`hepi.input`

Module Contents

Classes

`Order`	Computation orders.
`Input`	Input for computation and scans.

Functions

`get_input_dir`()	Get the input directory.
`get_output_dir`()	Get the input directory.
`get_pre`()	Gets the command prefix.
`set_input_dir`(ind)	Sets the input directory.
`set_output_dir`(outd, create: bool = True)	Sets the output directory.
`set_pre`(ppre)	Sets the command prefix.
`replace_macros`(s: str) → str
`order_to_string`(o: Order, json_style=False, no_macros=False) → str
`xsec_to_order`(s: str)
`is_gluino`(iid: int) → bool
`is_neutralino`(iid: int) → bool
`is_chargino`(iid: int) → bool
`is_weakino`(iid: int) → bool
`is_squark`(iid: int) → bool
`is_slepton`(iid: int) → bool
`update_slha`(i: Input)	Updates dependent parameters in Input i.
`scan`(l: List[Input], var: str, rrange: Iterable) → List[Input]	Scans a variable var over rrange in l.
`scan_multi`(li: List[Input], **kwargs) → List[Input]	Magically scans the variables passed to this function.
`scan_scale`(l: List[Input], rrange=3, distance=2.0)	Scans scale by varying mu_f and mu_r.
`scan_seven_point`(l: List[Input])	Scans scale by varying mu_f and mu_r by factors of two excluding relative factors of 4.
`change_where`(l: List[Input], dicts: dict, **kwargs)	Applies the values of dicts if the key value pairs in kwargs agree with a member of the list l.
`scan_invariant_mass`(l: List[Input], diff, points, low=0.001)	Logarithmic invariant_mass scan close to the production threshold.
`slha_write`(newname, d)
`masses_scan`(l: 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 l.
`mass_scan`(l: List[Input], var: int, rrange, diff_L_R=None) → List[Input]	Scans the PDG identified mass var over rrange in the list l.
`slha_scan`(l: List[Input], block, var, rrange: List) → List[Input]	Scan a generic
`slha_scan_rel`(l: List[Input], lambdas, rrange: List) → List[Input]	Scan a generic slha variable.
`scan_pdf`(l: List[Input])	Scans NLO PDF sets.

Attributes

`in_dir`	Input directory.
`out_dir`	Output directory.
`pre`	Prefix for run commands.
`multi_scan`
`scale_scan`
`seven_point_scan`
`pdf_scan`

hepi.input.in_dir = ./input/[source]: Input directory.

hepi.input.out_dir = ./output/[source]: Output directory.

hepi.input.pre = nice -n 5[source]: Prefix for run commands.

hepi.input.get_input_dir()[source]

Get the input directory.

Returns: in_dir
Return type: str

hepi.input.get_output_dir()[source]

Get the input directory.

Returns: out_dir
Return type: str

hepi.input.get_pre()[source]

Gets the command prefix.

Returns: pre
Return type: str

hepi.input.set_input_dir(ind)[source]

Sets the input directory.

Parameters: ind (str) – new input directory.

hepi.input.set_output_dir(outd, create: bool = True)[source]

Sets the output directory.

Parameters: outd (str) – new output directory. create (bool): create directory if not existing

hepi.input.set_pre(ppre)[source]

Sets the command prefix.

Parameters: ppre (str) – new command prefix.

class hepi.input.Order[source]

Bases: enum.IntEnum

Computation orders.

Initialize self. See help(type(self)) for accurate signature.

LO = 0[source]: Leading Order

NLO = 1[source]: Next-to-Leading Order

NLO_PLUS_NLL = 2[source]: Next-to-Leading Order plus Next-to-Leading Logarithms

aNNLO_PLUS_NNLL = 3[source]: Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms

hepi.input.replace_macros(s: str) → str[source]

hepi.input.order_to_string(o: Order, json_style=False, no_macros=False) → str[source]

hepi.input.xsec_to_order(s: str)[source]

class hepi.input.Input(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)[source]

Bases: hepi.util.DictData

Input for computation and scans.

Variables

order (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.

has_gluino(self) → bool[source]

has_neutralino(self) → bool[source]

has_charginos(self) → bool[source]

has_weakino(self) → bool[source]

has_squark(self) → bool[source]

has_slepton(self) → bool[source]

hepi.input.is_gluino(iid: int) → bool[source]

hepi.input.is_neutralino(iid: int) → bool[source]

hepi.input.is_chargino(iid: int) → bool[source]

hepi.input.is_weakino(iid: int) → bool[source]

hepi.input.is_squark(iid: int) → bool[source]

hepi.input.is_slepton(iid: int) → bool[source]

hepi.input.update_slha(i: Input)[source]

Updates dependent parameters in Input i.

Mainly concerns the mu value used by madgraph.

hepi.input.scan(l: List[Input], var: str, rrange: Iterable) → List[Input][source]

Scans a variable var over rrange in l.

Note

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

Parameters

l (list of Input) – Input parameters that get scanned each.
var (str) – Scan variable name.
rrange (Iterable) – Range of var to be scanned.

Returns

Modified list with scan runs added.

Return type

list of Input

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}

hepi.input.scan_multi(li: List[Input], **kwargs) → List[Input][source]

Magically scans the variables passed to this function.

Parameters: **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}

hepi.input.multi_scan[source]

hepi.input.scan_scale(l: List[Input], rrange=3, distance=2.0)[source]

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}

hepi.input.scale_scan[source]

hepi.input.scan_seven_point(l: List[Input])[source]

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}

hepi.input.seven_point_scan[source]

hepi.input.change_where(l: List[Input], dicts: dict, **kwargs)[source]

Applies the values of dicts if the key value pairs in kwargs agree with a member of the list l.

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}

hepi.input.scan_invariant_mass(l: List[Input], diff, points, low=0.001)[source]: Logarithmic invariant_mass scan close to the production threshold.

hepi.input.slha_write(newname, d)[source]

hepi.input.masses_scan(l: List[Input], varis: List[int], rrange, diff_L_R=None, negate=None) → List[Input][source]: Scans the PDG identified masses in varis over rrange in the list l. diff_L_R allows to set a fixed difference between masses of left- and right-handed particles.

hepi.input.mass_scan(l: List[Input], var: int, rrange, diff_L_R=None) → List[Input][source]: 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.

hepi.input.slha_scan(l: List[Input], block, var, rrange: List) → List[Input][source]: Scan a generic

hepi.input.slha_scan_rel(l: List[Input], lambdas, rrange: List) → List[Input][source]: Scan a generic slha variable.

hepi.input.scan_pdf(l: List[Input])[source]

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}
{'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': 6, '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': 7, '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': 8, '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': 9, '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': 10, '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': 11, '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': 12, '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': 13, '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': 14, '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': 15, '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': 16, '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': 17, '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': 18, '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': 19, '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': 20, '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': 21, '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': 22, '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': 23, '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': 24, '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': 25, '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': 26, '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': 27, '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': 28, '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': 29, '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': 30, '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': 31, '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': 32, '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': 33, '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': 34, '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': 35, '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': 36, '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': 37, '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': 38, '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': 39, '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': 40, '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': 41, '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': 42, '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': 43, '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': 44, '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': 45, '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': 46, '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': 47, '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': 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}

hepi.input.pdf_scan[source]

hepi.input

Module Contents

Classes

Functions

Attributes

`hepi.input`