view CONTRIBUTING @ 41277:61f9ef23a12f

dagop: minor python optimization to `headrevs`

Less lookup and less function call never hurt. This provides a small speedup
on various run of the 'heads()' revset. This also buys back some of the slow
down we observed in the previous changesets for single value lookup.

Performance number:
0) before dagop.headrevs usage
1) after dagop.headrevs usage
2) after this change

revset: heads(all())
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 0.036503      0.032564      0.030024      0.032378      0.030887      0.036367      0.031713      0.032205      0.036467      0.032286      0.030300
1) 0.036668      0.035347 108% 0.035611 118% 0.035358 109% 0.035726 115% 0.036411      0.035261 111% 0.036096 112% 0.036052      0.035095 108% 0.035792 118%
2) 0.034254  93% 0.034482      0.035003      0.034353      0.033754  94% 0.034689      0.034361      0.035059      0.034636      0.034662      0.035465

revset: heads(-10000:-1)
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 0.003936      0.003218      0.003227      0.003302      0.003328      0.003848      0.003305      0.003252      0.003839      0.003306      0.003279
1) 0.003870      0.003785 117% 0.003821 118% 0.003780 114% 0.003769 113% 0.003776      0.003792 114% 0.003805 117% 0.003810      0.003798 114% 0.003840 117%
2) 0.003666  94% 0.003577  94% 0.003632      0.003644      0.003614      0.003638      0.003652      0.003632      0.003661      0.003660      0.003658

revset: (-5000:-1000) and heads(-10000:-1)
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 0.004244      0.003368      0.003313      0.003367      0.003327      0.004325      0.003401      0.003379      0.004310      0.003359      0.003396
1) 0.003969  93% 0.003862 114% 0.003834 115% 0.003810 113% 0.003822 114% 0.003940  91% 0.003908 114% 0.003814 112% 0.003986  92% 0.003954 117% 0.003816 112%
2) 0.003728  93% 0.003638  94% 0.003659      0.003685      0.003628  94% 0.003716  94% 0.003653  93% 0.003655      0.003748  94% 0.003740  94% 0.003686

revset: heads(matching(tip, "author"))
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 7.574666      7.545950      7.570743      7.578697      7.525725      7.509929      7.443854      7.488442      7.452880      7.445411      7.689107
1) 7.549390      7.389162      7.529790      7.536297      7.450467      7.555347      7.404586      7.514948      7.542794      7.524787      7.536918
2) 7.568294      7.479326      7.578624      7.380375      7.440102      7.454218      7.515189      7.556511      7.524585      7.537566      7.507418

revset: heads(matching(tip, "author")) and -10000:-1
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 7.512533      7.605877      7.382894      7.462109      7.420086      7.575034      7.448452      7.549374      7.457880      7.450308      7.515019
1) 7.548677      7.551832      7.629598      7.494857      7.550554      7.521838      7.451794      error         7.321781      7.546885      7.557523
2) 7.451985      7.541044      7.506563      7.470928      7.512618      7.474988      7.498887      7.547930      7.560276      7.618599      7.465442

revset: (-10000:-1) and heads(matching(tip, "author"))
   plain         min           max           first         last          reverse       rev..rst      rev..ast      sort          sor..rst      sor..ast
0) 7.465419      7.570089      7.439594      7.521221      7.498716      7.492922      7.479108      7.552397      7.407888      error         7.468264
1) 7.539866      7.548045      7.491761      7.517170      7.469824      7.501990      7.579102      7.502568      7.578102      7.555754      7.567622
2) 7.370463      7.514712      7.497024      7.679428      7.638138      7.490775      7.472273      7.652587      7.584139      7.511893      7.466384

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