`hepi`

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

Submodules

Package Contents

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_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`
`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)

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__`

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) –

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}
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{'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 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)[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) –

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

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_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) –

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)

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)

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)

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)

hepi.fig[source]

hepi.axs[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.

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) –

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.

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) –

class hepi.Runner(path, in_dir=None, out_dir=None, pre=None)[source]

Parameters:

path (str) –
in_dir (str) –
out_dir (str) –

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]

hepi

Subpackages

Submodules

Package Contents

Classes

Functions

Attributes

`hepi`