Source code for hepi.input

from enum import IntEnum
import os
import shutil
import warnings
import copy

import numpy as np
from typing import Iterable, List

import pyslha
from .util import DictData, get_LR_partner, lhapdf_name_to_id

import lhapdf

[docs]in_dir = "./input/"
"""Input directory."""
[docs]out_dir = "./output/"
"""Output directory."""
[docs]pre = "nice -n 5"
"""Prefix for run commands."""


[docs]def get_input_dir():
    """
    Get the input directory.

    Returns:
        str: :attr:`in_dir`
    """
    global in_dir
    return in_dir


[docs]def get_output_dir():
    """
    Get the input directory.

    Returns:
        str: :attr:`out_dir`
    """
    global out_dir
    return out_dir


[docs]def get_pre():
    """
    Gets the command prefix.

    Returns:
        str: :attr:`pre`
    """
    global pre
    return pre


[docs]def set_input_dir(ind):
    """
    Sets the input directory.

    Args:
        ind (str): new input directory.
    """
    global in_dir
    in_dir = ind


[docs]def set_output_dir(outd, create: bool = True):
    """
    Sets the output directory.

    Args:
        outd (str): new output directory.
		create (bool): create directory if not existing
    """
    global out_dir
    if create:
        os.makedirs(outd, exist_ok=True)
    out_dir = outd


[docs]def set_pre(ppre):
    """
    Sets the command prefix.

    Args:
        ppre (str): new command prefix.
    """
    global pre
    pre = ppre


[docs]class Order(IntEnum):
    """
    Computation orders.
    """
[docs]    LO = 0
    """Leading Order"""
[docs]    NLO = 1
    """Next-to-Leading Order"""
[docs]    NLO_PLUS_NLL = 2
    """Next-to-Leading Order plus Next-to-Leading Logarithms"""
[docs]    aNNLO_PLUS_NNLL = 3
    """Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms"""


[docs]def replace_macros(s: str) -> str:
    return s.replace("_PLUS_", "+").replace(" ", "\\ ")


[docs]def order_to_string(o: Order, json_style=False, no_macros=False) -> str:
    ret = ""
    if o == Order.LO:
        ret = "LO"
    elif o == Order.NLO:
        ret = "NLO"
    elif o == Order.NLO_PLUS_NLL:
        ret = "NLO_PLUS_NLL"
    elif o == Order.aNNLO_PLUS_NNLL:
        if json_style:
            ret = "NNLOapprox+NNLL"
        else:
            ret = "aNNLO_PLUS_NNLL"
    else:
        raise ValueError("Order '" + o + "' not supported by HEPi.")
    if no_macros:
        return replace_macros(ret)
    return ret


[docs]def xsec_to_order(s: str):
    if s == "NNLOapprox+NNLL":
        return Order.aNNLO_PLUS_NNLL
    elif s == "NLO+NLL":
        return Order.NLO_PLUS_NLL
    elif s == "NLO":
        return Order.NLO
    elif s == "LO":
        return Order.LO
    else:
        raise ValueError("Unknown Order '" + s + "', not supported by HEPi.")


[docs]class Input(DictData):
    """
    Input for computation and scans.

    Attributes:
        order (:class:`Order`): LO, NLO or NLO+NLL computation.
        energy (int): CMS energy in GeV.
        energyhalf (int): Halfed `energy`.
        particle1 (int): PDG identifier of the first final state particle.
        particle2 (int): PDG identifier of the second final state particle.
        slha (str): File path of for the base slha.
            Modified slha files will be used if a scan requires a change of the input.
        pdf_lo (str): LO PDF name.
        pdf_nlo (str): NLO PDF name.
        pdfset_lo (int): LO PDF member/set id.
        pdfset_nlo (int): NLO PDF member/set id.
        pdf_lo_id (int):  LO PDF first member/set id.
        pdf_nlo_id (int): NLO PDF first member/set id.
        mu (double): central scale factor.
        mu_f (double): Factorization scale factor.
        mu_r (double): Renormalization scale factor.
        precision (double): Desired numerical relative precision.
        max_iters (int): Upper limit on integration iterations.
        invariant_mass (str): Invariant mass mode 'auto = sqrt((p1+p2)^2)' else value.
        pt (str): Transverse Momentum mode 'auto' or value.
        result (str): Result type 'total'/'pt'/'ptj'/'m'.
        id (str): Set an id of this run. 
        model (str): Path for MadGraph model.
        update (bool): Update dependent `mu` else set to zero.
    """

    # TODO allow unspecified input? Maybe with kwargs + defaults
    def __init__(self,
                 order: Order,
                 energy: float,
                 particle1: int,
                 particle2: int,
                 slha: str,
                 pdf_lo: str,
                 pdf_nlo: str,
                 mu_f=1.0,
                 mu_r=1.0,
                 pdfset_lo=0,
                 pdfset_nlo=0,
                 precision=0.01,
                 max_iters=50,
                 invariant_mass="auto",
                 result="total",
                 pt="auto",
                 id="",
                 model="",
                 update=True):
        self.order = order
        self.energy = energy
        self.energyhalf = energy / 2.
        self.particle1 = particle1
        self.particle2 = particle2
        self.slha = slha
        self.pdf_lo = pdf_lo
        self.pdfset_lo = pdfset_lo
        self.pdf_nlo = pdf_nlo
        self.pdfset_nlo = pdfset_nlo
        self.pdf_lo_id = lhapdf_name_to_id(pdf_lo)
        self.pdf_nlo_id = lhapdf_name_to_id(pdf_nlo)
        self.mu_f = mu_f
        self.mu_r = mu_r
        self.precision = precision
        self.max_iters = max_iters
        self.invariant_mass = invariant_mass
        self.pt = pt
        self.result = result
        self.id = id
        self.model = model
        self.mu = 0.
        if os.path.isfile(get_input_dir() + self.slha):
            shutil.copy(get_input_dir() + self.slha,
                        get_output_dir() + self.slha)
        if update:
            update_slha(self)

[docs]    def has_gluino(self) -> bool:
        return is_gluino(self.particle1) or is_gluino(self.particle2)

[docs]    def has_neutralino(self) -> bool:
        return is_neutralino(self.particle1) or is_neutralino(self.particle2)

[docs]    def has_charginos(self) -> bool:
        return is_chargino(self.particle1) or is_chargino(self.particle2)

[docs]    def has_weakino(self) -> bool:
        return self.has_charginos() or self.has_neutralino()

[docs]    def has_squark(self) -> bool:
        return is_squark(self.particle1) or is_squark(self.particle2)

[docs]    def has_slepton(self) -> bool:
        return is_slepton(self.particle1) or is_slepton(self.particle2)


[docs]def is_gluino(iid: int) -> bool:
    return iid == 1000021


[docs]def is_neutralino(iid: int) -> bool:
    neutralinos = [1000022, 1000023, 1000025, 1000035]
    return abs(iid) in neutralinos


[docs]def is_chargino(iid: int) -> bool:
    charginos = [1000024, 1000037]
    return abs(iid) in charginos


[docs]def is_weakino(iid: int) -> bool:
    return is_chargino(iid) or is_neutralino(iid)


[docs]def is_squark(iid: int) -> bool:
    l_squark = range(1000001, 1000007)
    r_squark = range(2000001, 2000007)
    return abs(iid) in l_squark or abs(iid) in r_squark


[docs]def is_slepton(iid: int) -> bool:
    l_slepton = range(1000011, 1000016)
    r_slepton = range(2000011, 2000016)  # TODO remove righthandend snu's
    return abs(iid) in l_slepton or abs(iid) in r_slepton


[docs]def update_slha(i: Input):
    """
    Updates dependent parameters in Input `i`.

    Mainly concerns the `mu` value used by `madgraph`.
    """
    b = pyslha.read(get_output_dir() + i.slha, ignorenomass=True)
    try:
        i.mu = (abs(b.blocks["MASS"][abs(i.particle1)]) +
                abs(b.blocks["MASS"][abs(i.particle2)])) / 2.
    except Exception:
        warnings.warn("Could not set new central scale to average of masses.",
                      RuntimeWarning)

[docs]def scan(input_list: List[Input], var: str, rrange: Iterable) -> List[Input]:
    """
    Scans a variable `var` over `rrange` in `input_list`.

    Note:
        This function does not ensure that dependent vairables are updated (see `energyhalf` in Examples).

    Args:
        input_list (:obj:`list` of :class:`Input`): Input parameters that get scanned each.
        var (str): Scan variable name.
        rrange (Iterable): Range of `var` to be scanned.

    Returns:
        :obj:`list` of :class:`Input`: Modified list with scan runs added.

    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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, '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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
 
    """
    ret = []
    for s in input_list:
        for r in rrange:
            tmp = copy.copy(s)
            setattr(tmp, var, r)
            ret.append(tmp)
    return ret


[docs]def scan_multi(li: List[Input], **kwargs) -> List[Input]:
    """
    Magically scans the variables passed to this function.

    Args:
        **kwargs: 

    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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, '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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO_PLUS_NLL: 2>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
 
    """
    for k, v in kwargs.items():
        li = scan(li, var=k, rrange=v)
    return li


[docs]multi_scan = scan_multi


[docs]def scan_scale(l: List[Input], rrange=3, distance=2.):
    """
    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.

    Examples:
        >>> li = [Input(Order.LO, 13000,  1000022,1000022, "None", "CT14lo","CT14lo",update=False)]
        >>> for e in scan_scale(li):
        ...     print(e)
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
    """
    ret = []
    for s in l:
        # not on error pdfs
        if s.pdfset_nlo == 0:
            tmp = scan([s], "mu_f",
                       np.logspace(np.log10(1. / distance), np.log10(distance),
                                   rrange))
            tmp = scan(
                tmp, "mu_r",
                np.logspace(np.log10(1. / distance), np.log10(distance),
                            rrange))
            for t in tmp:
                if t.mu_f == 1.0 or t.mu_r == 1.0 or t.mu_f == t.mu_r or t.mu_f == distance or t.mu_f == 1. / distance or t.mu_r == distance or t.mu_r == 1. / distance:
                    ret.append(t)

        else:
            ret.append(s)

    return ret


[docs]scale_scan = scan_scale


[docs]def scan_seven_point(input_list: List[Input]):
    """
    Scans scale by varying `mu_f` and `mu_r` by factors of two excluding relative factors of 4.

    Examples:
        >>> li = [Input(Order.LO, 13000,  1000022,1000022, "None", "CT14lo","CT14lo",update=False)]
        >>> for e in scan_seven_point(li):
        ...     print(e)
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
    """
    rrange = 3
    distance = 2.
    ret = []
    for s in input_list:
        # not on error pdfs
        if s.pdfset_nlo == 0 and s.mu_f == 1.0 and s.mu_r == 1.0:
            tmp = scan([s], "mu_f",
                       np.logspace(np.log10(1. / distance), np.log10(distance),
                                   rrange))
            tmp = scan(
                tmp, "mu_r",
                np.logspace(np.log10(1. / distance), np.log10(distance),
                            rrange))
            for t in tmp:
                if not ((t.mu_f == distance and t.mu_r == 1. / distance) or
                        (t.mu_r == distance and t.mu_f == 1. / distance)):
                    ret.append(t)

        else:
            ret.append(s)

    return ret


[docs]seven_point_scan = scan_seven_point


[docs]def keep_where(input_list: List[Input], condition):
    """
    Only keep the inputs where the condition is true.

    Inversion of the `remove_where` function.

    Parameters
    ----------
    input_list : List[Input]
        The list of inputs to filter.
    condition : Callable[[Input.__dict__], bool]
        The condition to filter on.
    """
    return remove_where(input_list, lambda x: not condition(x))


[docs]def remove_where(input_list: List[Input], condition, **kwargs):
    """
    Remove elements in list which satisfy condition.

    Parameters
    ----------
    input_list : List[Input]
        The list of inputs to filter.
    condition : Callable[[Input.__dict__], bool]
        The condition to filter on.

    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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
    """
    nl = []
    for e in input_list:
        if not condition(e.__dict__):
            nl.append(e)
    return nl


[docs]def change_where(input_list: List[Input], dicts: dict, **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.

    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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, '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': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.LO: 0>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
    """
    ret = []
    for s in input_list:
        ok = True
        for k, v in kwargs.items():
            if getattr(s, k) != v:
                ok = False
        if ok:
            tmp = copy.copy(s)
            for k, v in dicts.items():
                setattr(tmp, k, v)
            ret.append(tmp)
        else:
            ret.append(s)

    return ret


[docs]def scan_invariant_mass(input_list: List[Input], diff, points, low=0.001):
    """
    Logarithmic `invariant_mass` scan close to the production threshold.
    """
    ret = []
    for s in input_list:
        for r in s.mu * 2. + low + (np.logspace(
                np.log10(low), np.log10(1 + low), points) - low) * diff:
            tmp = copy.copy(s)
            setattr(tmp, "invariant_mass", r)
            tmp.result = "m"
            ret.append(tmp)
    return ret


[docs]def slha_write(newname, d):
    if len(newname) >= 256:
        warnings.warn("probably too long filename")
    f = get_output_dir() + newname
    pyslha.write(f, d)
    with open(f) as reader, open(f, 'r+') as writer:
        for line in reader:
            if line.strip():
                writer.write(line)
            else:
                writer.write("#\n")
        writer.truncate()


[docs]def masses_scan(input_list: List[Input],
                varis: List[int],
                rrange,
                diff_L_R=None,
                negate=None) -> List[Input]:
    """
    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.
    """
    if negate is None:
        negate = []
    ret = []
    for s in input_list:
        for r in rrange:
            d = None
            try:
                d = pyslha.read(s.slha)
            except Exception:
                d = pyslha.read(get_output_dir() + s.slha)

            mlist = ""
            for var in varis:
                d.blocks["MASS"][abs(var)] = r if not var in negate else -r
                if not (diff_L_R is None):
                    is_L, v = get_LR_partner(abs(var))
                    d.blocks["MASS"][abs(v)] = r + is_L * diff_L_R
                mlist = mlist + "_" + str(var)

            newname = s.slha + "_mass" + mlist + "_" + str(r)
            slha_write(newname, d)

            tmp = copy.copy(s)
            setattr(tmp, "mass" + mlist, r)
            setattr(tmp, "slha", newname)
            update_slha(tmp)
            ret.append(tmp)
    return ret


[docs]def mass_scan(input_list: List[Input], var: int, rrange, diff_L_R=None) -> List[Input]:
    """
    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.
    """
    return masses_scan(input_list, [var], rrange, diff_L_R)


[docs]def slha_scan(input_list: List[Input], block, var, rrange: List) -> List[Input]:
    """
    Scan a generic
    """
    return slha_scan_rel(input_list, lambda r, : [(block, var, r)], rrange)


[docs]def slha_scan_rel(input_list: List[Input], lambdas, rrange: List) -> List[Input]:
    """
    Scan a generic slha variable.
    """
    ret = []
    for s in input_list:
        for r in rrange:
            d = None
            tmp = copy.copy(s)
            newname = s.slha
            try:
                d = pyslha.read(s.slha, ignorenomass=True)
            except:
                d = pyslha.read(get_output_dir() + s.slha, ignorenomass=True)
            ls = lambdas(r)
            for b, v, res in ls:
                d.blocks[b][v] = res
                setattr(tmp, b + "_" + str(v), res)
                newname = newname + "_" + str(b) + "_" + str(v).replace(" ","") + "_" + str(
                    res)
            #pyslha.write(get_output_dir()+newname, d)
            slha_write(newname, d)

            setattr(tmp, "slha", newname)
            update_slha(tmp)
            ret.append(tmp)
    return ret


[docs]def scan_pdf(input_list: List[Input]):
    """
    Scans NLO PDF sets.

    The PDF sets are infered from `lhapdf.getPDFSet` with the argument of `pdfset_nlo`.

    Examples:
        >>> li = [Input(Order.NLO, 13000,  1000022,1000022, "None", "CT14lo","CT14nlo",update=False)]
        >>> for e in scan_pdf(li):
        ...     print(e)
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
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        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 48, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 49, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 50, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 51, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 52, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 53, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
        {'order': <Order.NLO: 1>, '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': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
    """
    ret = []
    for s in input_list:
        # only central scale
        if s.mu_f == 1.0 and s.mu_r == 1.0:
            sset = lhapdf.getPDFSet(s.pdf_nlo)
            for r in range(sset.size):
                tmp = copy.copy(s)
                setattr(tmp, "pdfset_nlo", r)
                ret.append(tmp)
        else:
            ret.append(s)
    return ret


[docs]pdf_scan = scan_pdf