hepi

The HEPi package aims to automize cluster computations for parameter scans with the option to produce plots.

Submodules

Attributes

in_dir

Input directory.

out_dir

Output directory.

pre

Prefix for run commands.

multi_scan

scale_scan

seven_point_scan

pdf_scan

load

unv

usd

tex_table

map_vplot

scatter_vplot

fig

axs

lines

labels

required_numerical_uncertainty_factor

unv

usd

package

__version__

Classes

Order

Computation orders.

DictData

Input

Input for computation and scans.

Input

Input for computation and scans.

Order

Computation orders.

Input

Input for computation and scans.

DictData

Result

General result class. All uncertainties are of numerical origin.

Input

Input for computation and scans.

Order

Computation orders.

Result

General result class. All uncertainties are of numerical origin.

DictData

RunParam

Abstract class that is similar to a dictionary but with fixed keys.

Runner

DictData

Functions

order_to_string(o[, json_style, no_macros])

replace_macros(s)

xsec_to_order(s)

lhapdf_name_to_id(name)

Converts a LHAPDF name to the sets id.

get_input_dir()

Get the input directory.

get_output_dir()

Get the output directory.

get_pre()

Gets the command prefix.

set_input_dir(ind)

Sets the input directory.

set_output_dir(outd[, create])

Sets the output directory.

set_pre(ppre)

Sets the command prefix.

is_gluino(iid)

is_neutralino(iid)

is_chargino(iid)

is_weakino(iid)

is_squark(iid)

is_slepton(iid)

update_slha(i)

Updates dependent parameters in Input i.

scan(input_list, var, rrange)

Scans a variable var over rrange in input_list.

scan_multi(li, **kwargs)

Magically scans the variables passed to this function.

scan_scale(l[, rrange, distance])

Scans scale by varying mu_f and mu_r.

scan_seven_point(input_list)

Scans scale by varying mu_f and mu_r by factors of two excluding relative factors of 4.

keep_where(input_list, condition)

Only keep the inputs where the condition is true.

remove_where(input_list, condition, **kwargs)

Remove elements in list which satisfy condition.

change_where(input_list, dicts, **kwargs)

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

scan_invariant_mass(input_list, diff, points[, low])

Logarithmic invariant_mass scan close to the production threshold.

slha_write(newname, d)

masses_scan(input_list, varis, rrange[, diff_L_R, negate])

Scans the PDG identified masses in varis over rrange in the list input_list.

mass_scan(input_list, var, rrange[, diff_L_R])

Scans the PDG identified mass var over rrange in the list l.

slha_scan(input_list, block, var, rrange)

Scan a generic

slha_scan_rel(input_list, lambdas, rrange)

Scan a generic slha variable.

scan_pdf(input_list)

Scans NLO PDF sets.

interpolate_1d(df, x, y, xrange[, only_interpolation])

Last key is the value to be interpolated, while the rest are cooridnates.

interpolate_2d(df, x, y, z, xrange, yrange[, ...])

Last key is the value to be interpolated, while the rest are cooridnates.

order_to_string(o[, json_style, no_macros])

xsec_to_order(s)

DL2DF(ld)

Convert a dict of list`s to a `pandas.DataFrame.

LD2DL(l[, actual_dict])

Convert a list of objects into a dictionary of lists.

load_json_with_metadata(file)

Load xsec data from json in to something that works within hepi's plotting.

load_json(f[, dimensions])

order_to_string(o[, json_style, no_macros])

DL2DF(ld)

Convert a dict of list`s to a `pandas.DataFrame.

write_twiki(rs_dl, mass, main)

write_latex_table_transposed_header(dict_list, t, ...)

write_latex_table_transposed(dict_list, t, fname[, ...])

write_latex(dict_list, t, key, fname[, scale, pdf, yscale])

Saves a dict of list`s to `filename as latex table.

write_csv(dict_list, filename)

Saves a dict of list`s to `filename as csv table.

write_json(dict_list, o, parameters, output[, error, ...])

Saves a dict of list`s to `filename as json.

get_output_dir()

Get the output directory.

replace_macros(s)

get_name(pid)

Get the latex name of a particle.

title(i[, axe, scenario, diff_L_R, extra, cms_energy, ...])

Sets the title on axis axe.

energy_plot(dict_list, y[, yscale, xaxis, yaxis, label])

Plot energy on the x-axis.

combined_mass_plot(dict_list, y, part[, label])

combined_plot(dict_list, x, y[, label])

mass_plot(dict_list, y, part[, logy, yaxis, yscale, ...])

mass_vplot(dict_list, y, part[, logy, yaxis, yscale, ...])

get_mass(l, iid)

Get the mass of particle with id iid out of the list in the "slha" element in the dict.

plot(dict_list, x, y[, label, xaxis, yaxis, ratio, K, ...])

Creates a plot based on the entries x`and `y in dict_list.

index_open(var, idx)

slha_data(li, index_list)

slha_plot(li, x, y, **kwargs)

vplot(x, y[, label, xaxis, yaxis, logy, yscale, ...])

Creates a plot based on the values in x`and `y.

mass_mapplot(dict_list, part1, part2, z[, logz, ...])

mapplot(dict_list, x, y, z[, xaxis, yaxis, zaxis])

Examples

scatterplot(dict_list, x, y, z[, xaxis, yaxis, zaxis])

Scatter map 2d.

err_plt(axes, x, y[, label, error])

scale_plot(dict_list, vl[, seven_point_band, cont, ...])

Creates a scale variance plot with 5 panels (xline).

central_scale_plot(dict_list, vl[, cont, error, ...])

Creates a scale variance plot with 3 panels (ystacked).

init_double_plot([figsize, sharex, sharey, gridspec_kw])

Initialze subplot for Ratio/K plots with another figure below.

pdf_errors(li, dl[, ordernames, confidence_level, n_jobs])

Just like pdf_error but over a list of ordernames.

_pdf_error_single(members, i, dl, ordername[, ...])

pdf_error(li, dl[, ordername, confidence_level, n_jobs])

Computes Parton Density Function (PDF) uncertainties through lhapdf.set.uncertainty().

scale_errors(li, dl[, ordernames, n_jobs])

Just like scale_error but over a list of ordernames.

_scale_error_single(members, i, dl[, ordername])

scale_error(li, dl[, ordername, n_jobs])

Computes seven-point scale uncertainties from the results where the renormalization and factorization scales are varied by factors of 2 and relative factors of four are excluded (cf. seven_point_scan()).

combine_errors(dl[, ordernames])

Just like combine_error but over a list of ordernames.

combine_error(dl[, ordername])

Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition.

asym_to_sym_error(central, errminus, errplus)

add_errors(error1, error2)

asym_to_sym_combined_error(central, errminus1, ...)

get_input_dir()

Get the input directory.

get_output_dir()

Get the output directory.

get_pre()

Gets the command prefix.

DL2DF(ld)

Convert a dict of list`s to a `pandas.DataFrame.

LD2DL(l[, actual_dict])

Convert a list of objects into a dictionary of lists.

namehash(n)

Creates a sha256 hash from the objects string representation.

LD2DL(l[, actual_dict])

Convert a list of objects into a dictionary of lists.

DL2DF(ld)

Convert a dict of list`s to a `pandas.DataFrame.

namehash(n)

Creates a sha256 hash from the objects string representation.

lhapdf_name_to_id(name)

Converts a LHAPDF name to the sets id.

lhapdf_id_to_name(lid)

Package Contents

class hepi.Order[source]

Bases: enum.IntEnum

Computation orders.

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

LO = 0

Leading Order

NLO = 1

Next-to-Leading Order

NLO_PLUS_NLL = 2

Next-to-Leading Order plus Next-to-Leading Logarithms

aNNLO_PLUS_NNLL = 3

Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms

hepi.order_to_string(o, json_style=False, no_macros=False)[source]
Parameters:

o (Order)

Return type:

str

hepi.replace_macros(s)[source]
Parameters:

s (str)

Return type:

str

hepi.xsec_to_order(s)[source]
Parameters:

s (str)

class hepi.DictData[source]
__str__()[source]

Returns attributes as dict as string

hepi.lhapdf_name_to_id(name)[source]

Converts a LHAPDF name to the sets id.

Parameters:

name (str) – LHAPDF set name.

Returns:

id of the LHAPDF set.

Return type:

int

Examples

>>> lhapdf_name_to_id("CT14lo")
13200
hepi.in_dir = './input/'[source]

Input directory.

hepi.out_dir = './output/'[source]

Output directory.

hepi.pre = 'nice -n 5'[source]

Prefix for run commands.

hepi.get_input_dir()[source]

Get the input directory.

Returns:

in_dir

Return type:

str

hepi.get_output_dir()[source]

Get the output directory.

Returns:

out_dir

Return type:

str

hepi.get_pre()[source]

Gets the command prefix.

Returns:

pre

Return type:

str

hepi.set_input_dir(ind)[source]

Sets the input directory.

Parameters:

ind (str) – new input directory.

hepi.set_output_dir(outd, create=True)[source]

Sets the output directory.

Parameters:

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

  • create (bool)

hepi.set_pre(ppre)[source]

Sets the command prefix.

Parameters:

ppre (str) – new command prefix.

class hepi.Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[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.

Parameters:

  • order (hepi.order.Order)

  • energy (float)

  • particle1 (int)

  • particle2 (int)

  • slha (str)

  • pdf_lo (str)

  • pdf_nlo (str)

order
energy
energyhalf
particle1
particle2
slha
pdf_lo
pdfset_lo = 0
pdf_nlo
pdfset_nlo = 0
pdf_lo_id = 0
pdf_nlo_id = 0
mu_f = 1.0
mu_r = 1.0
precision = 0.001
max_iters = 50
invariant_mass = 'auto'
pt = 'auto'
result = 'total'
id = ''
model = ''
mu = 0.0
has_gluino()[source]
Return type:

bool

has_neutralino()[source]
Return type:

bool

has_charginos()[source]
Return type:

bool

has_weakino()[source]
Return type:

bool

has_squark()[source]
Return type:

bool

has_slepton()[source]
Return type:

bool

hepi.is_gluino(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.is_neutralino(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.is_chargino(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.is_weakino(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.is_squark(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.is_slepton(iid)[source]
Parameters:

iid (int)

Return type:

bool

hepi.update_slha(i)[source]

Updates dependent parameters in Input i.

Mainly concerns the mu value used by madgraph.

Parameters:

i (Input)

hepi.scan(input_list, var, rrange)[source]

Scans a variable var over rrange in input_list.

Note

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

Parameters:

  • input_list (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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
>>> for e in scan(li,"order",[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]):
...     print(e)
{'order': <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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
hepi.scan_multi(li, **kwargs)[source]

Magically scans the variables passed to this function.

Parameters:

  • **kwargs

  • li (List[Input])

Return type:

List[Input]

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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
>>> for e in scan_multi(oli,energy=range(10000,13000,1000),order=[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]):
...     print(e)
{'order': <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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
hepi.multi_scan[source]
hepi.scan_scale(l, 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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
Parameters:

l (List[Input])

hepi.scale_scan[source]
hepi.scan_seven_point(input_list)[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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
Parameters:

input_list (List[Input])

hepi.seven_point_scan[source]
hepi.keep_where(input_list, condition)[source]

Only keep the inputs where the condition is true.

Inversion of the remove_where function.

Parameters:

  • input_list (List[Input]) – List[Input] The list of inputs to filter.

  • condition – Callable[[Input.__dict__], bool] The condition to filter on.

hepi.remove_where(input_list, condition, **kwargs)[source]

Remove elements in list which satisfy condition.

Parameters:

  • input_list (List[Input]) – 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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
hepi.change_where(input_list, dicts, **kwargs)[source]

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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
>>> li = scan_multi([Input(Order.LO, 13000,  1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,12000,1000),mu_f=range(1,3))
>>> for e in change_where(li,{'order':Order.NLO},energy=11000,mu_f=1):
...     print(e)
{'order': <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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
Parameters:

  • input_list (List[Input])

  • dicts (dict)

hepi.scan_invariant_mass(input_list, diff, points, low=0.001)[source]

Logarithmic invariant_mass scan close to the production threshold.

Parameters:

input_list (List[Input])

hepi.slha_write(newname, d)[source]
hepi.masses_scan(input_list, varis, rrange, diff_L_R=None, negate=None)[source]

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.

Parameters:

  • input_list (List[Input])

  • varis (List[int])

Return type:

List[Input]

hepi.mass_scan(input_list, var, rrange, diff_L_R=None)[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.

Parameters:

  • input_list (List[Input])

  • var (int)

Return type:

List[Input]

hepi.slha_scan(input_list, block, var, rrange)[source]

Scan a generic

Parameters:

  • input_list (List[Input])

  • rrange (List)

Return type:

List[Input]

hepi.slha_scan_rel(input_list, lambdas, rrange)[source]

Scan a generic slha variable.

Parameters:

  • input_list (List[Input])

  • rrange (List)

Return type:

List[Input]

hepi.scan_pdf(input_list)[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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, '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': 0.001, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
Parameters:

input_list (List[Input])

hepi.pdf_scan[source]
hepi.interpolate_1d(df, x, y, xrange, only_interpolation=True, **kwargs)[source]

Last key is the value to be interpolated, while the rest are cooridnates.

Parameters:

df (pandas.DataFrame) – results

hepi.interpolate_2d(df, x, y, z, xrange, yrange, only_interpolation=True, **kwargs)[source]

Last key is the value to be interpolated, while the rest are cooridnates.

Parameters:

df (pandas.DataFrame) – results

class hepi.Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[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.

Parameters:

  • order (hepi.order.Order)

  • energy (float)

  • particle1 (int)

  • particle2 (int)

  • slha (str)

  • pdf_lo (str)

  • pdf_nlo (str)

order
energy
energyhalf
particle1
particle2
slha
pdf_lo
pdfset_lo = 0
pdf_nlo
pdfset_nlo = 0
pdf_lo_id = 0
pdf_nlo_id = 0
mu_f = 1.0
mu_r = 1.0
precision = 0.001
max_iters = 50
invariant_mass = 'auto'
pt = 'auto'
result = 'total'
id = ''
model = ''
mu = 0.0
has_gluino()[source]
Return type:

bool

has_neutralino()[source]
Return type:

bool

has_charginos()[source]
Return type:

bool

has_weakino()[source]
Return type:

bool

has_squark()[source]
Return type:

bool

has_slepton()[source]
Return type:

bool

hepi.order_to_string(o, json_style=False, no_macros=False)[source]
Parameters:

o (Order)

Return type:

str

hepi.xsec_to_order(s)[source]
Parameters:

s (str)

hepi.DL2DF(ld)[source]

Convert a dict of list`s to a `pandas.DataFrame.

Parameters:

ld (dict)

Return type:

pandas.DataFrame

hepi.LD2DL(l, actual_dict=False)[source]

Convert a list of objects into a dictionary of lists.

The values of each object are first converted to a dict through the __dict__ attribute.

Parameters:

  • l (List) – list of objects.

  • actual_dict (bool) – objects are already dicts

Returns:

dictionary of numpy arrays.

Return type:

dict

Examples

>>> class Param:
...      def __init__(self,a,b,c):
...         self.a = a
...         self.b = b
...         self.c = c
>>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ])
{'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])}
hepi.load_json_with_metadata(file)[source]

Load xsec data from json in to something that works within hepi’s plotting.

Parameters:

  • f – readable object, e.g. open(filepath:str).

  • dimensions (int) – 1 or 2 currently supported.

hepi.load_json(f, dimensions=1)[source]
hepi.load[source]
class hepi.Order[source]

Bases: enum.IntEnum

Computation orders.

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

LO = 0

Leading Order

NLO = 1

Next-to-Leading Order

NLO_PLUS_NLL = 2

Next-to-Leading Order plus Next-to-Leading Logarithms

aNNLO_PLUS_NNLL = 3

Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms

hepi.order_to_string(o, json_style=False, no_macros=False)[source]
Parameters:

o (Order)

Return type:

str

hepi.DL2DF(ld)[source]

Convert a dict of list`s to a `pandas.DataFrame.

Parameters:

ld (dict)

Return type:

pandas.DataFrame

hepi.unv[source]
hepi.usd[source]
hepi.write_twiki(rs_dl, mass, main)[source]
hepi.write_latex_table_transposed_header(dict_list, t, fname, key, yscale=1.0)[source]
hepi.write_latex_table_transposed(dict_list, t, fname, scale=True, pdf=True, yscale=1.0, max_rows=None)[source]
hepi.write_latex(dict_list, t, key, fname, scale=True, pdf=True, yscale=1.0)[source]

Saves a dict of list`s to `filename as latex table.

hepi.tex_table[source]
hepi.write_csv(dict_list, filename)[source]

Saves a dict of list`s to `filename as csv table.

Examples

>>> import hepi
>>> import urllib.request
>>> dl = hepi.load(urllib.request.urlopen(
... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json"
... ),dimensions=2)
>>> with open("test.csv", 'w') as f:
...     hepi.write_csv(dl, f)
>>> with open('test.csv', 'r') as f:
...     print(f.read())
order,energy,energyhalf,particle1,particle2,slha,pdf_lo,pdfset_lo,pdf_nlo,pdfset_nlo,pdf_lo_id,pdf_nlo_id,mu_f,mu_r,precision,max_iters,invariant_mass,pt,result,id,model,mu,runner,N2,N1,NLO_PLUS_NLL_NOERR,NLO_PLUS_NLL_COMBINED
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,81.5,80.0,7.746232,7.746+/-0.023
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,82.0,80.0,7.646339,7.646+/-0.024
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,83.0,80.0,7.450843,7.451+/-0.024
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,85.0,80.0,7.079679,7.080+/-0.024
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,90.0,80.0,6.248933,6.249+/-0.025
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,95.0,80.0,5.53691,5.537+/-0.025
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,100.0,60.0,7.613015,7.613+/-0.024
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,100.0,80.0,4.924686,4.925+/-0.025
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,101.5,100.0,3.201246,3.201+/-0.026
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,102.0,100.0,3.169948,3.170+/-0.027
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,103.0,100.0,3.109625,3.110+/-0.027
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,105.0,100.0,2.993584,2.994+/-0.027
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,110.0,100.0,2.725548,2.726+/-0.027
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,110.0,80.0,3.933723,3.934+/-0.026
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,115.0,100.0,2.485705,2.486+/-0.028
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,100.0,2.271269,2.271+/-0.028
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,60.0,4.504708,4.505+/-0.025
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,120.0,80.0,3.180276,3.180+/-0.027
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,126.5,125.0,1.383578,1.384+/-0.030
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,127.0,125.0,1.373155,1.373+/-0.030
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,128.0,125.0,1.352257,1.352+/-0.031
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,130.0,100.0,1.905211,1.905+/-0.029
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,130.0,125.0,1.3128,1.313+/-0.031
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,135.0,125.0,1.219904,1.220+/-0.031
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,100.0,1.608394,1.608+/-0.029
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,125.0,1.134614,1.135+/-0.031
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,140.0,80.0,2.142151,2.142+/-0.028
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,145.0,125.0,1.056242,1.056+/-0.032
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,152.0,150.0,0.699925,0.700+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,153.0,150.0,0.691281,0.691+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,155.0,125.0,0.917808,0.918+/-0.032
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,155.0,150.0,0.674484,0.674+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,160.0,100.0,1.165897,1.166+/-0.031
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,160.0,150.0,0.6345,0.634+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,165.0,125.0,0.800281,0.800+/-0.033
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,165.0,150.0,0.597167,0.597+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,170.0,150.0,0.562441,0.562+/-0.035
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,178.0,175.0,0.391649,0.39+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,180.0,150.0,0.499633,0.500+/-0.035
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,180.0,175.0,0.383418,0.38+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,185.0,125.0,0.614697,0.615+/-0.034
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,185.0,175.0,0.363707,0.36+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,190.0,150.0,0.444892,0.44+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,190.0,175.0,0.345126,0.35+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,195.0,175.0,0.327625,0.33+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,202.0,200.0,0.2403,0.24+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,203.0,200.0,0.238047,0.24+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,205.0,200.0,0.233619,0.23+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,210.0,150.0,0.354984,0.35+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,210.0,200.0,0.222947,0.22+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,215.0,200.0,0.212818,0.21+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,220.0,200.0,0.203209,0.20+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,230.0,200.0,0.18536,0.19+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,230.0,225.0,0.150189,0.15+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,235.0,225.0,0.14399,0.14+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,240.0,200.0,0.169381,0.17+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,240.0,225.0,0.138083,0.14+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,252.0,250.0,0.102807,0.10+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,253.0,250.0,0.102017,0.10+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,255.0,250.0,0.100453,0.10+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,260.0,200.0,0.141817,0.14+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,260.0,250.0,0.096658,0.10+/-0.04
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,265.0,250.0,0.092955,0.09+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,270.0,250.0,0.089536,0.09+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,280.0,250.0,0.082931,0.08+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,290.0,250.0,0.076979,0.08+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,302.0,300.0,0.050316,0.05+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,303.0,300.0,0.049985,0.05+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,305.0,300.0,0.049326,0.05+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,310.0,250.0,0.066363,0.07+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,310.0,300.0,0.047719,0.05+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,315.0,300.0,0.046111,0.05+/-0.05
2,13000.0,6500.0,-1,-1,$\tilde\chi_2^0\tilde\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.001,50,auto,auto,total,,,0.0,Resummino,320.0,300.0,0.044674,0.04+/-0.05
Parameters:

  • dict_list (list)

  • filename (str)

hepi.write_json(dict_list, o, parameters, output, error=True, error_sym=None, scale=True, pdf=True)[source]

Saves a dict of list`s to `filename as json.

Cf. https://github.com/fuenfundachtzig/xsec

Parameters:

  • output (writeable or file name str) – Should support a function .write().

  • dict_list (list)

  • o (hepi.order.Order)

  • parameters (List[str])

Examples

>>> import hepi
>>> import urllib.request
>>> dl = hepi.load(urllib.request.urlopen(
... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json"
... ),dimensions=2)
>>> with open("test.json", "w") as f:
...     hepi.write_json(dl, Order.NLO_PLUS_NLL,["N1"],f,error=False)
>>> with open('test.json', 'r') as f:
...     print(f.read())
{
    "initial state": "pp",
    "order": "NLO+NLL",
    "source": "hepi-...",
    "contact": "...",
    "tool": "Resummino",
    "process_latex": "$\\overline{d}\\overline{d}$",
    "comment": "",
    "reference": "?",
    "Ecom [GeV]": "13000.0",
    "process_id": "pp_13000.0_-1_-1",
    "PDF set": "CTEQ6.6 and MSTW2008nlo90cl",
    "parameters": [
        [
            "N1"
        ]
    ],
    "data": {
        "80.0": {
            "xsec_pb": 2.142151
        },
        "60.0": {
            "xsec_pb": 4.504708
        },
        "100.0": {
            "xsec_pb": 1.165897
        },
        "125.0": {
            "xsec_pb": 0.614697
        },
        "150.0": {
            "xsec_pb": 0.354984
        },
        "175.0": {
            "xsec_pb": 0.327625
        },
        "200.0": {
            "xsec_pb": 0.141817
        },
        "225.0": {
            "xsec_pb": 0.138083
        },
        "250.0": {
            "xsec_pb": 0.066363
        },
        "300.0": {
            "xsec_pb": 0.044674
        }
    }
}
class hepi.Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[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.

Parameters:

  • order (hepi.order.Order)

  • energy (float)

  • particle1 (int)

  • particle2 (int)

  • slha (str)

  • pdf_lo (str)

  • pdf_nlo (str)

order
energy
energyhalf
particle1
particle2
slha
pdf_lo
pdfset_lo = 0
pdf_nlo
pdfset_nlo = 0
pdf_lo_id = 0
pdf_nlo_id = 0
mu_f = 1.0
mu_r = 1.0
precision = 0.001
max_iters = 50
invariant_mass = 'auto'
pt = 'auto'
result = 'total'
id = ''
model = ''
mu = 0.0
has_gluino()[source]
Return type:

bool

has_neutralino()[source]
Return type:

bool

has_charginos()[source]
Return type:

bool

has_weakino()[source]
Return type:

bool

has_squark()[source]
Return type:

bool

has_slepton()[source]
Return type:

bool

hepi.get_output_dir()[source]

Get the output directory.

Returns:

out_dir

Return type:

str

hepi.replace_macros(s)[source]
Parameters:

s (str)

Return type:

str

hepi.get_name(pid)[source]

Get the latex name of a particle.

Parameters:

pid (int) – PDG Monte Carlo identifier for the particle.

Returns:

Latex name.

Return type:

str

Examples

>>> get_name(21)
'g'
>>> get_name(1000022)
'\\tilde{\\chi}_{1}^{0}'
hepi.title(i, axe=None, scenario=None, diff_L_R=None, extra='', cms_energy=True, pdf_info=True, id=False, **kwargs)[source]

Sets the title on axis axe.

Parameters:

i (hepi.input.Input)

hepi.energy_plot(dict_list, y, yscale=1.0, xaxis='E [GeV]', yaxis='$\\sigma$ [pb]', label=None, **kwargs)[source]

Plot energy on the x-axis.

hepi.combined_mass_plot(dict_list, y, part, label=None, **kwargs)[source]
hepi.combined_plot(dict_list, x, y, label=None, **kwargs)[source]
hepi.mass_plot(dict_list, y, part, logy=True, yaxis='$\\sigma$ [pb]', yscale=1.0, label=None, xaxis=None, **kwargs)[source]
hepi.mass_vplot(dict_list, y, part, logy=True, yaxis='$\\sigma$ [pb]', yscale=1.0, label=None, mask=None, **kwargs)[source]
hepi.get_mass(l, iid)[source]

Get the mass of particle with id iid out of the list in the “slha” element in the dict.

Returns

list of float : masses of particles in each element of the dict list.

Parameters:

  • l (dict)

  • iid (int)

hepi.plot(dict_list, x, y, label=None, xaxis='M [GeV]', yaxis='$\\sigma$ [pb]', ratio=False, K=False, K_plus_1=False, logy=True, yscale=1.0, mask=None, **kwargs)[source]

Creates a plot based on the entries x`and `y in dict_list.

Examples

>>> import urllib.request
>>> import hepi
>>> dl = hepi.load(urllib.request.urlopen(
... "https://raw.githubusercontent.com/fuenfundachtzig/xsec/master/json/pp13_hino_NLO%2BNLL.json"
... ))
>>> hepi.plot(dl,"N1","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]")

(Source code, png, hires.png, pdf)

../../_images/index-1.png
Return type:

None

hepi.index_open(var, idx)[source]
hepi.slha_data(li, index_list)[source]
hepi.slha_plot(li, x, y, **kwargs)[source]
hepi.vplot(x, y, label=None, xaxis='E [GeV]', yaxis='$\\sigma$ [pb]', logy=True, yscale=1.0, interpolate=True, plot_data=True, data_color=None, mask=-1, fill=False, data_fmt='.', fmt='-', print_area=False, sort=True, **kwargs)[source]

Creates a plot based on the values in x`and `y.

hepi.mass_mapplot(dict_list, part1, part2, z, logz=True, zaxis='$\\sigma$ [pb]', zscale=1.0, label=None)[source]
hepi.mapplot(dict_list, x, y, z, xaxis=None, yaxis=None, zaxis=None, **kwargs)[source]

Examples

>>> import urllib.request
>>> import hepi

>>> dl = hepi.load(urllib.request.urlopen(
... "https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_SGmodel_GGxsec_NLO%2BNLL.json"
... ),dimensions=2)
>>> hepi.mapplot(dl,"gl","sq","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{g}}$ [GeV]",yaxis="$m_{\\tilde{q}}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]")

(Source code, png, hires.png, pdf)

../../_images/index-2.png
hepi.map_vplot[source]
hepi.scatter_vplot[source]
hepi.scatterplot(dict_list, x, y, z, xaxis=None, yaxis=None, zaxis=None, **kwargs)[source]

Scatter map 2d. Central color is the central value, while the inner and outer ring are lower and upper bounds of the uncertainty interval.

Examples

>>> import urllib.request
>>> import hepi
>>> dl = hepi.load(urllib.request.urlopen(
... "https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json"
... ),dimensions=2)
>>> hepi.scatterplot(dl,"N1","N2","NLO_PLUS_NLL_COMBINED",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]",yaxis="$m_{\\tilde{\\chi}_2^0}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]")

(Source code, png, hires.png, pdf)

../../_images/index-3.png
hepi.fig = None[source]
hepi.axs = None[source]
hepi.lines = [][source]
hepi.labels = [][source]
hepi.err_plt(axes, x, y, label=None, error=False)[source]
hepi.scale_plot(dict_list, vl, seven_point_band=False, cont=False, error=True, li=None, plehn_color=False, yscale=1.0, unit='pb', yaxis=None, **kwargs)[source]

Creates a scale variance plot with 5 panels (xline).

hepi.central_scale_plot(dict_list, vl, cont=False, error=True, yscale=1.0, unit='pb', yaxis=None)[source]

Creates a scale variance plot with 3 panels (ystacked).

hepi.init_double_plot(figsize=(6, 8), sharex=True, sharey=False, gridspec_kw={'height_ratios': [3, 1]})[source]

Initialze subplot for Ratio/K plots with another figure below.

class hepi.DictData[source]
__str__()[source]

Returns attributes as dict as string

hepi.required_numerical_uncertainty_factor = 5[source]
hepi.unv[source]
hepi.usd[source]
class hepi.Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None)[source]

Bases: hepi.util.DictData

General result class. All uncertainties are of numerical origin.

Variables:

  • LO (double) – Leading Order result. Defaults to None.

  • NLO (double) – Next-to-Leading Order result. Defaults to None.

  • NLO_PLUS_NLL (double) – Next-to-Leading Order plus Next-to-Leading Logarithm result. Defaults to None.

  • K_LO (double) – LO divided by LO.

  • K_NLO (double) – NLO divided by LO result.

  • K_NLO_PLUS_NLL (double) – NLO+NLL divided by LO.

  • K_aNNLO_PLUS_NNLL (double) – aNNLO+NNLL divided by LO.

  • NLO_PLUS_NLL_OVER_NLO (double) – NLO+NLL divided by NLO.

  • aNNLO_PLUS_NNLL_OVER_NLO (double) – aNNLO+NNLL divided by NLO.

Sets given and computes dependent Attributes.

Parameters:

  • lo (double) – corresponds to LO.

  • nlo (double) – corresponds to NLO.

  • nlo_plus_nll (double) – corresponds to NLO_PLUS_NLL.

  • annlo_plus_nnll (double) – corresponds to aNNLO_PLUS_NNLL.

LO = None
NLO = None
NLO_PLUS_NLL = None
aNNLO_PLUS_NNLL = None
hepi.pdf_errors(li, dl, ordernames=None, confidence_level=90, n_jobs=None)[source]

Just like pdf_error but over a list of ordernames.

hepi._pdf_error_single(members, i, dl, ordername, confidence_level=90)[source]
hepi.pdf_error(li, dl, ordername='LO', confidence_level=90, n_jobs=None)[source]

Computes Parton Density Function (PDF) uncertainties through lhapdf.set.uncertainty().

Parameters:

  • li (list of Input) – Input list.

  • dl (dict) – Result dictionary with lists per entry.

  • ordername (str) – Name of the order.

  • confidence_level (double) – Confidence Level for PDF uncertainty

Returns:

Modified dl with new ordername_{PDF,PDF_CENTRAL,PDF_ERRPLUS,PDF_ERRMINUS,PDF_ERRSYM} entries.

  • (ordername)_`PDF` contains a symmetrized uncertainties object.

Return type:

dict

hepi.scale_errors(li, dl, ordernames=None, n_jobs=None)[source]

Just like scale_error but over a list of ordernames.

hepi._scale_error_single(members, i, dl, ordername='LO')[source]
hepi.scale_error(li, dl, ordername='LO', n_jobs=None)[source]

Computes seven-point scale uncertainties from the results where the renormalization and factorization scales are varied by factors of 2 and relative factors of four are excluded (cf. seven_point_scan()).

Parameters:

  • li (list of Input) – Input list.

  • dl (dict) – Result dictionary with lists per entry.

Returns:

Modified dl with new ordername_{SCALE,SCALE_ERRPLUS,SCALE_ERRMINUS} entries.

  • ordername_SCALE contains a symmetrized uncertainties object.

Return type:

dict

hepi.combine_errors(dl, ordernames=None)[source]

Just like combine_error but over a list of ordernames.

hepi.combine_error(dl, ordername='LO')[source]

Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition.

Note

Running scale_errors() and pdf_errors() before is necessary.

Parameters:

dl (dict) – Result dictionary with lists per entry.

Returns:

Modified dl with new ordername_{COMBINED,ERRPLUS,ERRMINUS} entries.

  • ordername_COMBINED contains a symmetrized uncertainties object.

Return type:

dict

hepi.asym_to_sym_error(central, errminus, errplus)[source]
hepi.add_errors(error1, error2)[source]
hepi.asym_to_sym_combined_error(central, errminus1, errplus1, errminus2, errplus2)[source]
class hepi.Input(order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.001, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[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.

Parameters:

  • order (hepi.order.Order)

  • energy (float)

  • particle1 (int)

  • particle2 (int)

  • slha (str)

  • pdf_lo (str)

  • pdf_nlo (str)

order
energy
energyhalf
particle1
particle2
slha
pdf_lo
pdfset_lo = 0
pdf_nlo
pdfset_nlo = 0
pdf_lo_id = 0
pdf_nlo_id = 0
mu_f = 1.0
mu_r = 1.0
precision = 0.001
max_iters = 50
invariant_mass = 'auto'
pt = 'auto'
result = 'total'
id = ''
model = ''
mu = 0.0
has_gluino()[source]
Return type:

bool

has_neutralino()[source]
Return type:

bool

has_charginos()[source]
Return type:

bool

has_weakino()[source]
Return type:

bool

has_squark()[source]
Return type:

bool

has_slepton()[source]
Return type:

bool

class hepi.Order[source]

Bases: enum.IntEnum

Computation orders.

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

LO = 0

Leading Order

NLO = 1

Next-to-Leading Order

NLO_PLUS_NLL = 2

Next-to-Leading Order plus Next-to-Leading Logarithms

aNNLO_PLUS_NNLL = 3

Approximate Next-to-next-to-Leading Order plus Next-to-next-to-Leading Logarithms

hepi.get_input_dir()[source]

Get the input directory.

Returns:

in_dir

Return type:

str

hepi.get_output_dir()[source]

Get the output directory.

Returns:

out_dir

Return type:

str

hepi.get_pre()[source]

Gets the command prefix.

Returns:

pre

Return type:

str

class hepi.Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None)[source]

Bases: hepi.util.DictData

General result class. All uncertainties are of numerical origin.

Variables:

  • LO (double) – Leading Order result. Defaults to None.

  • NLO (double) – Next-to-Leading Order result. Defaults to None.

  • NLO_PLUS_NLL (double) – Next-to-Leading Order plus Next-to-Leading Logarithm result. Defaults to None.

  • K_LO (double) – LO divided by LO.

  • K_NLO (double) – NLO divided by LO result.

  • K_NLO_PLUS_NLL (double) – NLO+NLL divided by LO.

  • K_aNNLO_PLUS_NNLL (double) – aNNLO+NNLL divided by LO.

  • NLO_PLUS_NLL_OVER_NLO (double) – NLO+NLL divided by NLO.

  • aNNLO_PLUS_NNLL_OVER_NLO (double) – aNNLO+NNLL divided by NLO.

Sets given and computes dependent Attributes.

Parameters:

  • lo (double) – corresponds to LO.

  • nlo (double) – corresponds to NLO.

  • nlo_plus_nll (double) – corresponds to NLO_PLUS_NLL.

  • annlo_plus_nnll (double) – corresponds to aNNLO_PLUS_NNLL.

LO = None
NLO = None
NLO_PLUS_NLL = None
aNNLO_PLUS_NNLL = None
hepi.DL2DF(ld)[source]

Convert a dict of list`s to a `pandas.DataFrame.

Parameters:

ld (dict)

Return type:

pandas.DataFrame

hepi.LD2DL(l, actual_dict=False)[source]

Convert a list of objects into a dictionary of lists.

The values of each object are first converted to a dict through the __dict__ attribute.

Parameters:

  • l (List) – list of objects.

  • actual_dict (bool) – objects are already dicts

Returns:

dictionary of numpy arrays.

Return type:

dict

Examples

>>> class Param:
...      def __init__(self,a,b,c):
...         self.a = a
...         self.b = b
...         self.c = c
>>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ])
{'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])}
class hepi.DictData[source]
__str__()[source]

Returns attributes as dict as string

hepi.namehash(n)[source]

Creates a sha256 hash from the objects string representation.

Parameters:

n (any) – object.

Returns:

sha256 of object.

Return type:

str

Examples

>>> p = {'a':1,'b':2}
>>> str(p)
"{'a': 1, 'b': 2}"
>>> namehash(str(p))
'3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8'
>>> namehash(p)
'3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8'
class hepi.RunParam(skip=False, in_file=None, out_file=None, execute=None, name=None)[source]

Bases: hepi.util.DictData

Abstract class that is similar to a dictionary but with fixed keys.

Parameters:

  • skip (bool)

  • in_file (str)

  • out_file (str)

  • execute (str)

  • name (str)

name = None
skip = False
in_file = None
out_file = None
execute = None
class hepi.Runner(path, in_dir=None, out_dir=None, pre=None)[source]
Parameters:

  • path (str)

  • in_dir (str)

  • out_dir (str)

path
orders()[source]

List of supported Orders in this runner.

Return type:

List[hepi.input.Order]

get_name()[source]

Returns the name of the runner.

Return type:

str

get_version()[source]
Return type:

str

_sub_run(coms)[source]
Parameters:

coms (List[str])

Return type:

str

_check_path()[source]

Checks if the passed path is valid.

Return type:

bool

_prepare(p, skip=False, assume_valid=False, **kwargs)[source]
Parameters:

p (hepi.input.Input)

Return type:

RunParam

_check_input(param, **kwargs)[source]
Parameters:

param (hepi.input.Input)

Return type:

bool

_prepare_all(params, skip=True, n_jobs=None, **kwargs)[source]

Prepares all parameters for execution.

Parameters:

  • params (List[hepi.Input]) – List of input parameters.

  • skip (bool, optional) – If True, the runner will check if the output file already exists and skip the execution if it does. Defaults to True.

  • n_jobs (int) – Number of parallel jobs. If None, use all available cores.

Return type:

List[RunParam]

run(params, skip=True, parse=True, parallel=True, sleep=0, run=True, ignore_error=False, n_jobs=None, **kwargs)[source]

Run the passed list of parameters.

Parameters:

  • params (list of hepi.Input) – All parameters that should be executed/queued.

  • skip (bool) – True means stored runs will be skipped. Else the are overwritten.

  • parse (bool) – Parse the results. This is not the prefered cluster/parallel mode, as there the function only queues the job.

  • parallel (bool) – Run jobs in parallel.

  • sleep (int) – Sleep seconds after starting job.

  • run (bool) – Actually start/queue runner.

  • ignore_error (bool) – Continue instead of raising Exceptions. Also ignores hash collisions.

  • n_jobs (int) – Number of parallel jobs. If None, use all available cores.

Returns:

combined dataframe of results and parameters. The dataframe is empty if parse is set to False.

Return type:

pd.DataFrame

_run(rps, wait=True, parallel=True, sleep=0, n_jobs=None, **kwargs)[source]

Runs Runner per RunParams.

Parameters:

  • rps (list of RunParams) – Extended run parameters.

  • bar (bool) – Enable info bar.

  • wait (bool) – Wait for parallel runs to finish.

  • sleep (int) – Sleep seconds after starting subprocess.

  • parallel (bool) – Run jobs in parallel.

  • n_jobs (int) – Number of parallel jobs. If None, use all available cores.

Returns:

return codes from jobs if no_parse is False.

Return type:

list of int

_is_valid(file, p, d, **kwargs)[source]

Verifies that a file is a complete output.

Parameters:

  • file (str) – File path to be parsed.

  • p (hepi.Input) – Onput parameters.

  • d (dict) – Param dictionary.

Returns:

True if file could be parsed.

Return type:

bool

parse(outputs, n_jobs=None)[source]

Parses Resummino output files and returns List of Results.

Args:

outputs (list of str): List of the filenames to be parsed.

n_jobs (int): Number of parallel jobs. If None, use all available cores.

Returns:

list of hepi.resummino.result.ResumminoResult

Parameters:

outputs (List[str])

Return type:

List[hepi.results.Result]

_parse_file(file)[source]

Extracts results from an output file.

Parameters:

file (str) – File path to be parsed.

Returns:

If a value is not found in the file None is used.

Return type:

Result

get_path()[source]

Get the Runner path.

Returns:

current Runner path.

Return type:

str

get_input_dir()[source]

Get the input directory.

Returns:

in_dir

Return type:

str

get_output_dir()[source]

Get the input directory.

Returns:

out_dir

Return type:

str

get_pre()[source]

Gets the command prefix.

Returns:

pre

Return type:

str

set_path(p)[source]

Set the path to the Runner folder containing the binary in ‘./bin’ or ‘./build/bin’.

Parameters:

p (str) – new path.

set_input_dir(indir)[source]

Sets the input directory.

Parameters:

indir (str) – new input directory.

set_output_dir(outdir, create=True)[source]

Sets the output directory.

Parameters:

  • outdir (str) – new output directory. create (bool): create directory if not existing.

  • create (bool)

set_pre(ppre)[source]

Sets the command prefix.

Parameters:

ppre (str) – new command prefix.

class hepi.DictData[source]
__str__()[source]

Returns attributes as dict as string

hepi.LD2DL(l, actual_dict=False)[source]

Convert a list of objects into a dictionary of lists.

The values of each object are first converted to a dict through the __dict__ attribute.

Parameters:

  • l (List) – list of objects.

  • actual_dict (bool) – objects are already dicts

Returns:

dictionary of numpy arrays.

Return type:

dict

Examples

>>> class Param:
...      def __init__(self,a,b,c):
...         self.a = a
...         self.b = b
...         self.c = c
>>> LD2DL([ Param(1,2,3), Param(4,5,6) , Param(7,8,9) ])
{'a': array([1, 4, 7]), 'b': array([2, 5, 8]), 'c': array([3, 6, 9])}
hepi.DL2DF(ld)[source]

Convert a dict of list`s to a `pandas.DataFrame.

Parameters:

ld (dict)

Return type:

pandas.DataFrame

hepi.namehash(n)[source]

Creates a sha256 hash from the objects string representation.

Parameters:

n (any) – object.

Returns:

sha256 of object.

Return type:

str

Examples

>>> p = {'a':1,'b':2}
>>> str(p)
"{'a': 1, 'b': 2}"
>>> namehash(str(p))
'3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8'
>>> namehash(p)
'3dffaea891e5dbadb390da33bad65f509dd667779330e2720df8165a253462b8'
hepi.lhapdf_name_to_id(name)[source]

Converts a LHAPDF name to the sets id.

Parameters:

name (str) – LHAPDF set name.

Returns:

id of the LHAPDF set.

Return type:

int

Examples

>>> lhapdf_name_to_id("CT14lo")
13200
hepi.lhapdf_id_to_name(lid)[source]
Parameters:

lid (int)

Return type:

str

hepi.package = 'hepi'[source]
hepi.__version__[source]