hepi ==== .. py:module:: hepi .. autoapi-nested-parse:: The HEPi package aims to automize cluster computations for parameter scans with the option to produce plots. Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/hepi/data/index /autoapi/hepi/fast/index /autoapi/hepi/input/index /autoapi/hepi/interpolate/index /autoapi/hepi/load/index /autoapi/hepi/order/index /autoapi/hepi/output/index /autoapi/hepi/particles/index /autoapi/hepi/plot/index /autoapi/hepi/results/index /autoapi/hepi/run/index /autoapi/hepi/util/index Attributes ---------- .. autoapisummary:: hepi.in_dir hepi.out_dir hepi.pre hepi.multi_scan hepi.scale_scan hepi.seven_point_scan hepi.pdf_scan hepi.load hepi.unv hepi.usd hepi.tex_table hepi.map_vplot hepi.scatter_vplot hepi.fig hepi.axs hepi.lines hepi.labels hepi.required_numerical_uncertainty_factor hepi.unv hepi.usd hepi.package hepi.__version__ Classes ------- .. autoapisummary:: hepi.Order hepi.DictData hepi.Input hepi.Input hepi.Order hepi.Input hepi.DictData hepi.Result hepi.Input hepi.Order hepi.Result hepi.DictData hepi.RunParam hepi.Runner hepi.DictData Functions --------- .. autoapisummary:: hepi.order_to_string hepi.replace_macros hepi.xsec_to_order hepi.lhapdf_name_to_id hepi.get_input_dir hepi.get_output_dir hepi.get_pre hepi.set_input_dir hepi.set_output_dir hepi.set_pre hepi.is_gluino hepi.is_neutralino hepi.is_chargino hepi.is_weakino hepi.is_squark hepi.is_slepton hepi.update_slha hepi.scan hepi.scan_multi hepi.scan_scale hepi.scan_seven_point hepi.keep_where hepi.remove_where hepi.change_where hepi.scan_invariant_mass hepi.slha_write hepi.masses_scan hepi.mass_scan hepi.slha_scan hepi.slha_scan_rel hepi.scan_pdf hepi.interpolate_1d hepi.interpolate_2d hepi.order_to_string hepi.xsec_to_order hepi.DL2DF hepi.LD2DL hepi.load_json_with_metadata hepi.load_json hepi.order_to_string hepi.DL2DF hepi.write_twiki hepi.write_latex_table_transposed_header hepi.write_latex_table_transposed hepi.write_latex hepi.write_csv hepi.write_json hepi.get_output_dir hepi.replace_macros hepi.get_name hepi.title hepi.energy_plot hepi.combined_mass_plot hepi.combined_plot hepi.mass_plot hepi.mass_vplot hepi.get_mass hepi.plot hepi.index_open hepi.slha_data hepi.slha_plot hepi.vplot hepi.mass_mapplot hepi.mapplot hepi.scatterplot hepi.err_plt hepi.scale_plot hepi.central_scale_plot hepi.init_double_plot hepi.pdf_errors hepi._pdf_error_single hepi.pdf_error hepi.scale_errors hepi._scale_error_single hepi.scale_error hepi.combine_errors hepi.combine_error hepi.asym_to_sym_error hepi.add_errors hepi.asym_to_sym_combined_error hepi.get_input_dir hepi.get_output_dir hepi.get_pre hepi.DL2DF hepi.LD2DL hepi.namehash hepi.LD2DL hepi.DL2DF hepi.namehash hepi.lhapdf_name_to_id hepi.lhapdf_id_to_name Package Contents ---------------- .. py:class:: Order Bases: :py:obj:`enum.IntEnum` Computation orders. Initialize self. See help(type(self)) for accurate signature. .. py:attribute:: LO :value: 0 Leading Order .. py:attribute:: NLO :value: 1 Next-to-Leading Order .. py:attribute:: NLO_PLUS_NLL :value: 2 Next-to-Leading Order plus Next-to-Leading Logarithms .. py:attribute:: aNNLO_PLUS_NNLL :value: 3 Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms .. py:function:: order_to_string(o, json_style=False, no_macros=False) .. py:function:: replace_macros(s) .. py:function:: xsec_to_order(s) .. py:class:: DictData .. py:method:: __str__() Returns attributes as dict as string .. py:function:: lhapdf_name_to_id(name) Converts a LHAPDF name to the sets id. :param name: LHAPDF set name. :type name: str :returns: id of the LHAPDF set. :rtype: int .. rubric:: Examples >>> lhapdf_name_to_id("CT14lo") 13200 .. py:data:: in_dir :value: './input/' Input directory. .. py:data:: out_dir :value: './output/' Output directory. .. py:data:: pre :value: 'nice -n 5' Prefix for run commands. .. py:function:: get_input_dir() Get the input directory. :returns: :attr:`in_dir` :rtype: str .. py:function:: get_output_dir() Get the output directory. :returns: :attr:`out_dir` :rtype: str .. py:function:: get_pre() Gets the command prefix. :returns: :attr:`pre` :rtype: str .. py:function:: set_input_dir(ind) Sets the input directory. :param ind: new input directory. :type ind: str .. py:function:: set_output_dir(outd, create = True) Sets the output directory. :param outd: new output directory. create (bool): create directory if not existing :type outd: str .. py:function:: set_pre(ppre) Sets the command prefix. :param ppre: new command prefix. :type ppre: str .. py:class:: Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True) Bases: :py:obj:`hepi.util.DictData` Input for computation and scans. :ivar order: LO, NLO or NLO+NLL computation. :vartype order: :class:`Order` :ivar energy: CMS energy in GeV. :vartype energy: int :ivar energyhalf: Halfed `energy`. :vartype energyhalf: int :ivar particle1: PDG identifier of the first final state particle. :vartype particle1: int :ivar particle2: PDG identifier of the second final state particle. :vartype particle2: int :ivar slha: File path of for the base slha. Modified slha files will be used if a scan requires a change of the input. :vartype slha: str :ivar pdf_lo: LO PDF name. :vartype pdf_lo: str :ivar pdf_nlo: NLO PDF name. :vartype pdf_nlo: str :ivar pdfset_lo: LO PDF member/set id. :vartype pdfset_lo: int :ivar pdfset_nlo: NLO PDF member/set id. :vartype pdfset_nlo: int :ivar pdf_lo_id: LO PDF first member/set id. :vartype pdf_lo_id: int :ivar pdf_nlo_id: NLO PDF first member/set id. :vartype pdf_nlo_id: int :ivar mu: central scale factor. :vartype mu: double :ivar mu_f: Factorization scale factor. :vartype mu_f: double :ivar mu_r: Renormalization scale factor. :vartype mu_r: double :ivar precision: Desired numerical relative precision. :vartype precision: double :ivar max_iters: Upper limit on integration iterations. :vartype max_iters: int :ivar invariant_mass: Invariant mass mode 'auto = sqrt((p1+p2)^2)' else value. :vartype invariant_mass: str :ivar pt: Transverse Momentum mode 'auto' or value. :vartype pt: str :ivar result: Result type 'total'/'pt'/'ptj'/'m'. :vartype result: str :ivar id: Set an id of this run. :vartype id: str :ivar model: Path for MadGraph model. :vartype model: str :ivar update: Update dependent `mu` else set to zero. :vartype update: bool .. py:attribute:: order .. py:attribute:: energy .. py:attribute:: energyhalf .. py:attribute:: particle1 .. py:attribute:: particle2 .. py:attribute:: slha .. py:attribute:: pdf_lo .. py:attribute:: pdfset_lo :value: 0 .. py:attribute:: pdf_nlo .. py:attribute:: pdfset_nlo :value: 0 .. py:attribute:: pdf_lo_id :value: 0 .. py:attribute:: pdf_nlo_id :value: 0 .. py:attribute:: mu_f :value: 1.0 .. py:attribute:: mu_r :value: 1.0 .. py:attribute:: precision :value: 0.001 .. py:attribute:: max_iters :value: 50 .. py:attribute:: invariant_mass :value: 'auto' .. py:attribute:: pt :value: 'auto' .. py:attribute:: result :value: 'total' .. py:attribute:: id :value: '' .. py:attribute:: model :value: '' .. py:attribute:: mu :value: 0.0 .. py:method:: has_gluino() .. py:method:: has_neutralino() .. py:method:: has_charginos() .. py:method:: has_weakino() .. py:method:: has_squark() .. py:method:: has_slepton() .. py:function:: is_gluino(iid) .. py:function:: is_neutralino(iid) .. py:function:: is_chargino(iid) .. py:function:: is_weakino(iid) .. py:function:: is_squark(iid) .. py:function:: is_slepton(iid) .. py:function:: update_slha(i) Updates dependent parameters in Input `i`. Mainly concerns the `mu` value used by `madgraph`. .. py:function:: scan(input_list, var, rrange) Scans a variable `var` over `rrange` in `input_list`. .. note:: This function does not ensure that dependent vairables are updated (see `energyhalf` in Examples). :param input_list: Input parameters that get scanned each. :type input_list: :obj:`list` of :class:`Input` :param var: Scan variable name. :type var: str :param rrange: Range of `var` to be scanned. :type rrange: Iterable :returns: Modified list with scan runs added. :rtype: :obj:`list` of :class:`Input` .. rubric:: Examples >>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> li = scan(li,"energy",range(10000,13000,1000)) >>> for e in li: ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> for e in scan(li,"order",[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]): ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:function:: scan_multi(li, **kwargs) Magically scans the variables passed to this function. :param \*\*kwargs: .. rubric:: Examples >>> oli = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> li = scan_multi(oli,energy=range(10000,13000,1000)) >>> for e in li: ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> for e in scan_multi(oli,energy=range(10000,13000,1000),order=[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]): ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:data:: multi_scan .. py:function:: scan_scale(l, rrange=3, distance=2.0) Scans scale by varying `mu_f` and `mu_r`. They take `rrange` values from 1/`distance` to `distance` in lograthmic spacing. Only points with `mu_f`=`mu_r` or `mu_r/f`=1 or `mu_r/f`=`distance` or `mu_r/f`=1/`distance` are returned. .. rubric:: Examples >>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> for e in scan_scale(li): ... print(e) {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 0.5, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 2.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 0.5, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 2.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 0.5, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 2.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:data:: scale_scan .. py:function:: scan_seven_point(input_list) Scans scale by varying `mu_f` and `mu_r` by factors of two excluding relative factors of 4. .. rubric:: Examples >>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> for e in scan_seven_point(li): ... print(e) {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 0.5, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 0.5, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 2.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 2.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:data:: seven_point_scan .. py:function:: keep_where(input_list, condition) Only keep the inputs where the condition is true. Inversion of the `remove_where` function. :param input_list: List[Input] The list of inputs to filter. :param condition: Callable[[Input.__dict__], bool] The condition to filter on. .. py:function:: remove_where(input_list, condition, **kwargs) Remove elements in list which satisfy condition. :param input_list: List[Input] The list of inputs to filter. :param condition: Callable[[Input.__dict__], bool] The condition to filter on. .. rubric:: Examples >>> li = scan_multi([Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,13000,1000)) >>> for e in remove_where(li,lambda dict : (dict["energy"] == 10000 or dict["energy"] == 11000)): ... print(e) {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:function:: change_where(input_list, dicts, **kwargs) Applies the values of `dicts` if the key value pairs in `kwargs` agree with a member of the list `input_list`. The changes only applied to the matching list members. .. rubric:: Examples >>> li = scan_multi([Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,13000,1000)) >>> for e in change_where(li,{'order':Order.NLO},energy=11000): ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> li = scan_multi([Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,12000,1000),mu_f=range(1,3)) >>> for e in change_where(li,{'order':Order.NLO},energy=11000,mu_f=1): ... print(e) {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:function:: scan_invariant_mass(input_list, diff, points, low=0.001) Logarithmic `invariant_mass` scan close to the production threshold. .. py:function:: slha_write(newname, d) .. py:function:: masses_scan(input_list, varis, rrange, diff_L_R=None, negate=None) Scans the PDG identified masses in `varis` over `rrange` in the list `input_list`. `diff_L_R` allows to set a fixed difference between masses of left- and right-handed particles. .. py:function:: mass_scan(input_list, var, rrange, diff_L_R=None) Scans the PDG identified mass `var` over `rrange` in the list `l`. `diff_L_R` allows to set a fixed difference between masses of left- and right-handed particles. .. py:function:: slha_scan(input_list, block, var, rrange) Scan a generic .. py:function:: slha_scan_rel(input_list, lambdas, rrange) Scan a generic slha variable. .. py:function:: scan_pdf(input_list) Scans NLO PDF sets. The PDF sets are infered from `lhapdf.getPDFSet` with the argument of `pdfset_nlo`. .. rubric:: Examples >>> li = [Input(Order.NLO, 13000, 1000022,1000022, "None", "CT14lo","CT14nlo",update=False)] >>> for e in scan_pdf(li): ... print(e) {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 1, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 2, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 3, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 4, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 5, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 6, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 7, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 8, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 9, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 10, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 11, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 12, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 13, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 14, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 15, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 16, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 17, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 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'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 54, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 55, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': , 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 56, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} .. py:data:: pdf_scan .. py:function:: interpolate_1d(df, x, y, xrange, only_interpolation=True, **kwargs) Last key is the value to be interpolated, while the rest are cooridnates. :param df: results :type df: pandas.DataFrame .. py:function:: interpolate_2d(df, x, y, z, xrange, yrange, only_interpolation=True, **kwargs) Last key is the value to be interpolated, while the rest are cooridnates. :param df: results :type df: pandas.DataFrame .. py:class:: Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True) Bases: :py:obj:`hepi.util.DictData` Input for computation and scans. :ivar order: LO, NLO or NLO+NLL computation. :vartype order: :class:`Order` :ivar energy: CMS energy in GeV. :vartype energy: int :ivar energyhalf: Halfed `energy`. :vartype energyhalf: int :ivar particle1: PDG identifier of the first final state particle. :vartype particle1: int :ivar particle2: PDG identifier of the second final state particle. :vartype particle2: int :ivar slha: File path of for the base slha. Modified slha files will be used if a scan requires a change of the input. :vartype slha: str :ivar pdf_lo: LO PDF name. :vartype pdf_lo: str :ivar pdf_nlo: NLO PDF name. :vartype pdf_nlo: str :ivar pdfset_lo: LO PDF member/set id. :vartype pdfset_lo: int :ivar pdfset_nlo: NLO PDF member/set id. :vartype pdfset_nlo: int :ivar pdf_lo_id: LO PDF first member/set id. :vartype pdf_lo_id: int :ivar pdf_nlo_id: NLO PDF first member/set id. :vartype pdf_nlo_id: int :ivar mu: central scale factor. :vartype mu: double :ivar mu_f: Factorization scale factor. :vartype mu_f: double :ivar mu_r: Renormalization scale factor. :vartype mu_r: double :ivar precision: Desired numerical relative precision. :vartype precision: double :ivar max_iters: Upper limit on integration iterations. :vartype max_iters: int :ivar invariant_mass: Invariant mass mode 'auto = sqrt((p1+p2)^2)' else value. :vartype invariant_mass: str :ivar pt: Transverse Momentum mode 'auto' or value. :vartype pt: str :ivar result: Result type 'total'/'pt'/'ptj'/'m'. :vartype result: str :ivar id: Set an id of this run. :vartype id: str :ivar model: Path for MadGraph model. :vartype model: str :ivar update: Update dependent `mu` else set to zero. :vartype update: bool .. py:attribute:: order .. py:attribute:: energy .. py:attribute:: energyhalf .. py:attribute:: particle1 .. py:attribute:: particle2 .. py:attribute:: slha .. py:attribute:: pdf_lo .. py:attribute:: pdfset_lo :value: 0 .. py:attribute:: pdf_nlo .. py:attribute:: pdfset_nlo :value: 0 .. py:attribute:: pdf_lo_id :value: 0 .. py:attribute:: pdf_nlo_id :value: 0 .. py:attribute:: mu_f :value: 1.0 .. py:attribute:: mu_r :value: 1.0 .. py:attribute:: precision :value: 0.001 .. py:attribute:: max_iters :value: 50 .. py:attribute:: invariant_mass :value: 'auto' .. py:attribute:: pt :value: 'auto' .. py:attribute:: result :value: 'total' .. py:attribute:: id :value: '' .. py:attribute:: model :value: '' .. py:attribute:: mu :value: 0.0 .. py:method:: has_gluino() .. py:method:: has_neutralino() .. py:method:: has_charginos() .. py:method:: has_weakino() .. py:method:: has_squark() .. py:method:: has_slepton() .. py:function:: order_to_string(o, json_style=False, no_macros=False) .. py:function:: xsec_to_order(s) .. py:function:: DL2DF(ld) Convert a `dict` of `list`s to a `pandas.DataFrame`. .. py:function:: LD2DL(l, actual_dict=False) Convert a list of objects into a dictionary of lists. The values of each object are first converted to a `dict` through the `__dict__` attribute. :param l: list of objects. :type l: List :param actual_dict: objects are already dicts :type actual_dict: bool :returns: dictionary of numpy arrays. :rtype: dict .. rubric:: Examples >>> class Param: ... def __init__(self,a,b,c): ... self.a = a ... self.b = b ... self.c = c >>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ]) {'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])} .. py:function:: load_json_with_metadata(file) Load xsec data from json in to something that works within hepi's plotting. :param f: readable object, e.g. `open(filepath:str)`. :param dimensions: 1 or 2 currently supported. :type dimensions: int .. py:function:: load_json(f, dimensions=1) .. py:data:: load .. py:class:: Order Bases: :py:obj:`enum.IntEnum` Computation orders. Initialize self. See help(type(self)) for accurate signature. .. py:attribute:: LO :value: 0 Leading Order .. py:attribute:: NLO :value: 1 Next-to-Leading Order .. py:attribute:: NLO_PLUS_NLL :value: 2 Next-to-Leading Order plus Next-to-Leading Logarithms .. py:attribute:: aNNLO_PLUS_NNLL :value: 3 Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms .. py:function:: order_to_string(o, json_style=False, no_macros=False) .. py:function:: DL2DF(ld) Convert a `dict` of `list`s to a `pandas.DataFrame`. .. py:data:: unv .. py:data:: usd .. py:function:: write_twiki(rs_dl, mass, main) .. py:function:: write_latex_table_transposed_header(dict_list, t, fname, key, yscale=1.0) .. py:function:: write_latex_table_transposed(dict_list, t, fname, scale=True, pdf=True, yscale=1.0, max_rows=None) .. py:function:: write_latex(dict_list, t, key, fname, scale=True, pdf=True, yscale=1.0) Saves a `dict` of `list`s to `filename` as latex table. .. py:data:: tex_table .. py:function:: write_csv(dict_list, filename) Saves a `dict` of `list`s to `filename` as csv table. .. rubric:: Examples >>> import hepi >>> import urllib.request >>> dl = hepi.load(urllib.request.urlopen( ... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json" ... ),dimensions=2) >>> with open("test.csv", 'w') as f: ... hepi.write_csv(dl, f) >>> with open('test.csv', 'r') as f: ... print(f.read()) order,energy,energyhalf,particle1,particle2,slha,pdf_lo,pdfset_lo,pdf_nlo,pdfset_nlo,pdf_lo_id,pdf_nlo_id,mu_f,mu_r,precision,max_iters,invariant_mass,pt,result,id,model,mu,runner,N2,N1,NLO_PLUS_NLL_NOERR,NLO_PLUS_NLL_COMBINED 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,81.5,80.0,7.746232,7.746+/-0.023 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,82.0,80.0,7.646339,7.646+/-0.024 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,83.0,80.0,7.450843,7.451+/-0.024 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,85.0,80.0,7.079679,7.080+/-0.024 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,90.0,80.0,6.248933,6.249+/-0.025 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,95.0,80.0,5.53691,5.537+/-0.025 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,100.0,60.0,7.613015,7.613+/-0.024 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,100.0,80.0,4.924686,4.925+/-0.025 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,101.5,100.0,3.201246,3.201+/-0.026 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,102.0,100.0,3.169948,3.170+/-0.027 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,103.0,100.0,3.109625,3.110+/-0.027 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,105.0,100.0,2.993584,2.994+/-0.027 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,110.0,100.0,2.725548,2.726+/-0.027 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,110.0,80.0,3.933723,3.934+/-0.026 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,115.0,100.0,2.485705,2.486+/-0.028 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,100.0,2.271269,2.271+/-0.028 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,60.0,4.504708,4.505+/-0.025 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,80.0,3.180276,3.180+/-0.027 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,126.5,125.0,1.383578,1.384+/-0.030 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,127.0,125.0,1.373155,1.373+/-0.030 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,128.0,125.0,1.352257,1.352+/-0.031 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,130.0,100.0,1.905211,1.905+/-0.029 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,130.0,125.0,1.3128,1.313+/-0.031 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,135.0,125.0,1.219904,1.220+/-0.031 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,100.0,1.608394,1.608+/-0.029 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,125.0,1.134614,1.135+/-0.031 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,80.0,2.142151,2.142+/-0.028 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,145.0,125.0,1.056242,1.056+/-0.032 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,152.0,150.0,0.699925,0.700+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,153.0,150.0,0.691281,0.691+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,155.0,125.0,0.917808,0.918+/-0.032 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,155.0,150.0,0.674484,0.674+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,160.0,100.0,1.165897,1.166+/-0.031 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,160.0,150.0,0.6345,0.634+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,165.0,125.0,0.800281,0.800+/-0.033 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,165.0,150.0,0.597167,0.597+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,170.0,150.0,0.562441,0.562+/-0.035 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,178.0,175.0,0.391649,0.39+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,180.0,150.0,0.499633,0.500+/-0.035 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,180.0,175.0,0.383418,0.38+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,185.0,125.0,0.614697,0.615+/-0.034 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,185.0,175.0,0.363707,0.36+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,190.0,150.0,0.444892,0.44+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,190.0,175.0,0.345126,0.35+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,195.0,175.0,0.327625,0.33+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,202.0,200.0,0.2403,0.24+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,203.0,200.0,0.238047,0.24+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,205.0,200.0,0.233619,0.23+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,210.0,150.0,0.354984,0.35+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,210.0,200.0,0.222947,0.22+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,215.0,200.0,0.212818,0.21+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,220.0,200.0,0.203209,0.20+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,230.0,200.0,0.18536,0.19+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,230.0,225.0,0.150189,0.15+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,235.0,225.0,0.14399,0.14+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,240.0,200.0,0.169381,0.17+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,240.0,225.0,0.138083,0.14+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,252.0,250.0,0.102807,0.10+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,253.0,250.0,0.102017,0.10+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,255.0,250.0,0.100453,0.10+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,260.0,200.0,0.141817,0.14+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,260.0,250.0,0.096658,0.10+/-0.04 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,265.0,250.0,0.092955,0.09+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,270.0,250.0,0.089536,0.09+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,280.0,250.0,0.082931,0.08+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,290.0,250.0,0.076979,0.08+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,302.0,300.0,0.050316,0.05+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,303.0,300.0,0.049985,0.05+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,305.0,300.0,0.049326,0.05+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,310.0,250.0,0.066363,0.07+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,310.0,300.0,0.047719,0.05+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,315.0,300.0,0.046111,0.05+/-0.05 2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,320.0,300.0,0.044674,0.04+/-0.05 .. py:function:: write_json(dict_list, o, parameters, output, error=True, error_sym=None, scale=True, pdf=True) Saves a `dict` of `list`s to `filename` as json. Cf. https://github.com/fuenfundachtzig/xsec :param output: Should support a function `.write()`. :type output: writeable or file name str .. rubric:: Examples >>> import hepi >>> import urllib.request >>> dl = hepi.load(urllib.request.urlopen( ... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json" ... ),dimensions=2) >>> with open("test.json", "w") as f: ... hepi.write_json(dl, Order.NLO_PLUS_NLL,["N1"],f,error=False) >>> with open('test.json', 'r') as f: ... print(f.read()) { "initial state": "pp", "order": "NLO+NLL", "source": "hepi-...", "contact": "...", "tool": "Resummino", "process_latex": "$\\overline{d}\\overline{d}$", "comment": "", "reference": "?", "Ecom [GeV]": "13000.0", "process_id": "pp_13000.0_-1_-1", "PDF set": "CTEQ6.6 and MSTW2008nlo90cl", "parameters": [ [ "N1" ] ], "data": { "80.0": { "xsec_pb": 2.142151 }, "60.0": { "xsec_pb": 4.504708 }, "100.0": { "xsec_pb": 1.165897 }, "125.0": { "xsec_pb": 0.614697 }, "150.0": { "xsec_pb": 0.354984 }, "175.0": { "xsec_pb": 0.327625 }, "200.0": { "xsec_pb": 0.141817 }, "225.0": { "xsec_pb": 0.138083 }, "250.0": { "xsec_pb": 0.066363 }, "300.0": { "xsec_pb": 0.044674 } } } .. py:class:: Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True) Bases: :py:obj:`hepi.util.DictData` Input for computation and scans. :ivar order: LO, NLO or NLO+NLL computation. :vartype order: :class:`Order` :ivar energy: CMS energy in GeV. :vartype energy: int :ivar energyhalf: Halfed `energy`. :vartype energyhalf: int :ivar particle1: PDG identifier of the first final state particle. :vartype particle1: int :ivar particle2: PDG identifier of the second final state particle. :vartype particle2: int :ivar slha: File path of for the base slha. Modified slha files will be used if a scan requires a change of the input. :vartype slha: str :ivar pdf_lo: LO PDF name. :vartype pdf_lo: str :ivar pdf_nlo: NLO PDF name. :vartype pdf_nlo: str :ivar pdfset_lo: LO PDF member/set id. :vartype pdfset_lo: int :ivar pdfset_nlo: NLO PDF member/set id. :vartype pdfset_nlo: int :ivar pdf_lo_id: LO PDF first member/set id. :vartype pdf_lo_id: int :ivar pdf_nlo_id: NLO PDF first member/set id. :vartype pdf_nlo_id: int :ivar mu: central scale factor. :vartype mu: double :ivar mu_f: Factorization scale factor. :vartype mu_f: double :ivar mu_r: Renormalization scale factor. :vartype mu_r: double :ivar precision: Desired numerical relative precision. :vartype precision: double :ivar max_iters: Upper limit on integration iterations. :vartype max_iters: int :ivar invariant_mass: Invariant mass mode 'auto = sqrt((p1+p2)^2)' else value. :vartype invariant_mass: str :ivar pt: Transverse Momentum mode 'auto' or value. :vartype pt: str :ivar result: Result type 'total'/'pt'/'ptj'/'m'. :vartype result: str :ivar id: Set an id of this run. :vartype id: str :ivar model: Path for MadGraph model. :vartype model: str :ivar update: Update dependent `mu` else set to zero. :vartype update: bool .. py:attribute:: order .. py:attribute:: energy .. py:attribute:: energyhalf .. py:attribute:: particle1 .. py:attribute:: particle2 .. py:attribute:: slha .. py:attribute:: pdf_lo .. py:attribute:: pdfset_lo :value: 0 .. py:attribute:: pdf_nlo .. py:attribute:: pdfset_nlo :value: 0 .. py:attribute:: pdf_lo_id :value: 0 .. py:attribute:: pdf_nlo_id :value: 0 .. py:attribute:: mu_f :value: 1.0 .. py:attribute:: mu_r :value: 1.0 .. py:attribute:: precision :value: 0.001 .. py:attribute:: max_iters :value: 50 .. py:attribute:: invariant_mass :value: 'auto' .. py:attribute:: pt :value: 'auto' .. py:attribute:: result :value: 'total' .. py:attribute:: id :value: '' .. py:attribute:: model :value: '' .. py:attribute:: mu :value: 0.0 .. py:method:: has_gluino() .. py:method:: has_neutralino() .. py:method:: has_charginos() .. py:method:: has_weakino() .. py:method:: has_squark() .. py:method:: has_slepton() .. py:function:: get_output_dir() Get the output directory. :returns: :attr:`out_dir` :rtype: str .. py:function:: replace_macros(s) .. py:function:: get_name(pid) Get the latex name of a particle. :param pid: PDG Monte Carlo identifier for the particle. :type pid: int :returns: Latex name. :rtype: str .. rubric:: Examples >>> get_name(21) 'g' >>> get_name(1000022) '\\tilde{\\chi}_{1}^{0}' .. py:function:: title(i, axe=None, scenario=None, diff_L_R=None, extra='', cms_energy=True, pdf_info=True, id=False, **kwargs) Sets the title on axis `axe`. .. py:function:: energy_plot(dict_list, y, yscale=1.0, xaxis='E [GeV]', yaxis='$\\sigma$ [pb]', label=None, **kwargs) Plot energy on the x-axis. .. py:function:: combined_mass_plot(dict_list, y, part, label=None, **kwargs) .. py:function:: combined_plot(dict_list, x, y, label=None, **kwargs) .. py:function:: mass_plot(dict_list, y, part, logy=True, yaxis='$\\sigma$ [pb]', yscale=1.0, label=None, xaxis=None, **kwargs) .. py:function:: mass_vplot(dict_list, y, part, logy=True, yaxis='$\\sigma$ [pb]', yscale=1.0, label=None, mask=None, **kwargs) .. py:function:: get_mass(l, iid) Get the mass of particle with id `iid` out of the list in the "slha" element in the dict. Returns :obj:`list` of float : masses of particles in each element of the dict list. .. py:function:: plot(dict_list, x, y, label=None, xaxis='M [GeV]', yaxis='$\\sigma$ [pb]', ratio=False, K=False, K_plus_1=False, logy=True, yscale=1.0, mask=None, **kwargs) Creates a plot based on the entries `x`and `y` in `dict_list`. Examples .. plot:: :include-source: >>> import urllib.request >>> import hepi >>> dl = hepi.load(urllib.request.urlopen( ... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hino_NLO%2BNLL.json" ... )) >>> hepi.plot(dl,"N1","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]") .. py:function:: index_open(var, idx) .. py:function:: slha_data(li, index_list) .. py:function:: slha_plot(li, x, y, **kwargs) .. py:function:: vplot(x, y, label=None, xaxis='E [GeV]', yaxis='$\\sigma$ [pb]', logy=True, yscale=1.0, interpolate=True, plot_data=True, data_color=None, mask=-1, fill=False, data_fmt='.', fmt='-', print_area=False, sort=True, **kwargs) Creates a plot based on the values in `x`and `y`. .. py:function:: mass_mapplot(dict_list, part1, part2, z, logz=True, zaxis='$\\sigma$ [pb]', zscale=1.0, label=None) .. py:function:: mapplot(dict_list, x, y, z, xaxis=None, yaxis=None, zaxis=None, **kwargs) Examples .. plot:: :include-source: >>> import urllib.request >>> import hepi >>> dl = hepi.load(urllib.request.urlopen( ... "https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_SGmodel_GGxsec_NLO%2BNLL.json" ... ),dimensions=2) >>> hepi.mapplot(dl,"gl","sq","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{g}}$ [GeV]",yaxis="$m_{\\tilde{q}}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]") .. py:data:: map_vplot .. py:data:: scatter_vplot .. py:function:: scatterplot(dict_list, x, y, z, xaxis=None, yaxis=None, zaxis=None, **kwargs) Scatter map 2d. Central color is the central value, while the inner and outer ring are lower and upper bounds of the uncertainty interval. Examples .. plot:: :include-source: >>> import urllib.request >>> import hepi >>> dl = hepi.load(urllib.request.urlopen( ... "https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json" ... ),dimensions=2) >>> hepi.scatterplot(dl,"N1","N2","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]",yaxis="$m_{\\tilde{\\chi}_2^0}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]") .. py:data:: fig :value: None .. py:data:: axs :value: None .. py:data:: lines :value: [] .. py:data:: labels :value: [] .. py:function:: err_plt(axes, x, y, label=None, error=False) .. py:function:: scale_plot(dict_list, vl, seven_point_band=False, cont=False, error=True, li=None, plehn_color=False, yscale=1.0, unit='pb', yaxis=None, **kwargs) Creates a scale variance plot with 5 panels (xline). .. py:function:: central_scale_plot(dict_list, vl, cont=False, error=True, yscale=1.0, unit='pb', yaxis=None) Creates a scale variance plot with 3 panels (ystacked). .. py:function:: init_double_plot(figsize=(6, 8), sharex=True, sharey=False, gridspec_kw={'height_ratios': [3, 1]}) Initialze subplot for Ratio/K plots with another figure below. .. py:class:: DictData .. py:method:: __str__() Returns attributes as dict as string .. py:data:: required_numerical_uncertainty_factor :value: 5 .. py:data:: unv .. py:data:: usd .. py:class:: Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None) Bases: :py:obj:`hepi.util.DictData` General result class. All uncertainties are of numerical origin. :ivar LO: Leading Order result. Defaults to None. :vartype LO: :obj:`double` :ivar NLO: Next-to-Leading Order result. Defaults to None. :vartype NLO: :obj:`double` :ivar NLO_PLUS_NLL: Next-to-Leading Order plus Next-to-Leading Logarithm result. Defaults to None. :vartype NLO_PLUS_NLL: :obj:`double` :ivar K_LO: LO divided by LO. :vartype K_LO: :obj:`double` :ivar K_NLO: NLO divided by LO result. :vartype K_NLO: :obj:`double` :ivar K_NLO_PLUS_NLL: NLO+NLL divided by LO. :vartype K_NLO_PLUS_NLL: :obj:`double` :ivar K_aNNLO_PLUS_NNLL: aNNLO+NNLL divided by LO. :vartype K_aNNLO_PLUS_NNLL: :obj:`double` :ivar NLO_PLUS_NLL_OVER_NLO: NLO+NLL divided by NLO. :vartype NLO_PLUS_NLL_OVER_NLO: :obj:`double` :ivar aNNLO_PLUS_NNLL_OVER_NLO: aNNLO+NNLL divided by NLO. :vartype aNNLO_PLUS_NNLL_OVER_NLO: :obj:`double` Sets given and computes dependent ``Attributes``. :param lo: corresponds to :attr:`LO`. :type lo: :obj:`double` :param nlo: corresponds to :attr:`NLO`. :type nlo: :obj:`double` :param nlo_plus_nll: corresponds to :attr:`NLO_PLUS_NLL`. :type nlo_plus_nll: :obj:`double` :param annlo_plus_nnll: corresponds to :attr:`aNNLO_PLUS_NNLL`. :type annlo_plus_nnll: :obj:`double` .. py:attribute:: LO :value: None .. py:attribute:: NLO :value: None .. py:attribute:: NLO_PLUS_NLL :value: None .. py:attribute:: aNNLO_PLUS_NNLL :value: None .. py:function:: pdf_errors(li, dl, ordernames=None, confidence_level=90, n_jobs=None) Just like `pdf_error` but over a list of ordernames. .. py:function:: _pdf_error_single(members, i, dl, ordername, confidence_level=90) .. py:function:: pdf_error(li, dl, ordername='LO', confidence_level=90, n_jobs=None) Computes Parton Density Function (PDF) uncertainties through :func:`lhapdf.set.uncertainty`. :param li: Input list. :type li: :obj:`list` of :class:`Input` :param dl: :class:`Result` dictionary with lists per entry. :type dl: :obj:`dict` :param ordername: Name of the order. :type ordername: `str` :param confidence_level: Confidence Level for PDF uncertainty :type confidence_level: :obj:`double` :returns: Modified `dl` with new `ordername_{PDF,PDF_CENTRAL,PDF_ERRPLUS,PDF_ERRMINUS,PDF_ERRSYM}` entries. - (`ordername`)_`PDF` contains a symmetrized :mod:`uncertainties` object. :rtype: :obj:`dict` .. py:function:: scale_errors(li, dl, ordernames=None, n_jobs=None) Just like `scale_error` but over a list of ordernames. .. py:function:: _scale_error_single(members, i, dl, ordername='LO') .. py:function:: scale_error(li, dl, ordername='LO', n_jobs=None) Computes seven-point scale uncertainties from the results where the renormalization and factorization scales are varied by factors of 2 and relative factors of four are excluded (cf. :meth:`seven_point_scan`). :param li: Input list. :type li: :obj:`list` of :class:`Input` :param dl: :class:`Result` dictionary with lists per entry. :type dl: :obj:`dict` :returns: Modified `dl` with new `ordername_{SCALE,SCALE_ERRPLUS,SCALE_ERRMINUS}` entries. - `ordername_SCALE` contains a symmetrized :mod:`uncertainties` object. :rtype: :obj:`dict` .. py:function:: combine_errors(dl, ordernames=None) Just like `combine_error` but over a list of ordernames. .. py:function:: combine_error(dl, ordername='LO') Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition. .. note:: Running :func:`scale_errors` and :func:`pdf_errors` before is necessary. :param dl: :class:`Result` dictionary with lists per entry. :type dl: :obj:`dict` :returns: Modified `dl` with new `ordername_{COMBINED,ERRPLUS,ERRMINUS}` entries. - `ordername_COMBINED` contains a symmetrized :mod:`uncertainties` object. :rtype: :obj:`dict` .. py:function:: asym_to_sym_error(central, errminus, errplus) .. py:function:: add_errors(error1, error2) .. py:function:: asym_to_sym_combined_error(central, errminus1, errplus1, errminus2, errplus2) .. py:class:: Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True) Bases: :py:obj:`hepi.util.DictData` Input for computation and scans. :ivar order: LO, NLO or NLO+NLL computation. :vartype order: :class:`Order` :ivar energy: CMS energy in GeV. :vartype energy: int :ivar energyhalf: Halfed `energy`. :vartype energyhalf: int :ivar particle1: PDG identifier of the first final state particle. :vartype particle1: int :ivar particle2: PDG identifier of the second final state particle. :vartype particle2: int :ivar slha: File path of for the base slha. Modified slha files will be used if a scan requires a change of the input. :vartype slha: str :ivar pdf_lo: LO PDF name. :vartype pdf_lo: str :ivar pdf_nlo: NLO PDF name. :vartype pdf_nlo: str :ivar pdfset_lo: LO PDF member/set id. :vartype pdfset_lo: int :ivar pdfset_nlo: NLO PDF member/set id. :vartype pdfset_nlo: int :ivar pdf_lo_id: LO PDF first member/set id. :vartype pdf_lo_id: int :ivar pdf_nlo_id: NLO PDF first member/set id. :vartype pdf_nlo_id: int :ivar mu: central scale factor. :vartype mu: double :ivar mu_f: Factorization scale factor. :vartype mu_f: double :ivar mu_r: Renormalization scale factor. :vartype mu_r: double :ivar precision: Desired numerical relative precision. :vartype precision: double :ivar max_iters: Upper limit on integration iterations. :vartype max_iters: int :ivar invariant_mass: Invariant mass mode 'auto = sqrt((p1+p2)^2)' else value. :vartype invariant_mass: str :ivar pt: Transverse Momentum mode 'auto' or value. :vartype pt: str :ivar result: Result type 'total'/'pt'/'ptj'/'m'. :vartype result: str :ivar id: Set an id of this run. :vartype id: str :ivar model: Path for MadGraph model. :vartype model: str :ivar update: Update dependent `mu` else set to zero. :vartype update: bool .. py:attribute:: order .. py:attribute:: energy .. py:attribute:: energyhalf .. py:attribute:: particle1 .. py:attribute:: particle2 .. py:attribute:: slha .. py:attribute:: pdf_lo .. py:attribute:: pdfset_lo :value: 0 .. py:attribute:: pdf_nlo .. py:attribute:: pdfset_nlo :value: 0 .. py:attribute:: pdf_lo_id :value: 0 .. py:attribute:: pdf_nlo_id :value: 0 .. py:attribute:: mu_f :value: 1.0 .. py:attribute:: mu_r :value: 1.0 .. py:attribute:: precision :value: 0.001 .. py:attribute:: max_iters :value: 50 .. py:attribute:: invariant_mass :value: 'auto' .. py:attribute:: pt :value: 'auto' .. py:attribute:: result :value: 'total' .. py:attribute:: id :value: '' .. py:attribute:: model :value: '' .. py:attribute:: mu :value: 0.0 .. py:method:: has_gluino() .. py:method:: has_neutralino() .. py:method:: has_charginos() .. py:method:: has_weakino() .. py:method:: has_squark() .. py:method:: has_slepton() .. py:class:: Order Bases: :py:obj:`enum.IntEnum` Computation orders. Initialize self. See help(type(self)) for accurate signature. .. py:attribute:: LO :value: 0 Leading Order .. py:attribute:: NLO :value: 1 Next-to-Leading Order .. py:attribute:: NLO_PLUS_NLL :value: 2 Next-to-Leading Order plus Next-to-Leading Logarithms .. py:attribute:: aNNLO_PLUS_NNLL :value: 3 Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms .. py:function:: get_input_dir() Get the input directory. :returns: :attr:`in_dir` :rtype: str .. py:function:: get_output_dir() Get the output directory. :returns: :attr:`out_dir` :rtype: str .. py:function:: get_pre() Gets the command prefix. :returns: :attr:`pre` :rtype: str .. py:class:: Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None) Bases: :py:obj:`hepi.util.DictData` General result class. All uncertainties are of numerical origin. :ivar LO: Leading Order result. Defaults to None. :vartype LO: :obj:`double` :ivar NLO: Next-to-Leading Order result. Defaults to None. :vartype NLO: :obj:`double` :ivar NLO_PLUS_NLL: Next-to-Leading Order plus Next-to-Leading Logarithm result. Defaults to None. :vartype NLO_PLUS_NLL: :obj:`double` :ivar K_LO: LO divided by LO. :vartype K_LO: :obj:`double` :ivar K_NLO: NLO divided by LO result. :vartype K_NLO: :obj:`double` :ivar K_NLO_PLUS_NLL: NLO+NLL divided by LO. :vartype K_NLO_PLUS_NLL: :obj:`double` :ivar K_aNNLO_PLUS_NNLL: aNNLO+NNLL divided by LO. :vartype K_aNNLO_PLUS_NNLL: :obj:`double` :ivar NLO_PLUS_NLL_OVER_NLO: NLO+NLL divided by NLO. :vartype NLO_PLUS_NLL_OVER_NLO: :obj:`double` :ivar aNNLO_PLUS_NNLL_OVER_NLO: aNNLO+NNLL divided by NLO. :vartype aNNLO_PLUS_NNLL_OVER_NLO: :obj:`double` Sets given and computes dependent ``Attributes``. :param lo: corresponds to :attr:`LO`. :type lo: :obj:`double` :param nlo: corresponds to :attr:`NLO`. :type nlo: :obj:`double` :param nlo_plus_nll: corresponds to :attr:`NLO_PLUS_NLL`. :type nlo_plus_nll: :obj:`double` :param annlo_plus_nnll: corresponds to :attr:`aNNLO_PLUS_NNLL`. :type annlo_plus_nnll: :obj:`double` .. py:attribute:: LO :value: None .. py:attribute:: NLO :value: None .. py:attribute:: NLO_PLUS_NLL :value: None .. py:attribute:: aNNLO_PLUS_NNLL :value: None .. py:function:: DL2DF(ld) Convert a `dict` of `list`s to a `pandas.DataFrame`. .. py:function:: LD2DL(l, actual_dict=False) Convert a list of objects into a dictionary of lists. The values of each object are first converted to a `dict` through the `__dict__` attribute. :param l: list of objects. :type l: List :param actual_dict: objects are already dicts :type actual_dict: bool :returns: dictionary of numpy arrays. :rtype: dict .. rubric:: Examples >>> class Param: ... def __init__(self,a,b,c): ... self.a = a ... self.b = b ... self.c = c >>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ]) {'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])} .. py:class:: DictData .. py:method:: __str__() Returns attributes as dict as string .. py:function:: namehash(n) Creates a sha256 hash from the objects string representation. :param n: object. :type n: any :returns: sha256 of object. :rtype: str .. rubric:: Examples >>> p = {'a':1,'b':2} >>> str(p) "{'a': 1, 'b': 2}" >>> namehash(str(p)) '3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8' >>> namehash(p) '3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8' .. py:class:: RunParam(skip = False, in_file = None, out_file = None, execute = None, name = None) Bases: :py:obj:`hepi.util.DictData` Abstract class that is similar to a dictionary but with fixed keys. .. py:attribute:: name :value: None .. py:attribute:: skip :value: False .. py:attribute:: in_file :value: None .. py:attribute:: out_file :value: None .. py:attribute:: execute :value: None .. py:class:: Runner(path, in_dir = None, out_dir = None, pre=None) .. py:attribute:: path .. py:method:: orders() List of supported Orders in this runner. .. py:method:: get_name() Returns the name of the runner. .. py:method:: get_version() .. py:method:: _sub_run(coms) .. py:method:: _check_path() Checks if the passed path is valid. .. py:method:: _prepare(p, skip=False, assume_valid=False, **kwargs) .. py:method:: _check_input(param, **kwargs) .. py:method:: _prepare_all(params, skip=True, n_jobs=None, **kwargs) Prepares all parameters for execution. :param params: List of input parameters. :type params: List[:class:`hepi.Input`] :param skip: If True, the runner will check if the output file already exists and skip the execution if it does. Defaults to True. :type skip: bool, optional :param n_jobs: Number of parallel jobs. If None, use all available cores. :type n_jobs: int .. py:method:: run(params, skip=True, parse=True, parallel=True, sleep=0, run=True, ignore_error=False, n_jobs=None, **kwargs) Run the passed list of parameters. :param params: All parameters that should be executed/queued. :type params: :obj:`list` of :class:`hepi.Input` :param skip: True means stored runs will be skipped. Else the are overwritten. :type skip: bool :param parse: Parse the results. This is not the prefered cluster/parallel mode, as there the function only queues the job. :type parse: bool :param parallel: Run jobs in parallel. :type parallel: bool :param sleep: Sleep seconds after starting job. :type sleep: int :param run: Actually start/queue runner. :type run: bool :param ignore_error: Continue instead of raising Exceptions. Also ignores hash collisions. :type ignore_error: bool :param n_jobs: Number of parallel jobs. If None, use all available cores. :type n_jobs: int :returns: combined dataframe of results and parameters. The dataframe is empty if `parse` is set to False. :rtype: :obj:`pd.DataFrame` .. py:method:: _run(rps, wait=True, parallel=True, sleep=0, n_jobs=None, **kwargs) Runs Runner per :class:`RunParams`. :param rps: Extended run parameters. :type rps: :obj:`list` of :class:`RunParams` :param bar: Enable info bar. :type bar: bool :param wait: Wait for parallel runs to finish. :type wait: bool :param sleep: Sleep seconds after starting subprocess. :type sleep: int :param parallel: Run jobs in parallel. :type parallel: bool :param n_jobs: Number of parallel jobs. If None, use all available cores. :type n_jobs: int :returns: return codes from jobs if `no_parse` is False. :rtype: :obj:`list` of int .. py:method:: _is_valid(file, p, d, **kwargs) Verifies that a file is a complete output. :param file: File path to be parsed. :type file: str :param p: Onput parameters. :type p: :class:`hepi.Input` :param d: Param dictionary. :type d: :obj:`dict` :returns: True if `file` could be parsed. :rtype: bool .. py:method:: parse(outputs, n_jobs=None) Parses Resummino output files and returns List of Results. Args: outputs (:obj:`list` of `str`): List of the filenames to be parsed. n_jobs (int): Number of parallel jobs. If None, use all available cores. Returns: :obj:`list` of :class:`hepi.resummino.result.ResumminoResult` .. py:method:: _parse_file(file) Extracts results from an output file. :param file: File path to be parsed. :type file: str :returns: If a value is not found in the file None is used. :rtype: :class:`Result` .. py:method:: get_path() Get the Runner path. :returns: current Runner path. :rtype: str .. py:method:: get_input_dir() Get the input directory. :returns: :attr:`in_dir` :rtype: str .. py:method:: get_output_dir() Get the input directory. :returns: :attr:`out_dir` :rtype: str .. py:method:: get_pre() Gets the command prefix. :returns: :attr:`pre` :rtype: str .. py:method:: set_path(p) Set the path to the Runner folder containing the binary in './bin' or './build/bin'. :param p: new path. :type p: str .. py:method:: set_input_dir(indir) Sets the input directory. :param indir: new input directory. :type indir: str .. py:method:: set_output_dir(outdir, create = True) Sets the output directory. :param outdir: new output directory. create (bool): create directory if not existing. :type outdir: str .. py:method:: set_pre(ppre) Sets the command prefix. :param ppre: new command prefix. :type ppre: str .. py:class:: DictData .. py:method:: __str__() Returns attributes as dict as string .. py:function:: LD2DL(l, actual_dict=False) Convert a list of objects into a dictionary of lists. The values of each object are first converted to a `dict` through the `__dict__` attribute. :param l: list of objects. :type l: List :param actual_dict: objects are already dicts :type actual_dict: bool :returns: dictionary of numpy arrays. :rtype: dict .. rubric:: Examples >>> class Param: ... def __init__(self,a,b,c): ... self.a = a ... self.b = b ... self.c = c >>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ]) {'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])} .. py:function:: DL2DF(ld) Convert a `dict` of `list`s to a `pandas.DataFrame`. .. py:function:: namehash(n) Creates a sha256 hash from the objects string representation. :param n: object. :type n: any :returns: sha256 of object. :rtype: str .. rubric:: Examples >>> p = {'a':1,'b':2} >>> str(p) "{'a': 1, 'b': 2}" >>> namehash(str(p)) '3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8' >>> namehash(p) '3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8' .. py:function:: lhapdf_name_to_id(name) Converts a LHAPDF name to the sets id. :param name: LHAPDF set name. :type name: str :returns: id of the LHAPDF set. :rtype: int .. rubric:: Examples >>> lhapdf_name_to_id("CT14lo") 13200 .. py:function:: lhapdf_id_to_name(lid) .. py:data:: package :value: 'hepi' .. py:data:: __version__