A python package to read (big/many) SLHA files

# xSLHA

xSLHA is a python parser for files written in the SLHA format. It is optimised for fast reading of a large sample of files.

## Installation

The package can be installed via

pip install xslha

and is loaded in python by

import xslha

## Reading a single spectrum file

Reading a spectrum file file and stroing the information in a class object spc is done via the command

One has afterwards access to the different information by using the Value command, e.g

print("tan(beta): ",spc.Value('MINPAR',[3]))
print("T_u(3,3): ",spc.Value('TU',[3,3]))
print("m_h [GeV]: ",spc.Value('MASS',[25]))
print("Gamma(h) [GeV]: ",spc.Value('WIDTH',25))
print("BR(h->W^+W^-): ",spc.Value('BR',[25,[-13,13]]))
print("Sigma(pp->N1 N1,Q=8TeV): ",spc.Value('XSECTION',[8000,(2212,2212),(1000021,1000021)]))

produces the following output

tan(beta):  16.870458
T_u(3,3):  954.867627
m_h [GeV]:  117.758677
Gamma(h) [GeV]:  0.00324670136
BR(h->W^+W^-):  0.000265688227
Sigma(pp->N1 N1,Q=8TeV): [[(0, 2, 0, 0, 0, 0), 0.00496483158]]

Thus, the conventions are:

• for information given in the different SLHA blocks is returned by using using the name of the block as input as well as the numbers in the block as list
• the widths of particles are returned via the keyword WIDHT and the pdg of the particle
• for branching ratios, the keyword BRis used together with a nested list which states the pdg of the decay particle as well as of the final states
• for cross-sections the keyword XSECTION is used together with a nested list which states the center-of-mass energy and the pdgs of the initial/final states. The result is a list containing all calculated cross-sections for the given options for the renormalisation scheme, the QED & QCD order, etc. (see the SLHA recommendations for details).

Another possibility to access the information in the spectrum file is to look at the different dictionaries

spc.blocks
spc.widths
spc.br
spc.xsctions

which contain all information

## Reading all spectrum files from a directory

In order to read several spectrum files located in a directory dir, one can make use of the command

This generates a list list_spc where each entry corresponds to one spectrum. Thus, one can for instance use

[[x.Value('MINPAR',[1]),x.Value('MASS',[25])] for x in list_spc]

to extract the input for a 2D-scatter plot.

## Fast read-in of many files

Reading many spectrum files can be time consuming. However, many of the information which is given in a SLHA file is often not needed for a current study. Therefore, one can speed up the reading by extracting first all relevant information. This generates smaller files which are faster to read in. This can be done via the optional argument entries for read_dir:

list_spc_fast=xslha.read_dir("/home/$USER/Documents/spc1000",entries=["# m0","# m12","# hh_1"])` entries defines a list of strings which can be used to extract the necessary lines from the SLHA file by using grep. The speed improvement can be easily an order of magnitude if only some entries from a SLHA file are actually needed. ### Speed The impact of this optimisation for reading 1000 files is as follows: %%time list_spc=xslha.read_dir("/home/$USER/Documents/spc1000")

CPU times: user 5.05 s, sys: 105 ms, total: 5.15 s
Wall time: 5.51 s

compared to

%%time
list_spc_fast=xslha.read_dir("/home/$USER/Documents/spc1000",entries=["# m0","# m12","# hh_1"]) CPU times: user 147 ms, sys: 132 ms, total: 280 ms Wall time: 917 ms One can also compares this with other available python parser: • pylha: %%time all_spc=[] for filename in os.listdir("/home/$USER/Documents/spc1000/"):
with open("~/Documents/spc1000/"+filename) as f:

CPU times: user 21.5 s, sys: 174 ms, total: 21.7 s
Wall time: 21.7 s
• pyslha{
%%time
all_spc=[]
for filename in os.listdir("/home/$USER/Documents/spc1000/"): all_spc.append(pyslha.read(("/home/$USER/Documents/spc1000/"+filename)))

CPU times: user 13.3 s, sys: 152 ms, total: 13.5 s
Wall time: 13.5 s

## Reading spectra stored in one file

Another common approach for saving spectrum files is to produce one huge file in which the different spectra are separated by a keyword. xSLHA can read such files by setting the optional argument separator for read:

In order to speed up the reading of many spectra also in this case, it is possible to define the entries as well which are need:

In this casexSLHA will produce first a smaller spectrum file using cat and grep. For instance, in order to read efficiently files produced with SSP, one can use:

list_spc=xslha.read("SpectrumFiles.spc",separator="ENDOFPARAMETERFILE",entries=["# m0", "# m12", "# hh_1"])

## Special blocks

There are some programs which use blocks that are not supported by the official SLHA conventions:

• HiggsBounds expects the effective coupling ratios in blocks HIGGSBOUNDSINPUTHIGGSCOUPLINGSBOSONS and HIGGSBOUNDSINPUTHIGGSCOUPLINGSFERMIONS which are differently order compared to other blocks (first the numerical entries are stated before the PDGs of the involved particles follow)
• SPheno version generated by SARAH can calculate one-loop corrections to the decays. The results are given in the blocks DECAY1L which appear in parallel to DECAY containing the standard calculation. xSLHA will distinguish these cases when reading the file and offer the two following options for Values in addtion:
spc.Values('WIDTH1L',1000022)
spc.Values('BR1L',[1000023,[25,1000022]])

## Writing files

Files in the SLHA format can be written via

xslha.write(blocks,file)

where it might be the best to use ordered dictionaries to define the blocks and the values in the blocks. For instance

import collections
out_blocks=collections.OrderedDict([
('MODSEL',collections.OrderedDict([('1', 1), ('2', 2),('6',0)])),
('MINPAR',collections.OrderedDict([('1', 1000.),('2', 2000),('3',10),('4',1),('5',0)]))
])
xslha.write(out_blocks,"/home/\$USER/Documents/LH.in")