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<title>Event Statistics</title>
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<h2>Event Statistics</h2>
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At the end of the run you will want to write out the final statistics
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on number of events generated, the corresponding cross sections and
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the number of errors encountered. This is done either with the
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<code>pythia.stat()</code> method or the <code>pythia.statistics()</code>
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one, assuming <code>pythia</code> is an instance of the
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<code>Pythia</code> class.The former method is steered entirely by
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settings values, see <a href="MainProgramSettings.html" target="page">here</a>.
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The latter, deprecated one instead takes two arguments:
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<a name="method1"></a>
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<p/><strong>void Pythia::statistics(bool all = false, bool reset = false) </strong> <br/>
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write out statistics on cross sections and errors. This is based on
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calls to the methods below, for the two kinds of information.
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<br/><code>argument</code><strong> all </strong> :
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if <code>true</code> it allows a more extensive listing than the default
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one, see multiparton-interactions statistics below.
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<br/><code>argument</code><strong> reset </strong> : if <code>true</code> it implies that all counters,
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e.g on events generated and errors experienced, are reset to zero whenever
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the routine is called. The default instead is that all stored
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statistics information is unaffected by the call.
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Counters are automatically reset in each new <code>Pythia::init()</code>
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call, however, so the only time the <code>reset</code> option makes a
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difference is if <code>statistics(...)</code> is called several times
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<h3>Cross-section statistics</h3>
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The <code>ProcessLevel::statistics()</code> method cannot be accessed
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directly, but only via the <code>Pythia::stat()</code> and
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<code>Pythia::statistics(...)</code> calls above.
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When called it will loop over the list of existing processes, and for
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each write out name, code, the number of tried, selected and accepted
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events, the cross section and the estimated error on the latter.
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The three different event numbers are related to the Monte Carlo method
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used, whereby an initial upper estimate of the cross section is used to
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select a large number of trial phase-space points, whereof then not all
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survive. Rejections are normally done by the internal machinery, but can
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also be obtained by <a href="UserHooks.html" target="page">user hooks</a>.
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<li><b>tried</b> events reflect the original number of
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phase-space points probed, as part of the upper estimate;</li>
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<li><b>selected</b> events correspond to those that survive
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the internal Monte-Carlo selection procedure;</li>
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<li><b>accepted</b> events are those that also survive
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the additional user cuts.</li>
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In most runs there would be no user hooks implemented, and then the
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numbers of selected and of accepted events will agree. Aborted events
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(see below) usually appear in the selected statistics but not in the
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For Les Houches events the total cross section will be correctly
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displayed; however the (optional) error value will not be used, so that
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the reported error will be smaller than the correct statistical ones,
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and often vanish completely. Furthermore, while the number of events
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is shown for each user process, the cross section is only for the sum
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<h3>Error messages</h3>
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When Pythia is run, errors may occur, and give rise to warning messages.
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These may be of varying severity, as follows:
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<li><b>Abort</b> means things went seriously wrong, and the
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initialization or event generation failed. In the former case it is
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not possible to generate events at all, in the latter the current
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event is flawed and should be skipped. In either case the respective
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method, <code>Pythia::init()</code> or <code>Pythia::next()</code>,
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then also returns the value <code>false</code>. There are occasions
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where an abort may be deliberate, such as when a file of Les Houches
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Events is read and the end of the file is reached.</li>
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<li><b>Error</b> normally is less severe. Typically the program will
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back up one step and try again. There are cases where this is not possible,
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in particular during the initialization and the generation of a hard
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process, and then the error may be followed by an abort as a direct
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consequence (with two separate messages).</li>
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<li><b>Warning</b> is even less severe. In some cases the program will
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try again, with good chances of success, in others no measure at all
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need to be taken.</li>
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The error messages is handled by a small part of the <code>Info</code>
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class. It is handed any abort, error or warning messages during the event
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generation phase, and will store each distinct message, with a counter
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for how many times it is issued. Thus it is possible to limit the number
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of identical messages issued, currently hardcoded so that each kind of
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error message is only printed once
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(<code>static const int TIMESTOPRINT = 1</code>).
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This can be overridden by the calling routine, so that all messages of
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this kind are shown, which is particularly relevant for the
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initialization stage.
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The summary table printed by <code>Pythia::statistics()</code>
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provides a table with all the different messages issued, in
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alphabetical order, with the total number of times each was generated.
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<h3>Multiparton-interactions statistics</h3>
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If you call <code>Pythia::statistics(true)</code>, i.e. with the first
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optional argument <code>true</code>, also statistics on multiparton
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interactions is printed, comprising a list of all allowed subprocesses
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with how many times each of them has been generated. For the minimum-bias
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process this also includes the hardest interaction, while else the
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hardest process is excluded from the statistics. (This is because
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the hardest process is of the same character and generated by the same
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machinery in the former case but not in the latter. Also, for the
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former case only, the standard statistics listing only lists
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minimum bias as one single process, i.e. does not further specify
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the character of the hardest subprocess, so there is not any overlap
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