hepi
The HEPi package aims to automize cluster computations for parameter scans with the option to produce plots.
Subpackages
Submodules
Package Contents
Classes
Computation orders. |
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Computation orders. |
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Input for computation and scans. |
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General result class. |
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Input for computation and scans. |
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Input for computation and scans. |
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Computation orders. |
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General result class. |
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Abstract class that is similar to a dictionary but with fixed keys. |
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Input for computation and scans. |
Functions
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Convert a dict of list`s to a `pandas.DataFrame. |
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Get the latex name of a particle. |
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Saves a dict of list`s to `filename as latex table. |
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Saves a dict of list`s to `filename as csv table. |
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Saves a dict of list`s to `filename as json. |
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Transforms a PDG id to it's left-right partner. |
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Converts a LHAPDF name to the sets id. |
Get the input directory. |
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Get the input directory. |
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Gets the command prefix. |
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Sets the input directory. |
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Sets the output directory. |
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Sets the command prefix. |
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Updates dependent parameters in Input i. |
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Scans a variable var over rrange in l. |
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Magically scans the variables passed to this function. |
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Scans scale by varying mu_f and mu_r. |
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Scans scale by varying mu_f and mu_r by factors of two excluding relative factors of 4. |
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Applies the values of dicts if the key value pairs in kwargs agree with a member of the list l. |
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Logarithmic invariant_mass scan close to the production threshold. |
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Scans the PDG identified masses in varis over rrange in the list l. |
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Scans the PDG identified mass var over rrange in the list l. |
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Scan a generic |
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Scan a generic slha variable. |
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Scans NLO PDF sets. |
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Just like pdf_error but over a list of ordernames. |
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Computes Parton Density Function (PDF) uncertainties through |
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Computes Parton Density Function (PDF) uncertainties through |
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Just like scale_error but over a list of ordernames. |
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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. |
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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. |
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Just like combine_error but over a list of ordernames. |
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Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition. |
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Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition. |
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Convert a list of objects into a dictionary of lists. |
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Convert a dict of list`s to a `pandas.DataFrame. |
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Get the latex name of a particle. |
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Transforms a PDG id to it's left-right partner. |
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Creates a sha256 hash from the objects string representation. |
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Converts a LHAPDF name to the sets id. |
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Last key is the value to be interpolated, while the rest are cooridnates. |
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Last key is the value to be interpolated, while the rest are cooridnates. |
Get the input directory. |
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Get the latex name of a particle. |
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Sets the title on axis axe. |
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Plot energy on the x-axis. |
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Get the mass of particle with id iid out of the list in the "slha" element in the dict. |
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Creates a plot based on the values in x`and `y. |
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Examples |
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Scatter map 2d. |
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Creates a scale variance plot with 5 panels (xline). |
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Creates a scale variance plot with 3 panels (ystacked). |
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Initialze subplot for Ratio/K plots with another figure below. |
Get the input directory. |
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Get the input directory. |
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Gets the command prefix. |
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Convert a dict of list`s to a `pandas.DataFrame. |
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Convert a list of objects into a dictionary of lists. |
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Creates a sha256 hash from the objects string representation. |
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Convert a list of objects into a dictionary of lists. |
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Convert a dict of list`s to a `pandas.DataFrame. |
Attributes
Input directory. |
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Output directory. |
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Prefix for run commands. |
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If the numerical uncertainty is |
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- class hepi.Order
Bases:
enum.IntEnumComputation 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.DL2DF(ld: dict) pandas.DataFrame
Convert a dict of list`s to a `pandas.DataFrame.
- hepi.get_name(pid: int) str
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.write_latex(dict_list, key, fname, scale=True, pdf=True, yscale=1.0)[source]
Saves a dict of list`s to `filename as latex table.
- hepi.write_csv(dict_list: list, filename: str)[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) >>> hepi.write_csv(dl, open("test.csv", 'w')) >>> 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 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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,81.5,80.0,7.746+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,82.0,80.0,7.646+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,83.0,80.0,7.451+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,85.0,80.0,7.080+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,90.0,80.0,6.249+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,95.0,80.0,5.537+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,100.0,60.0,7.613+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,100.0,80.0,4.925+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,101.5,100.0,3.201+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,102.0,100.0,3.170+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,103.0,100.0,3.110+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,105.0,100.0,2.994+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,110.0,100.0,2.726+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,110.0,80.0,3.934+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,115.0,100.0,2.486+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,120.0,100.0,2.271+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,120.0,60.0,4.505+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,120.0,80.0,3.180+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,126.5,125.0,1.384+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,127.0,125.0,1.373+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,128.0,125.0,1.352+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,130.0,100.0,1.905+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,130.0,125.0,1.313+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,135.0,125.0,1.220+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,140.0,100.0,1.608+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,140.0,125.0,1.135+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,140.0,80.0,2.142+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,145.0,125.0,1.056+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,152.0,150.0,0.700+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,153.0,150.0,0.691+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,155.0,125.0,0.918+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,155.0,150.0,0.674+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,160.0,100.0,1.166+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,160.0,150.0,0.634+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,165.0,125.0,0.800+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,165.0,150.0,0.597+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,170.0,150.0,0.562+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,178.0,175.0,0.39+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,180.0,150.0,0.500+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,180.0,175.0,0.38+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,185.0,125.0,0.615+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,185.0,175.0,0.36+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,190.0,150.0,0.44+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,190.0,175.0,0.35+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,195.0,175.0,0.33+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,202.0,200.0,0.24+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,203.0,200.0,0.24+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,205.0,200.0,0.23+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,210.0,150.0,0.35+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,210.0,200.0,0.22+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,215.0,200.0,0.21+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,220.0,200.0,0.20+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,230.0,200.0,0.19+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,230.0,225.0,0.15+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,235.0,225.0,0.14+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,240.0,200.0,0.17+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,240.0,225.0,0.14+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,252.0,250.0,0.10+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,253.0,250.0,0.10+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,255.0,250.0,0.10+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,260.0,200.0,0.14+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,260.0,250.0,0.10+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,265.0,250.0,0.09+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,270.0,250.0,0.09+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,280.0,250.0,0.08+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,290.0,250.0,0.08+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,302.0,300.0,0.05+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,303.0,300.0,0.05+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,305.0,300.0,0.05+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,310.0,250.0,0.07+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,310.0,300.0,0.05+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,315.0,300.0,0.05+/-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,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,320.0,300.0,0.04+/-0.05
- hepi.write_json(dict_list: list, o: hepi.input.Order, parameter: str, output, error_sym=False, error_asym=False)[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().
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) >>> hepi.write_json(dl, Order.NLO_PLUS_NLL,"N1",open("test.json", 'w')) >>> 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", "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}}, "parameters": [["N1"]]}
- hepi.get_LR_partner(pid: int) Tuple[int, int]
Transforms a PDG id to it’s left-right partner.
- Parameters
pid (int) – PDG Monte Carlo identifier for the particle.
- Returns
First int is -1 for Left and 1 for Right. Second int is the PDG id.
- Return type
tuple
Examples
>>> get_LR_partner(1000002) (-1, 2000002)
- hepi.lhapdf_name_to_id(name: str) int
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.set_input_dir(ind)[source]
Sets the input directory.
- Parameters
ind (str) – new input directory.
- hepi.set_output_dir(outd, create: bool = True)[source]
Sets the output directory.
- Parameters
outd (str) – new output directory. create (bool): create directory if not existing
- class hepi.Order
Bases:
enum.IntEnumComputation 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
- class hepi.Input(order: Order, energy: float, particle1: int, particle2: int, slha: str, pdf_lo: str, pdf_nlo: str, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.01, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[source]
Bases:
hepi.util.DictDataInput for computation and scans.
- Variables
order (
Order) – LO, NLO or NLO+NLL computation.energy (int) – CMS energy in GeV.
energyhalf (int) – Halfed energy.
particle1 (int) – PDG identifier of the first final state particle.
particle2 (int) – PDG identifier of the second final state particle.
slha (str) – File path of for the base slha. Modified slha files will be used if a scan requires a change of the input.
pdf_lo (str) – LO PDF name.
pdf_nlo (str) – NLO PDF name.
pdfset_lo (int) – LO PDF member/set id.
pdfset_nlo (int) – NLO PDF member/set id.
pdf_lo_id (int) – LO PDF first member/set id.
pdf_nlo_id (int) – NLO PDF first member/set id.
mu (double) – central scale factor.
mu_f (double) – Factorization scale factor.
mu_r (double) – Renormalization scale factor.
precision (double) – Desired numerical relative precision.
max_iters (int) – Upper limit on integration iterations.
invariant_mass (str) – Invariant mass mode ‘auto = sqrt((p1+p2)^2)’ else value.
pt (str) – Transverse Momentum mode ‘auto’ or value.
result (str) – Result type ‘total’/’pt’/’ptj’/’m’.
id (str) – Set an id of this run.
model (str) – Path for MadGraph model.
update (bool) – Update dependent mu else set to zero.
- has_gluino(self) bool
- has_neutralino(self) bool
- has_charginos(self) bool
- has_weakino(self) bool
- has_squark(self) bool
- has_slepton(self) bool
- hepi.update_slha(i: Input)[source]
Updates dependent parameters in Input i.
Mainly concerns the mu value used by madgraph.
- hepi.scan(l: List[Input], var: str, rrange: Iterable) List[Input][source]
Scans a variable var over rrange in l.
Note
This function does not ensure that dependent vairables are updated (see energyhalf in Examples).
- Parameters
l (
listofInput) – 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
listofInput
Examples
>>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> li = scan(li,"energy",range(10000,13000,1000)) >>> for e in li: ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> for e in scan(li,"order",[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]): ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.scan_multi(li: List[Input], **kwargs) List[Input][source]
Magically scans the variables passed to this function.
- Parameters
**kwargs –
Examples
>>> oli = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> li = scan_multi(oli,energy=range(10000,13000,1000)) >>> for e in li: ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> for e in scan_multi(oli,energy=range(10000,13000,1000),order=[Order.LO,Order.NLO,Order.NLO_PLUS_NLL]): ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO_PLUS_NLL: 2>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.scan_scale(l: List[Input], rrange=3, distance=2.0)[source]
Scans scale by varying mu_f and mu_r.
They take rrange values from 1/distance to distance in lograthmic spacing. Only points with mu_f`=`mu_r or mu_r/f`=1 or `mu_r/f`=`distance or mu_r/f`=1/`distance are returned.
Examples
>>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> for e in scan_scale(li): ... print(e) {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 0.5, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 2.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 0.5, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 2.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 0.5, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 2.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.scan_seven_point(l: List[Input])[source]
Scans scale by varying mu_f and mu_r by factors of two excluding relative factors of 4.
Examples
>>> li = [Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)] >>> for e in scan_seven_point(li): ... print(e) {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 0.5, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 0.5, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 0.5, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 2.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2.0, 'mu_r': 2.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.change_where(l: List[Input], dicts: dict, **kwargs)[source]
Applies the values of dicts if the key value pairs in kwargs agree with a member of the list l.
The changes only applied to the matching list members.
Examples
>>> li = scan_multi([Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,13000,1000)) >>> for e in change_where(li,{'order':Order.NLO},energy=11000): ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 12000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} >>> li = scan_multi([Input(Order.LO, 13000, 1000022,1000022, "None", "CT14lo","CT14lo",update=False)],energy=range(10000,12000,1000),mu_f=range(1,3)) >>> for e in change_where(li,{'order':Order.NLO},energy=11000,mu_f=1): ... print(e) {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 10000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 1, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.LO: 0>, 'energy': 11000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14lo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13200, 'mu_f': 2, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.scan_invariant_mass(l: List[Input], diff, points, low=0.001)[source]
Logarithmic invariant_mass scan close to the production threshold.
- hepi.masses_scan(l: List[Input], varis: List[int], rrange, diff_L_R=None, negate=None) List[Input][source]
Scans the PDG identified masses in varis over rrange in the list l. diff_L_R allows to set a fixed difference between masses of left- and right-handed particles.
- hepi.mass_scan(l: List[Input], var: int, rrange, diff_L_R=None) List[Input][source]
Scans the PDG identified mass var over rrange in the list l. diff_L_R allows to set a fixed difference between masses of left- and right-handed particles.
- hepi.slha_scan_rel(l: List[Input], lambdas, rrange: List) List[Input][source]
Scan a generic slha variable.
- hepi.scan_pdf(l: List[Input])[source]
Scans NLO PDF sets.
The PDF sets are infered from lhapdf.getPDFSet with the argument of pdfset_nlo.
Examples
>>> li = [Input(Order.NLO, 13000, 1000022,1000022, "None", "CT14lo","CT14nlo",update=False)] >>> for e in scan_pdf(li): ... print(e) {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 0, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 1, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 2, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 3, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 4, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 5, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 6, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 7, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 8, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 9, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 10, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 11, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 12, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 13, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 14, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 15, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 16, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 17, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 18, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 19, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 20, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 21, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 22, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 23, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 24, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 25, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 26, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 27, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 28, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 29, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 30, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 31, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 32, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 33, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 34, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 35, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 36, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 37, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 38, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 39, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 40, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 41, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 42, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 43, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 44, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 45, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 46, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 47, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 48, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 49, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 50, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 51, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 52, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 53, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 54, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 55, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0} {'order': <Order.NLO: 1>, 'energy': 13000, 'energyhalf': 6500.0, 'particle1': 1000022, 'particle2': 1000022, 'slha': 'None', 'pdf_lo': 'CT14lo', 'pdfset_lo': 0, 'pdf_nlo': 'CT14nlo', 'pdfset_nlo': 56, 'pdf_lo_id': 13200, 'pdf_nlo_id': 13100, 'mu_f': 1.0, 'mu_r': 1.0, 'precision': 0.01, 'max_iters': 50, 'invariant_mass': 'auto', 'pt': 'auto', 'result': 'total', 'id': '', 'model': '', 'mu': 0.0}
- hepi.required_numerical_uncertainty_factor = 10[source]
If the numerical uncertainty is
required_numerical_uncertainty_factortimes higher than the scale or pdf uncertainty a warning is shown.
- class hepi.Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None)[source]
Bases:
hepi.util.DictDataGeneral result class.
- 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 toLO.nlo (
double) – corresponds toNLO.nlo_plus_nll (
double) – corresponds toNLO_PLUS_NLL.annlo_plus_nnll (
double) – corresponds toaNNLO_PLUS_NNLL.
- hepi.pdf_errors(li, dl, ordernames=['LO', 'NLO', 'aNNLO_PLUS_NNLL'], confidence_level=90)[source]
Just like pdf_error but over a list of ordernames.
- hepi.pdf_error(li, dl, ordername='LO', confidence_level=90)[source]
Computes Parton Density Function (PDF) uncertainties through
lhapdf.set.uncertainty().- Parameters
- Returns
- Modified dl with new LO/NLO/NLO_PLUS_NLL _ PDF/PDF_CENTRAL/PDF_ERRPLUS/PDF_ERRMINUS/PDF_ERRSYM entries.
LO/NLO/NLO_PLUS_NLL _ PDF contains a symmetrized
uncertaintiesobject.
- Return type
dict
- hepi.pdf_error_old(li, dl, confidence_level=90)[source]
Computes Parton Density Function (PDF) uncertainties through
lhapdf.set.uncertainty().- Parameters
- Returns
- Modified dl with new LO/NLO/NLO_PLUS_NLL _ PDF/PDF_CENTRAL/PDF_ERRPLUS/PDF_ERRMINUS/PDF_ERRSYM entries.
LO/NLO/NLO_PLUS_NLL _ PDF contains a symmetrized
uncertaintiesobject.
- Return type
dict
- hepi.scale_errors(li, dl, ordernames=['LO', 'NLO', 'aNNLO_PLUS_NNLL'])[source]
Just like scale_error but over a list of ordernames.
- hepi.scale_error(li, dl, ordername='LO')[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()).
- hepi.scale_error_old(li, dl)[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()).
- hepi.combine_errors(dl, ordernames=['LO', 'NLO', 'aNNLO_PLUS_NNLL'])[source]
Just like combine_error but over a list of ordernames.
- hepi.combine_error(dl: dict, ordername='LO')[source]
Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition.
Note
Running
scale_errors()andpdf_errors()before is necessary.- Parameters
dl (
dict) –Resultdictionary with lists per entry.- Returns
- Modified dl with new LO/NLO/NLO_PLUS_NLL _ COMBINED/ERRPLUS/ERRMINUS entries.
LO/NLO/NLO_PLUS_NLL _ COMBINED contains a symmetrized
uncertaintiesobject.
- Return type
dict
- hepi.combine_errors_old(dl: dict)[source]
Combines seven-point scale uncertainties and pdf uncertainties from the results by Pythagorean addition.
Note
Running
scale_errors()andpdf_errors()before is necessary.- Parameters
dl (
dict) –Resultdictionary with lists per entry.- Returns
- Modified dl with new LO/NLO/NLO_PLUS_NLL _ COMBINED/ERRPLUS/ERRMINUS entries.
LO/NLO/NLO_PLUS_NLL _ COMBINED contains a symmetrized
uncertaintiesobject.
- Return type
dict
- hepi.LD2DL(l: List, actual_dict=False) dict[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: dict) pandas.DataFrame
Convert a dict of list`s to a `pandas.DataFrame.
- hepi.get_name(pid: int) str
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.get_LR_partner(pid: int) Tuple[int, int]
Transforms a PDG id to it’s left-right partner.
- Parameters
pid (int) – PDG Monte Carlo identifier for the particle.
- Returns
First int is -1 for Left and 1 for Right. Second int is the PDG id.
- Return type
tuple
Examples
>>> get_LR_partner(1000002) (-1, 2000002)
- hepi.namehash(n: any) str[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: str) int
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.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: Order, energy: float, particle1: int, particle2: int, slha: str, pdf_lo: str, pdf_nlo: str, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.01, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[source]
Bases:
hepi.util.DictDataInput for computation and scans.
- Variables
order (
Order) – LO, NLO or NLO+NLL computation.energy (int) – CMS energy in GeV.
energyhalf (int) – Halfed energy.
particle1 (int) – PDG identifier of the first final state particle.
particle2 (int) – PDG identifier of the second final state particle.
slha (str) – File path of for the base slha. Modified slha files will be used if a scan requires a change of the input.
pdf_lo (str) – LO PDF name.
pdf_nlo (str) – NLO PDF name.
pdfset_lo (int) – LO PDF member/set id.
pdfset_nlo (int) – NLO PDF member/set id.
pdf_lo_id (int) – LO PDF first member/set id.
pdf_nlo_id (int) – NLO PDF first member/set id.
mu (double) – central scale factor.
mu_f (double) – Factorization scale factor.
mu_r (double) – Renormalization scale factor.
precision (double) – Desired numerical relative precision.
max_iters (int) – Upper limit on integration iterations.
invariant_mass (str) – Invariant mass mode ‘auto = sqrt((p1+p2)^2)’ else value.
pt (str) – Transverse Momentum mode ‘auto’ or value.
result (str) – Result type ‘total’/’pt’/’ptj’/’m’.
id (str) – Set an id of this run.
model (str) – Path for MadGraph model.
update (bool) – Update dependent mu else set to zero.
- has_gluino(self) bool
- has_neutralino(self) bool
- has_charginos(self) bool
- has_weakino(self) bool
- has_squark(self) bool
- has_slepton(self) bool
- hepi.get_name(pid: int) str
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: hepi.input.Input, axe=None, scenario='', diff_L_R=None, extra='', cms_energy=True, pdf_info=True, id=False, **kwargs)[source]
Sets the title on axis axe.
- 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.mass_plot(dict_list, y, part, logy=True, yaxis='$\\sigma$ [pb]', yscale=1.0, label=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: dict, iid: int)[source]
Get the mass of particle with id iid out of the list in the “slha” element in the dict.
- Returns
listof float : masses of particles in each element of the dict list.
- hepi.plot(dict_list, x, y, label=None, xaxis='E [GeV]', yaxis='$\\sigma$ [pb]', ratio=False, K=False, K_plus_1=False, logy=True, yscale=1.0, mask=None, **kwargs) None
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",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]")
(Source code, png, hires.png, pdf)
- 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",xaxis="$m_{\\tilde{g}}$ [GeV]",yaxis="$m_{\\tilde{q}}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]")
(Source code, png, hires.png, pdf)
- 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",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.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.Input(order: Order, energy: float, particle1: int, particle2: int, slha: str, pdf_lo: str, pdf_nlo: str, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.01, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[source]
Bases:
hepi.util.DictDataInput for computation and scans.
- Variables
order (
Order) – LO, NLO or NLO+NLL computation.energy (int) – CMS energy in GeV.
energyhalf (int) – Halfed energy.
particle1 (int) – PDG identifier of the first final state particle.
particle2 (int) – PDG identifier of the second final state particle.
slha (str) – File path of for the base slha. Modified slha files will be used if a scan requires a change of the input.
pdf_lo (str) – LO PDF name.
pdf_nlo (str) – NLO PDF name.
pdfset_lo (int) – LO PDF member/set id.
pdfset_nlo (int) – NLO PDF member/set id.
pdf_lo_id (int) – LO PDF first member/set id.
pdf_nlo_id (int) – NLO PDF first member/set id.
mu (double) – central scale factor.
mu_f (double) – Factorization scale factor.
mu_r (double) – Renormalization scale factor.
precision (double) – Desired numerical relative precision.
max_iters (int) – Upper limit on integration iterations.
invariant_mass (str) – Invariant mass mode ‘auto = sqrt((p1+p2)^2)’ else value.
pt (str) – Transverse Momentum mode ‘auto’ or value.
result (str) – Result type ‘total’/’pt’/’ptj’/’m’.
id (str) – Set an id of this run.
model (str) – Path for MadGraph model.
update (bool) – Update dependent mu else set to zero.
- has_gluino(self) bool
- has_neutralino(self) bool
- has_charginos(self) bool
- has_weakino(self) bool
- has_squark(self) bool
- has_slepton(self) bool
- class hepi.Order
Bases:
enum.IntEnumComputation 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
- class hepi.Result(lo=None, nlo=None, nlo_plus_nll=None, annlo_plus_nnll=None)[source]
Bases:
hepi.util.DictDataGeneral result class.
- 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 toLO.nlo (
double) – corresponds toNLO.nlo_plus_nll (
double) – corresponds toNLO_PLUS_NLL.annlo_plus_nnll (
double) – corresponds toaNNLO_PLUS_NNLL.
- hepi.DL2DF(ld: dict) pandas.DataFrame
Convert a dict of list`s to a `pandas.DataFrame.
- hepi.LD2DL(l: List, actual_dict=False) dict[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.namehash(n: any) str[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: bool = False, in_file: str = None, out_file: str = None, execute: str = None, name: str = None)[source]
Bases:
hepi.util.DictDataAbstract class that is similar to a dictionary but with fixed keys.
- class hepi.Runner(path: str, in_dir: str = None, out_dir: str = None, pre=None)[source]
- orders(self) List[hepi.input.Order]
List of supported Orders in this runner.
- get_name(self) str
Returns the name of the runner.
- get_version(self) str
- _sub_run(self, coms: List[str]) str
- _check_path(self) bool
Checks if the passed path is valid.
- _prepare(self, p: hepi.input.Input, **kwargs) RunParam
- _check_input(self, param: hepi.input.Input, **kwargs) bool
- _prepare_all(self, params: List[hepi.input.Input], skip=True, **kwargs) List[RunParam]
- run(self, params: List[hepi.input.Input], skip=True, parse=True, parallel=True, sleep=0, run=True, ignore_error=False, **kwargs)
Run the passed list of parameters.
- Args:
params (
listofhepi.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.
- Returns:
pd.DataFrame: combined dataframe of results and parameters. The dataframe is empty if parse is set to False.
- _run(self, rps: List[RunParam], wait=True, parallel=True, sleep=0, **kwargs)
Runs Runner per
RunParams.- Parameters
rps (
listofRunParams) – 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.
- Returns
return codes from jobs if no_parse is False.
- Return type
listof int
- _is_valid(self, file: str, p: hepi.input.Input, d, **kwargs) bool
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(self, outputs: List[str]) List[hepi.results.Result]
Parses Resummino output files and returns List of Results.
- Parameters
outputs (
listof str) – List of the filenames to be parsed.- Returns
- _parse_file(self, file: str) hepi.results.Result
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
- get_path(self) str
Get the Runner path.
- Returns
current Runner path.
- Return type
str
- set_path(self, p: str)
Set the path to the Runner folder containing the binary in ‘./bin’ or ‘./build/bin’.
- Parameters
p (str) – new path.
- set_input_dir(self, indir: str)
Sets the input directory.
- Parameters
indir (str) – new input directory.
- set_output_dir(self, outdir: str, create: bool = True)
Sets the output directory.
- Parameters
outdir (str) – new output directory. create (bool): create directory if not existing.
- set_pre(self, ppre: str)
Sets the command prefix.
- Parameters
ppre (str) – new command prefix.
- class hepi.Input(order: Order, energy: float, particle1: int, particle2: int, slha: str, pdf_lo: str, pdf_nlo: str, mu_f=1.0, mu_r=1.0, pdfset_lo=0, pdfset_nlo=0, precision=0.01, max_iters=50, invariant_mass='auto', result='total', pt='auto', id='', model='', update=True)[source]
Bases:
hepi.util.DictDataInput for computation and scans.
- Variables
order (
Order) – LO, NLO or NLO+NLL computation.energy (int) – CMS energy in GeV.
energyhalf (int) – Halfed energy.
particle1 (int) – PDG identifier of the first final state particle.
particle2 (int) – PDG identifier of the second final state particle.
slha (str) – File path of for the base slha. Modified slha files will be used if a scan requires a change of the input.
pdf_lo (str) – LO PDF name.
pdf_nlo (str) – NLO PDF name.
pdfset_lo (int) – LO PDF member/set id.
pdfset_nlo (int) – NLO PDF member/set id.
pdf_lo_id (int) – LO PDF first member/set id.
pdf_nlo_id (int) – NLO PDF first member/set id.
mu (double) – central scale factor.
mu_f (double) – Factorization scale factor.
mu_r (double) – Renormalization scale factor.
precision (double) – Desired numerical relative precision.
max_iters (int) – Upper limit on integration iterations.
invariant_mass (str) – Invariant mass mode ‘auto = sqrt((p1+p2)^2)’ else value.
pt (str) – Transverse Momentum mode ‘auto’ or value.
result (str) – Result type ‘total’/’pt’/’ptj’/’m’.
id (str) – Set an id of this run.
model (str) – Path for MadGraph model.
update (bool) – Update dependent mu else set to zero.
- has_gluino(self) bool
- has_neutralino(self) bool
- has_charginos(self) bool
- has_weakino(self) bool
- has_squark(self) bool
- has_slepton(self) bool
- hepi.LD2DL(l: List, actual_dict=False) dict[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: dict) pandas.DataFrame
Convert a dict of list`s to a `pandas.DataFrame.
- hepi.load(f, dimensions=1)
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.