of "values" must have a key "column": %s', json_obj) obj = cls() obj.column = json_obj['column'] if ('unc' in json_obj) and ('unc+' in json_obj or 'unc-' in json_obj): raise ValueError('Invalid uncertainties (asymmetric and symmetric): %s', obj.column) for attr_name, key_name in [('unc_p', 'unc+'), ('unc_m', 'unc-')]: u = json_obj.get(key_name) or json_obj.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]]]] """Serialize the object to a json data. Returns ------- dict(str, str or float) The json data describing the 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 table-wide annotations for general-purpose table data. A table structure is given by `!columns`, while in semantics a table consists of `!parameters` and `!values`. The information about them is stored as lists of `ColumnInfo`, `ParameterInfo`, and `ValueInfo` objects. In addition, `!reader_options` can be specified, which is directly passed to :func:`pandas.read_csv`. The attribute `!document` is provided just for documentation. The information is guaranteed not to modify any functionality of codes or packages, and thus can be anything. Developers must not use `!document` information except for displaying them. If one needs to interpret some information, one should extend this class to provide other data-storage for such information, as is done in `CrossSectionInfo` class. Attributes ---------- document : dict(Any, Any) Any information for documentation without physical meanings. meanings. columns : list of ColumnInfo The list of columns. parameters: list of ParameterInfo The list of parameters to define a data point. values: list of ValueInfo The list of values described in the table. reader_options: dict(str, Any) Options to read the CSV The values are directly passed to :func:`pandas.read_csv` as keyword arguments, so all the options of :func:`pandas.read_csv` are available. """ def __init__(self, document=None, # type: Mapping[Any, Any] columns=None, # type: List[ColumnInfo] parameters=None, # type: List[ParameterInfo] values=None, # type: List[ValueInfo] reader_options=None, # type: Mapping[str, Any] ): # type: (...)->None self.document = document or {} self.columns = columns or [] self.parameters = parameters or [] self.values = values or [] self.reader_options = reader_options or {} def validate(self): # noqa: C901 # type: ()->None """Validate the content.""" if not isinstance(self.document, MutableMapping): raise TypeError('document must be a dict.') for name in ['columns', 'parameters', 'values']: if not isinstance(getattr(self, name), List): raise TypeError('TableInfo.%s must be a list', name) for obj in getattr(self, name): obj.validate() if not isinstance(self.reader_options, MutableMapping): raise TypeError('reader_options must be a dict(str, Any).') if not all(isinstance(k, str) for k in self.reader_options.keys()): raise TypeError('reader_options must be a dict(str, Any).') # validate columns (`index` matches actual index, names are unique) names_dict = {} # type: MutableMapping[str, bool] for i, column in enumerate(self.columns): 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 # validate params and values for p in self.parameters: if p.column not in names_dict: raise ValueError('Unknown column name: %s', p.column) for v in self.values: for col in itertools.chain([v.column], v.unc_p.keys(), v.unc_m.keys()): if col not in names_dict: raise ValueError('Unknown column name: %s', v.column) @classmethod def load(cls, source): # type: (Union[pathlib.Path, str])->TableInfo """Load and construct TableInfo from a json file. Parameters ---------- source: pathlib.Path or str Path to the json file. Returns ------- TableInfo Constructed instance. """ obj = cls() with open(source.__str__()) as f: # py2 obj._load(**(json.load(f))) obj.validate() return obj def _load(self, **kw): # type: (Any)->None """Load and construct TableInfo from keyword arguments.""" 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.get('columns') or [])] self.parameters = [ParameterInfo.from_json(p) for p in kw.get('parameters') or []] self.values = [ValueInfo.from_json(p) for p in kw.get('values') or []] self.reader_options = kw.get('reader_options') or {} # emit warnings if not self.document: logger.warning('No document is given.') for key in kw: if key not in ['document', 'columns', 'parameters', 'values', 'reader_options']: logger.warning('Unrecognized attribute "%s"', key) def get_column(self, name): # type: (str)->ColumnInfo """Return a column with specified name. Return `ColumnInfo` of a column with name :ar:`name`. Arguments --------- name The name of column to get. Returns ------- ColumnInfo The column with name :ar:`name`. Raises ------ KeyError If no column is found. """ for c in self.columns: if c.name == name: return c raise KeyError(name) def dump(self): # type: ()->str """Return the formatted string. Returns ------- str Dumped data. """ results = ['[Document]'] for k, v in self.document.items(): results.append(' {}: {}'.format(k, v)) return '\n'.join(results) PK!P,f!f! susy_cross_section/base/table.py"""Tables representing values with asymmetric uncertainties. This module provides a class to handle CSV-like table data representing values with asymmetric uncertainties. Such tables are provided in various format; for example, the uncertainty may be relative or absolute, or with multiple sources. The class :class:`BaseTable` interprets such tables based on `TableInfo` annotations. """ from __future__ import absolute_import, division, print_function # py2 import json import logging import pathlib # noqa: F401 import sys from typing import ( # noqa: F401 Any, List, Mapping, MutableMapping, Optional, Sequence, Union, ) import pandas from susy_cross_section.base.info import TableInfo 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__) JSONDecodeError = Exception if sys.version_info[0] < 3 else json.decoder.JSONDecodeError # py2 class BaseTable(object): """Table data with information. A table object has two main attributes: `!info` (:typ:`TableInfo`) as the annotation and `!data` (:typ:`dict` of :typ:`pandas.DataFrame`) as the data tables. Arguments --------- table_path: str or pathlib.Path Path to the csv data file. info_path: str or pathlib.Path, optional Path to the corresponding info file. If unspecified, `!table_path` with suffix changed to ``".info"`` is used. Attributes ---------- table_path: pathlib.Path Path to the csv data file. info_path: pathlib.Path Path to the info file. raw_data: pandas.DataFrame the content of `!table_path`. info: TableInfo the content of `!info_path`. data: dict(str, pandas.DataFrame) The table parsed according to the annotation. Keys are the name of data, and values are `pandas.DataFrame` objects. Each DataFrame object is indexed according to the parameter specified in `!info` and has exactly three value-columns: ``"value"``, ``"unc+"``, and ``"unc-"``, which stores the central value and positive- and negative- directed **absolute** uncertainty, respectively. The content of ``"unc-"`` is non-positive. units: dict(str, Utility.Unit) The unit of values. Note that ``"value"``, ``"unc+"``, and ``"unc-"`` have the same unit. """ 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 = TableInfo.load(self.info_path) # type: TableInfo self.raw_data = self._read_csv(self.table_path) # type: pandas.DataFrame # contents are filled in _load_data self.data = {} # type: MutableMapping[str, pandas.DataFrame] self.units = {} # type: MutableMapping[str, str] self.info.validate() # validate annotation before actual load self._load_data() self.validate() def _read_csv(self, path): # type: (pathlib.Path)->pandas.DataFrame """Read a csv file and return the content. Internally, call :meth:`pandas.read_csv()` with `!reader_options`. """ reader_options = { 'skiprows': [0], 'names': [c.name for c in self.info.columns], } # default values reader_options.update(self.info.reader_options) return pandas.read_csv(path, **reader_options) def _load_data(self): # type: ()->None """Load and prepare data from the specified paths.""" self.data = {} # type: MutableMapping[str, pandas.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 by the quantized values 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) # define functions to apply to DataFrame to get uncertainty. 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, name=name, unc_sources=value_info.unc_p, factors=unc_p_factors): # type: (Any, str, Mapping[str, str], Mapping[str, float])->float return self._combine_uncertainties(row, name, unc_sources, factors) def unc_m(row, name=name, unc_sources=value_info.unc_m, factors=unc_m_factors): # type: (Any, str, Mapping[str, str], Mapping[str, float])->float return self._combine_uncertainties(row, name, unc_sources, 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 _uncertainty_factors(self, value_unit, uncertainty_info): # type: (Unit, Mapping[str, str])->Mapping[str, float] """Return the factor of uncertainty column relative to value column.""" 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, unc_sources, factors): # type: (Any, str, Mapping[str, str], Mapping[str, float])->float """Return absolute combined uncertainty.""" uncertainties = [] for source_name, source_type in unc_sources.items(): uncertainties.append(row[source_name] * factors[source_name] * ( row[value_name] if source_type == 'relative' else 1 )) return sum(x**2 for x in uncertainties) ** 0.5 def validate(self): # type: ()->None """Validate the Table data.""" for key, data in self.data.items(): duplication = data.index[data.index.duplicated()] for d in duplication: raise ValueError('Found duplicated entries: %s, %s', key, d) if len(duplication) > 5: raise ValueError('Maybe parameter granularity is set too large?') # ------------------ # # accessor functions # # ------------------ # def __getitem__(self, key): # type: (str)->pandas.DataFrame """Return the specied table data. Arguments --------- key: str One of The key of the data to return. Returns ------- pandas.DataFrame One of the data tables specified by :ar:`key`. """ return self.data[key] def dump(self, keys=None): # type: (Optional[List[str]])->str """Return the dumped string of the data tables. Arguments --------- keys: list of str, optional if specified, specified data are only dumped. Returns ------- str Dumped data. """ results = [] # type: List[str] line = '-' * 72 keys_to_show = self.data.keys() if keys is None else keys for k in keys_to_show: results.append(line) results.append('DATA "{}" (unit: {})'.format(k, self.units[k])) results.append(line) results.append(self.data[k].__str__()) # py2 results.append('') return '\n'.join(results) PK!-  susy_cross_section/config.py"""Configuration data of this package.""" 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]]] """ Preset table names and paths to files. A :typ:`dict` object, where the values show the paths to table and info files. Values are a tuple `!(table_file_path, info_file_path)`, or `!table_file_path` if info_file_path is given by replacing the extension of table_file_path to `!.info`. Relative paths are interpreted from this package directory (i.e., the directory having this file). :Type: :typ:`dict[str, str or tuple[str, str]]` """ PK!jjCsusy_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", "pdf_name": "??" }, "columns": [ { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "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!Ͽ@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", "pdf_name": "??" }, "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 }, "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 300 950 0.0381117531516 0.0101339897301 300 1000 0.0245145877474 0.00684204765268 300 1050 0.0159680259241 0.00465696369245 300 1100 0.0104555446567 0.00321442660313 300 1150 0.00688127736389 0.00220972080362 300 1200 0.00454817919795 0.00152166981218 300 1250 0.00302063111747 0.00105609504574 300 1300 0.00201663841197 0.000733343815891 300 1350 0.00135278638424 0.000514201343915 300 1400 0.000909502207156 0.000361978167951 300 1450 0.000611618596727 0.000253948902635 300 1500 0.000412205750476 0.000178121180603 300 1550 0.000278192549323 0.000125313547257 300 1600 0.000187733216408 8.85351745698e-05 300 1650 0.000126847475622 6.22354899505e-05 300 1700 8.54331000852e-05 4.34979126438e-05 300 1750 5.74610984685e-05 3.0423247179e-05 300 1800 3.86354301071e-05 2.12325828507e-05 300 1850 2.59006531294e-05 1.47828222015e-05 300 1900 1.73522244136e-05 1.02740639803e-05 300 1950 1.15796049231e-05 7.09870582878e-06 300 2000 7.70902518442e-06 4.89552682537e-06 350 200 963.540883684 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 0.000249726646442 350 1500 0.000400431689929 0.000175221045695 350 1550 0.00027029448598 0.00012305145385 350 1600 0.0001829102986 8.68522264096e-05 350 1650 0.000123679089663 6.10067093685e-05 350 1700 8.34704580052e-05 4.27803956466e-05 350 1750 5.62334274841e-05 2.99671142188e-05 350 1800 3.78224728507e-05 2.09656499635e-05 350 1850 2.54154211535e-05 1.45950265644e-05 350 1900 1.70499119386e-05 1.01534090421e-05 350 1950 1.13759709237e-05 7.01057779752e-06 350 2000 7.57547248783e-06 4.82969443501e-06 400 200 974.333980673 150.982224328 400 250 285.247418112 44.6215760787 400 300 94.9319775974 13.9027968471 400 350 35.6952763206 4.90429674295 400 400 15.0138974342 2.00186131625 400 450 7.01202768893 1.00091725653 400 500 3.53560764938 0.547460373696 400 550 1.89972629747 0.320877451512 400 600 1.07095617109 0.195984207127 400 650 0.62218015232 0.121191815236 400 700 0.370375354076 0.0766739067921 400 750 0.222981164148 0.0498618846372 400 800 0.136775331029 0.0325326434384 400 850 0.0853409610543 0.0214178902768 400 900 0.0539619382166 0.0142518388626 400 950 0.0344452849022 0.00960418523304 400 1000 0.0222328805078 0.00649729036672 400 1050 0.0144863062399 0.00444938400529 400 1100 0.00953558099203 0.00304028110317 400 1150 0.00630403716802 0.0020938271507 400 1200 0.00418948770761 0.00144810965192 400 1250 0.0028097827178 0.0010104211585 400 1300 0.00188363536961 0.000707473224545 400 1350 0.00126414684545 0.000495346604554 400 1400 0.000850643265809 0.000348524825679 400 1450 0.000573433158139 0.000244991879346 400 1500 0.000387808302568 0.000172519623851 400 1550 0.00026260666412 0.000120874270504 400 1600 0.000177621268025 8.46794901732e-05 400 1650 0.000120146314185 5.95784577935e-05 400 1700 8.12854408085e-05 4.20253039766e-05 400 1750 5.48283972682e-05 2.95124801137e-05 400 1800 3.69596350024e-05 2.0641767308e-05 400 1850 2.48950923157e-05 1.43658083246e-05 400 1900 1.66646442277e-05 9.9660236048e-06 400 1950 1.11687631244e-05 6.923946723e-06 400 2000 7.43364238435e-06 4.7649400682e-06 450 200 983.446412406 153.62566132 450 250 285.947501624 43.8960866921 450 300 97.1244941372 14.7683731029 450 350 36.9259753031 5.34713181747 450 400 15.42722033 2.12970173561 450 450 7.05087585795 1.02047540723 450 500 3.47411877013 0.536445246061 450 550 1.82864397428 0.303409286505 450 600 1.01180150416 0.180965570909 450 650 0.585002645299 0.1122179226 450 700 0.349293637158 0.072246490381 450 750 0.210776924804 0.0472566812983 450 800 0.129590937744 0.0311005078171 450 850 0.0810208420548 0.0206593719387 450 900 0.0512557236743 0.013782909604 450 950 0.0328181164989 0.00928648466399 450 1000 0.0212202459846 0.00631020063106 450 1050 0.0138398133956 0.00428649596549 450 1100 0.00909315746446 0.00295749535093 450 1150 0.00601930425992 0.00204388184831 450 1200 0.00400502682939 0.00141558952276 450 1250 0.00268467018091 0.000987539265691 450 1300 0.00180033365881 0.000691632557362 450 1350 0.00121395738603 0.000483647520146 450 1400 0.000819911727564 0.000340909397355 450 1450 0.000553520911788 0.00023996458926 450 1500 0.000375215031651 0.000168753357843 450 1550 0.000254179805363 0.000119054210796 450 1600 0.000172400185208 8.34721341914e-05 450 1650 0.00011696657527 5.89149348721e-05 450 1700 7.90349275407e-05 4.12834105234e-05 450 1750 5.34243823559e-05 2.89644910426e-05 450 1800 3.60175954696e-05 2.0251833495e-05 450 1850 2.42554768923e-05 1.41319107766e-05 450 1900 1.63056635781e-05 9.82312381593e-06 450 1950 1.09091103436e-05 6.79449196497e-06 450 2000 7.29254050368e-06 4.69803576235e-06 500 200 986.097408278 151.759715982 500 250 283.663714006 42.4073729157 500 300 98.1670122427 14.9917965908 500 350 38.1064674431 5.8099057398 500 400 15.9608097426 2.31250466522 500 450 7.23795928018 1.07817558479 500 500 3.48432077193 0.541383215085 500 550 1.7878585955 0.295538204733 500 600 0.970771539016 0.171645594659 500 650 0.556081285319 0.105383497169 500 700 0.330505257978 0.0678072159954 500 750 0.200205693793 0.0446326307481 500 800 0.123390350868 0.0297636597686 500 850 0.0769914485616 0.0198748094704 500 900 0.0486917009538 0.0133167257701 500 950 0.0311739417296 0.00899556549539 500 1000 0.0201930391817 0.00612365299064 500 1050 0.0131763074498 0.00422102393142 500 1100 0.00867459963134 0.00288403697593 500 1150 0.00575117804258 0.00199191704453 500 1200 0.00383104982792 0.00138291044463 500 1250 0.00256725609134 0.000960988637313 500 1300 0.00172344760726 0.000672797577409 500 1350 0.00116309561017 0.000472311720353 500 1400 0.000788066599145 0.000333057026317 500 1450 0.00053335672995 0.000234521354298 500 1500 0.000361477849728 0.000164903600446 500 1550 0.000245820760423 0.000116218612895 500 1600 0.000167187419997 8.23108477069e-05 500 1650 0.000113241866295 5.76363177213e-05 500 1700 7.67657450763e-05 4.05238006317e-05 500 1750 5.19145733357e-05 2.83959718819e-05 500 1800 3.50919591667e-05 1.98570667827e-05 500 1850 2.36928477607e-05 1.38682152081e-05 500 1900 1.59117486959e-05 9.65616806934e-06 500 1950 1.06912293682e-05 6.71597947709e-06 500 2000 7.13617139466e-06 4.62569149002e-06 550 200 992.898631619 155.725553991 550 250 287.5857555 44.1487656804 550 300 99.2378437021 15.2190185047 550 350 38.5867224194 5.85897177755 550 400 16.2757220686 2.40526148513 550 450 7.42698579741 1.13892707714 550 500 3.56752005707 0.568547568437 550 550 1.80864943324 0.303596860944 550 600 0.960675382646 0.170381016036 550 650 0.539667275478 0.101771261117 550 700 0.315651058824 0.0637370634377 550 750 0.189746990241 0.0421991042614 550 800 0.11713840665 0.0283478442181 550 850 0.0730978432097 0.0190055199407 550 900 0.0462860332519 0.0128491982712 550 950 0.0297237845361 0.00871739146703 550 1000 0.0192565242097 0.00594049904182 550 1050 0.012552548766 0.00410800050541 550 1100 0.00827161781849 0.00281059831468 550 1150 0.00548862341179 0.00194000957906 550 1200 0.00366186853971 0.00134606381208 550 1250 0.00245469152285 0.000941025248402 550 1300 0.00165123125573 0.000658348845256 550 1350 0.00111996135096 0.000465161899465 550 1400 0.000756824714399 0.00032568903966 550 1450 0.00051333449666 0.000229205441734 550 1500 0.000348281936877 0.000161560174177 550 1550 0.00023717251682 0.000114241325438 550 1600 0.000161551280437 8.06150619109e-05 550 1650 0.000109874923704 5.68542262067e-05 550 1700 7.43969835949e-05 3.9732392986e-05 550 1750 5.04320590341e-05 2.78513803938e-05 550 1800 3.41214310234e-05 1.95165588375e-05 550 1850 2.30258645944e-05 1.35998873407e-05 550 1900 1.54623525963e-05 9.43783794516e-06 550 1950 1.04222559675e-05 6.58496007752e-06 550 2000 6.97316579038e-06 4.54598199729e-06 600 200 993.795659678 154.850478705 600 250 292.254108873 46.0191618127 600 300 100.277805102 15.3841673202 600 350 38.8565514132 5.82356626318 600 400 16.4837745995 2.43922222695 600 450 7.58537745975 1.18513194279 600 500 3.67301412056 0.60424558701 600 550 1.84985110183 0.315553678824 600 600 0.969380063235 0.173693990302 600 650 0.532743444749 0.100965982541 600 700 0.30521560634 0.0615286443286 600 750 0.181543041471 0.0404030046539 600 800 0.111692131393 0.0271925642953 600 850 0.0696427767025 0.0182534339236 600 900 0.0441384055169 0.0123424869374 600 950 0.0282411510872 0.00841957059177 600 1000 0.0183120926497 0.00576334230269 600 1050 0.0119586642414 0.00394637688698 600 1100 0.00789249698395 0.00273515938978 600 1150 0.0052377370461 0.00188179727952 600 1200 0.00349946631607 0.00130994974309 600 1250 0.00235615050812 0.000915603831597 600 1300 0.00158944186921 0.000645919350881 600 1350 0.00107209612765 0.000451955607066 600 1400 0.0007265799915 0.000318556730756 600 1450 0.000493357114922 0.000223882242299 600 1500 0.000335697334755 0.000157792918961 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9.27901816646e-06 1650 1900 8.93018925805e-06 6.47157056178e-06 1650 1950 6.05613863968e-06 4.48552262999e-06 1650 2000 4.09958235956e-06 3.10296784084e-06 1700 200 1003.18927524 150.930227429 1700 250 298.729682772 44.8278749281 1700 300 105.223913933 15.6033626287 1700 350 41.9370479505 6.18697707769 1700 400 18.2695284498 2.6884848363 1700 450 8.57035930638 1.30094905001 1700 500 4.2598090158 0.685422482728 1700 550 2.21566335909 0.375679564148 1700 600 1.19205736339 0.213881128598 1700 650 0.663206490055 0.125357302341 1700 700 0.379125286563 0.0752640379042 1700 750 0.219220534007 0.0478372854633 1700 800 0.129151036138 0.0305875145932 1700 850 0.0773195168511 0.0196544540934 1700 900 0.0469000224832 0.0127436792701 1700 950 0.0287747589726 0.00841502239169 1700 1000 0.0178360919459 0.0055783504591 1700 1050 0.0111645291611 0.00373454880259 1700 1100 0.00707695213895 0.00255197257709 1700 1150 0.00449926661262 0.00173437459055 1700 1200 0.0028780699161 0.00118214210388 1700 1250 0.00184945889773 0.00080533916547 1700 1300 0.00119767389768 0.00054868897809 1700 1350 0.000781699543951 0.000378733284402 1700 1400 0.000512640104054 0.000262200162705 1700 1450 0.000337242194679 0.000180783076254 1700 1500 0.000222823563741 0.000124829578468 1700 1550 0.000148138946149 8.65790853851e-05 1700 1600 9.87381051466e-05 6.00914888555e-05 1700 1650 6.54039091578e-05 4.11774461578e-05 1700 1700 4.3365582536e-05 2.81340927238e-05 1700 1750 2.88860019479e-05 1.9331899521e-05 1700 1800 1.93025053646e-05 1.33039122565e-05 1700 1850 1.30487993898e-05 9.24501934333e-06 1700 1900 8.83819643737e-06 6.41997300694e-06 1700 1950 5.98431158807e-06 4.44890610218e-06 1700 2000 4.0447389562e-06 3.07342840737e-06 1750 200 1003.18927524 150.930227429 1750 250 298.065610694 44.1981947319 1750 300 105.174870458 15.5589044734 1750 350 41.8824429367 6.23237206385 1750 400 18.2695284498 2.6884848363 1750 450 8.58546238302 1.30145178465 1750 500 4.27108062549 0.684807492796 1750 550 2.21633298276 0.375863222946 1750 600 1.20200433283 0.214108627422 1750 650 0.665948103605 0.125424950193 1750 700 0.381096284071 0.075343510398 1750 750 0.220655699598 0.0474351884343 1750 800 0.130219919292 0.0306603237881 1750 850 0.0778165400748 0.0196064553101 1750 900 0.0473201765724 0.0128071337971 1750 950 0.0290704688747 0.00844321730056 1750 1000 0.0180343657181 0.00560674915623 1750 1050 0.0113170925827 0.00380340476711 1750 1100 0.00714939131559 0.00255682918627 1750 1150 0.00454483706397 0.00173694259963 1750 1200 0.00290918705131 0.00118642121222 1750 1250 0.00187048377443 0.0008087474244 1750 1300 0.00120891770386 0.00054931782525 1750 1350 0.000790863130028 0.000381093378819 1750 1400 0.00051795559021 0.000263286926801 1750 1450 0.000340441001312 0.000181432776403 1750 1500 0.000224498790532 0.000125811388143 1750 1550 0.000149076788196 8.70060257019e-05 1750 1600 9.95903770461e-05 6.0640287386e-05 1750 1650 6.60872142711e-05 4.15480798866e-05 1750 1700 4.39737033268e-05 2.86071727432e-05 1750 1750 2.91883797378e-05 1.95779755947e-05 1750 1800 1.940775921e-05 1.33944069556e-05 1750 1850 1.30350310032e-05 9.25195878332e-06 1750 1900 8.76707725603e-06 6.3832482376e-06 1750 1950 5.91800792615e-06 4.41670313903e-06 1750 2000 4.00317032046e-06 3.04759589903e-06 1800 200 1003.18927524 150.930227429 1800 250 298.407692409 43.6712610685 1800 300 105.156723832 15.5407578475 1800 350 41.932585898 6.27493802657 1800 400 18.320710093 2.73966647958 1800 450 8.59499236479 1.30176412757 1800 500 4.28096727647 0.685581367891 1800 550 2.22596971368 0.376491702694 1800 600 1.20200433283 0.214108627422 1800 650 0.668906970369 0.125217699456 1800 700 0.382089315288 0.075254707571 1800 750 0.22112831498 0.0470489166926 1800 800 0.130581569161 0.0300957115854 1800 850 0.0784117608346 0.019513096908 1800 900 0.0477133000434 0.0128431133904 1800 950 0.0293283971383 0.00853083636809 1800 1000 0.0181977850562 0.00568355757332 1800 1050 0.0114195316585 0.00381141198108 1800 1100 0.00721958265214 0.00256876064018 1800 1150 0.00459112487412 0.00174770887757 1800 1200 0.00294028162669 0.00119002444648 1800 1250 0.0018932980416 0.000814798208865 1800 1300 0.00122761142405 0.000557326724729 1800 1350 0.000799757083242 0.000383684273257 1800 1400 0.000523759870404 0.000264900178347 1800 1450 0.000344252968862 0.000182733417337 1800 1500 0.000226669296423 0.000126286965216 1800 1550 0.000150086042742 8.73703095358e-05 1800 1600 9.98867918318e-05 6.06222927726e-05 1800 1650 6.67106205888e-05 4.19527998474e-05 1800 1700 4.45110925676e-05 2.89312160615e-05 1800 1750 2.95301903311e-05 1.97687980131e-05 1800 1800 1.96155113518e-05 1.35459646991e-05 1800 1850 1.30611685964e-05 9.27601779843e-06 1800 1900 8.73522947248e-06 6.36956118465e-06 1800 1950 5.8765905981e-06 4.39142268276e-06 1800 2000 3.95953983612e-06 3.02099065941e-06 1850 200 1003.18927524 150.930227429 1850 250 297.907692409 44.1712610685 1850 300 105.156723832 15.5407578475 1850 350 41.9821345738 6.20699115075 1850 400 18.3161996303 2.73515601685 1850 450 8.61089528071 1.29923316804 1850 500 4.28934948042 0.684851776024 1850 550 2.23536831166 0.376883997976 1850 600 1.21265877642 0.215029492747 1850 650 0.67196584277 0.125227781006 1850 700 0.384157369287 0.0752810270503 1850 750 0.223021143183 0.0470262390761 1850 800 0.131527083383 0.0301858716043 1850 850 0.078932343404 0.0196158857092 1850 900 0.048008254484 0.0128651341559 1850 950 0.0295315440922 0.00854988826133 1850 1000 0.0183885321312 0.00569743503949 1850 1050 0.0115247666183 0.00382539930018 1850 1100 0.00728858889862 0.00257881362751 1850 1150 0.00464180939444 0.00175695716839 1850 1200 0.00297606866088 0.00119115609251 1850 1250 0.00191930595502 0.000824008062039 1850 1300 0.00124638945184 0.000564879381308 1850 1350 0.000810742161604 0.000387245815099 1850 1400 0.000529525148871 0.000266692569523 1850 1450 0.000347744233952 0.000183896518541 1850 1500 0.000228732652521 0.00012667630526 1850 1550 0.000151671354059 8.80388467735e-05 1850 1600 0.000100829610219 6.09546605405e-05 1850 1650 6.71199978077e-05 4.21116367138e-05 1850 1700 4.47584949164e-05 2.90254370396e-05 1850 1750 2.98371189798e-05 2.00029338108e-05 1850 1800 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 0.00176186274564 1900 1200 0.00301166205167 0.0012020606066 1900 1250 0.00194927983096 0.00082714953606 1900 1300 0.00126179004965 0.00056935175434 1900 1350 0.000821263619206 0.000390867806888 1900 1400 0.000536296059802 0.000268437143383 1900 1450 0.000351549469533 0.000185184968383 1900 1500 0.00023160268854 0.000127858870514 1900 1550 0.000153598675354 8.87246758754e-05 1900 1600 0.000101791551694 6.12585866567e-05 1900 1650 6.75668577069e-05 4.22113426938e-05 1900 1700 4.49544863526e-05 2.91278930045e-05 1900 1750 3.00784643193e-05 2.01784656323e-05 1900 1800 2.01381209381e-05 1.39421826053e-05 1900 1850 1.33938495391e-05 9.53103479579e-06 1900 1900 8.8643219942e-06 6.4638908069e-06 1900 1950 5.88312386973e-06 4.39903840894e-06 1900 2000 3.91057965576e-06 2.98854000891e-06 1950 200 1003.68112673 150.446555659 1950 250 298.06855623 44.2011402673 1950 300 105.152133976 15.5453477036 1950 350 42.0718648631 6.20999668657 1950 400 18.3546851284 2.68765789846 1950 450 8.63678476315 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!x[O@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", "pdf_name": "??" }, "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 }, "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 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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!jjCsusy_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", "pdf_name": "??" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "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!3 wino0 wino-", "collider": "pp", "ecm": "13TeV", "order": "NLO+NLL", "pdf_name": "CTEQ6.6" }, "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 }, "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!^%?susy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_cteq_p.csvm χ̃ [GeV], xsec [fb], -scale unc [%], -pdf unc [%], +scale unc [%], +pdf unc [%] 100.0, 13927.0, -0.8, -3.2, 0.0, 3.1 125.0, 6248.4, -0.6, -3.3, 0.0, 3.2 150.0, 3264.6, -0.7, -3.4, 0.0, 3.3 175.0, 1880.8, -0.6, -3.6, 0.0, 3.4 200.0, 1161.6, -0.6, -3.8, 0.2, 3.6 225.0, 755.82, -0.5, -4.0, 0.4, 3.7 250.0, 511.85, -0.5, -4.2, 0.4, 3.9 275.0, 357.99, -0.5, -4.4, 0.4, 4.0 300.0, 256.93, -0.5, -4.6, 0.4, 4.2 325.0, 188.55, -0.5, -4.8, 0.4, 4.3 350.0, 140.95, -0.5, -5.0, 0.4, 4.4 375.0, 107.02, -0.4, -5.2, 0.5, 4.6 400.0, 82.39, -0.4, -5.4, 0.4, 4.7 425.0, 64.17, -0.4, -5.6, 0.4, 4.9 450.0, 50.53, -0.4, -5.8, 0.4, 5.0 475.0, 40.14, -0.3, -5.9, 0.4, 5.2 500.0, 32.17, -0.3, -6.1, 0.3, 5.3 525.0, 25.99, -0.4, -6.4, 0.2, 5.4 550.0, 21.11, -0.3, -6.5, 0.1, 5.6 575.0, 17.26, -0.2, -6.6, 0.1, 5.8 600.0, 14.2, -0.3, -7.0, 0.1, 5.8 625.0, 11.74, -0.3, -7.2, 0.0, 6.0 650.0, 9.74, -0.2, -7.1, 0.0, 6.2 675.0, 8.12, -0.2, -7.4, 0.0, 6.3 700.0, 6.79, -0.2, -7.5, 0.0, 6.5 725.0, 5.7, -0.3, -7.6, 0.0, 6.7 750.0, 4.8, -0.4, -7.8, 0.0, 6.9 775.0, 4.06, -0.5, -8.1, 0.0, 7.0 800.0, 3.44, -0.5, -8.3, 0.1, 7.1 825.0, 2.92, -0.6, -8.5, 0.1, 7.3 850.0, 2.49, -0.6, -8.6, 0.1, 7.3 875.0, 2.12, -0.7, -9.2, 0.0, 7.5 900.0, 1.81, -0.4, -8.4, 0.2, 8.3 925.0, 1.55, -0.3, -8.7, 0.4, 8.2 950.0, 1.33, -1.2, -9.9, 0.0, 7.3 975.0, 1.15, -1.4, -10.5, 0.0, 7.2 1000.0, 0.98, -0.6, -9.0, 0.4, 9.2 1025.0, 0.85, -0.9, -9.2, 0.5, 9.4 1050.0, 0.73, -0.9, -8.5, 0.9, 10.0 1075.0, 0.64, -1.6, -10.5, 0.5, 8.6 1100.0, 0.55, -1.2, -11.1, 0.3, 8.7 1125.0, 0.48, -1.0, -10.7, 0.6, 9.9 1150.0, 0.41, -0.7, -11.3, 0.4, 9.1 1175.0, 0.36, -0.1, -8.2, 1.3, 13.2 1200.0, 0.31, -1.0, -11.5, 0.6, 10.2 1225.0, 0.27, -0.7, -11.4, 0.6, 10.7 1250.0, 0.24, -0.8, -11.6, 0.8, 11.0 1275.0, 0.21, -0.9, -12.4, 0.9, 10.9 1300.0, 0.18, -0.8, -12.6, 1.3, 11.2 1325.0, 0.16, -0.9, -12.4, 1.0, 11.8 1350.0, 0.14, -0.9, -12.9, 1.1, 11.8 1375.0, 0.12, -0.6, -12.1, 1.8, 13.1 1400.0, 0.1, -0.2, -11.9, 2.0, 13.5 1425.0, 0.09, -0.5, -12.7, 1.8, 13.8 1450.0, 0.08, -0.5, -12.9, 2.2, 14.5 1475.0, 0.07, -0.9, -15.0, 1.9, 13.4 1500.0, 0.0597115, -1.4, -14.2, 2.6, 15.0 1525.0, 0.0522203, -1.4, -14.8, 2.6, 15.0 1550.0, 0.0455892, -1.5, -15.1, 2.9, 16.4 1575.0, 0.0398661, -1.5, -14.3, 3.0, 16.8 1600.0, 0.0348743, -1.6, -14.8, 1.6, 18.7 1625.0, 0.0302986, -1.7, -13.2, 1.7, 22.1 1650.0, 0.0265033, -1.7, -14.1, 1.7, 21.5 1675.0, 0.0231864, -1.7, -15.6, 1.8, 22.3 1700.0, 0.0202908, -1.8, -18.0, 1.8, 23.1 1725.0, 0.0177606, -2.6, -18.2, 1.9, 22.6 1750.0, 0.0155431, -2.7, -18.9, 2.0, 23.0 1775.0, 0.0136071, -2.7, -19.6, 2.0, 23.7 1800.0, 0.0119185, -2.8, -20.5, 2.8, 24.5 1825.0, 0.0104378, -2.9, -20.7, 3.4, 23.8 1850.0, 0.0091365, -2.9, -21.3, 3.8, 25.4 1875.0, 0.0080001, -2.9, -18.6, 4.1, 26.8 1900.0, 0.00706487, -3.8, -21.5, 3.0, 26.2 1925.0, 0.00622541, -4.4, -20.6, 2.4, 25.9 1950.0, 0.00547282, -4.8, -23.5, 2.4, 25.3 1975.0, 0.00479632, -4.8, -21.1, 2.7, 27.6 2000.0, 0.00419527, -2.9, -20.2, 2.7, 29.6 PK!h@susy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_cteq_p.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": "CTEQ6.6" }, "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 }, "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!/=@susy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_cteq_pm.csvm χ̃ [GeV], xsec [fb], -scale unc [%], -pdf unc [%], +scale unc [%], +pdf unc [%] 100.0, 22504.0, -0.95, -3.3, 0.0, 3.2 125.0, 9936.7, -0.67, -3.4, 0.0, 3.3 150.0, 5118.9, -0.63, -3.5, 0.0, 3.5 175.0, 2912.9, -0.56, -3.7, 0.0, 3.7 200.0, 1779.1, -0.57, -3.9, 0.2, 3.9 225.0, 1145.7, -0.47, -4.2, 0.26, 4.1 250.0, 768.61, -0.47, -4.4, 0.3, 4.3 275.0, 532.81, -0.47, -4.6, 0.37, 4.5 300.0, 379.23, -0.47, -4.8, 0.4, 4.7 325.0, 276.17, -0.44, -5.1, 0.4, 4.8 350.0, 204.91, -0.44, -5.2, 0.37, 5.1 375.0, 154.54, -0.37, -5.4, 0.44, 5.3 400.0, 118.22, -0.37, -5.7, 0.34, 5.4 425.0, 91.52, -0.34, -5.9, 0.31, 5.7 450.0, 71.65, -0.34, -6.1, 0.31, 5.8 475.0, 56.6, -0.27, -6.2, 0.28, 6.1 500.0, 45.12, -0.21, -6.4, 0.21, 6.2 525.0, 36.27, -0.31, -6.7, 0.14, 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> wino+ 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 }, "parameters": [{ "column": "m_wino", "granularity": 1 }], "values": [ { "column": "xsec", "unc": [{ "column": "uncertainty", "type": "absolute" }] } ] } PK!N  =susy_cross_section/data/lhc_susy_xs_wg/13TeVx1x1wino_mstw.csvm χ̃ [GeV], xsec [fb], -scale unc [%], -pdf unc [%], +scale unc [%], +pdf unc [%] 100.0, 11743.0, -1.1, -3.0, 0.0, 3.3 125.0, 5169.4, -0.7, -3.1, 0.0, 3.3 150.0, 2662.6, -0.7, -3.1, 0.0, 3.3 175.0, 1514.2, -0.6, -3.2, 0.0, 3.4 200.0, 923.92, -0.5, -3.3, 0.2, 3.5 225.0, 594.17, -0.5, -3.5, 0.3, 3.6 250.0, 398.07, -0.5, -3.6, 0.3, 3.7 275.0, 275.57, -0.5, -3.7, 0.3, 3.8 300.0, 195.9, -0.4, -3.8, 0.5, 3.9 325.0, 142.37, -0.4, -3.9, 0.6, 4.1 350.0, 105.49, -0.4, -4.0, 0.5, 4.2 375.0, 79.43, -0.4, -4.1, 0.4, 4.3 400.0, 60.62, -0.4, -4.1, 0.4, 4.5 425.0, 46.87, -0.4, -4.4, 0.2, 4.4 450.0, 36.6, -0.4, -4.5, 0.3, 4.6 475.0, 28.86, -0.3, -4.4, 0.2, 4.8 500.0, 22.95, -0.3, -4.5, 0.2, 4.9 525.0, 18.4, -0.1, -4.4, 0.1, 5.1 550.0, 14.85, -0.2, -4.5, 0.0, 5.3 575.0, 12.06, -0.2, -4.5, 0.0, 5.5 600.0, 9.85, -0.2, -4.6, 0.0, 5.7 625.0, 8.1, -0.2, -4.7, 0.0, 5.8 650.0, 6.69, -0.2, -4.9, 0.0, 5.7 675.0, 5.54, -0.4, -4.7, 0.0, 6.1 700.0, 4.61, -0.4, -5.0, 0.0, 6.2 725.0, 3.85, -0.7, -4.9, 0.0, 6.3 750.0, 3.22, -0.7, -4.9, 0.0, 6.8 775.0, 2.71, -0.6, -5.0, 0.0, 7.0 800.0, 2.29, -0.5, -5.3, 0.0, 7.3 825.0, 1.93, -0.7, -5.3, 0.1, 6.9 850.0, 1.64, -0.9, -5.4, 0.1, 7.4 875.0, 1.39, -0.6, -5.6, 0.2, 7.7 900.0, 1.19, -0.7, -5.4, 0.3, 8.3 925.0, 1.01, -1.0, -5.5, 0.4, 8.5 950.0, 0.87, -1.2, -5.7, 0.3, 8.6 975.0, 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!L/->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 }, "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!ݩ||%susy_cross_section/interp/__init__.pyr"""A subpackage to perform interpolation. At the subpackage-level, the following modules and class aliases are defined. =============================== =============================================== module `interp.axes_wrapper` has axis preprocessors for interpolation module `interp.interpolator` has interpolator classes `!interp.Scipy1dInterpolator` = `interp.interpolator.Scipy1dInterpolator` `!interp.ScipyGridInterpolator` = `interp.interpolator.ScipyGridInterpolator` =============================== =============================================== This subpackage contains the following class. Actual interpolators are subclasses of `AbstractInterpolator` and not listed here. ========================================== ==================================== classes ========================================== ==================================== `interp.axes_wrapper.AxesWrapper` axis preprocessor `interp.interpolator.Interpolation` interpolation result `interp.interpolator.AbstractInterpolator` base class for interpolators ========================================== ==================================== Note ---- Interpolation of :math:`N` data points, .. math:: (x_{n1}, \dots, x_{nd}; y_n) for :math:`n=1, ..., N` and :math:`d` is the dimension of parameter space, i.e., the number of parameters, returns a continuous function :math:`f` satisfying :math:`f({\boldsymbol x}_n)=y_n`. Caution ------- One should distinguish an interpolation :math:`f` from fitting functions. An interpolation satisfies :math:`f({\boldsymbol x}_n)=y_n` but this does not necessarily hold for fitting functions. Meanwhile, an interpolation is defined patch by patch, so its first or higher derivative can be discontinuous, while usually a fit function is globally defined and class :math:`C^\infty`. """ from .interpolator import Scipy1dInterpolator, ScipyGridInterpolator # noqa: F401 PK!v<;;)susy_cross_section/interp/axes_wrapper.pyr"""Axis preprocessor for table interpolation. This module provides a class `AxesWrapper` for advanced interpolation. 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. Note ---- Here we summarize interpolations with modified axes. In axis-modification process, we modify the data points .. math:: (x_{n1}, \dots, x_{nd}; y_n), with :math:`d+1` functions :math:`w_1, \dots, w_d` and :math:`w_{\mathrm y}` into .. math:: X_{ni}=w_i(x_{ni}), \qquad Y_{n} = w_{\mathrm y}(y_n) and derive the interpolation function :math:`\bar f` based on :math:`({\boldsymbol X}_n; Y_n)`. Then, the interpolated result is given by .. math:: f({\boldsymbol x}) = w_{\mathrm y}^{-1}\Bigl(\bar f\bigl(w_1(x_1), \dots, w_d(x_d)\bigr)\Bigr). :Type Aliases: .. py:data:: VT :annotation: (= float) Type representing elements of data points. .. py:data:: FT :annotation: (= Callable[[VT], VT]) Type for wrapper functions :m:`w`. .. py:data:: XT :annotation: (= List[VT]) Type for parameters :m:`x`. .. py:data:: YT :annotation: (= VT) Type for the value :m:`y`. .. role:: data_typ(typ) :reftype: data .. |VT| replace:: :data_typ:`VT` .. |FT| replace:: :data_typ:`FT` .. |XT| replace:: :data_typ:`XT` .. |YT| replace:: :data_typ:`YT` """ 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] XT = Sequence[VT] # X-point is always a sequence, even if one-parameter. YT = VT def _is_number(obj): # type: (Any)->bool """Return whether obj is a number (int, float, dim-0 numpy array).""" if isinstance(obj, numpy.ndarray): return bool(obj.ndim == 0 and obj.dtype.kind in 'fiub') else: return isinstance(obj, float) or isinstance(obj, int) def _is_number_sequence(obj, length): # type: (Any, int)->bool """Return whether obj is a sequence of numbers with specified length.""" if isinstance(obj, numpy.ndarray): return obj.shape == (length,) and obj.dtype.kind in 'fiub' try: return len(obj) == length and all(_is_number(i) for i in obj) except TypeError: return False class AxesWrapper: """Toolkit to modify the x- and y- axes before interpolation. In initialization, one can specify wrapper functions predefined, where one can omit :ar:`wy_inv` argument. The following functions are predefined. - "identity" (or "id", "linear") - "log10" (or "log") - "exp10" (or "exp") Attributes ---------- wx: *list of* |FT| Wrapper functions (or names) for parameters x. wy: |FT| Wrapper function for the value y. wy_inv: |FT| The inverse function of :attr:`wy`. """ @staticmethod def identity(x): # type: (VT)->VT """Identity function as a wrapper.""" return x @staticmethod def log10(x): # type: (VT)->VT """Log function (base 10) as a wrapper. Note that this is equivalent to natural-log function as a wrapper. """ return cast(VT, numpy.log10(x)) @staticmethod def exp10(x): # type: (VT)->VT """Exp function (base 10) as a wrapper. Note that this is equivalent to natural-exp function as a wrapper. """ return 10 ** x # we use base 10 because they are equivalent and easier to debug. # keys include aliases, and values are the name of staticmethods. _predefined_function_names = { 'identity': 'identity', 'id': 'identity', 'linear': 'identity', 'log': 'log10', 'log10': 'log10', 'exp': 'exp10', 'exp10': 'exp10', } # type: Mapping[str, str] _inverse_function_names = { 'identity': 'identity', 'log10': 'exp10', 'exp10': 'log10', } # type: Mapping[str, str] @classmethod def _get_function(cls, obj): # type: (Union[FT, str])->FT """Return wrapper function. The argument can be a function itself or a function name. The returned functions are dressed by `numpy.vectorize` so that it can be applied to numpy objects. """ if isinstance(obj, str): name = cls._predefined_function_names.get(obj) if not name: raise KeyError('Function %s is not predefined in AxesWrapper', obj) return cast(FT, numpy.vectorize(getattr(cls, name))) else: return cast(FT, numpy.vectorize(obj)) @classmethod def _get_inverse_function(cls, name): # type: (str)->FT """Return the inverse function with numpy-dress.""" name = cls._inverse_function_names[cls._predefined_function_names[name]] return cast(FT, numpy.vectorize(getattr(cls, 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 elif isinstance(wy, str): self.wy_inv = self._get_inverse_function(wy) # guess wy_inv else: raise TypeError('wy_inv must be specified.') def wrapped_x(self, xs): # type: (XT)->XT r"""Return the parameter values after axes modification. Arguments --------- xs: |XT| Parameters in the original axes Returns ------- XT Parameters in the wrapped axes. Note ---- The argument :ar:`xs` is :math:`(x_1, x_2, \dots, x_d)`, while the returned value is :math:`(X_1, \dots, X_d) = (w_1(x_1), \dots, w_d(x_d))`. """ return [w(x) for w, x in zip(self.wx, xs)] def wrapped_f(self, f_bar, type_check=True): # type: (Callable[[XT], YT], bool)->Callable[[XT], YT] r"""Return interpolating function for original data. Return the interpolating function applicable to the original data set, given the interpolating function in the modified axes. Arguments --------- f_bar: function of |XT| to |YT| The interpolating function in the modified axes. type_check: bool To perform type-check or not. Returns ------- function of XT to YT The interpolating function in the original axes. Note ---- The argument :ar:`f_bar` is :math:`\bar f`, which is the interpolation function for :math:`({\boldsymbol X}_n; Y_n)`, and this method returns the function :math:`f`, which is .. math:: f({\boldsymbol x}) = w_{\mathrm y}^{-1}\bigl(\bar f({\boldsymbol X})\bigr), where :math:`\boldsymbol X` is given by applying :meth:`wrapped_x` to :math:`\boldsymbol x`. """ x_len = len(self.wx) if type_check else None def _f(x, _f_bar=f_bar, _len=x_len): # type: (XT, Callable[[XT], YT], Union[int, None])->YT if _len is not None and not _is_number_sequence(x, _len): raise TypeError('Invalid arguments for %d-dim fit: %s', _len, x) return self.wy_inv(_f_bar(self.wrapped_x(x))) return _f PK!O*u?u?)susy_cross_section/interp/interpolator.pyr"""Interpolators of cross-section data. :Type Aliases: .. py:data:: InterpType :annotation: (= Callable[[Sequence[float]], float]) Type representing an interpolation function. .. role:: data_typ(typ) :reftype: data .. |InterpType| replace:: :data_typ:`InterpType` """ from __future__ import absolute_import, division, print_function # py2 import logging import re import sys from typing import Any, Callable, List, Mapping, Optional, Sequence, Tuple, Union, cast import numpy import pandas # noqa: F401 import scipy.interpolate as sci_interp from susy_cross_section.table import Table from .axes_wrapper import AxesWrapper if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) InterpType = Callable[[Sequence[float]], float] class Interpolation: """An interpolation result for values with uncertainties. This class handles an interpolation of data points, where each data point is given with uncertainties, but does not handle uncertainties due to interpolation. In initialization, the interpolation results :ar:`f0`, :ar:`fp`, and :ar:`fm` should be specified as functions accepting a list of float, i.e., ``f0([x1, ..., xd])`` etc. If the argument :ar:`param_names` is also specified, the attribute :attr:`param_index` is set, which allows users to call the interpolating functions with keyword arguments. Arguments --------- f0: |InterpType| Interpolating function of the central values. fp: |InterpType| Interpolating function of values with positive uncertainty added. fm: |InterpType| Interpolating function of values with negative uncertainty subtracted. param_names: list[str], optional Names of parameters. Attributes ---------- param_index: dict(str, int) Dictionary to look up parameter's position from a parameter name. """ def __init__(self, f0, fp, fm, param_names=None): # type: (InterpType, InterpType, InterpType, List[str])->None self._f0 = f0 self._fp = fp self._fm = fm self.param_index = { name: index for index, name in enumerate(param_names or []) } # type: Mapping[str, int] def _interpret_args(self, *args, **kwargs): # type: (Union[Sequence[float], float], float)->Sequence[float] """Interpret the argument and return a list-like of float. Note that strict type-check is not performed. """ if len(args) == 1: # single list, dim-1 array, or only one argument if isinstance(args[0], numpy.ndarray) and args[0].ndim == 1: xs = cast(List[float], args[0]) elif isinstance(args[0], numpy.ndarray) and args[0].ndim == 0: xs = cast(List[float], [args[0]]) elif hasattr(args[0], '__iter__'): xs = cast(List[float], args[0]) else: xs = cast(List[float], args) else: xs = cast(List[float], args) if not kwargs: return xs # parse kwargs; before parsing, convert (possibly) ndarray to list. x_list = [x for x in xs] # type: List[Union[float, None]] for key, value in kwargs.items(): try: index = self.param_index[key] x_list += [None for _ in range(len(x_list), index + 1)] x_list[index] = value except KeyError: raise TypeError('Unexpected param name: %s', key) if any(v is None for v in x_list): raise TypeError('Arguments insufficient: %s, %s', args, kwargs) return cast(List[float], x_list) def f0(self, *args, **kwargs): # type: (float, float)->float r"""Return the interpolation result of central value. The parameters can be specified as arguments, a sequence, or as keyword arguments if :attr:`param_index` is set. Returns ------- float interpolated central value. Examples -------- For an interpolation with names "foo", "bar", and "baz", the following calls are equivalent: - ``f0([100, 20, -1])`` - ``f0(100, 20, -1)`` - ``f0(numpy.array([100, 20, -1]))`` - ``f0(100, 20, baz=-1)`` - ``f0(foo=100, bar=20, baz=-1)`` - ``f0(0, 0, -1, bar=20, foo=100)`` """ return self._f0(self._interpret_args(*args, **kwargs)) __call__ = f0 """Function call is alias of :meth:`f0`.""" def fp(self, *args, **kwargs): # type: (Union[Sequence[float], float], float)->float """Return the interpolation result of upper-fluctuated value. Returns ------- float interpolated result of central value plus positive uncertainty. """ return self._fp(self._interpret_args(*args, **kwargs)) def fm(self, *args, **kwargs): # type: (Union[Sequence[float], float], float)->float """Return the interpolation result of downer-fluctuated value. Returns ------- float interpolated result of central value minus negative uncertainty. """ return self._fm(self._interpret_args(*args, **kwargs)) def tuple_at(self, *args, **kwargs): # type: (Union[Sequence[float], float], float)->Tuple[float, float, float] """Return the tuple(central, +unc, -unc) at the point. Returns ------- tuple(float, float, float) interpolated central value and positive and negative uncertainties. """ x = self._interpret_args(*args, **kwargs) return self._f0(x), self.unc_p_at(*x), self.unc_m_at(*x) def unc_p_at(self, *args, **kwargs): # type: (Union[Sequence[float], float], float)->float """Return the interpolated value of positive uncertainty. This is calculated not by interpolating the positive uncertainty table but as a difference of the interpolation result of the central and upper - fluctuated values. Returns ------- float interpolated result of positive uncertainty. Warning ------- This is not the positive uncertainty of the interpolation because the interpolating uncertainty is not included. The same warning applies for: meth: `unc_m_at`. """ x = self._interpret_args(*args, **kwargs) return self._fp(x) - self._f0(x) def unc_m_at(self, *args, **kwargs): # type: (float, float)->float """Return the interpolated value of negative uncertainty. Returns ------- float interpolated result of negative uncertainty. """ x = self._interpret_args(*args, **kwargs) return -(self._f0(x) - self._fm(x)) class AbstractInterpolator: """A base class of interpolator for values with uncertainties. Actual interpolator should implement :meth:`_interpolate` method, which accepts a `pandas.DataFrame` object with one value-column and returns an interpolating function (|InterpType|). """ def interpolate(self, cross_section_table, name): # type: (Table, str)->Interpolation """Perform interpolation for values with uncertainties. Arguments --------- cross_section_table: Table A cross-section data table. name: str Value name of the table to interpolate. Returns ------- Interpolation The interpolation result. """ df = cross_section_table.data[name] return Interpolation( 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)->InterpType raise NotImplementedError class Scipy1dInterpolator(AbstractInterpolator): r"""Interpolator for one-dimensional data based on scipy interpolators. Arguments --------- kind: str Specifies the interpolator types. :linear: uses `scipy.interpolate.interp1d` (`!kind="linear"`), which performs piece-wise linear interpolation. :spline: uses `scipy.interpolate.CubicSpline`, which performs cubic-spline interpolation. The natural boundary condition is imposed. This is simple and works well if the grid is even-spaced, but is unstable and not recommended if not even-spaced. :pchip: uses `scipy.interpolate.PchipInterpolator`. This method is recommended for most cases, especially if monotonic, but not suitable for oscillatory data. :akima: uses `scipy.interpolate.Akima1DInterpolator`. For oscillatory data this is preferred to Pchip interpolation. axes: str Specifies the axes preprocess types. :linear: does no preprocess. :log: uses log-axis for values (y). :loglinear: uses log-axis for parameters (x). :loglog: uses log-axis for parameters and values. Warnings -------- Users should notice the cons of each interpolator, e.g., "spline" and "akima" methods are worse for the first and last intervals or if the grid is not even-spaced, or "pchip" cannot capture oscillations. Note ---- :attr:`kind` also accepts all the options for `scipy.interpolate.interp1d`, but they except for "cubic" are not recommended for cross-section data. The option "cubic" calls `scipy.interpolate.interp1d`, but it uses the not-a-knot boundary condition, while "spline" uses the natural condition, which imposes the second derivatives at the both ends to be zero. Note ---- Polynomial interpolations (listed below) are not included because they are not suitable for cross-section data. They yield in globally-defined polynomials, but such uniformity is not necessary for our purpose and they suffer from so-called Runge phenomenon. If data is expected to be fit by a polynomial, one may use "linear" with `!axes="loglog"`. - `scipy.interpolate.BarycentricInterpolator` - `scipy.interpolate.KroghInterpolator` See Also -------- * `MATLAB pchip - MathWorks`_ * `Spline methods comparison`_ .. _MATLAB pchip - MathWorks: https://mathworks.com/help/matlab/ref/pchip.html .. _Spline methods comparison: https://gist.github.com/misho104/46032fa730088a0cb4c2e0556c59260b """ def __init__(self, kind='linear', axes='linear'): # type: (str, str)->None self.kind = kind.lower() # type: str self.axes = axes.lower() # type: str def _interpolate(self, df): # type: (pandas.DataFrame)->InterpType if self.axes == 'linear': wrapper = AxesWrapper(['linear'], 'linear') elif self.axes == 'log': wrapper = AxesWrapper(['linear'], 'log') elif self.axes == 'loglinear': wrapper = AxesWrapper(['log'], 'linear') elif self.axes == 'loglog': wrapper = AxesWrapper(['log'], 'log') else: raise ValueError('Invalid axes wrapper: %s', self.axes) if df.index.nlevels != 1: raise ValueError('Scipy1dInterpolator not handle multiindex data.') # axes modification; note that the wrappers are numpy.vectorize()-ed. xs = wrapper.wx[0](df.index.to_numpy()) ys = wrapper.wy(df.to_numpy()) if self.kind == 'spline': f_bar = sci_interp.CubicSpline(xs, ys, bc_type='natural', extrapolate=False) elif self.kind == 'pchip': f_bar = sci_interp.PchipInterpolator(xs, ys, extrapolate=False) elif self.kind == 'akima': f_bar = sci_interp.Akima1DInterpolator(xs, ys) f_bar.extrapolate = False else: f_bar = sci_interp.interp1d(xs, ys, self.kind, bounds_error=True) # now `f_bar` is float->float; we should convert it to Tuple[float]->float. def _f_bar(x, f_bar=f_bar): # noqa: B008 # type: (Sequence[float], Callable[[float], float])->float return f_bar(*x) return wrapper.wrapped_f(_f_bar) class ScipyGridInterpolator(AbstractInterpolator): r"""Interpolator for multi-dimensional structural data. Arguments --------- kind: str Specifies the interpolator types. Spline interpolators can be available only for two-parameter interpolations. :linear: uses `scipy.interpolate.RegularGridInterpolator` with method="linear", which linearly interpolates the grid mesh. :spline: alias of "spline33". :spline33: uses `scipy.interpolate.RectBivariateSpline` with order (3, 3); the numbers may be 1 to 5, but "spline11" is equivalent to "linear". axes_wrapper: AxesWrapper, optional Object for axes preprocess. If unspecified, no preprocess is performed. """ def __init__(self, kind='linear', axes_wrapper=None): # type: (str, Optional[AxesWrapper])->None self.kind = kind.lower() # type: str self.axes_wrapper = axes_wrapper # type: Optional[AxesWrapper] def _interpolate(self, df): # type: (pandas.DataFrame)->InterpType xs = df.index.levels ys = df.unstack().to_numpy() # xs: list with n_dim elements; each is a list of grid points along an axis. # ys: a numpy matrix with ndim = n_dim, i.e., "unstacked" tensor. # wrap if self.axes_wrapper: if len(self.axes_wrapper.wx) != len(xs): raise ValueError( 'Axes wrapper for %d-dim is specified for %d-dim interp.', len(self.axes_wrapper.wx), len(xs), ) xs = [w(axis) for w, axis in zip(self.axes_wrapper.wx, xs)] ys = self.axes_wrapper.wy(ys) # call scipy if self.kind == 'linear': f_bar = self._interpolate_linear(xs, ys) elif self.kind == 'spline': f_bar = self._interpolate_spline(xs, ys, 3, 3) elif re.match(r"\Aspline[1-5][1-5]\Z", self.kind): f_bar = self._interpolate_spline(xs, ys, int(self.kind[-2]), int(self.kind[-1])) else: raise ValueError('Invalid kind: %s', self.kind) if self.axes_wrapper: return self.axes_wrapper.wrapped_f(f_bar) else: return f_bar def _interpolate_linear(self, xs, ys): # type: (Any, Any)->Callable[[Sequence[float]], float] interp = sci_interp.RegularGridInterpolator(xs, ys, method='linear') interp.bounds_error = True def f_bar(x, _f_bar=interp): # type: (Sequence[float], Any)->float return float(_f_bar(x)) return f_bar def _interpolate_spline(self, xs, ys, kx, ky): # type: (Any, Any, int, int)->Callable[[Sequence[float]], float] if len(xs) != 2: raise ValueError('ScipyGridInterpolator with spline is only for 2d data.') if numpy.isnan(xs).any() or numpy.isnan(ys).any(): raise ValueError('Spline interpolation does not allow missing grid points.') interp = sci_interp.RectBivariateSpline(xs[0], xs[1], ys, s=0, kx=kx, ky=ky) def f_bar(x, _f_bar=interp): # type: (Sequence[float], Any)->float return float(_f_bar(*x)) return f_bar # 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. # """ PK!Y9susy_cross_section/scripts.py"""Scripts for user's ease of handling the data. Two command-line scripts are provided: susy-xs-get: Interpolate cross-section grid table and return the cross section. This script corresponds to `!scripts.command_get` method. susy-xs-show: Display cross-section grid table with information. This script corresponds to `!scripts.command_show` method. For details, see the manual or try to execute with ``--help`` option. """ from __future__ import absolute_import, division, print_function # py2 import logging import sys from typing import Any # noqa: F401 import click import susy_cross_section.utility as Util from susy_cross_section.interp.axes_wrapper import AxesWrapper from susy_cross_section.interp.interpolator import ( AbstractInterpolator, Scipy1dInterpolator, ScipyGridInterpolator, ) from susy_cross_section.table import Table __author__ = 'Sho Iwamoto' __copyright__ = 'Copyright (C) 2018-2019 Sho Iwamoto / Misho' __license__ = 'MIT' __packagename__ = 'susy_cross_section' __version__ = '0.0.4' if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) _DEFAULT_VALUE_NAME = 'xsec' def _display_usage_for_table(context, table_obj, **kw): # type: (click.Context, Table, Any)->None """Display usage of the specified table.""" arg_zero = context.info_name # program name arg_table = kw['table'] # list of param_name and unit usage_line = 'Usage: {a0} [OPTIONS] {table} {args}'.format( a0=arg_zero, table=arg_table, args=' '.join(p.column.upper() for p in table_obj.info.parameters), ) params_lines = [' {title:11} {name} [unit: {unit}]'.format( title='Parameters:' if i == 0 else '', name=p.column.upper(), unit=table_obj.info.get_column(p.column).unit, ) for i, p in enumerate(table_obj.info.parameters)] values_lines = [' {title:17} --name={name} [unit: {unit}] {default}'.format( title='Table-specific options:' if i == 0 else '', name=name, unit=unit, default='(default)' if name == _DEFAULT_VALUE_NAME else '', ) for i, (name, unit) in enumerate(table_obj.units.items())] click.echo(usage_line) click.echo() for line in params_lines: click.echo(line) click.echo() for line in values_lines: click.echo(line) @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=__packagename__ + ' interpolator') @click.pass_context def command_get(context, **kw): # type: (Any, Any)->None """Script for cross-section interpolation.""" # handle arguments args = kw['args'] or [] value_name = kw['name'] or _DEFAULT_VALUE_NAME try: table_path, info_path = Util.get_paths(kw['table'], kw['info']) except (FileNotFoundError, RuntimeError) as e: click.echo(e.__str__()) # py2 exit(1) try: table = Table(table_path, info_path) except (ValueError, TypeError) as e: click.echo(e.__str__()) # py2 exit(1) # without arguments or with invalid number of arguments, show the table information. if len(args) != len(table.info.parameters): _display_usage_for_table(context, table, **kw) exit(1) # data evaluation if len(args) == 1: interp = Scipy1dInterpolator(axes='loglog', kind='linear') # type: AbstractInterpolator else: wrapper = AxesWrapper(['log' for _ in args], 'log') interp = ScipyGridInterpolator(axes_wrapper=wrapper, 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(Util.value_format(central, unc_p, unc_m, unit=table.units[value_name] if kw['unit'] else None)) 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=__packagename__ + ' table viewer') def command_show(**kw): # type: (Any)->None """Script for cross-section interpolation.""" # handle arguments try: table_path, info_path = Util.get_paths(kw['table'], kw['info']) except (FileNotFoundError, RuntimeError) as e: click.echo(e.__str__()) # py2 exit(1) try: table = Table(table_path, info_path) except (ValueError, TypeError) as e: click.echo(e.__str__()) # py2 exit(1) click.echo(table.dump()) click.echo(table.info.dump()) exit(0) PK!t+oosusy_cross_section/table.py"""Classes for annotations to a table. ====================== ================================================ CrossSectionAttributes physical property of cross section. CrossSectionInfo `TableInfo` with `CrossSectionAttributes`. Table grid data with `CrossSectionInfo`. ====================== ================================================ """ from __future__ import absolute_import, division, print_function # py2 import logging import pathlib import sys import textwrap from typing import ( # noqa: F401 Any, List, Mapping, MutableMapping, Optional, Sequence, SupportsFloat, Tuple, Union, cast, ) import pandas # noqa: F401 from susy_cross_section.base.info import TableInfo from susy_cross_section.base.table import BaseTable 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. These information is intended to be handled by program codes, so the content should be neat, clear, and ready to be standardized. Attributes ---------- processes: list of str The processes included in the cross section values. MadGraph5 syntax is recommended. Definiteness should be best respected. collider: str The type of collider, e.g., ``"pp"``, ``"e+e-"``. ecm: str The initial collision energy with unit. order: str The order of the cross-section calculation. pdf_name: str The name of PDF used in calculation. The `LHAPDF's set name `_ is recommended. """ def __init__(self, processes='', collider='', ecm='', order='', pdf_name=''): # type: (Union[str, List[str]], str, str, str, str)->None if not processes: self.processes = [] # type: List[str] elif isinstance(processes, str): self.processes = [processes] else: self.processes = processes self.collider = collider # type: str self.ecm = ecm # type: str # because it is always with units self.order = order # type: str self.pdf_name = pdf_name # type: str def validate(self): # type: ()->None """Validate the content. Type is also strictly checked in order to validate info files. Raises ------ TypeError If any attributes are invalid type of instance. ValueError If any attributes have invalid content. """ for attr, typ in [('processes', List), ('collider', str), ('ecm', str), ('order', str), ('pdf_name', str)]: value = getattr(self, attr) if not value: raise ValueError('attributes: %s is empty.', attr) if not isinstance(value, typ): raise TypeError('attributes: %s must be %s', attr, typ) if not all(isinstance(s, str) and s for s in self.processes): raise TypeError('attributes: processes must be a list of string.') def dump(self): # type: ()->str """Return the formatted string. Returns ------- str Dumped data. """ lines = [ 'collider: {}-collider, ECM={}'.format(self.collider, self.ecm), 'calculation order: {}'.format(self.order), 'PDF: {}'.format(self.pdf_name), 'included processes:', ] for p in self.processes: lines.append(' ' + p) return '\n'.join(lines) class CrossSectionInfo(TableInfo): """Stores annotations of a cross section table. Annotation for cross section tables are `TableInfo` plus one `CrossSectionAttributes` object. Attributes ---------- attributes: CrossSectionAttributes Information provided particularly for the cross section. General information intended to the users should be stored in `!document`, while the content of `!attributes` should be neat, standardized, and easy-to-parse objects. """ def __init__(self, attributes=None, **kw): # type: (CrossSectionAttributes, Any)->None self.attributes = attributes or CrossSectionAttributes() # type: CrossSectionAttributes 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. Parameters ---------- source: pathlib.Path or str Path to the json file. Returns ------- CrossSectionInfo Constructed instance. """ return cast(CrossSectionInfo, super(CrossSectionInfo, cls).load(source)) # py2 def _load(self, **kw): # type: (Any)->None """Construct CrossSectionInfo from json data. Raises ------ KeyError If json data lacks value for 'attributes'. """ attributes = CrossSectionAttributes(**kw['attributes']) del kw['attributes'] super(CrossSectionInfo, self)._load(**kw) # py2 self.attributes = attributes def validate(self): # type: ()->None """Validate the content. This method calls children's and superclass' `!validate()` method, so exceptions are raised from them. """ super(CrossSectionInfo, self).validate() # py2 self.attributes.validate() def dump(self): # type: ()->str """Return the formatted string. Returns ------- str Dumped data. """ return '\n'.join([ super(CrossSectionInfo, self).dump(), '', '[Cross section attributes]', textwrap.indent(self.attributes.dump(), prefix=' '), ]) class Table(BaseTable): """Data of a cross section with parameters, read from a table file. Arguments --------- table_path: str or pathlib.Path Path to the csv data file. info_path: str or pathlib.Path, optional Path to the corresponding info file. If unspecified, `!table_path` with suffix changed to ``".info"`` is used. Attributes ---------- table_path: pathlib.Path Path to the csv data file. info_path: pathlib.Path Path to the info file. self. """ 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: TableInfo self.raw_data = self._read_csv(self.table_path) # type: pandas.DataFrame # contents are filled in _load_data self.data = {} # type: MutableMapping[str, pandas.DataFrame] self.units = {} # type: MutableMapping[str, str] self.info.validate() # validate annotation before actual load self._load_data() self.validate() PK!- N$susy_cross_section/tests/__init__.py"""Test codes.""" PK!=f: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", "pdf_name": "??" }, "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 }, "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 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350 0.10460298409 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 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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!!!-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_, ok_ # noqa: F401 from susy_cross_section.interp import Scipy1dInterpolator, ScipyGridInterpolator from susy_cross_section.interp.axes_wrapper import AxesWrapper from susy_cross_section.table import Table 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.""" cwd = pathlib.Path(__file__).parent self.dirs = { 'lhc_wg': cwd / '..' / 'data' / 'lhc_susy_xs_wg', 'fastlim8': cwd / '..' / 'data' / 'fastlim' / '8TeV' / 'NLO+NLL', 'fastlim8mod': cwd / 'data', } def test_scipy_1d_interpolator(self): """Verify Scipy1dInterpolator.""" table = Table(self.dirs['lhc_wg'] / '13TeVn2x1wino_cteq_pm.csv') for kind in ['linear', 'akima', 'spline', 'pchip']: for axes in ['linear', 'log', 'loglog', 'loglinear']: fit = Scipy1dInterpolator(kind, 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), 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) # 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 = Table(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,)) 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 arrays eq_(test_method([333.3]), value) 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, 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 = Table(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']: wrapper = AxesWrapper([x1a, x2a], ya) fit = ScipyGridInterpolator(kind, wrapper).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) # 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 = Table(self.dirs['fastlim8mod'] / 'sg_8TeV_NLONLL_modified.xsec') for kind in ['linear', 'spline']: fit = ScipyGridInterpolator(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 invalid arrays or numpy.ndarray with >0 dim. for bad_input in ([[777]], [[777, 888], [789, 890]], [777, 888, 999]): with assert_raises((ValueError, TypeError, IndexError)): test_method(bad_input) with assert_raises((ValueError, TypeError, IndexError)): test_method(numpy.array(bad_input)) with assert_raises(TypeError): test_method(777, 888, m_wino=100) with assert_raises(TypeError): test_method(777, m_wino=100) with assert_raises(TypeError): test_method(m_wino=100) with assert_raises((IndexError, TypeError)): test_method() with assert_raises((IndexError, TypeError)): test_method(777) PK!I(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', mass.__str__()], ) # py2 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', mass.__str__()], ) # py2 output[mass] = [float(x) for x in result[mass].output.strip().split(' ')] logger.debug('Exit code %s: %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!K->'$'$susy_cross_section/utility.py"""Utility functions and classes. ============== ======================================================== `Unit` describing a physical unit. `value_format` give human-friendly string representation of values. `get_paths` parse and give paths to data and info files ============== ======================================================== """ from __future__ import absolute_import, division, print_function # py2 import itertools import logging import pathlib import sys from typing import List, Mapping, MutableMapping, Optional, Tuple, Union # noqa: F401 import numpy import susy_cross_section.config if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 FileNotFoundError = OSError # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) PathLike = Union[str, pathlib.Path] class Unit: """A class to handle units of physical values. This class handles units associated to physical values. Units can be multiplied, inverted, or converted. A new instance is equivalent to the product of `!*args`; each argument can be a str, a Unit, or a float (as a numerical factor). Parameters ---------- *args: float, str, or Unit Factors of the new instance. """ definitions = { '': [1], '%': [0.01], 'pb': [1000, 'fb'], } # type: Mapping[str, List[Union[float, str]]] """:typ:`dict[str, list of (float or str)]`: The replacement rules of units. This dictionary defines the replacement rules for unit conversion. Each key should be replaced with the product of its values.""" @classmethod def _get_base_units(cls, name): # type: (Union[float, str])->List[Union[float, str]] """Expand the unit name to the base units. Parameters ---------- name: float or str The unit name to be expanded, or possibly a numerical factor. Returns ------- list[float or str] Expansion result as a list of factors and base unit names. """ if isinstance(name, str) and name in cls.definitions: nested = [cls._get_base_units(u) for u in cls.definitions[name]] return list(itertools.chain.from_iterable(nested)) # flatten else: return [name] 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: self *= u def inverse(self): # type: ()->Unit """Return an inverted unit. Returns ------- Unit The inverted unit of `!self`. """ result = Unit() result._factor = 1 / self._factor result._units = {k: -v for k, v in self._units.items()} return result def __imul__(self, other): # type: (Union[float, str, Unit])->Unit """Multiply by another unit. Parameters ---------- other: float, str, or Unit Another unit as a multiplier. """ if isinstance(other, Unit): self._factor *= other._factor for k, v in other._units.items(): self._units[k] = self._units.get(k, 0) + v else: for b in self._get_base_units(other): 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', other) return self def __mul__(self, other): # type: (Union[float, str, Unit])->Unit """Return products of two units. Parameters ---------- other: float, str, or Unit Another unit as a multiplier. Returns ------- Unit The product. """ return Unit(self, other) def __truediv__(self, other): # type: (Union[float, str, Unit])->Unit """Return division of two units. Parameters ---------- other: float, str, or Unit Another unit as a divider. Returns ------- Unit The quotient. """ return Unit(self, Unit(other).inverse()) def __float__(self): # type: ()->float """Evaluate as a float value if this is a dimension-less unit. Returns ------- float The number corresponding to this dimension-less unit. Raises ------ ValueError If not dimension-less unit. """ 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 """Return human-friendly text of an uncertainty-accompanied value. Parameters ---------- value: float Central value. unc_p: float Positive-direction absolute uncertainty. unc_m: float Negative-direction absolute uncertainty. unit: str, optional Unit of the value and the uncertainties. Returns ------- str Formatted string describing the given value. """ delta = min(abs(unc_p), abs(unc_m)) suffix = ' {}'.format(unit) if unit else '' # will be appended to the body. if delta == 0: # without uncertainty body = '{:g} +0 -0'.format(value) else: v_order = int(numpy.log10(value)) if abs(v_order) > 3: # force to use scientific notation suffix = '*1e{:d}'.format(v_order) + suffix divider = 10 ** v_order digits_to_show = max(int(-numpy.log10(delta / value) - 0.005) + 2, 3) else: divider = 1 digits_to_show = max(int(-numpy.log10(delta) - 0.005) + (1 if delta > 1 else 2), 0) v_format = '{f} +{f} -{f}'.format(f='{{:.{}f}}'.format(digits_to_show)) body = v_format.format(value / divider, unc_p / divider, abs(unc_m / divider)) return '({}){}'.format(body, suffix) if suffix else body def get_paths(data_name, info_path=None): # type: (PathLike, Optional[PathLike])->Tuple[PathLike, PathLike] """Return paths to data file and info file. Relative path is evaluted from the package directory (i.e., the directory having this file). Parameters ---------- data_name: pathlib.Path or str Path to data file or a table name found in configuration. If the string is found in configuration's table_names, the configured paths are returned. Otherwise, :ar:`data_name` is interpreted as a path to the data file itself. info_path: pathlib.Path or str, optional Path to info file. If not given, the path to data file with suffix changed to ".info" is returned. Returns ------- Tuple[pathlib.Path, pathlib.Path] A tuple with paths to data file and info file. Raises ------ FileNotFoundError If one of the specified files are not found. RuntimeError If one of the specified files are not a file. 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