IO.rst

Input & Output

We use yaml runcards for piping input and output files.

Input

The input is split into two runcards: a theory runcard and an operator runcard. Note that we are not assuming any default values for the keywords, but instead the user has to provide the full definition. However, for the benchmarking environment </development/Benchmarks> we do provide some default settings.

Theory Runcard

The theory runcard (compatible with the NNPDF theory database) defines the physical setup and environment. The benchmark settings are available at banana.data.theories.default_card.

theory input runcard

Name	Type	Description
PTO	:pyint	perturbation theory order: 0 = , 1 = , 2 = , 3 = .
ModEv	:pystr	Evolution method. Possible options are: iterate-exact abbreviated with EXA, decompose-exact, perturbative-exact, iterate-expanded abbreviated with EXP, decompose-expanded, perturbative-expanded, truncated abbreviated with TRN, ordered-truncated.
XIF	:pyfloat	Factorization to renormalization scale ratio. 1 means no scale variation.
ModSV	:pystr	Scale variation method, used only if XIF!=1. Possible options are: expanded or exponentiated.
Q0	:pyfloat	Initial evolution scale (in GeV).
nf0	:pyint or :pyNone	Number of flavors active ant the Q0 scale. If not provided it is inferred from the heavy quark threshold scales.
MaxNfPdf	:pyint	Maximum number of flavors in the evolution.
alphas	:pyfloat	Reference value of the strong coupling αs (Note that we have to use αs here, instead of as for legacy reasons).
Qref	:pyfloat	Reference scale at which the alphas value is given (in GeV).
nfref	:pyint or :pyNone	Number of flavors active at the Qref scale. If not provided it is inferred from the heavy quark threshold scales.
MaxNfAs	:pyint	Maximum number of flavors in the strong coupling evolution.
QED	:pyint	If 1 include evolution.
alphaqed	:pyfloat	Reference value of the electromagnetic coupling αem.
Qedref	:pyfloat	Reference scale at which the alphaqed value is given (in GeV).
HQ	:pystr	Heavy quark scheme: if POLE use heavy quark pole masses, if MSBAR use heavy quark masses.
mc	:pyfloat	Charm quark mass (in GeV).
Qmc	:pyfloat	Reference scale at which the charm quark mass is given (in GeV). Used only with HQ='MSBAR'.
kcThr	:pyfloat	Ratio between the charm mass scale and the nf=4 threshold scale.
mb	:pyfloat	Bottom quark mass (in GeV).
Qmb	:pyfloat	Reference scale at which the bottom quark mass is given (in GeV). Used only with HQ='MSBAR'.
kbThr	:pyfloat	Ratio between the bottom mass scale and the nf=5 threshold scale.
mt	:pyfloat	Top quark mass (in GeV).
Qmt	:pyfloat	Reference scale at which the top quark mass is given (in GeV). Used only with HQ='MSBAR'.
ktThr	:pyfloat	Ratio between the top mass scale and the nf=6 threshold scale.
IC	:pybool	If 1 allow for intrinsic charm evolution.
IB	:pybool	If 1 allow for intrinsic bottom evolution.

Operator Runcard

The operator runcard defines the numerical setup and the requested operators. The benchmark settings are available at ekomark.data.operators.

operator input runcard

Name	Type	description
interpolation_xgrid	:pylist(float)	x-grid at which the is computed.
mugrid	:pylist(float)	target grid at which the is computed (in GeV).
interpolation_is_log	:pybool	If True use logarithmic interpolation.
interpolation_polynomial_degree	:pyint	Polynomial degree of the interpolating function.
debug_skip_non_singlet	:pybool	If True skip the non singlet sector, useful for debug purposes.
debug_skip_singlet	:pybool	If True skip the singlet sector, useful for debug purposes.
ev_op_max_order	:pyint	Perturbative expansion order of unitary evolution matrix. Needed only for perturbative evolution methods.
ev_op_iterations	:pyint	Number of evolution steps.
backward_inversion	:pystr	Backward matching inversion method, relevant only for backward evolution in .
n_integration_cores	:pyint	Number of cores used during the integration. 0 means use all; -1 all minus 1.

Output

The eko output is represented by the class ~eko.output.Output. An instance of this class is a :pydict and contains the following keys:

output runcard

Name	Type	Description
Q2grid	:pydict	All operators at the requested values of Q2 represented by the key
eko_version	:pyfloat	The version
inputgrid	:pylist(float)	The input x-grid
inputpids	:pylist(int)	The input list of participating partons listed by their .
interpolation_is_log	:pybool	If True use logarithmic interpolation.
interpolation_polynomial_degree	:pyint	Polynomial degree of the interpolating function.
targetgrid	:pylist(float)	The target x-grid
targetpids	:pylist(int)	The target list of participating partons listed by their

Since the final is a rank 4-tensor we store in the output all the different grids for each dimension:targetpids,targetgrid,inputpids,inputgrid. The Q2grid values are the actual tensor for the requested Q². Each of them contains two keys:

• operators a :pydict with all evolution kernel operators where the key indicates which distribution is generated by which other one and the value represents the eko in matrix representation - this can either be the plain list representation or the binary representation (as provided by :pynumpy.ndarray.tobytes)
• errors a :pydict with the integration errors associated to the respective operators following the same conventions as the operator dictionary

Each element () is a rank-4 tensor with the indices ordered in the following way: EKO[pid_out][x_out][pid_in][x_in] where pid_out and x_out refer to the outgoing and pid_in and x_in to the incoming . The ordering of pid_out/pid_in is determined by the targetpids/inputpids parameter of the output and the order of x_out/x_in by targetgrid/inputgrid.

To further explore how an ~eko.output.Output object looks like you can follow this tutorial </overview/tutorials/output>.