At the end of the run you will want to write out the final statistics
on number of events generated, the corresponding cross sections and
the number of errors encountered. This is done either with the
pythia.stat() method or the
pythia is an instance of the
Pythia class.The former method is steered entirely by
settings values, see here.
The latter, deprecated one instead takes two arguments:
void Pythia::statistics(bool all = false, bool reset = false)
write out statistics on cross sections and errors. This is based on
calls to the methods below, for the two kinds of information.
argument all :
true it allows a more extensive listing than the default
one, see multiparton-interactions statistics below.
argument reset : if
true it implies that all counters,
e.g on events generated and errors experienced, are reset to zero whenever
the routine is called. The default instead is that all stored
statistics information is unaffected by the call.
Counters are automatically reset in each new
call, however, so the only time the
reset option makes a
difference is if
statistics(...) is called several times
in a (sub)run.
ProcessLevel::statistics() method cannot be accessed
directly, but only via the
Pythia::statistics(...) calls above.
When called it will loop over the list of existing processes, and for
each write out name, code, the number of tried, selected and accepted
events, the cross section and the estimated error on the latter.
The three different event numbers are related to the Monte Carlo method
used, whereby an initial upper estimate of the cross section is used to
select a large number of trial phase-space points, whereof then not all
survive. Rejections are normally done by the internal machinery, but can
also be obtained by user hooks.
In most runs there would be no user hooks implemented, and then the
numbers of selected and of accepted events will agree. Aborted events
(see below) usually appear in the selected statistics but not in the
For Les Houches events the total cross section will be correctly
displayed; however the (optional) error value will not be used, so that
the reported error will be smaller than the correct statistical ones,
and often vanish completely. Furthermore, while the number of events
is shown for each user process, the cross section is only for the sum
- tried events reflect the original number of
phase-space points probed, as part of the upper estimate;
- selected events correspond to those that survive
the internal Monte-Carlo selection procedure;
- accepted events are those that also survive
the additional user cuts.
When Pythia is run, errors may occur, and give rise to warning messages.
These may be of varying severity, as follows:
The error messages is handled by a small part of the
- Abort means things went seriously wrong, and the
initialization or event generation failed. In the former case it is
not possible to generate events at all, in the latter the current
event is flawed and should be skipped. In either case the respective
then also returns the value
false. There are occasions
where an abort may be deliberate, such as when a file of Les Houches
Events is read and the end of the file is reached.
- Error normally is less severe. Typically the program will
back up one step and try again. There are cases where this is not possible,
in particular during the initialization and the generation of a hard
process, and then the error may be followed by an abort as a direct
consequence (with two separate messages).
- Warning is even less severe. In some cases the program will
try again, with good chances of success, in others no measure at all
need to be taken.
class. It is handed any abort, error or warning messages during the event
generation phase, and will store each distinct message, with a counter
for how many times it is issued. Thus it is possible to limit the number
of identical messages issued, currently hardcoded so that each kind of
error message is only printed once
static const int TIMESTOPRINT = 1).
This can be overridden by the calling routine, so that all messages of
this kind are shown, which is particularly relevant for the
The summary table printed by
provides a table with all the different messages issued, in
alphabetical order, with the total number of times each was generated.
If you call
Pythia::statistics(true), i.e. with the first
true, also statistics on multiparton
interactions is printed, comprising a list of all allowed subprocesses
with how many times each of them has been generated. For the nondiffractive
process this also includes the hardest interaction, while else the
hardest process is excluded from the statistics. (This is because
the hardest process is of the same character and generated by the same
machinery in the former case but not in the latter. Also, for the
former case only, the standard statistics listing only lists
nondiffractive as one single process, i.e. does not further specify
the character of the hardest subprocess, so there is not any overlap
between the two.)