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15 """Classes for diagram generation. Amplitude performs the diagram
16 generation, DecayChainAmplitude keeps track of processes with decay
17 chains, and MultiProcess allows generation of processes with
18 multiparticle definitions. DiagramTag allows to identify diagrams
19 based on relevant properties.
20 """
21
22 import array
23 import copy
24 import itertools
25 import logging
26
27 import madgraph.core.base_objects as base_objects
28 import madgraph.various.misc as misc
29 from madgraph import InvalidCmd, MadGraph5Error
30
31 logger = logging.getLogger('madgraph.diagram_generation')
35
41 """Class to tag diagrams based on objects with some __lt__ measure, e.g.
42 PDG code/interaction id (for comparing diagrams from the same amplitude),
43 or Lorentz/coupling/mass/width (for comparing AMPs from different MEs).
44 Algorithm: Create chains starting from external particles:
45 1 \ / 6
46 2 /\______/\ 7
47 3_ / | \_ 8
48 4 / 5 \_ 9
49 \ 10
50 gives ((((9,10,id910),8,id9108),(6,7,id67),id910867)
51 (((1,2,id12),(3,4,id34)),id1234),
52 5,id91086712345)
53 where idN is the id of the corresponding interaction. The ordering within
54 chains is based on chain length (depth; here, 1234 has depth 3, 910867 has
55 depth 4, 5 has depht 0), and if equal on the ordering of the chain elements.
56 The determination of central vertex is based on minimizing the chain length
57 for the longest subchain.
58 This gives a unique tag which can be used to identify diagrams
59 (instead of symmetry), as well as identify identical matrix elements from
60 different processes."""
61
63 """Exception for any problems in DiagramTags"""
64 pass
65
66 - def __init__(self, diagram, model=None, ninitial=2):
67 """Initialize with a diagram. Create DiagramTagChainLinks according to
68 the diagram, and figure out if we need to shift the central vertex."""
69
70
71 leg_dict = {}
72
73 for vertex in diagram.get('vertices'):
74
75 legs = vertex.get('legs')[:-1]
76 lastvx = vertex == diagram.get('vertices')[-1]
77 if lastvx:
78
79 legs = vertex.get('legs')
80
81 link = DiagramTagChainLink([leg_dict.setdefault(leg.get('number'),
82 DiagramTagChainLink(self.link_from_leg(leg, model))) \
83 for leg in legs],
84 self.vertex_id_from_vertex(vertex,
85 lastvx,
86 model,
87 ninitial))
88
89 if not lastvx:
90 leg_dict[vertex.get('legs')[-1].get('number')] = link
91
92
93 self.tag = link
94
95
96
97 done = max([l.depth for l in self.tag.links]) == 0
98 while not done:
99
100 longest_chain = self.tag.links[0]
101
102 new_link = DiagramTagChainLink(self.tag.links[1:],
103 self.flip_vertex(\
104 self.tag.vertex_id,
105 longest_chain.vertex_id,
106 self.tag.links[1:]))
107
108 other_links = list(longest_chain.links) + [new_link]
109 other_link = DiagramTagChainLink(other_links,
110 self.flip_vertex(\
111 longest_chain.vertex_id,
112 self.tag.vertex_id,
113 other_links))
114
115 if other_link.links[0] < self.tag.links[0]:
116
117 self.tag = other_link
118 else:
119
120 done = True
121
126
128 """Output a diagram from a DiagramTag. Note that each daughter
129 class must implement the static functions id_from_vertex_id
130 (if the vertex id is something else than an integer) and
131 leg_from_link (to pass the correct info from an end link to a
132 leg)."""
133
134
135 diagram = base_objects.Diagram({'vertices': \
136 self.vertices_from_link(self.tag,
137 model,
138 True)})
139 diagram.calculate_orders(model)
140 return diagram
141
142 @classmethod
144 """Recursively return the leg corresponding to this link and
145 the list of all vertices from all previous links"""
146
147 if link.end_link:
148
149 return cls.leg_from_link(link), []
150
151
152 leg_vertices = [cls.vertices_from_link(l, model) for l in link.links]
153
154 legs = base_objects.LegList(sorted([l for l,v in leg_vertices],
155 lambda l1,l2: l2.get('number') - \
156 l1.get('number')))
157
158 vertices = base_objects.VertexList(sum([v for l, v in leg_vertices],
159 []))
160
161 if not first_vertex:
162
163
164 last_leg = cls.leg_from_legs(legs,link.vertex_id,model)
165 legs.append(last_leg)
166
167
168 vertices.append(cls.vertex_from_link(legs,
169 link.vertex_id,
170 model))
171 if first_vertex:
172
173 return vertices
174 else:
175
176 return last_leg, vertices
177
178 @classmethod
180 """Returns the list of external PDGs of the interaction corresponding
181 to this vertex_id."""
182
183
184
185
186 if (len(vertex_id)>=3 and 'PDGs' in vertex_id[2]):
187 return vertex_id[2]['PDGs']
188 else:
189 return [part.get_pdg_code() for part in model.get_interaction(
190 cls.id_from_vertex_id(vertex_id)).get('particles')]
191
192 @classmethod
194 """Return a leg from a leg list and the model info"""
195
196 pdgs = list(cls.legPDGs_from_vertex_id(vertex_id, model))
197
198
199 for pdg in [leg.get('id') for leg in legs]:
200 pdgs.remove(pdg)
201
202 assert len(pdgs) == 1
203
204 pdg = model.get_particle(pdgs[0]).get_anti_pdg_code()
205 number = min([l.get('number') for l in legs])
206
207 state = (len([l for l in legs if l.get('state') == False]) != 1)
208
209 onshell= False
210
211 return base_objects.Leg({'id': pdg,
212 'number': number,
213 'state': state,
214 'onshell': onshell})
215
216 @classmethod
229
230 @staticmethod
232 """Return a leg from a link"""
233
234 if link.end_link:
235
236 return base_objects.Leg({'number':link.links[0][1],
237 'id':link.links[0][0][0],
238 'state':(link.links[0][0][1] == 0),
239 'onshell':False})
240
241
242 assert False
243
244 @staticmethod
246 """Return the numerical vertex id from a link.vertex_id"""
247
248 return vertex_id[0][0]
249
250 @staticmethod
252 """Return the loop_info stored in this vertex id. Notice that the
253 IdentifyME tag does not store the loop_info, but should normally never
254 need access to it."""
255
256 return vertex_id[2]
257
258 @staticmethod
260 """Reorder a permutation with respect to start_perm. Note that
261 both need to start from 1."""
262 if perm == start_perm:
263 return range(len(perm))
264 order = [i for (p,i) in \
265 sorted([(p,i) for (i,p) in enumerate(perm)])]
266 return [start_perm[i]-1 for i in order]
267
268 @staticmethod
270 """Returns the default end link for a leg: ((id, state), number).
271 Note that the number is not taken into account if tag comparison,
272 but is used only to extract leg permutations."""
273 if leg.get('state'):
274
275 return [((leg.get('id'), 0), leg.get('number'))]
276 else:
277
278 return [((leg.get('id'), leg.get('number')), leg.get('number'))]
279
280 @staticmethod
282 """Returns the default vertex id: just the interaction id
283 Note that in the vertex id, like the leg, only the first entry is
284 taken into account in the tag comparison, while the second is for
285 storing information that is not to be used in comparisons and the
286 third for additional info regarding the shrunk loop vertex."""
287
288 if isinstance(vertex,base_objects.ContractedVertex):
289
290 return ((vertex.get('id'),vertex.get('loop_tag')),(),
291 {'PDGs':vertex.get('PDGs')})
292 else:
293 return ((vertex.get('id'),()),(),{})
294
295 @staticmethod
297 """Returns the default vertex flip: just the new_vertex"""
298 return new_vertex
299
301 """Equal if same tag"""
302 if type(self) != type(other):
303 return False
304 return self.tag == other.tag
305
307 return not self.__eq__(other)
308
311
313 return self.tag < other.tag
314
316 return self.tag > other.tag
317
318 __repr__ = __str__
319
321 """Chain link for a DiagramTag. A link is a tuple + vertex id + depth,
322 with a comparison operator defined"""
323
324 - def __init__(self, objects, vertex_id = None):
325 """Initialize, either with a tuple of DiagramTagChainLinks and
326 a vertex_id (defined by DiagramTag.vertex_id_from_vertex), or
327 with an external leg object (end link) defined by
328 DiagramTag.link_from_leg"""
329
330 if vertex_id == None:
331
332 self.links = tuple(objects)
333 self.vertex_id = (0,)
334 self.depth = 0
335 self.end_link = True
336 return
337
338 self.links = tuple(sorted(list(tuple(objects)), reverse=True))
339 self.vertex_id = vertex_id
340
341
342 self.depth = sum([l.depth for l in self.links],
343 max(1, len(self.links)-1))
344 self.end_link = False
345
347 """Get the permutation of external numbers (assumed to be the
348 second entry in the end link tuples)"""
349
350 if self.end_link:
351 return [self.links[0][1]]
352
353 return sum([l.get_external_numbers() for l in self.links], [])
354
356 """Compare self with other in the order:
357 1. depth 2. len(links) 3. vertex id 4. measure of links"""
358
359 if self == other:
360 return False
361
362 if self.depth != other.depth:
363 return self.depth < other.depth
364
365 if len(self.links) != len(other.links):
366 return len(self.links) < len(other.links)
367
368 if self.vertex_id[0] != other.vertex_id[0]:
369 return self.vertex_id[0] < other.vertex_id[0]
370
371 for i, link in enumerate(self.links):
372 if i > len(other.links) - 1:
373 return False
374 if link != other.links[i]:
375 return link < other.links[i]
376
378 return self != other and not self.__lt__(other)
379
381 """For end link,
382 consider equal if self.links[0][0] == other.links[0][0],
383 i.e., ignore the leg number (in links[0][1])."""
384
385 if self.end_link and other.end_link and self.depth == other.depth \
386 and self.vertex_id == other.vertex_id:
387 return self.links[0][0] == other.links[0][0]
388
389 return self.end_link == other.end_link and self.depth == other.depth \
390 and self.vertex_id[0] == other.vertex_id[0] \
391 and self.links == other.links
392
394 return not self.__eq__(other)
395
396
398 if self.end_link:
399 return str(self.links)
400 return "%s, %s; %d" % (str(self.links),
401 str(self.vertex_id),
402 self.depth)
403
404 __repr__ = __str__
405
406
407
408
409 -class Amplitude(base_objects.PhysicsObject):
410 """Amplitude: process + list of diagrams (ordered)
411 Initialize with a process, then call generate_diagrams() to
412 generate the diagrams for the amplitude
413 """
414
416 """Default values for all properties"""
417
418 self['process'] = base_objects.Process()
419 self['diagrams'] = None
420
421
422 self['has_mirror_process'] = False
423
436
437 - def filter(self, name, value):
453
454 - def get(self, name):
463
464
465
467 """Return diagram property names as a nicely sorted list."""
468
469 return ['process', 'diagrams', 'has_mirror_process']
470
472 """Returns number of diagrams for this amplitude"""
473 return len(self.get('diagrams'))
474
476 """Return an AmplitudeList with just this amplitude.
477 Needed for DecayChainAmplitude."""
478
479 return AmplitudeList([self])
480
482 """Returns a nicely formatted string of the amplitude content."""
483 return self.get('process').nice_string(indent) + "\n" + \
484 self.get('diagrams').nice_string(indent)
485
487 """Returns a nicely formatted string of the amplitude process."""
488 return self.get('process').nice_string(indent)
489
491 """Returns the number of initial state particles in the process."""
492 return self.get('process').get_ninitial()
493
495 """ Returns wether this amplitude has a loop process."""
496
497 return self.get('process').get('perturbation_couplings')
498
500 """Generate diagrams. Algorithm:
501
502 1. Define interaction dictionaries:
503 * 2->0 (identity), 3->0, 4->0, ... , maxlegs->0
504 * 2 -> 1, 3 -> 1, ..., maxlegs-1 -> 1
505
506 2. Set flag from_group=true for all external particles.
507 Flip particle/anti particle for incoming particles.
508
509 3. If there is a dictionary n->0 with n=number of external
510 particles, create if possible the combination [(1,2,3,4,...)]
511 with *at least two* from_group==true. This will give a
512 finished (set of) diagram(s) (done by reduce_leglist)
513
514 4. Create all allowed groupings of particles with at least one
515 from_group==true (according to dictionaries n->1):
516 [(1,2),3,4...],[1,(2,3),4,...],...,
517 [(1,2),(3,4),...],...,[(1,2,3),4,...],...
518 (done by combine_legs)
519
520 5. Replace each group with a (list of) new particle(s) with number
521 n = min(group numbers). Set from_group true for these
522 particles and false for all other particles. Store vertex info.
523 (done by merge_comb_legs)
524
525 6. Stop algorithm when at most 2 particles remain.
526 Return all diagrams (lists of vertices).
527
528 7. Repeat from 3 (recursion done by reduce_leglist)
529
530 8. Replace final p=p vertex
531
532 Be aware that the resulting vertices have all particles outgoing,
533 so need to flip for incoming particles when used.
534
535 SPECIAL CASE: For A>BC... processes which are legs in decay
536 chains, we need to ensure that BC... combine first, giving A=A
537 as a final vertex. This case is defined by the Process
538 property is_decay_chain = True.
539 This function can also be called by the generate_diagram function
540 of LoopAmplitudes, in which case the generated diagrams here must not
541 be directly assigned to the 'diagrams' attributed but returned as a
542 DiagramList by the function. This is controlled by the argument
543 returndiag.
544 """
545
546 process = self.get('process')
547 model = process.get('model')
548 legs = process.get('legs')
549
550 for key in process.get('overall_orders').keys():
551 try:
552 process.get('orders')[key] = \
553 min(process.get('orders')[key],
554 process.get('overall_orders')[key])
555 except KeyError:
556 process.get('orders')[key] = process.get('overall_orders')[key]
557
558 assert model.get('particles'), \
559 "particles are missing in model: %s" % model.get('particles')
560
561 assert model.get('interactions'), \
562 "interactions are missing in model"
563
564
565 res = base_objects.DiagramList()
566
567 if len(filter(lambda leg: model.get('particle_dict')[\
568 leg.get('id')].is_fermion(), legs)) % 2 == 1:
569 if not returndiag:
570 self['diagrams'] = res
571 raise InvalidCmd, 'The number of fermion is odd'
572 else:
573 return False, res
574
575
576
577 if not model.get('got_majoranas') and \
578 len(filter(lambda leg: leg.is_incoming_fermion(model), legs)) != \
579 len(filter(lambda leg: leg.is_outgoing_fermion(model), legs)):
580 if not returndiag:
581 self['diagrams'] = res
582 raise InvalidCmd, 'The number of of incoming/outcoming fermions are different'
583 else:
584 return False, res
585
586
587
588 for charge in model.get('conserved_charge'):
589 total = 0
590 for leg in legs:
591 part = model.get('particle_dict')[leg.get('id')]
592 try:
593 value = part.get(charge)
594 except (AttributeError, base_objects.PhysicsObject.PhysicsObjectError):
595 try:
596 value = getattr(part, charge)
597 except AttributeError:
598 value = 0
599
600 if (leg.get('id') != part['pdg_code']) != leg['state']:
601 total -= value
602 else:
603 total += value
604
605 if abs(total) > 1e-10:
606 if not returndiag:
607 self['diagrams'] = res
608 raise InvalidCmd, 'No %s conservation for this process ' % charge
609 return res
610 else:
611 raise InvalidCmd, 'No %s conservation for this process ' % charge
612 return res, res
613
614 if not returndiag:
615 logger.info("Trying %s " % process.nice_string().replace('Process', 'process'))
616
617
618 for i in range(0, len(process.get('legs'))):
619
620 leg = copy.copy(process.get('legs')[i])
621 process.get('legs')[i] = leg
622 if leg.get('number') == 0:
623 leg.set('number', i + 1)
624
625
626
627 leglist = self.copy_leglist(process.get('legs'))
628
629 for leg in leglist:
630
631
632
633 leg.set('from_group', True)
634
635
636
637 if leg.get('state') == False:
638 part = model.get('particle_dict')[leg.get('id')]
639 leg.set('id', part.get_anti_pdg_code())
640
641
642
643 max_multi_to1 = max([len(key) for key in \
644 model.get('ref_dict_to1').keys()])
645
646
647
648
649
650
651
652
653 is_decay_proc = process.get_ninitial() == 1
654 if is_decay_proc:
655 part = model.get('particle_dict')[leglist[0].get('id')]
656
657
658
659 ref_dict_to0 = {(part.get_pdg_code(),part.get_anti_pdg_code()):[0],
660 (part.get_anti_pdg_code(),part.get_pdg_code()):[0]}
661
662
663 leglist[0].set('from_group', None)
664 reduced_leglist = self.reduce_leglist(leglist,
665 max_multi_to1,
666 ref_dict_to0,
667 is_decay_proc,
668 process.get('orders'))
669 else:
670 reduced_leglist = self.reduce_leglist(leglist,
671 max_multi_to1,
672 model.get('ref_dict_to0'),
673 is_decay_proc,
674 process.get('orders'))
675
676
677
678
679 self.convert_dgleg_to_leg(reduced_leglist)
680
681 if reduced_leglist:
682 for vertex_list in reduced_leglist:
683 res.append(self.create_diagram(base_objects.VertexList(vertex_list)))
684
685
686
687 failed_crossing = not res
688
689
690
691
692
693
694 if process.get('required_s_channels') and \
695 process.get('required_s_channels')[0]:
696
697
698 lastvx = -1
699
700
701
702 if is_decay_proc: lastvx = -2
703 ninitial = len(filter(lambda leg: leg.get('state') == False,
704 process.get('legs')))
705
706 old_res = res
707 res = base_objects.DiagramList()
708 for id_list in process.get('required_s_channels'):
709 res_diags = filter(lambda diagram: \
710 all([req_s_channel in \
711 [vertex.get_s_channel_id(\
712 process.get('model'), ninitial) \
713 for vertex in diagram.get('vertices')[:lastvx]] \
714 for req_s_channel in \
715 id_list]), old_res)
716
717 res.extend([diag for diag in res_diags if diag not in res])
718
719
720
721
722
723 if process.get('forbidden_s_channels'):
724 ninitial = len(filter(lambda leg: leg.get('state') == False,
725 process.get('legs')))
726 if ninitial == 2:
727 res = base_objects.DiagramList(\
728 filter(lambda diagram: \
729 not any([vertex.get_s_channel_id(\
730 process.get('model'), ninitial) \
731 in process.get('forbidden_s_channels')
732 for vertex in diagram.get('vertices')[:-1]]),
733 res))
734 else:
735
736
737 newres= []
738 for diagram in res:
739 leg1 = 1
740
741
742
743 vertex = diagram.get('vertices')[-1]
744 if any([l['number'] ==1 for l in vertex.get('legs')]):
745 leg1 = [l['number'] for l in vertex.get('legs') if l['number'] !=1][0]
746 to_loop = range(len(diagram.get('vertices'))-1)
747 if leg1 >1:
748 to_loop.reverse()
749 for i in to_loop:
750 vertex = diagram.get('vertices')[i]
751 if leg1:
752 if any([l['number'] ==leg1 for l in vertex.get('legs')]):
753 leg1 = 0
754 continue
755 if vertex.get_s_channel_id(process.get('model'), ninitial)\
756 in process.get('forbidden_s_channels'):
757 break
758 else:
759 newres.append(diagram)
760 res = base_objects.DiagramList(newres)
761
762
763
764
765 if process.get('forbidden_onsh_s_channels'):
766 ninitial = len(filter(lambda leg: leg.get('state') == False,
767 process.get('legs')))
768
769 verts = base_objects.VertexList(sum([[vertex for vertex \
770 in diagram.get('vertices')[:-1]
771 if vertex.get_s_channel_id(\
772 process.get('model'), ninitial) \
773 in process.get('forbidden_onsh_s_channels')] \
774 for diagram in res], []))
775 for vert in verts:
776
777 newleg = copy.copy(vert.get('legs').pop(-1))
778 newleg.set('onshell', False)
779 vert.get('legs').append(newleg)
780
781
782 for diagram in res:
783 diagram.calculate_orders(model)
784
785
786
787
788
789
790
791
792 if not returndiag and len(res)>0:
793 res = self.apply_squared_order_constraints(res)
794
795 if diagram_filter:
796 res = self.apply_user_filter(res)
797
798
799 if not process.get('is_decay_chain'):
800 for diagram in res:
801 vertices = diagram.get('vertices')
802 if len(vertices) > 1 and vertices[-1].get('id') == 0:
803
804
805
806
807 vertices = copy.copy(vertices)
808 lastvx = vertices.pop()
809 nexttolastvertex = copy.copy(vertices.pop())
810 legs = copy.copy(nexttolastvertex.get('legs'))
811 ntlnumber = legs[-1].get('number')
812 lastleg = filter(lambda leg: leg.get('number') != ntlnumber,
813 lastvx.get('legs'))[0]
814
815 if lastleg.get('onshell') == False:
816 lastleg.set('onshell', None)
817
818 legs[-1] = lastleg
819 nexttolastvertex.set('legs', legs)
820 vertices.append(nexttolastvertex)
821 diagram.set('vertices', vertices)
822
823 if res and not returndiag:
824 logger.info("Process has %d diagrams" % len(res))
825
826
827 self.trim_diagrams(diaglist=res)
828
829
830 pertur = 'QCD'
831 if self.get('process')['perturbation_couplings']:
832 pertur = sorted(self.get('process')['perturbation_couplings'])[0]
833 self.get('process').get('legs').sort(pert=pertur)
834
835
836 if not returndiag:
837 self['diagrams'] = res
838 return not failed_crossing
839 else:
840 return not failed_crossing, res
841
843 """Applies the user specified squared order constraints on the diagram
844 list in argument."""
845
846 res = copy.copy(diag_list)
847
848
849
850 for name, (value, operator) in self['process'].get('constrained_orders').items():
851 res.filter_constrained_orders(name, value, operator)
852
853
854
855
856 while True:
857 new_res = res.apply_positive_sq_orders(res,
858 self['process'].get('squared_orders'),
859 self['process']['sqorders_types'])
860
861 if len(res)==len(new_res):
862 break
863 elif (len(new_res)>len(res)):
864 raise MadGraph5Error(
865 'Inconsistency in function apply_squared_order_constraints().')
866
867 res = new_res
868
869
870
871
872 neg_orders = [(order, value) for order, value in \
873 self['process'].get('squared_orders').items() if value<0]
874 if len(neg_orders)==1:
875 neg_order, neg_value = neg_orders[0]
876
877 res, target_order = res.apply_negative_sq_order(res, neg_order,\
878 neg_value, self['process']['sqorders_types'][neg_order])
879
880
881
882
883 self['process']['squared_orders'][neg_order]=target_order
884 elif len(neg_orders)>1:
885 raise InvalidCmd('At most one negative squared order constraint'+\
886 ' can be specified, not %s.'%str(neg_orders))
887
888 return res
889
891 """Applies the user specified squared order constraints on the diagram
892 list in argument."""
893
894 if True:
895 remove_diag = misc.plugin_import('user_filter',
896 'user filter required to be defined in PLUGIN/user_filter.py with the function remove_diag(ONEDIAG) which returns True if the diagram has to be removed',
897 fcts=['remove_diag'])
898 else:
899
900 def remove_diag(diag):
901 for vertex in diag['vertices']:
902 if vertex['id'] == 0:
903 continue
904 if vertex['legs'][-1]['number'] < 3:
905 if abs(vertex['legs'][-1]['id']) <6:
906 return True
907 return False
908
909 res = diag_list.__class__()
910 nb_removed = 0
911 for diag in diag_list:
912 if remove_diag(diag):
913 nb_removed +=1
914 else:
915 res.append(diag)
916
917 if nb_removed:
918 logger.warning('Diagram filter is ON and removed %s diagrams for this subprocess.' % nb_removed)
919
920 return res
921
922
923
925 """ Return a Diagram created from the vertex list. This function can be
926 overloaded by daughter classes."""
927 return base_objects.Diagram({'vertices':vertexlist})
928
930 """ In LoopAmplitude, it converts back all DGLoopLegs into Legs.
931 In Amplitude, there is nothing to do. """
932
933 return True
934
936 """ Simply returns a copy of the leg list. This function is
937 overloaded in LoopAmplitude so that a DGLoopLeg list is returned.
938 The DGLoopLeg has some additional parameters only useful during
939 loop diagram generation"""
940
941 return base_objects.LegList(\
942 [ copy.copy(leg) for leg in legs ])
943
944 - def reduce_leglist(self, curr_leglist, max_multi_to1, ref_dict_to0,
945 is_decay_proc = False, coupling_orders = None):
946 """Recursive function to reduce N LegList to N-1
947 For algorithm, see doc for generate_diagrams.
948 """
949
950
951
952 res = []
953
954
955
956 if curr_leglist is None:
957 return None
958
959
960 model = self.get('process').get('model')
961 ref_dict_to1 = self.get('process').get('model').get('ref_dict_to1')
962
963
964
965
966
967
968 if curr_leglist.can_combine_to_0(ref_dict_to0, is_decay_proc):
969
970
971 vertex_ids = self.get_combined_vertices(curr_leglist,
972 copy.copy(ref_dict_to0[tuple(sorted([leg.get('id') for \
973 leg in curr_leglist]))]))
974
975 final_vertices = [base_objects.Vertex({'legs':curr_leglist,
976 'id':vertex_id}) for \
977 vertex_id in vertex_ids]
978
979 for final_vertex in final_vertices:
980 if self.reduce_orders(coupling_orders, model,
981 [final_vertex.get('id')]) != False:
982 res.append([final_vertex])
983
984
985 if len(curr_leglist) == 2:
986 if res:
987 return res
988 else:
989 return None
990
991
992 comb_lists = self.combine_legs(curr_leglist,
993 ref_dict_to1, max_multi_to1)
994
995
996 leg_vertex_list = self.merge_comb_legs(comb_lists, ref_dict_to1)
997
998
999 for leg_vertex_tuple in leg_vertex_list:
1000
1001
1002 if self.get('process').get('forbidden_particles') and \
1003 any([abs(vertex.get('legs')[-1].get('id')) in \
1004 self.get('process').get('forbidden_particles') \
1005 for vertex in leg_vertex_tuple[1]]):
1006 continue
1007
1008
1009 new_coupling_orders = self.reduce_orders(coupling_orders,
1010 model,
1011 [vertex.get('id') for vertex in \
1012 leg_vertex_tuple[1]])
1013 if new_coupling_orders == False:
1014
1015 continue
1016
1017
1018
1019 reduced_diagram = self.reduce_leglist(leg_vertex_tuple[0],
1020 max_multi_to1,
1021 ref_dict_to0,
1022 is_decay_proc,
1023 new_coupling_orders)
1024
1025 if reduced_diagram:
1026 vertex_list_list = [list(leg_vertex_tuple[1])]
1027 vertex_list_list.append(reduced_diagram)
1028 expanded_list = expand_list_list(vertex_list_list)
1029 res.extend(expanded_list)
1030
1031 return res
1032
1033 - def reduce_orders(self, coupling_orders, model, vertex_id_list):
1034 """Return False if the coupling orders for any coupling is <
1035 0, otherwise return the new coupling orders with the vertex
1036 orders subtracted. If coupling_orders is not given, return
1037 None (which counts as success).
1038 WEIGHTED is a special order, which corresponds to the sum of
1039 order hierarchies for the couplings.
1040 We ignore negative constraints as these cannot be taken into
1041 account on the fly but only after generation."""
1042
1043 if not coupling_orders:
1044 return None
1045
1046 present_couplings = copy.copy(coupling_orders)
1047 for id in vertex_id_list:
1048
1049 if not id:
1050 continue
1051 inter = model.get("interaction_dict")[id]
1052 for coupling in inter.get('orders').keys():
1053
1054
1055 if coupling in present_couplings and \
1056 present_couplings[coupling]>=0:
1057
1058 present_couplings[coupling] -= \
1059 inter.get('orders')[coupling]
1060 if present_couplings[coupling] < 0:
1061
1062 return False
1063
1064 if 'WEIGHTED' in present_couplings and \
1065 present_couplings['WEIGHTED']>=0:
1066 weight = sum([model.get('order_hierarchy')[c]*n for \
1067 (c,n) in inter.get('orders').items()])
1068 present_couplings['WEIGHTED'] -= weight
1069 if present_couplings['WEIGHTED'] < 0:
1070
1071 return False
1072
1073 return present_couplings
1074
1075 - def combine_legs(self, list_legs, ref_dict_to1, max_multi_to1):
1076 """Recursive function. Take a list of legs as an input, with
1077 the reference dictionary n-1->1, and output a list of list of
1078 tuples of Legs (allowed combinations) and Legs (rest). Algorithm:
1079
1080 1. Get all n-combinations from list [123456]: [12],..,[23],..,[123],..
1081
1082 2. For each combination, say [34]. Check if combination is valid.
1083 If so:
1084
1085 a. Append [12[34]56] to result array
1086
1087 b. Split [123456] at index(first element in combination+1),
1088 i.e. [12],[456] and subtract combination from second half,
1089 i.e.: [456]-[34]=[56]. Repeat from 1. with this array
1090
1091 3. Take result array from call to 1. (here, [[56]]) and append
1092 (first half in step b - combination) + combination + (result
1093 from 1.) = [12[34][56]] to result array
1094
1095 4. After appending results from all n-combinations, return
1096 resulting array. Example, if [13] and [45] are valid
1097 combinations:
1098 [[[13]2456],[[13]2[45]6],[123[45]6]]
1099 """
1100
1101 res = []
1102
1103
1104 for comb_length in range(2, max_multi_to1 + 1):
1105
1106
1107 if comb_length > len(list_legs):
1108 return res
1109
1110
1111
1112 for comb in itertools.combinations(list_legs, comb_length):
1113
1114
1115 if base_objects.LegList(comb).can_combine_to_1(ref_dict_to1):
1116
1117
1118
1119 res_list = copy.copy(list_legs)
1120 for leg in comb:
1121 res_list.remove(leg)
1122 res_list.insert(list_legs.index(comb[0]), comb)
1123 res.append(res_list)
1124
1125
1126
1127
1128
1129
1130 res_list1 = list_legs[0:list_legs.index(comb[0])]
1131 res_list2 = list_legs[list_legs.index(comb[0]) + 1:]
1132 for leg in comb[1:]:
1133 res_list2.remove(leg)
1134
1135
1136 res_list = res_list1
1137 res_list.append(comb)
1138
1139
1140 for item in self.combine_legs(res_list2,
1141 ref_dict_to1,
1142 max_multi_to1):
1143 final_res_list = copy.copy(res_list)
1144 final_res_list.extend(item)
1145 res.append(final_res_list)
1146
1147 return res
1148
1149
1151 """Takes a list of allowed leg combinations as an input and returns
1152 a set of lists where combinations have been properly replaced
1153 (one list per element in the ref_dict, so that all possible intermediate
1154 particles are included). For each list, give the list of vertices
1155 corresponding to the executed merging, group the two as a tuple.
1156 """
1157
1158 res = []
1159
1160 for comb_list in comb_lists:
1161
1162 reduced_list = []
1163 vertex_list = []
1164
1165 for entry in comb_list:
1166
1167
1168 if isinstance(entry, tuple):
1169
1170
1171
1172 leg_vert_ids = copy.copy(ref_dict_to1[\
1173 tuple(sorted([leg.get('id') for leg in entry]))])
1174
1175
1176 number = min([leg.get('number') for leg in entry])
1177
1178
1179 if len(filter(lambda leg: leg.get('state') == False,
1180 entry)) == 1:
1181 state = False
1182 else:
1183 state = True
1184
1185
1186
1187
1188
1189 new_leg_vert_ids = []
1190 if leg_vert_ids:
1191 new_leg_vert_ids = self.get_combined_legs(entry,
1192 leg_vert_ids,
1193 number,
1194 state)
1195
1196 reduced_list.append([l[0] for l in new_leg_vert_ids])
1197
1198
1199
1200
1201
1202 vlist = base_objects.VertexList()
1203 for (myleg, vert_id) in new_leg_vert_ids:
1204
1205 myleglist = base_objects.LegList(list(entry))
1206
1207 myleglist.append(myleg)
1208
1209 vlist.append(base_objects.Vertex(
1210 {'legs':myleglist,
1211 'id':vert_id}))
1212
1213 vertex_list.append(vlist)
1214
1215
1216
1217 else:
1218 cp_entry = copy.copy(entry)
1219
1220
1221
1222 if cp_entry.get('from_group') != None:
1223 cp_entry.set('from_group', False)
1224 reduced_list.append(cp_entry)
1225
1226
1227 flat_red_lists = expand_list(reduced_list)
1228 flat_vx_lists = expand_list(vertex_list)
1229
1230
1231 for i in range(0, len(flat_vx_lists)):
1232 res.append((base_objects.LegList(flat_red_lists[i]), \
1233 base_objects.VertexList(flat_vx_lists[i])))
1234
1235 return res
1236
1238 """Create a set of new legs from the info given. This can be
1239 overloaded by daughter classes."""
1240
1241 mylegs = [(base_objects.Leg({'id':leg_id,
1242 'number':number,
1243 'state':state,
1244 'from_group':True}),
1245 vert_id)\
1246 for leg_id, vert_id in leg_vert_ids]
1247
1248 return mylegs
1249
1251 """Allow for selection of vertex ids. This can be
1252 overloaded by daughter classes."""
1253
1254 return vert_ids
1255
1257 """Reduce the number of legs and vertices used in memory.
1258 When called by a diagram generation initiated by LoopAmplitude,
1259 this function should not trim the diagrams in the attribute 'diagrams'
1260 but rather a given list in the 'diaglist' argument."""
1261
1262 legs = []
1263 vertices = []
1264
1265 if diaglist is None:
1266 diaglist=self.get('diagrams')
1267
1268
1269 process = self.get('process')
1270 for leg in process.get('legs'):
1271 if leg.get('state') and leg.get('id') in decay_ids:
1272 leg.set('onshell', True)
1273
1274 for diagram in diaglist:
1275
1276 leg_external = set()
1277 for ivx, vertex in enumerate(diagram.get('vertices')):
1278 for ileg, leg in enumerate(vertex.get('legs')):
1279
1280 if leg.get('state') and leg.get('id') in decay_ids and \
1281 leg.get('number') not in leg_external:
1282
1283
1284 leg = copy.copy(leg)
1285 leg.set('onshell', True)
1286 try:
1287 index = legs.index(leg)
1288 except ValueError:
1289 vertex.get('legs')[ileg] = leg
1290 legs.append(leg)
1291 else:
1292 vertex.get('legs')[ileg] = legs[index]
1293 leg_external.add(leg.get('number'))
1294 try:
1295 index = vertices.index(vertex)
1296 diagram.get('vertices')[ivx] = vertices[index]
1297 except ValueError:
1298 vertices.append(vertex)
1299
1300
1301
1302
1303 -class AmplitudeList(base_objects.PhysicsObjectList):
1304 """List of Amplitude objects
1305 """
1306
1308 """ Check the content of all processes of the amplitudes in this list to
1309 see if there is any which defines perturbation couplings. """
1310
1311 for amp in self:
1312 if amp.has_loop_process():
1313 return True
1314
1316 """Test if object obj is a valid Amplitude for the list."""
1317
1318 return isinstance(obj, Amplitude)
1319
1324 """A list of amplitudes + a list of decay chain amplitude lists;
1325 corresponding to a ProcessDefinition with a list of decay chains
1326 """
1327
1333
1334 - def __init__(self, argument = None, collect_mirror_procs = False,
1335 ignore_six_quark_processes = False, loop_filter=None, diagram_filter=False):
1336 """Allow initialization with Process and with ProcessDefinition"""
1337
1338 if isinstance(argument, base_objects.Process):
1339 super(DecayChainAmplitude, self).__init__()
1340 from madgraph.loop.loop_diagram_generation import LoopMultiProcess
1341 if argument['perturbation_couplings']:
1342 MultiProcessClass=LoopMultiProcess
1343 else:
1344 MultiProcessClass=MultiProcess
1345 if isinstance(argument, base_objects.ProcessDefinition):
1346 self['amplitudes'].extend(\
1347 MultiProcessClass.generate_multi_amplitudes(argument,
1348 collect_mirror_procs,
1349 ignore_six_quark_processes,
1350 loop_filter=loop_filter,
1351 diagram_filter=diagram_filter))
1352 else:
1353 self['amplitudes'].append(\
1354 MultiProcessClass.get_amplitude_from_proc(argument,
1355 loop_filter=loop_filter,
1356 diagram_filter=diagram_filter))
1357
1358
1359 process = copy.copy(self.get('amplitudes')[0].get('process'))
1360 process.set('decay_chains', base_objects.ProcessList())
1361 self['amplitudes'][0].set('process', process)
1362
1363 for process in argument.get('decay_chains'):
1364 if process.get('perturbation_couplings'):
1365 raise MadGraph5Error,\
1366 "Decay processes can not be perturbed"
1367 process.set('overall_orders', argument.get('overall_orders'))
1368 if not process.get('is_decay_chain'):
1369 process.set('is_decay_chain',True)
1370 if not process.get_ninitial() == 1:
1371 raise InvalidCmd,\
1372 "Decay chain process must have exactly one" + \
1373 " incoming particle"
1374 self['decay_chains'].append(\
1375 DecayChainAmplitude(process, collect_mirror_procs,
1376 ignore_six_quark_processes))
1377
1378
1379 decay_ids = sum([[a.get('process').get('legs')[0].get('id') \
1380 for a in dec.get('amplitudes')] for dec in \
1381 self['decay_chains']], [])
1382 decay_ids = set(decay_ids)
1383 for amp in self['amplitudes']:
1384 amp.trim_diagrams(decay_ids)
1385
1386
1387 for amp in self['amplitudes']:
1388 for l in amp.get('process').get('legs'):
1389 if l.get('id') in decay_ids:
1390 decay_ids.remove(l.get('id'))
1391
1392 if decay_ids:
1393 model = amp.get('process').get('model')
1394 names = [model.get_particle(id).get('name') for id in decay_ids]
1395
1396 logger.warning(
1397 "$RED Decay without corresponding particle in core process found.\n" + \
1398 "Decay information for particle(s) %s is discarded.\n" % ','.join(names) + \
1399 "Please check your process definition carefully. \n" + \
1400 "This warning usually means that you forgot parentheses in presence of subdecay.\n" + \
1401 "Example of correct syntax: p p > t t~, ( t > w+ b, w+ > l+ vl)")
1402
1403
1404 for dc in reversed(self['decay_chains']):
1405 for a in reversed(dc.get('amplitudes')):
1406
1407 if a.get('process').get('legs')[0].get('id') in decay_ids:
1408 dc.get('amplitudes').remove(a)
1409 if not dc.get('amplitudes'):
1410
1411 self['decay_chains'].remove(dc)
1412
1413
1414
1415 bad_procs = []
1416 for dc in self['decay_chains']:
1417 for amp in dc.get('amplitudes'):
1418 legs = amp.get('process').get('legs')
1419 fs_parts = [abs(l.get('id')) for l in legs if
1420 l.get('state')]
1421 is_part = [l.get('id') for l in legs if not
1422 l.get('state')][0]
1423 if abs(is_part) in fs_parts:
1424 bad_procs.append(amp.get('process'))
1425
1426 if bad_procs:
1427 logger.warning(
1428 "$RED Decay(s) with particle decaying to itself:\n" + \
1429 '\n'.join([p.nice_string() for p in bad_procs]) + \
1430 "\nPlease check your process definition carefully. \n")
1431
1432
1433 elif argument != None:
1434
1435 super(DecayChainAmplitude, self).__init__(argument)
1436 else:
1437
1438 super(DecayChainAmplitude, self).__init__()
1439
1440 - def filter(self, name, value):
1441 """Filter for valid amplitude property values."""
1442
1443 if name == 'amplitudes':
1444 if not isinstance(value, AmplitudeList):
1445 raise self.PhysicsObjectError, \
1446 "%s is not a valid AmplitudeList" % str(value)
1447 if name == 'decay_chains':
1448 if not isinstance(value, DecayChainAmplitudeList):
1449 raise self.PhysicsObjectError, \
1450 "%s is not a valid DecayChainAmplitudeList object" % \
1451 str(value)
1452 return True
1453
1455 """Return diagram property names as a nicely sorted list."""
1456
1457 return ['amplitudes', 'decay_chains']
1458
1459
1460
1462 """Returns number of diagrams for this amplitude"""
1463 return sum(len(a.get('diagrams')) for a in self.get('amplitudes')) \
1464 + sum(d.get_number_of_diagrams() for d in \
1465 self.get('decay_chains'))
1466
1468 """Returns a nicely formatted string of the amplitude content."""
1469 mystr = ""
1470 for amplitude in self.get('amplitudes'):
1471 mystr = mystr + amplitude.nice_string(indent) + "\n"
1472
1473 if self.get('decay_chains'):
1474 mystr = mystr + " " * indent + "Decays:\n"
1475 for dec in self.get('decay_chains'):
1476 mystr = mystr + dec.nice_string(indent + 2) + "\n"
1477
1478 return mystr[:-1]
1479
1481 """Returns a nicely formatted string of the amplitude processes."""
1482 mystr = ""
1483 for amplitude in self.get('amplitudes'):
1484 mystr = mystr + amplitude.nice_string_processes(indent) + "\n"
1485
1486 if self.get('decay_chains'):
1487 mystr = mystr + " " * indent + "Decays:\n"
1488 for dec in self.get('decay_chains'):
1489 mystr = mystr + dec.nice_string_processes(indent + 2) + "\n"
1490
1491 return mystr[:-1]
1492
1494 """Returns the number of initial state particles in the process."""
1495 return self.get('amplitudes')[0].get('process').get_ninitial()
1496
1498 """Returns a set of all particle ids for which a decay is defined"""
1499
1500 decay_ids = []
1501
1502
1503 for amp in sum([dc.get('amplitudes') for dc \
1504 in self['decay_chains']], []):
1505
1506 decay_ids.append(amp.get('process').get_initial_ids()[0])
1507
1508
1509 return list(set(decay_ids))
1510
1512 """ Returns wether this amplitude has a loop process."""
1513 return self['amplitudes'].has_any_loop_process()
1514
1516 """Recursive function to extract all amplitudes for this process"""
1517
1518 amplitudes = AmplitudeList()
1519
1520 amplitudes.extend(self.get('amplitudes'))
1521 for decay in self.get('decay_chains'):
1522 amplitudes.extend(decay.get_amplitudes())
1523
1524 return amplitudes
1525
1531 """List of DecayChainAmplitude objects
1532 """
1533
1535 """Test if object obj is a valid DecayChainAmplitude for the list."""
1536
1537 return isinstance(obj, DecayChainAmplitude)
1538
1539
1540
1541
1542
1543 -class MultiProcess(base_objects.PhysicsObject):
1544 """MultiProcess: list of process definitions
1545 list of processes (after cleaning)
1546 list of amplitudes (after generation)
1547 """
1548
1550 """Default values for all properties"""
1551
1552 self['process_definitions'] = base_objects.ProcessDefinitionList()
1553
1554
1555
1556 self['amplitudes'] = AmplitudeList()
1557
1558 self['collect_mirror_procs'] = False
1559
1560
1561 self['ignore_six_quark_processes'] = []
1562
1563
1564 self['use_numerical'] = False
1565
1566 - def __init__(self, argument=None, collect_mirror_procs = False,
1567 ignore_six_quark_processes = [], optimize=False,
1568 loop_filter=None, diagram_filter=None):
1596
1597
1598 - def filter(self, name, value):
1599 """Filter for valid process property values."""
1600
1601 if name == 'process_definitions':
1602 if not isinstance(value, base_objects.ProcessDefinitionList):
1603 raise self.PhysicsObjectError, \
1604 "%s is not a valid ProcessDefinitionList object" % str(value)
1605
1606 if name == 'amplitudes':
1607 if not isinstance(value, AmplitudeList):
1608 raise self.PhysicsObjectError, \
1609 "%s is not a valid AmplitudeList object" % str(value)
1610
1611 if name in ['collect_mirror_procs']:
1612 if not isinstance(value, bool):
1613 raise self.PhysicsObjectError, \
1614 "%s is not a valid boolean" % str(value)
1615
1616 if name == 'ignore_six_quark_processes':
1617 if not isinstance(value, list):
1618 raise self.PhysicsObjectError, \
1619 "%s is not a valid list" % str(value)
1620
1621 return True
1622
1623 - def get(self, name):
1624 """Get the value of the property name."""
1625
1626 if (name == 'amplitudes') and not self[name]:
1627 for process_def in self.get('process_definitions'):
1628 if process_def.get('decay_chains'):
1629
1630
1631 self['amplitudes'].append(\
1632 DecayChainAmplitude(process_def,
1633 self.get('collect_mirror_procs'),
1634 self.get('ignore_six_quark_processes'),
1635 diagram_filter=self['diagram_filter']))
1636 else:
1637 self['amplitudes'].extend(\
1638 self.generate_multi_amplitudes(process_def,
1639 self.get('collect_mirror_procs'),
1640 self.get('ignore_six_quark_processes'),
1641 self['use_numerical'],
1642 loop_filter=self['loop_filter'],
1643 diagram_filter=self['diagram_filter']))
1644
1645 return MultiProcess.__bases__[0].get(self, name)
1646
1648 """Return process property names as a nicely sorted list."""
1649
1650 return ['process_definitions', 'amplitudes']
1651
1652 @classmethod
1653 - def generate_multi_amplitudes(cls,process_definition,
1654 collect_mirror_procs = False,
1655 ignore_six_quark_processes = [],
1656 use_numerical=False,
1657 loop_filter=None,
1658 diagram_filter=False):
1659 """Generate amplitudes in a semi-efficient way.
1660 Make use of crossing symmetry for processes that fail diagram
1661 generation, but not for processes that succeed diagram
1662 generation. Doing so will risk making it impossible to
1663 identify processes with identical amplitudes.
1664 """
1665 assert isinstance(process_definition, base_objects.ProcessDefinition), \
1666 "%s not valid ProcessDefinition object" % \
1667 repr(process_definition)
1668
1669
1670
1671 if not process_definition['born_orders']:
1672 process_definition.set('orders', MultiProcess.\
1673 find_optimal_process_orders(process_definition,
1674 diagram_filter))
1675
1676 process_definition.check_expansion_orders()
1677
1678 processes = base_objects.ProcessList()
1679 amplitudes = AmplitudeList()
1680
1681
1682
1683 failed_procs = []
1684 success_procs = []
1685
1686 non_permuted_procs = []
1687
1688 permutations = []
1689
1690
1691
1692 model = process_definition['model']
1693
1694 isids = [leg['ids'] for leg in process_definition['legs'] \
1695 if leg['state'] == False]
1696 fsids = [leg['ids'] for leg in process_definition['legs'] \
1697 if leg['state'] == True]
1698
1699
1700 for prod in itertools.product(*isids):
1701 islegs = [\
1702 base_objects.Leg({'id':id, 'state': False}) \
1703 for id in prod]
1704
1705
1706
1707
1708 red_fsidlist = []
1709
1710 for prod in itertools.product(*fsids):
1711
1712
1713 if tuple(sorted(prod)) in red_fsidlist:
1714 continue
1715
1716 red_fsidlist.append(tuple(sorted(prod)));
1717
1718
1719 leg_list = [copy.copy(leg) for leg in islegs]
1720
1721 leg_list.extend([\
1722 base_objects.Leg({'id':id, 'state': True}) \
1723 for id in prod])
1724
1725 legs = base_objects.LegList(leg_list)
1726
1727
1728 sorted_legs = sorted([(l,i+1) for (i,l) in \
1729 enumerate(legs.get_outgoing_id_list(model))])
1730 permutation = [l[1] for l in sorted_legs]
1731
1732 sorted_legs = array.array('i', [l[0] for l in sorted_legs])
1733
1734
1735 if ignore_six_quark_processes and \
1736 len([i for i in sorted_legs if abs(i) in \
1737 ignore_six_quark_processes]) >= 6:
1738 continue
1739
1740
1741
1742 if sorted_legs in failed_procs:
1743 continue
1744
1745
1746 if use_numerical:
1747
1748 initial_mass = abs(model['parameter_dict'][model.get_particle(legs[0].get('id')).get('mass')])
1749 if initial_mass == 0:
1750 continue
1751 for leg in legs[1:]:
1752 m = model['parameter_dict'][model.get_particle(leg.get('id')).get('mass')]
1753 initial_mass -= abs(m)
1754 if initial_mass.real <= 0:
1755 continue
1756
1757
1758 process = process_definition.get_process_with_legs(legs)
1759
1760 fast_proc = \
1761 array.array('i',[leg.get('id') for leg in legs])
1762 if collect_mirror_procs and \
1763 process_definition.get_ninitial() == 2:
1764
1765 mirror_proc = \
1766 array.array('i', [fast_proc[1], fast_proc[0]] + \
1767 list(fast_proc[2:]))
1768 try:
1769 mirror_amp = \
1770 amplitudes[non_permuted_procs.index(mirror_proc)]
1771 except Exception:
1772
1773 pass
1774 else:
1775
1776 mirror_amp.set('has_mirror_process', True)
1777 logger.info("Process %s added to mirror process %s" % \
1778 (process.base_string(),
1779 mirror_amp.get('process').base_string()))
1780 continue
1781
1782
1783
1784 if not process.get('required_s_channels') and \
1785 not process.get('forbidden_onsh_s_channels') and \
1786 not process.get('forbidden_s_channels') and \
1787 not process.get('is_decay_chain'):
1788 try:
1789 crossed_index = success_procs.index(sorted_legs)
1790
1791
1792
1793
1794 if 'loop_diagrams' in amplitudes[crossed_index]:
1795 raise ValueError
1796 except ValueError:
1797
1798 pass
1799 else:
1800
1801 amplitude = MultiProcess.cross_amplitude(\
1802 amplitudes[crossed_index],
1803 process,
1804 permutations[crossed_index],
1805 permutation)
1806 amplitudes.append(amplitude)
1807 success_procs.append(sorted_legs)
1808 permutations.append(permutation)
1809 non_permuted_procs.append(fast_proc)
1810 logger.info("Crossed process found for %s, reuse diagrams." % \
1811 process.base_string())
1812 continue
1813
1814
1815 amplitude = cls.get_amplitude_from_proc(process,
1816 loop_filter=loop_filter)
1817
1818 try:
1819 result = amplitude.generate_diagrams(diagram_filter=diagram_filter)
1820 except InvalidCmd as error:
1821 failed_procs.append(sorted_legs)
1822 else:
1823
1824 if amplitude.get('diagrams'):
1825 amplitudes.append(amplitude)
1826 success_procs.append(sorted_legs)
1827 permutations.append(permutation)
1828 non_permuted_procs.append(fast_proc)
1829 elif not result:
1830
1831 failed_procs.append(sorted_legs)
1832
1833
1834 if not amplitudes:
1835 if len(failed_procs) == 1 and 'error' in locals():
1836 raise error
1837 else:
1838 raise NoDiagramException, \
1839 "No amplitudes generated from process %s. Please enter a valid process" % \
1840 process_definition.nice_string()
1841
1842
1843
1844 return amplitudes
1845
1846 @classmethod
1848 """ Return the correct amplitude type according to the characteristics of
1849 the process proc. The only option that could be specified here is
1850 loop_filter and it is of course not relevant for a tree amplitude."""
1851
1852 return Amplitude({"process": proc})
1853
1854
1855 @staticmethod
1857 """Find the minimal WEIGHTED order for this set of processes.
1858
1859 The algorithm:
1860
1861 1) Check the coupling hierarchy of the model. Assign all
1862 particles to the different coupling hierarchies so that a
1863 particle is considered to be in the highest hierarchy (i.e.,
1864 with lowest value) where it has an interaction.
1865
1866 2) Pick out the legs in the multiprocess according to the
1867 highest hierarchy represented (so don't mix particles from
1868 different hierarchy classes in the same multiparticles!)
1869
1870 3) Find the starting maximum WEIGHTED order as the sum of the
1871 highest n-2 weighted orders
1872
1873 4) Pick out required s-channel particle hierarchies, and use
1874 the highest of the maximum WEIGHTED order from the legs and
1875 the minimum WEIGHTED order extracted from 2*s-channel
1876 hierarchys plus the n-2-2*(number of s-channels) lowest
1877 leg weighted orders.
1878
1879 5) Run process generation with the WEIGHTED order determined
1880 in 3)-4) - # final state gluons, with all gluons removed from
1881 the final state
1882
1883 6) If no process is found, increase WEIGHTED order by 1 and go
1884 back to 5), until we find a process which passes. Return that
1885 order.
1886
1887 7) Continue 5)-6) until we reach (n-2)*(highest hierarchy)-1.
1888 If still no process has passed, return
1889 WEIGHTED = (n-2)*(highest hierarchy)
1890 """
1891
1892 assert isinstance(process_definition, base_objects.ProcessDefinition), \
1893 "%s not valid ProcessDefinition object" % \
1894 repr(process_definition)
1895
1896 processes = base_objects.ProcessList()
1897 amplitudes = AmplitudeList()
1898
1899
1900 if process_definition.get('orders') or \
1901 process_definition.get('overall_orders') or \
1902 process_definition.get('NLO_mode')=='virt':
1903 return process_definition.get('orders')
1904
1905
1906 if process_definition.get_ninitial() == 1 and not \
1907 process_definition.get('is_decay_chain'):
1908 return process_definition.get('orders')
1909
1910 logger.info("Checking for minimal orders which gives processes.")
1911 logger.info("Please specify coupling orders to bypass this step.")
1912
1913
1914 max_order_now, particles, hierarchy = \
1915 process_definition.get_minimum_WEIGHTED()
1916 coupling = 'WEIGHTED'
1917
1918 model = process_definition.get('model')
1919
1920
1921 isids = [leg['ids'] for leg in \
1922 filter(lambda leg: leg['state'] == False, process_definition['legs'])]
1923 fsids = [leg['ids'] for leg in \
1924 filter(lambda leg: leg['state'] == True, process_definition['legs'])]
1925
1926 max_WEIGHTED_order = \
1927 (len(fsids + isids) - 2)*int(model.get_max_WEIGHTED())
1928
1929
1930 hierarchydef = process_definition['model'].get('order_hierarchy')
1931 tmp = []
1932 hierarchy = hierarchydef.items()
1933 hierarchy.sort()
1934 for key, value in hierarchydef.items():
1935 if value>1:
1936 tmp.append('%s*%s' % (value,key))
1937 else:
1938 tmp.append('%s' % key)
1939 wgtdef = '+'.join(tmp)
1940
1941
1942 while max_order_now < max_WEIGHTED_order:
1943 logger.info("Trying coupling order WEIGHTED<=%d: WEIGTHED IS %s" % (max_order_now, wgtdef))
1944
1945 oldloglevel = logger.level
1946 logger.setLevel(logging.WARNING)
1947
1948
1949
1950 failed_procs = []
1951
1952
1953 for prod in apply(itertools.product, isids):
1954 islegs = [ base_objects.Leg({'id':id, 'state': False}) \
1955 for id in prod]
1956
1957
1958
1959
1960 red_fsidlist = []
1961
1962 for prod in apply(itertools.product, fsids):
1963
1964
1965 if tuple(sorted(prod)) in red_fsidlist:
1966 continue
1967
1968 red_fsidlist.append(tuple(sorted(prod)));
1969
1970
1971
1972 nglue = 0
1973 if 21 in particles[0]:
1974 nglue = len([id for id in prod if id == 21])
1975 prod = [id for id in prod if id != 21]
1976
1977
1978 leg_list = [copy.copy(leg) for leg in islegs]
1979
1980 leg_list.extend([\
1981 base_objects.Leg({'id':id, 'state': True}) \
1982 for id in prod])
1983
1984 legs = base_objects.LegList(leg_list)
1985
1986
1987
1988 coupling_orders_now = {coupling: max_order_now - \
1989 nglue * model['order_hierarchy']['QCD']}
1990
1991
1992 process = base_objects.Process({\
1993 'legs':legs,
1994 'model':model,
1995 'id': process_definition.get('id'),
1996 'orders': coupling_orders_now,
1997 'required_s_channels': \
1998 process_definition.get('required_s_channels'),
1999 'forbidden_onsh_s_channels': \
2000 process_definition.get('forbidden_onsh_s_channels'),
2001 'sqorders_types': \
2002 process_definition.get('sqorders_types'),
2003 'squared_orders': \
2004 process_definition.get('squared_orders'),
2005 'split_orders': \
2006 process_definition.get('split_orders'),
2007 'forbidden_s_channels': \
2008 process_definition.get('forbidden_s_channels'),
2009 'forbidden_particles': \
2010 process_definition.get('forbidden_particles'),
2011 'is_decay_chain': \
2012 process_definition.get('is_decay_chain'),
2013 'overall_orders': \
2014 process_definition.get('overall_orders'),
2015 'split_orders': \
2016 process_definition.get('split_orders')})
2017
2018
2019 process.check_expansion_orders()
2020
2021
2022 sorted_legs = sorted(legs.get_outgoing_id_list(model))
2023
2024
2025 if tuple(sorted_legs) in failed_procs:
2026 continue
2027
2028 amplitude = Amplitude({'process': process})
2029 try:
2030 amplitude.generate_diagrams(diagram_filter=diagram_filter)
2031 except InvalidCmd:
2032 failed_procs.append(tuple(sorted_legs))
2033 else:
2034 if amplitude.get('diagrams'):
2035
2036 logger.setLevel(oldloglevel)
2037 return {coupling: max_order_now}
2038 else:
2039 failed_procs.append(tuple(sorted_legs))
2040
2041
2042 max_order_now += 1
2043 logger.setLevel(oldloglevel)
2044
2045
2046 return {coupling: max_order_now}
2047
2048 @staticmethod
2050 """Return the amplitude crossed with the permutation new_perm"""
2051
2052 perm_map = dict(zip(org_perm, new_perm))
2053
2054 new_amp = copy.copy(amplitude)
2055
2056 for i, leg in enumerate(process.get('legs')):
2057 leg.set('number', i+1)
2058
2059 new_amp.set('process', process)
2060
2061 diagrams = base_objects.DiagramList([d.renumber_legs(perm_map,
2062 process.get('legs'),) for \
2063 d in new_amp.get('diagrams')])
2064 new_amp.set('diagrams', diagrams)
2065 new_amp.trim_diagrams()
2066
2067
2068 new_amp.set('has_mirror_process', False)
2069
2070 return new_amp
2071
2077 """Takes a list of lists and elements and returns a list of flat lists.
2078 Example: [[1,2], 3, [4,5]] -> [[1,3,4], [1,3,5], [2,3,4], [2,3,5]]
2079 """
2080
2081
2082 assert isinstance(mylist, list), "Expand_list argument must be a list"
2083
2084 res = []
2085
2086 tmplist = []
2087 for item in mylist:
2088 if isinstance(item, list):
2089 tmplist.append(item)
2090 else:
2091 tmplist.append([item])
2092
2093 for item in apply(itertools.product, tmplist):
2094 res.append(list(item))
2095
2096 return res
2097
2099 """Recursive function. Takes a list of lists and lists of lists
2100 and returns a list of flat lists.
2101 Example: [[1,2],[[4,5],[6,7]]] -> [[1,2,4,5], [1,2,6,7]]
2102 """
2103
2104 res = []
2105
2106 if not mylist or len(mylist) == 1 and not mylist[0]:
2107 return [[]]
2108
2109
2110 assert isinstance(mylist[0], list), \
2111 "Expand_list_list needs a list of lists and lists of lists"
2112
2113
2114 if len(mylist) == 1:
2115 if isinstance(mylist[0][0], list):
2116 return mylist[0]
2117 else:
2118 return mylist
2119
2120 if isinstance(mylist[0][0], list):
2121 for item in mylist[0]:
2122
2123
2124
2125 for rest in expand_list_list(mylist[1:]):
2126 reslist = copy.copy(item)
2127 reslist.extend(rest)
2128 res.append(reslist)
2129 else:
2130 for rest in expand_list_list(mylist[1:]):
2131 reslist = copy.copy(mylist[0])
2132 reslist.extend(rest)
2133 res.append(reslist)
2134
2135
2136 return res
2137