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1 #
2 ################################################################################
3 # Copyright (c) 2009 The MadGraph5_aMC@NLO Development team and Contributors
4 #
5 # This file is a part of the MadGraph5_aMC@NLO project, an application which
6 # automatically generates Feynman diagrams and matrix elements for arbitrary
7 # high-energy processes in the Standard Model and beyond.
8 #
9 # It is subject to the MadGraph5_aMC@NLO license which should accompany this
10 # distribution.
11 #
12 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
13 #
14 ################################################################################
15 """Classes for diagram generation with loop features.
16 """
17
18 import array
19 import copy
20 import itertools
21 import logging
22
23 import madgraph.loop.loop_base_objects as loop_base_objects
24 import madgraph.core.base_objects as base_objects
25 import madgraph.core.diagram_generation as diagram_generation
26 import madgraph.various.misc as misc
27
28 from madgraph import MadGraph5Error
29 from madgraph import InvalidCmd
30 logger = logging.getLogger('madgraph.loop_diagram_generation')
33 # This subroutine has typically quite large DEBUG info.
34 # So even in debug mode, they are turned off by default.
35 # Remove the line below for loop diagram generation diagnostic
36 if not force: return
37
38 flag = "LoopGenInfo: "
39 if len(msg)>40:
40 logger.debug(flag+msg[:35]+" [...] = %s"%str(val))
41 else:
42 logger.debug(flag+msg+''.join([' ']*(40-len(msg)))+' = %s'%str(val))
43
44 #===============================================================================
45 # LoopAmplitude
46 #===============================================================================
47 -class LoopAmplitude(diagram_generation.Amplitude):
48 """NLOAmplitude: process + list of diagrams (ordered)
49 Initialize with a process, then call generate_diagrams() to
50 generate the diagrams for the amplitude
51 """
52
54 """Default values for all properties"""
55
56 # The 'diagrams' entry from the mother class is inherited but will not
57 # be used in NLOAmplitude, because it is split into the four following
58 # different categories of diagrams.
59 super(LoopAmplitude, self).default_setup()
60 self['born_diagrams'] = None
61 self['loop_diagrams'] = None
62 self['loop_UVCT_diagrams'] = base_objects.DiagramList()
63 # This is in principle equal to self['born_diagram']==[] but it can be
64 # that for some reason the born diagram can be generated but do not
65 # contribute.
66 # This will decide wether the virtual is squared against the born or
67 # itself.
68 self['has_born'] = True
69 # This where the structures obtained for this amplitudes are stored
70 self['structure_repository'] = loop_base_objects.FDStructureList()
71
72 # A list that registers what Lcut particle have already been
73 # employed in order to forbid them as loop particles in the
74 # subsequent diagram generation runs.
75 self.lcutpartemployed=[]
76
78 """Allow initialization with Process.
79 If loop_filter is not None, then it will be applied to all subsequent
80 diagram generation from this LoopAmplitude."""
81
82 self.loop_filter = loop_filter
83
84 if isinstance(argument, base_objects.Process):
85 super(LoopAmplitude, self).__init__()
86 self.set('process', argument)
87 self.generate_diagrams()
88 elif argument != None:
89 # call the mother routine
90 super(LoopAmplitude, self).__init__(argument)
91 else:
92 # call the mother routine
93 super(LoopAmplitude, self).__init__()
94
96 """Return diagram property names as a nicely sorted list."""
97
98 return ['process', 'diagrams', 'has_mirror_process', 'born_diagrams',
99 'loop_diagrams','has_born',
100 'structure_repository']
101
103 """Filter for valid amplitude property values."""
104
105 if name == 'diagrams':
106 if not isinstance(value, base_objects.DiagramList):
107 raise self.PhysicsObjectError, \
108 "%s is not a valid DiagramList" % str(value)
109 for diag in value:
110 if not isinstance(diag,loop_base_objects.LoopDiagram) and \
111 not isinstance(diag,loop_base_objects.LoopUVCTDiagram):
112 raise self.PhysicsObjectError, \
113 "%s contains a diagram which is not an NLODiagrams." % str(value)
114 if name == 'born_diagrams':
115 if not isinstance(value, base_objects.DiagramList):
116 raise self.PhysicsObjectError, \
117 "%s is not a valid DiagramList" % str(value)
118 for diag in value:
119 if not isinstance(diag,loop_base_objects.LoopDiagram):
120 raise self.PhysicsObjectError, \
121 "%s contains a diagram which is not an NLODiagrams." % str(value)
122 if name == 'loop_diagrams':
123 if not isinstance(value, base_objects.DiagramList):
124 raise self.PhysicsObjectError, \
125 "%s is not a valid DiagramList" % str(value)
126 for diag in value:
127 if not isinstance(diag,loop_base_objects.LoopDiagram):
128 raise self.PhysicsObjectError, \
129 "%s contains a diagram which is not an NLODiagrams." % str(value)
130 if name == 'has_born':
131 if not isinstance(value, bool):
132 raise self.PhysicsObjectError, \
133 "%s is not a valid bool" % str(value)
134 if name == 'structure_repository':
135 if not isinstance(value, loop_base_objects.FDStructureList):
136 raise self.PhysicsObjectError, \
137 "%s is not a valid bool" % str(value)
138
139 else:
140 super(LoopAmplitude, self).filter(name, value)
141
142 return True
143
145 """Redefine set for the particular case of diagrams"""
146
147 if name == 'diagrams':
148 if self.filter(name, value):
149 self['born_diagrams']=base_objects.DiagramList([diag for diag in value if \
150 not isinstance(diag,loop_base_objects.LoopUVCTDiagram) and diag['type']==0])
151 self['loop_diagrams']=base_objects.DiagramList([diag for diag in value if \
152 not isinstance(diag,loop_base_objects.LoopUVCTDiagram) and diag['type']!=0])
153 self['loop_UVCT_diagrams']=base_objects.DiagramList([diag for diag in value if \
154 isinstance(diag,loop_base_objects.LoopUVCTDiagram)])
155
156 else:
157 return super(LoopAmplitude, self).set(name, value)
158
159 return True
160
162 """Redefine get for the particular case of '*_diagrams' property"""
163
164 if name == 'diagrams':
165 if self['process'] and self['loop_diagrams'] == None:
166 self.generate_diagrams()
167 return base_objects.DiagramList(self['born_diagrams']+\
168 self['loop_diagrams']+\
169 self['loop_UVCT_diagrams'])
170
171 if name == 'born_diagrams':
172 if self['born_diagrams'] == None:
173 # Have not yet generated born diagrams for this process
174 if self['process']['has_born']:
175 if self['process']:
176 self.generate_born_diagrams()
177 else:
178 self['born_diagrams']=base_objects.DiagramList()
179
180 return LoopAmplitude.__bases__[0].get(self, name) #return the mother routine
181
182 # Functions of the different tasks performed in generate_diagram
184 """ Choose the configuration of non-perturbed coupling orders to be
185 retained for all diagrams. This is used when the user did not specify
186 any order. """
187 chosen_order_config = {}
188 min_wgt = self['born_diagrams'].get_min_order('WEIGHTED')
189 # Scan the born diagrams of minimum weight to chose a configuration
190 # of non-perturbed orders.
191 min_non_pert_order_wgt = -1
192 for diag in [d for d in self['born_diagrams'] if \
193 d.get_order('WEIGHTED')==min_wgt]:
194 non_pert_order_wgt = min_wgt - sum([diag.get_order(order)*\
195 self['process']['model']['order_hierarchy'][order] for order in \
196 self['process']['perturbation_couplings']])
197 if min_non_pert_order_wgt == -1 or \
198 non_pert_order_wgt<min_non_pert_order_wgt:
199 chosen_order_config = self.get_non_pert_order_config(diag)
200 logger.info("Chosen coupling orders configuration: (%s)"\
201 %self.print_config(chosen_order_config))
202 return chosen_order_config
203
205 """If squared orders (other than WEIGHTED) are defined, then they can be
206 used for determining what is the expected upper bound for the order
207 restricting loop diagram generation."""
208 for order, value in self['process']['squared_orders'].items():
209 if order.upper()!='WEIGHTED' and order not in self['process']['orders']:
210 # If the bound is of type '>' we cannot say anything
211 if self['process'].get('sqorders_types')[order]=='>':
212 continue
213 # If there is no born, the min order will simply be 0 as it should.
214 bornminorder=self['born_diagrams'].get_min_order(order)
215 if value>=0:
216 self['process']['orders'][order]=value-bornminorder
217 elif self['process']['has_born']:
218 # This means the user want the leading if order=-1 or N^n
219 # Leading term if order=-n. If there is a born diag, we can
220 # infer the necessary maximum order in the loop:
221 # bornminorder+2*(n-1).
222 # If there is no born diag, then we cannot say anything.
223 self['process']['orders'][order]=bornminorder+2*(-value-1)
224
226 """Guess the upper bound for the orders for loop diagram generation
227 based on either no squared orders or simply 'Weighted'"""
228
229 hierarchy = self['process']['model']['order_hierarchy']
230
231 # Maximum of the hierarchy weigtht among all perturbed order
232 max_pert_wgt = max([hierarchy[order] for order in \
233 self['process']['perturbation_couplings']])
234
235 # In order to be sure to catch the corrections to all born diagrams that
236 # the user explicitly asked for with the amplitude orders, we take here
237 # the minimum weighted order as being the maximum between the min weighted
238 # order detected in the Born diagrams and the weight computed from the
239 # user input amplitude orders.
240 user_min_wgt = 0
241
242 # One can chose between the two behaviors below. It is debatable which
243 # one is best. The first one tries to only consider the loop which are
244 # dominant, even when the user selects the amplitude orders and the
245 # second chosen here makes sure that the user gets a correction of the
246 # desired type for all the born diagrams generated with its amplitude
247 # order specification.
248 # min_born_wgt=self['born_diagrams'].get_min_order('WEIGHTED')
249 min_born_wgt=max(self['born_diagrams'].get_min_order('WEIGHTED'),
250 sum([hierarchy[order]*val for order, val in user_orders.items() \
251 if order!='WEIGHTED']))
252
253 if 'WEIGHTED' not in [key.upper() for key in \
254 self['process']['squared_orders'].keys()]:
255 # Then we guess it from the born
256 self['process']['squared_orders']['WEIGHTED']= 2*(min_born_wgt+\
257 max_pert_wgt)
258
259 # Now we know that the remaining weighted orders which can fit in
260 # the loop diagram is (self['target_weighted_order']-
261 # min_born_weighted_order) so for each perturbed order we just have to
262 # take that number divided by its hierarchy weight to have the maximum
263 # allowed order for the loop diagram generation. Of course,
264 # we don't overwrite any order already defined by the user.
265 if self['process']['squared_orders']['WEIGHTED']>=0:
266 trgt_wgt=self['process']['squared_orders']['WEIGHTED']-min_born_wgt
267 else:
268 trgt_wgt=min_born_wgt+(-self['process']['squared_orders']['WEIGHTED']+1)*2
269 # We also need the minimum number of vertices in the born.
270 min_nvert=min([len([1 for vert in diag['vertices'] if vert['id']!=0]) \
271 for diag in self['born_diagrams']])
272 # And the minimum weight for the ordered declared as perturbed
273 min_pert=min([hierarchy[order] for order in \
274 self['process']['perturbation_couplings']])
275
276 for order, value in hierarchy.items():
277 if order not in self['process']['orders']:
278 # The four cases below come from a study of the maximal order
279 # needed in the loop for the weighted order needed and the
280 # number of vertices available.
281 if order in self['process']['perturbation_couplings']:
282 if value!=1:
283 self['process']['orders'][order]=\
284 int((trgt_wgt-min_nvert-2)/(value-1))
285 else:
286 self['process']['orders'][order]=int(trgt_wgt)
287 else:
288 if value!=1:
289 self['process']['orders'][order]=\
290 int((trgt_wgt-min_nvert-2*min_pert)/(value-1))
291 else:
292 self['process']['orders'][order]=\
293 int(trgt_wgt-2*min_pert)
294 # Now for the remaining orders for which the user has not set squared
295 # orders neither amplitude orders, we use the max order encountered in
296 # the born (and add 2 if this is a perturbed order).
297 # It might be that this upper bound is better than the one guessed
298 # from the hierarchy.
299 for order in self['process']['model']['coupling_orders']:
300 neworder=self['born_diagrams'].get_max_order(order)
301 if order in self['process']['perturbation_couplings']:
302 neworder+=2
303 if order not in self['process']['orders'].keys() or \
304 neworder<self['process']['orders'][order]:
305 self['process']['orders'][order]=neworder
306
308 """ Filter diags to select only the diagram with the non perturbed orders
309 configuration config and update discarded_configurations.Diags is the
310 name of the key attribute of this class containing the diagrams to
311 filter."""
312 newdiagselection = base_objects.DiagramList()
313 for diag in self[diags]:
314 diag_config = self.get_non_pert_order_config(diag)
315 if diag_config == config:
316 newdiagselection.append(diag)
317 elif diag_config not in discarded_configurations:
318 discarded_configurations.append(diag_config)
319 self[diags] = newdiagselection
320
322 """ Remove the loops which are zero because of Furry theorem. So as to
323 limit any possible mistake in case of BSM model, I limit myself here to
324 removing SM-quark loops with external legs with an odd number of photons,
325 possibly including exactly two gluons."""
326
327 new_diag_selection = base_objects.DiagramList()
328
329 n_discarded = 0
330 for diag in self['loop_diagrams']:
331 if diag.get('tag')==[]:
332 raise MadGraph5Error, "The loop diagrams should have been tagged"+\
333 " before going through the Furry filter."
334
335 loop_line_pdgs = diag.get_loop_lines_pdgs()
336 attached_pdgs = diag.get_pdgs_attached_to_loop(structs)
337 if (attached_pdgs.count(22)%2==1) and \
338 (attached_pdgs.count(21) in [0,2]) and \
339 (all(pdg in [22,21] for pdg in attached_pdgs)) and \
340 (abs(loop_line_pdgs[0]) in list(range(1,7))) and \
341 (all(abs(pdg)==abs(loop_line_pdgs[0]) for pdg in loop_line_pdgs)):
342 n_discarded += 1
343 else:
344 new_diag_selection.append(diag)
345
346 self['loop_diagrams'] = new_diag_selection
347
348 if n_discarded > 0:
349 logger.debug(("MadLoop discarded %i diagram%s because they appeared"+\
350 " to be zero because of Furry theorem.")%(n_discarded,'' if \
351 n_discarded<=1 else 's'))
352
353 @staticmethod
355 """ Returns a function which applies the filter corresponding to the
356 conditional expression encoded in filterdef."""
357
358 def filter(diag, structs, model, id):
359 """ The filter function generated '%s'."""%filterdef
360
361 loop_pdgs = diag.get_loop_lines_pdgs()
362 struct_pdgs = diag.get_pdgs_attached_to_loop(structs)
363 loop_masses = [model.get_particle(pdg).get('mass') for pdg in loop_pdgs]
364 struct_masses = [model.get_particle(pdg).get('mass') for pdg in struct_pdgs]
365 if not eval(filterdef.lower(),{'n':len(loop_pdgs),
366 'loop_pdgs':loop_pdgs,
367 'struct_pdgs':struct_pdgs,
368 'loop_masses':loop_masses,
369 'struct_masses':struct_masses,
370 'id':id}):
371 return False
372 else:
373 return True
374
375 return filter
376
378 """ User-defined user-filter. By default it is not called, but the expert
379 user can turn it on and code here is own filter. Some default examples
380 are provided here.
381 The tagging of the loop diagrams must be performed before using this
382 user loop filter"""
383
384 # By default the user filter does nothing if filter is not set,
385 # if you want to turn it on and edit it by hand, then set the
386 # variable edit_filter_manually to True
387 edit_filter_manually = False
388 if not edit_filter_manually and filter in [None,'None']:
389 return
390 if isinstance(filter,str) and filter.lower() == 'true':
391 edit_filter_manually = True
392 filter=None
393
394
395 if filter not in [None,'None']:
396 filter_func = LoopAmplitude.get_loop_filter(filter)
397 else:
398 filter_func = None
399
400 new_diag_selection = base_objects.DiagramList()
401 discarded_diags = base_objects.DiagramList()
402 i=0
403 for diag in self['loop_diagrams']:
404 if diag.get('tag')==[]:
405 raise MadGraph5Error, "Before using the user_filter, please "+\
406 "make sure that the loop diagrams have been tagged first."
407 valid_diag = True
408 i=i+1
409
410 # Apply the custom filter specified if any
411 if filter_func:
412 try:
413 valid_diag = filter_func(diag, structs, model, i)
414 except Exception as e:
415 raise InvalidCmd("The user-defined filter '%s' did not"%filter+
416 " returned the following error:\n > %s"%str(e))
417
418 # if any([abs(pdg) not in range(1,7) for pdg in diag.get_loop_lines_pdgs()]):
419 # valid_diag = False
420
421 # if any([abs(i)!=1000021 for i in diag.get_loop_lines_pdgs()]):
422 # valid_diag=False
423 # if len(diag.get_loop_lines_pdgs())<4:
424 # valid_diag = False
425
426 # connected_id = diag.get_pdgs_attached_to_loop(structs)
427 # if connected_id.count(22)!=2 or not all(abs(pdg) in range(7) for pdg in diag.get_loop_lines_pdgs()):
428 # valid_diag=False
429
430 # Ex. 0: Chose a specific diagram number, here the 8th one for ex.
431 # if i not in [31]:
432 # valid_diag = False
433
434 # Ex. 0: Keeps only the top quark loops.
435 # if any([pdg not in [6,-6] for pdg in diag.get_loop_lines_pdgs()]):
436 # valid_diag = False
437
438 # Ex. 1: Chose the topology, i.e. number of loop line.
439 # Notice that here particles and antiparticles are not
440 # differentiated and always the particle PDG is returned.
441 # In this example, only boxes are selected.
442 # if len(diag.get_loop_lines_pdgs())>2 and \
443 # any([i in diag.get_loop_lines_pdgs() for i in[24,-24,23]]):
444 # valid_diag=False
445
446 # Ex. 2: Use the pdgs of the particles directly attached to the loop.
447 # In this example, we forbid the Z to branch off the loop.
448 # connected_id = diag.get_pdgs_attached_to_loop(structs)
449 # if 22 not in connected_id:
450 # valid_diag=False
451
452 # Ex. 3: Filter based on the mass of the particles running in the
453 # loop. It shows how to access the particles properties from
454 # the PDG.
455 # In this example, only massive parts. are allowed in the loop.
456 # if 'ZERO' in [model.get_particle(pdg).get('mass') for pdg in \
457 # diag.get_loop_lines_pdgs()]:
458 # valid_diag=False
459
460 # Ex. 4: Complicated filter which gets rid of all bubble diagrams made
461 # of two vertices being the four gluon vertex and the effective
462 # glu-glu-Higgs vertex.
463 # if len(diag.get_loop_lines_pdgs())==2:
464 # bubble_lines_pdgs=[abs(diag.get('canonical_tag')[0][0]),
465 # abs(diag.get('canonical_tag')[0][0])]
466 # first_vertex_pdgs=bubble_lines_pdgs+\
467 # [abs(structs.get_struct(struct_ID).get('binding_leg').get('id')) \
468 # for struct_ID in diag.get('canonical_tag')[0][1]]
469 # second_vertex_pdgs=bubble_lines_pdgs+\
470 # [abs(structs.get_struct(struct_ID).get('binding_leg').get('id')) \
471 # for struct_ID in diag.get('canonical_tag')[1][1]]
472 # first_vertex_pdgs.sort()
473 # second_vertex_pdgs.sort()
474 # bubble_vertices=[first_vertex_pdgs,second_vertex_pdgs]
475 # bubble_vertices.sort()
476 # if bubble_vertices==[[21,21,21,21],[21,21,25]]:
477 # valid_diag=False
478
479 # If you need any more advanced function for your filter and cannot
480 # figure out how to implement them. Just contact the authors.
481
482 if valid_diag:
483 new_diag_selection.append(diag)
484 else:
485 discarded_diags.append(diag)
486
487 self['loop_diagrams'] = new_diag_selection
488 if filter in [None,'None']:
489 warn_msg = """
490 The user-defined loop diagrams filter is turned on and discarded %d loops."""\
491 %len(discarded_diags)
492 else:
493 warn_msg = """
494 The loop diagrams filter '%s' is turned on and discarded %d loops."""\
495 %(filter,len(discarded_diags))
496 logger.warning(warn_msg)
497
499 """ Filter the loop diagrams to make sure they belong to the class
500 of coupling orders perturbed. """
501
502 # First define what are the set of particles allowed to run in the loop.
503 allowedpart=[]
504 for part in self['process']['model']['particles']:
505 for order in self['process']['perturbation_couplings']:
506 if part.is_perturbating(order,self['process']['model']):
507 allowedpart.append(part.get_pdg_code())
508 break
509
510 newloopselection=base_objects.DiagramList()
511 warned=False
512 warning_msg = ("Some loop diagrams contributing to this process"+\
513 " are discarded because they are not pure (%s)-perturbation.\nMake sure"+\
514 " you did not want to include them.")%\
515 ('+'.join(self['process']['perturbation_couplings']))
516 for i,diag in enumerate(self['loop_diagrams']):
517 # Now collect what are the coupling orders building the loop which
518 # are also perturbed order.
519 loop_orders=diag.get_loop_orders(self['process']['model'])
520 pert_loop_order=set(loop_orders.keys()).intersection(\
521 set(self['process']['perturbation_couplings']))
522 # Then make sure that the particle running in the loop for all
523 # diagrams belong to the set above. Also make sure that there is at
524 # least one coupling order building the loop which is in the list
525 # of the perturbed order.
526 valid_diag=True
527 if (diag.get_loop_line_types()-set(allowedpart))!=set() or \
528 pert_loop_order==set([]):
529 valid_diag=False
530 if not warned:
531 logger.warning(warning_msg)
532 warned=True
533 if len([col for col in [
534 self['process'].get('model').get_particle(pdg).get('color') \
535 for pdg in diag.get_pdgs_attached_to_loop(\
536 self['structure_repository'])] if col!=1])==1:
537 valid_diag=False
538
539 if valid_diag:
540 newloopselection.append(diag)
541 self['loop_diagrams']=newloopselection
542 # To monitor what are the diagrams filtered, simply comment the line
543 # directly above and uncomment the two directly below.
544 # self['loop_diagrams'] = base_objects.DiagramList(
545 # [diag for diag in self['loop_diagrams'] if diag not in newloopselection])
546
548 """ Makes sure that all non perturbed orders factorize the born diagrams
549 """
550 warning_msg = "All Born diagrams do not factorize the same sum of power(s) "+\
551 "of the the perturbed order(s) %s.\nThis is potentially dangerous"+\
552 " as the real-emission diagrams from aMC@NLO will not be consistent"+\
553 " with these virtual contributions."
554 if self['process']['has_born']:
555 trgt_summed_order = sum([self['born_diagrams'][0].get_order(order)
556 for order in self['process']['perturbation_couplings']])
557 for diag in self['born_diagrams'][1:]:
558 if sum([diag.get_order(order) for order in self['process']
559 ['perturbation_couplings']])!=trgt_summed_order:
560 logger.warning(warning_msg%' '.join(self['process']
561 ['perturbation_couplings']))
562 break
563
564 warning_msg = "All born diagrams do not factorize the same power of "+\
565 "the order %s which is not perturbed and for which you have not"+\
566 "specified any amplitude order. \nThis is potentially dangerous"+\
567 " as the real-emission diagrams from aMC@NLO will not be consistent"+\
568 " with these virtual contributions."
569 if self['process']['has_born']:
570 for order in self['process']['model']['coupling_orders']:
571 if order not in self['process']['perturbation_couplings'] and \
572 order not in user_orders.keys():
573 order_power=self['born_diagrams'][0].get_order(order)
574 for diag in self['born_diagrams'][1:]:
575 if diag.get_order(order)!=order_power:
576 logger.warning(warning_msg%order)
577 break
578
579 # Helper function
581 """ Return a dictionary of all the coupling orders of this diagram which
582 are not the perturbed ones."""
583 return dict([(order, diagram.get_order(order)) for \
584 order in self['process']['model']['coupling_orders'] if \
585 not order in self['process']['perturbation_couplings'] ])
586
588 """Return a string describing the coupling order configuration"""
589 res = []
590 for order in self['process']['model']['coupling_orders']:
591 try:
592 res.append('%s=%d'%(order,config[order]))
593 except KeyError:
594 res.append('%s=*'%order)
595 return ','.join(res)
596
598 """ Generates all diagrams relevant to this Loop Process """
599
600 # Description of the algorithm to guess the leading contribution.
601 # The summed weighted order of each diagram will be compared to
602 # 'target_weighted_order' which acts as a threshold to decide which
603 # diagram to keep. Here is an example on how MG5 sets the
604 # 'target_weighted_order'.
605 #
606 # In the sm process uu~ > dd~ [QCD, QED] with hierarchy QCD=1, QED=2 we
607 # would have at leading order contribution like
608 # (QED=4) , (QED=2, QCD=2) , (QCD=4)
609 # leading to a summed weighted order of respectively
610 # (4*2=8) , (2*2+2*1=6) , (4*1=4)
611 # at NLO in QCD and QED we would have the following possible contributions
612 # (QED=6), (QED=4,QCD=2), (QED=2,QCD=4) and (QCD=6)
613 # which translate into the following weighted orders, respectively
614 # 12, 10, 8 and 6
615 # So, now we take the largest weighted order at born level, 4, and add two
616 # times the largest weight in the hierarchy among the order for which we
617 # consider loop perturbation, in this case 2*2 wich gives us a
618 # target_weighted_order of 8. based on this we will now keep all born
619 # contributions and exclude the NLO contributions (QED=6) and (QED=4,QCD=2)
620
621 # Use the globally defined loop_filter if the locally defined one is empty
622 if (not self.loop_filter is None) and (loop_filter is None):
623 loop_filter = self.loop_filter
624
625 logger.debug("Generating %s "\
626 %self['process'].nice_string().replace('Process', 'process'))
627
628 # Hierarchy and model shorthands
629 model = self['process']['model']
630 hierarchy = model['order_hierarchy']
631
632 # Later, we will specify the orders for the loop amplitude.
633 # It is a temporary change that will be reverted after loop diagram
634 # generation. We then back up here its value prior modification.
635 user_orders=copy.copy(self['process']['orders'])
636 # First generate the born diagram if the user asked for it
637 if self['process']['has_born']:
638 bornsuccessful = self.generate_born_diagrams()
639 ldg_debug_info("# born diagrams after first generation",\
640 len(self['born_diagrams']))
641 else:
642 self['born_diagrams'] = base_objects.DiagramList()
643 bornsuccessful = True
644 logger.debug("Born diagrams generation skipped by user request.")
645
646 # Make sure that all orders specified belong to the model:
647 for order in self['process']['orders'].keys()+\
648 self['process']['squared_orders'].keys():
649 if not order in model.get('coupling_orders') and \
650 order != 'WEIGHTED':
651 raise InvalidCmd("Coupling order %s not found"%order +\
652 " in any interaction of the current model %s."%model['name'])
653
654 # The decision of whether the virtual must be squared against the born or the
655 # virtual is made based on whether there are Born or not unless the user
656 # already asked for the loop squared.
657 if self['process']['has_born']:
658 self['process']['has_born'] = self['born_diagrams']!=[]
659 self['has_born'] = self['process']['has_born']
660
661 ldg_debug_info("User input born orders",self['process']['orders'])
662 ldg_debug_info("User input squared orders",
663 self['process']['squared_orders'])
664 ldg_debug_info("User input perturbation",\
665 self['process']['perturbation_couplings'])
666
667 # Now, we can further specify the orders for the loop amplitude.
668 # Those specified by the user of course remain the same, increased by
669 # two if they are perturbed. It is a temporary change that will be
670 # reverted after loop diagram generation.
671 user_orders=copy.copy(self['process']['orders'])
672 user_squared_orders=copy.copy(self['process']['squared_orders'])
673
674 # If the user did not specify any order, we can expect him not to be an
675 # expert. So we must make sure the born all factorize the same powers of
676 # coupling orders which are not perturbed. If not we chose a configuration
677 # of non-perturbed order which has the smallest total weight and inform
678 # the user about this. It is then stored below for later filtering of
679 # the loop diagrams.
680 chosen_order_config={}
681 if self['process']['squared_orders']=={} and \
682 self['process']['orders']=={} and self['process']['has_born']:
683 chosen_order_config = self.choose_order_config()
684
685 discarded_configurations = []
686 # The born diagrams are now filtered according to the chose configuration
687 if chosen_order_config != {}:
688 self.filter_from_order_config('born_diagrams', \
689 chosen_order_config,discarded_configurations)
690
691 # Before proceeding with the loop contributions, we must make sure that
692 # the born diagram generated factorize the same sum of power of the
693 # perturbed couplings. If this is not true, then it is very
694 # cumbersome to get the real radiation contribution correct and consistent
695 # with the computations of the virtuals (for now).
696 # Also, when MadLoop5 guesses the a loop amplitude order on its own, it
697 # might decide not to include some subleading loop which might be not
698 # be consistently neglected for now in the MadFKS5 so that its best to
699 # warn the user that he should enforce that target born amplitude order
700 # to any value of his choice.
701 self.check_factorization(user_orders)
702
703 # Now find an upper bound for the loop diagram generation.
704 self.guess_loop_orders_from_squared()
705
706 # If the user had not specified any fixed squared order other than
707 # WEIGHTED, we will use the guessed weighted order to assign a bound to
708 # the loop diagram order. Later we will check if the order deduced from
709 # the max order appearing in the born diagrams is a better upper bound.
710 # It will set 'WEIGHTED' to the desired value if it was not already set
711 # by the user. This is why you see the process defined with 'WEIGHTED'
712 # in the squared orders no matter the user input. Leave it like this.
713 if [k.upper() for k in self['process']['squared_orders'].keys()] in \
714 [[],['WEIGHTED']] and self['process']['has_born']:
715 self.guess_loop_orders(user_orders)
716
717 # Finally we enforce the use of the orders specified for the born
718 # (augmented by two if perturbed) by the user, no matter what was
719 # the best guess performed above.
720 for order in user_orders.keys():
721 if order in self['process']['perturbation_couplings']:
722 self['process']['orders'][order]=user_orders[order]+2
723 else:
724 self['process']['orders'][order]=user_orders[order]
725 if 'WEIGHTED' in user_orders.keys():
726 self['process']['orders']['WEIGHTED']=user_orders['WEIGHTED']+\
727 2*min([hierarchy[order] for order in \
728 self['process']['perturbation_couplings']])
729
730 ldg_debug_info("Orders used for loop generation",\
731 self['process']['orders'])
732
733 # Make sure to warn the user if we already possibly excluded mixed order
734 # loops by smartly setting up the orders
735 warning_msg = ("Some loop diagrams contributing to this process might "+\
736 "be discarded because they are not pure (%s)-perturbation.\nMake sure"+\
737 " there are none or that you did not want to include them.")%(\
738 ','.join(self['process']['perturbation_couplings']))
739
740 if self['process']['has_born']:
741 for order in model['coupling_orders']:
742 if order not in self['process']['perturbation_couplings']:
743 try:
744 if self['process']['orders'][order]< \
745 self['born_diagrams'].get_max_order(order):
746 logger.warning(warning_msg)
747 break
748 except KeyError:
749 pass
750
751 # Now we can generate the loop diagrams.
752 totloopsuccessful=self.generate_loop_diagrams()
753
754 # If there is no born neither loop diagrams, return now.
755 if not self['process']['has_born'] and not self['loop_diagrams']:
756 self['process']['orders'].clear()
757 self['process']['orders'].update(user_orders)
758 return False
759
760 # We add here the UV renormalization contribution built in
761 # LoopUVCTDiagram. It is done before the squared order selection because
762 # it is possible that some UV-renorm. diagrams are removed as well.
763 if self['process']['has_born']:
764 self.set_Born_CT()
765
766 ldg_debug_info("#UVCTDiags generated",len(self['loop_UVCT_diagrams']))
767
768 # Reset the orders to their original specification by the user
769 self['process']['orders'].clear()
770 self['process']['orders'].update(user_orders)
771
772 # If there was no born, we will guess the WEIGHT squared order only now,
773 # based on the minimum weighted order of the loop contributions, if it
774 # was not specified by the user.
775 if not self['process']['has_born'] and not \
776 self['process']['squared_orders'] and not\
777 self['process']['orders'] and hierarchy:
778 pert_order_weights=[hierarchy[order] for order in \
779 self['process']['perturbation_couplings']]
780 self['process']['squared_orders']['WEIGHTED']=2*(\
781 self['loop_diagrams'].get_min_order('WEIGHTED')+\
782 max(pert_order_weights)-min(pert_order_weights))
783
784 ldg_debug_info("Squared orders after treatment",\
785 self['process']['squared_orders'])
786 ldg_debug_info("#Diags after diagram generation",\
787 len(self['loop_diagrams']))
788
789
790 # If a special non perturbed order configuration was chosen at the
791 # beginning because of the absence of order settings by the user,
792 # the corresponding filter is applied now to loop diagrams.
793 # List of discarded configurations
794 if chosen_order_config != {}:
795 self.filter_from_order_config('loop_diagrams', \
796 chosen_order_config,discarded_configurations)
797 # # Warn about discarded configurations.
798 if discarded_configurations!=[]:
799 msg = ("The contribution%s of th%s coupling orders "+\
800 "configuration%s %s discarded :%s")%(('s','ese','s','are','\n')\
801 if len(discarded_configurations)>1 else ('','is','','is',' '))
802 msg = msg + '\n'.join(['(%s)'%self.print_config(conf) for conf \
803 in discarded_configurations])
804 msg = msg + "\nManually set the coupling orders to "+\
805 "generate %sthe contribution%s above."%(('any of ','s') if \
806 len(discarded_configurations)>1 else ('',''))
807 logger.info(msg)
808
809 # The minimum of the different orders used for the selections can
810 # possibly increase, after some loop diagrams are selected out.
811 # So this check must be iterated until the number of diagrams
812 # remaining is stable.
813 # We first apply the selection rules without the negative constraint.
814 # (i.e. QCD=1 for LO contributions only)
815 regular_constraints = dict([(key,val) for (key,val) in
816 self['process']['squared_orders'].items() if val>=0])
817 negative_constraints = dict([(key,val) for (key,val) in
818 self['process']['squared_orders'].items() if val<0])
819 while True:
820 ndiag_remaining=len(self['loop_diagrams']+self['born_diagrams'])
821 self.check_squared_orders(regular_constraints)
822 if len(self['loop_diagrams']+self['born_diagrams'])==ndiag_remaining:
823 break
824 # And then only the negative ones
825 if negative_constraints!={}:
826 # It would be meaningless here to iterate because <order>=-X would
827 # have a different meaning every time.
828 # notice that this function will change the negative values of
829 # self['process']['squared_orders'] to their corresponding positive
830 # constraint for the present process.
831 # For example, u u~ > d d~ QCD^2=-2 becomes u u~ > d d~ QCD=2
832 # because the LO QCD contribution has QED=4, QCD=0 and the NLO one
833 # selected with -2 is QED=2, QCD=2.
834 self.check_squared_orders(negative_constraints,user_squared_orders)
835
836 ldg_debug_info("#Diags after constraints",len(self['loop_diagrams']))
837 ldg_debug_info("#Born diagrams after constraints",len(self['born_diagrams']))
838 ldg_debug_info("#UVCTDiags after constraints",len(self['loop_UVCT_diagrams']))
839
840 # Now the loop diagrams are tagged and filtered for redundancy.
841 tag_selected=[]
842 loop_basis=base_objects.DiagramList()
843 for diag in self['loop_diagrams']:
844 diag.tag(self['structure_repository'],model)
845 # Make sure not to consider wave-function renormalization, vanishing tadpoles,
846 # or redundant diagrams
847 if not diag.is_wf_correction(self['structure_repository'], \
848 model) and not diag.is_vanishing_tadpole(model) and \
849 diag['canonical_tag'] not in tag_selected:
850 loop_basis.append(diag)
851 tag_selected.append(diag['canonical_tag'])
852
853 self['loop_diagrams']=loop_basis
854
855 # Now select only the loops corresponding to the perturbative orders
856 # asked for.
857 self.filter_loop_for_perturbative_orders()
858
859 if len(self['loop_diagrams'])==0 and len(self['born_diagrams'])!=0:
860 raise InvalidCmd('All loop diagrams discarded by user selection.\n'+\
861 'Consider using a tree-level generation or relaxing the coupling'+\
862 ' order constraints.')
863 # If there is no born neither loop diagrams after filtering, return now.
864 if not self['process']['has_born'] and not self['loop_diagrams']:
865 self['process']['squared_orders'].clear()
866 self['process']['squared_orders'].update(user_squared_orders)
867 return False
868
869
870 # Discard diagrams which are zero because of Furry theorem
871 self.remove_Furry_loops(model,self['structure_repository'])
872
873 # Apply here some user-defined filter.
874 # For expert only, you can edit your own filter by modifying the
875 # user_filter() function which by default does nothing but in which you
876 # will find examples of common filters.
877 self.user_filter(model,self['structure_repository'], filter=loop_filter)
878
879 # Set the necessary UV/R2 CounterTerms for each loop diagram generated
880 self.set_LoopCT_vertices()
881
882 # Now revert the squared order. This function typically adds to the
883 # squared order list the target WEIGHTED order which has been detected.
884 # This is typically not desired because if the user types in directly
885 # what it sees on the screen, it does not get back the same process.
886 # for example, u u~ > d d~ [virt=QCD] becomes
887 # u u~ > d d~ [virt=QCD] WEIGHTED=6
888 # but of course the photon-gluon s-channel Born interference is not
889 # counted in.
890 # However, if you type it in generate again with WEIGHTED=6, you will
891 # get it.
892 self['process']['squared_orders'].clear()
893 self['process']['squared_orders'].update(user_squared_orders)
894
895 # The computation below is just to report what split order are computed
896 # and which one are considered (i.e. kept using the order specifications)
897 self.print_split_order_infos()
898
899 # Give some info about the run
900 nLoopDiag = 0
901 nCT={'UV':0,'R2':0}
902 for ldiag in self['loop_UVCT_diagrams']:
903 nCT[ldiag['type'][:2]]+=len(ldiag['UVCT_couplings'])
904 for ldiag in self['loop_diagrams']:
905 nLoopDiag+=1
906 nCT['UV']+=len(ldiag.get_CT(model,'UV'))
907 nCT['R2']+=len(ldiag.get_CT(model,'R2'))
908
909 # The identification of numerically equivalent diagrams is done here.
910 # Simply comment the line above to remove it for testing purposes
911 # (i.e. to make sure it does not alter the result).
912 nLoopsIdentified = self.identify_loop_diagrams()
913 if nLoopsIdentified > 0:
914 logger.debug("A total of %d loop diagrams "%nLoopsIdentified+\
915 "were identified with equivalent ones.")
916 logger.info("Contributing diagrams generated: "+\
917 "%d Born, %d%s loops, %d R2, %d UV"%(len(self['born_diagrams']),
918 len(self['loop_diagrams']),'(+%d)'%nLoopsIdentified \
919 if nLoopsIdentified>0 else '' ,nCT['R2'],nCT['UV']))
920
921 ldg_debug_info("#Diags after filtering",len(self['loop_diagrams']))
922 ldg_debug_info("# of different structures identified",\
923 len(self['structure_repository']))
924
925 return (bornsuccessful or totloopsuccessful)
926
928 """ Uses a loop_tag characterizing the loop with only physical
929 information about it (mass, coupling, width, color, etc...) so as to
930 recognize numerically equivalent diagrams and group them together,
931 such as massless quark loops in pure QCD gluon loop amplitudes."""
932
933 # This dictionary contains key-value pairs of the form
934 # (loop_tag, DiagramList) where the loop_tag key unambiguously
935 # characterizes a class of equivalent diagrams and the DiagramList value
936 # lists all the diagrams belonging to this class.
937 # In the end, the first diagram of this DiagramList will be used as
938 # the reference included in the numerical code for the loop matrix
939 # element computations and all the others will be omitted, being
940 # included via a simple multiplicative factor applied to the first one.
941 diagram_identification = {}
942
943 for i, loop_diag in enumerate(self['loop_diagrams']):
944 loop_tag = loop_diag.build_loop_tag_for_diagram_identification(
945 self['process']['model'], self.get('structure_repository'),
946 use_FDStructure_ID_for_tag = True)
947 # We store the loop diagrams in a 2-tuple that keeps track of 'i'
948 # so that we don't lose their original order. It is just for
949 # convenience, and not strictly necessary.
950 try:
951 diagram_identification[loop_tag].append((i+1,loop_diag))
952 except KeyError:
953 diagram_identification[loop_tag] = [(i+1,loop_diag)]
954
955 # Now sort the loop_tag keys according to their order of appearance
956 sorted_loop_tag_keys = sorted(diagram_identification.keys(),
957 key=lambda k:diagram_identification[k][0][0])
958
959 new_loop_diagram_base = base_objects.DiagramList([])
960 n_loops_identified = 0
961 for loop_tag in sorted_loop_tag_keys:
962 n_diag_in_class = len(diagram_identification[loop_tag])
963 n_loops_identified += n_diag_in_class-1
964 new_loop_diagram_base.append(diagram_identification[loop_tag][0][1])
965 # We must add the counterterms of all the identified loop diagrams
966 # to the reference one.
967 new_loop_diagram_base[-1]['multiplier'] = n_diag_in_class
968 for ldiag in diagram_identification[loop_tag][1:]:
969 new_loop_diagram_base[-1].get('CT_vertices').extend(
970 copy.copy(ldiag[1].get('CT_vertices')))
971 if n_diag_in_class > 1:
972 ldg_debug_info("# Diagram equivalence class detected","#(%s) -> #%d"\
973 %(','.join('%d'%diag[0] for diag in diagram_identification[loop_tag][1:])+
974 (',' if n_diag_in_class==2 else ''),diagram_identification[loop_tag][0][0]))
975
976
977 self.set('loop_diagrams',new_loop_diagram_base)
978 return n_loops_identified
979
981 """This function is solely for monitoring purposes. It reports what are
982 the coupling order combination which are obtained with the diagram
983 genarated and among those which ones correspond to those selected by
984 the process definition and which ones are the extra combinations which
985 comes as a byproduct of the computation of the desired one. The typical
986 example is that if you ask for d d~ > u u~ QCD^2==2 [virt=QCD, QED],
987 you will not only get (QCD,QED)=(2,2);(2,4) which are the desired ones
988 but the code output will in principle also be able to return
989 (QCD,QED)=(4,0);(4,2);(0,4);(0,6) because they involve the same amplitudes
990 """
991
992 hierarchy = self['process']['model']['order_hierarchy']
993
994 sqorders_types=copy.copy(self['process'].get('sqorders_types'))
995 # The WEIGHTED order might have been automatically assigned to the
996 # squared order constraints, so we must assign it a type if not specified
997 if 'WEIGHTED' not in sqorders_types:
998 sqorders_types['WEIGHTED']='<='
999
1000 sorted_hierarchy = [order[0] for order in \
1001 sorted(hierarchy.items(), key=lambda el: el[1])]
1002
1003 loop_SOs = set(tuple([d.get_order(order) for order in sorted_hierarchy])
1004 for d in self['loop_diagrams']+self['loop_UVCT_diagrams'])
1005
1006 if self['process']['has_born']:
1007 born_SOs = set(tuple([d.get_order(order) for order in \
1008 sorted_hierarchy]) for d in self['born_diagrams'])
1009 else:
1010 born_SOs = set([])
1011
1012 born_sqSOs = set(tuple([x + y for x, y in zip(b1_SO, b2_SO)]) for b1_SO
1013 in born_SOs for b2_SO in born_SOs)
1014 if self['process']['has_born']:
1015 ref_amps = born_SOs
1016 else:
1017 ref_amps = loop_SOs
1018 loop_sqSOs = set(tuple([x + y for x, y in zip(b_SO, l_SO)]) for b_SO in
1019 ref_amps for l_SO in loop_SOs)
1020
1021 # Append the corresponding WEIGHT of each contribution
1022 sorted_hierarchy.append('WEIGHTED')
1023 born_sqSOs = sorted([b_sqso+(sum([b*hierarchy[sorted_hierarchy[i]] for
1024 i, b in enumerate(b_sqso)]),) for b_sqso in born_sqSOs],
1025 key=lambda el: el[1])
1026 loop_sqSOs = sorted([l_sqso+(sum([l*hierarchy[sorted_hierarchy[i]] for
1027 i, l in enumerate(l_sqso)]),) for l_sqso in loop_sqSOs],
1028 key=lambda el: el[1])
1029
1030
1031 logger.debug("Coupling order combinations considered:"+\
1032 " (%s)"%','.join(sorted_hierarchy))
1033
1034 # Now check what is left
1035 born_considered = []
1036 loop_considered = []
1037 for i, sqSOList in enumerate([born_sqSOs,loop_sqSOs]):
1038 considered = []
1039 extra = []
1040 for sqSO in sqSOList:
1041 for sqo, constraint in self['process']['squared_orders'].items():
1042 sqo_index = sorted_hierarchy.index(sqo)
1043 # Notice that I assume here that the negative coupling order
1044 # constraint should have been replaced here (by its
1045 # corresponding positive value).
1046 if (sqorders_types[sqo]=='==' and
1047 sqSO[sqo_index]!=constraint ) or \
1048 (sqorders_types[sqo] in ['=','<='] and
1049 sqSO[sqo_index]>constraint) or \
1050 (sqorders_types[sqo] in ['>'] and
1051 sqSO[sqo_index]<=constraint):
1052 extra.append(sqSO)
1053 break;
1054
1055 # Set the ones considered to be the complement of the omitted ones
1056 considered = [sqSO for sqSO in sqSOList if sqSO not in extra]
1057
1058 if i==0:
1059 born_considered = considered
1060 name = "Born"
1061 if not self['process']['has_born']:
1062 logger.debug(" > No Born contributions for this process.")
1063 continue
1064 elif i==1:
1065 loop_considered = considered
1066 name = "loop"
1067
1068 if len(considered)==0:
1069 logger.debug(" > %s : None"%name)
1070 else:
1071 logger.debug(" > %s : %s"%(name,' '.join(['(%s,W%d)'%(
1072 ','.join(list('%d'%s for s in c[:-1])),c[-1])
1073 for c in considered])))
1074
1075 if len(extra)!=0:
1076 logger.debug(" > %s (not selected but available): %s"%(name,' '.
1077 join(['(%s,W%d)'%(','.join(list('%d'%s for s in e[:-1])),
1078 e[-1]) for e in extra])))
1079
1080 # In case it is needed, the considered orders are returned
1081 # (it is used by some of the unit tests)
1082 return (born_considered,
1083 [sqSO for sqSO in born_sqSOs if sqSO not in born_considered],
1084 loop_considered,
1085 [sqSO for sqSO in loop_sqSOs if sqSO not in loop_considered])
1086
1087
1089 """ Generates all born diagrams relevant to this NLO Process """
1090
1091 bornsuccessful, self['born_diagrams'] = \
1092 diagram_generation.Amplitude.generate_diagrams(self,True)
1093
1094 return bornsuccessful
1095
1097 """ Generates all loop diagrams relevant to this NLO Process """
1098
1099 # Reinitialize the loop diagram container
1100 self['loop_diagrams']=base_objects.DiagramList()
1101 totloopsuccessful=False
1102
1103 # Make sure to start with an empty l-cut particle list.
1104 self.lcutpartemployed=[]
1105
1106 for order in self['process']['perturbation_couplings']:
1107 ldg_debug_info("Perturbation coupling generated now ",order)
1108 lcutPart=[particle for particle in \
1109 self['process']['model']['particles'] if \
1110 (particle.is_perturbating(order, self['process']['model']) and \
1111 particle.get_pdg_code() not in \
1112 self['process']['forbidden_particles'])]
1113 # lcutPart = [lp for lp in lcutPart if abs(lp.get('pdg_code'))==6]
1114 # misc.sprint("lcutPart=",[part.get('name') for part in lcutPart])
1115 for part in lcutPart:
1116 if part.get_pdg_code() not in self.lcutpartemployed:
1117 # First create the two L-cut particles to add to the process.
1118 # Remember that in the model only the particles should be
1119 # tagged as contributing to the a perturbation. Never the
1120 # anti-particle. We chose here a specific orientation for
1121 # the loop momentum flow, say going IN lcutone and OUT
1122 # lcuttwo. We also define here the 'positive' loop fermion
1123 # flow by always setting lcutone to be a particle and
1124 # lcuttwo the corresponding anti-particle.
1125 ldg_debug_info("Generating loop diagram with L-cut type",\
1126 part.get_name())
1127 lcutone=base_objects.Leg({'id': part.get_pdg_code(),
1128 'state': True,
1129 'loop_line': True})
1130 lcuttwo=base_objects.Leg({'id': part.get_anti_pdg_code(),
1131 'state': True,
1132 'loop_line': True})
1133 self['process'].get('legs').extend([lcutone,lcuttwo])
1134 # WARNING, it is important for the tagging to notice here
1135 # that lcuttwo is the last leg in the process list of legs
1136 # and will therefore carry the highest 'number' attribute as
1137 # required to insure that it will never be 'propagated' to
1138 # any output leg.
1139
1140 # We generate the diagrams now
1141 loopsuccessful, lcutdiaglist = \
1142 super(LoopAmplitude, self).generate_diagrams(True)
1143
1144 # Now get rid of all the previously defined l-cut particles.
1145 leg_to_remove=[leg for leg in self['process']['legs'] \
1146 if leg['loop_line']]
1147 for leg in leg_to_remove:
1148 self['process']['legs'].remove(leg)
1149
1150 # The correct L-cut type is specified
1151 for diag in lcutdiaglist:
1152 diag.set('type',part.get_pdg_code())
1153 self['loop_diagrams']+=lcutdiaglist
1154
1155 # Update the list of already employed L-cut particles such
1156 # that we never use them again in loop particles
1157 self.lcutpartemployed.append(part.get_pdg_code())
1158 self.lcutpartemployed.append(part.get_anti_pdg_code())
1159
1160 ldg_debug_info("#Diags generated w/ this L-cut particle",\
1161 len(lcutdiaglist))
1162 # Accordingly update the totloopsuccessful tag
1163 if loopsuccessful:
1164 totloopsuccessful=True
1165
1166 # Reset the l-cut particle list
1167 self.lcutpartemployed=[]
1168
1169 return totloopsuccessful
1170
1171
1173 """ Scan all born diagrams and add for each all the corresponding UV
1174 counterterms. It creates one LoopUVCTDiagram per born diagram and set
1175 of possible coupling_order (so that QCD and QED wavefunction corrections
1176 are not in the same LoopUVCTDiagram for example). Notice that this takes
1177 care only of the UV counterterm which factorize with the born and the
1178 other contributions like the UV mass renormalization are added in the
1179 function setLoopCTVertices"""
1180
1181 # return True
1182 # ============================================
1183 # Including the UVtree contributions
1184 # ============================================
1185
1186 # The following lists the UV interactions potentially giving UV counterterms
1187 # (The UVmass interactions is accounted for like the R2s)
1188 UVCTvertex_interactions = base_objects.InteractionList()
1189 for inter in self['process']['model']['interactions'].get_UV():
1190 if inter.is_UVtree() and len(inter['particles'])>1 and \
1191 inter.is_perturbating(self['process']['perturbation_couplings']) \
1192 and (set(inter['orders'].keys()).intersection(\
1193 set(self['process']['perturbation_couplings'])))!=set([]) and \
1194 (any([set(loop_parts).intersection(set(self['process']\
1195 ['forbidden_particles']))==set([]) for loop_parts in \
1196 inter.get('loop_particles')]) or \
1197 inter.get('loop_particles')==[[]]):
1198 UVCTvertex_interactions.append(inter)
1199
1200 # Temporarly give the tagging order 'UVCT_SPECIAL' to those interactions
1201 self['process']['model'].get('order_hierarchy')['UVCT_SPECIAL']=0
1202 self['process']['model'].get('coupling_orders').add('UVCT_SPECIAL')
1203 for inter in UVCTvertex_interactions:
1204 neworders=copy.copy(inter.get('orders'))
1205 neworders['UVCT_SPECIAL']=1
1206 inter.set('orders',neworders)
1207 # Refresh the model interaction dictionary while including those special
1208 # interactions
1209 self['process']['model'].actualize_dictionaries(useUVCT=True)
1210
1211 # Generate the UVCTdiagrams (born diagrams with 'UVCT_SPECIAL'=0 order
1212 # will be generated along)
1213 self['process']['orders']['UVCT_SPECIAL']=1
1214
1215 UVCTsuccessful, UVCTdiagrams = \
1216 super(LoopAmplitude, self).generate_diagrams(True)
1217
1218 for UVCTdiag in UVCTdiagrams:
1219 if UVCTdiag.get_order('UVCT_SPECIAL')==1:
1220 newUVCTDiag = loop_base_objects.LoopUVCTDiagram({\
1221 'vertices':copy.deepcopy(UVCTdiag['vertices'])})
1222 UVCTinter = newUVCTDiag.get_UVCTinteraction(self['process']['model'])
1223 newUVCTDiag.set('type',UVCTinter.get('type'))
1224 # This interaction counter-term must be accounted for as many times
1225 # as they are list of loop_particles defined and allowed for by
1226 # the process.
1227 newUVCTDiag.get('UVCT_couplings').append((len([1 for loop_parts \
1228 in UVCTinter.get('loop_particles') if set(loop_parts).intersection(\
1229 set(self['process']['forbidden_particles']))==set([])])) if
1230 loop_parts!=[[]] else 1)
1231 self['loop_UVCT_diagrams'].append(newUVCTDiag)
1232
1233 # Remove the additional order requirement in the born orders for this
1234 # process
1235 del self['process']['orders']['UVCT_SPECIAL']
1236 # Remove the fake order added to the selected UVCT interactions
1237 del self['process']['model'].get('order_hierarchy')['UVCT_SPECIAL']
1238 self['process']['model'].get('coupling_orders').remove('UVCT_SPECIAL')
1239 for inter in UVCTvertex_interactions:
1240 del inter.get('orders')['UVCT_SPECIAL']
1241 # Revert the model interaction dictionaries to default
1242 self['process']['model'].actualize_dictionaries(useUVCT=False)
1243
1244 # Set the correct orders to the loop_UVCT_diagrams
1245 for UVCTdiag in self['loop_UVCT_diagrams']:
1246 UVCTdiag.calculate_orders(self['process']['model'])
1247
1248 # ============================================
1249 # Wavefunction renormalization
1250 # ============================================
1251
1252 if not self['process']['has_born']:
1253 return UVCTsuccessful
1254
1255 # We now scan each born diagram, adding the necessary wavefunction
1256 # renormalizations
1257 for bornDiag in self['born_diagrams']:
1258 # This dictionary takes for keys the tuple
1259 # (('OrderName1',power1),...,('OrderNameN',powerN) representing
1260 # the power brought by the counterterm and the value is the
1261 # corresponding LoopUVCTDiagram.
1262 # The last entry is of the form ('EpsilonOrder', value) to put the
1263 # contribution of each different EpsilonOrder to different
1264 # LoopUVCTDiagrams.
1265 LoopUVCTDiagramsAdded={}
1266 for leg in self['process']['legs']:
1267 counterterm=self['process']['model'].get_particle(abs(leg['id'])).\
1268 get('counterterm')
1269 for key, value in counterterm.items():
1270 if key[0] in self['process']['perturbation_couplings']:
1271 for laurentOrder, CTCoupling in value.items():
1272 # Create the order key of the UV counterterm
1273 orderKey=[(key[0],2),]
1274 orderKey.sort()
1275 orderKey.append(('EpsilonOrder',-laurentOrder))
1276 CTCouplings=[CTCoupling for loop_parts in key[1] if
1277 set(loop_parts).intersection(set(self['process']\
1278 ['forbidden_particles']))==set([])]
1279 if CTCouplings!=[]:
1280 try:
1281 LoopUVCTDiagramsAdded[tuple(orderKey)].get(\
1282 'UVCT_couplings').extend(CTCouplings)
1283 except KeyError:
1284 LoopUVCTDiagramsAdded[tuple(orderKey)]=\
1285 loop_base_objects.LoopUVCTDiagram({\
1286 'vertices':copy.deepcopy(bornDiag['vertices']),
1287 'type':'UV'+('' if laurentOrder==0 else
1288 str(-laurentOrder)+'eps'),
1289 'UVCT_orders':{key[0]:2},
1290 'UVCT_couplings':CTCouplings})
1291
1292 for LoopUVCTDiagram in LoopUVCTDiagramsAdded.values():
1293 LoopUVCTDiagram.calculate_orders(self['process']['model'])
1294 self['loop_UVCT_diagrams'].append(LoopUVCTDiagram)
1295
1296 return UVCTsuccessful
1297
1299 """ Scan each loop diagram and recognizes what are the R2/UVmass
1300 CounterTerms associated to them """
1301 #return # debug
1302 # We first create a base dictionary with as a key (tupleA,tupleB). For
1303 # each R2/UV interaction, tuple B is the ordered tuple of the loop
1304 # particles (not anti-particles, so that the PDG is always positive!)
1305 # listed in its loop_particles attribute. Tuple A is the ordered tuple
1306 # of external particles PDGs. making up this interaction. The values of
1307 # the dictionary are a list of the interaction ID having the same key
1308 # above.
1309 CT_interactions = {}
1310 for inter in self['process']['model']['interactions']:
1311 if inter.is_UVmass() or inter.is_UVloop() or inter.is_R2() and \
1312 len(inter['particles'])>1 and inter.is_perturbating(\
1313 self['process']['perturbation_couplings']):
1314 # This interaction might have several possible loop particles
1315 # yielding the same CT. So we add this interaction ID
1316 # for each entry in the list loop_particles.
1317 for i, lparts in enumerate(inter['loop_particles']):
1318 keya=copy.copy(lparts)
1319 keya.sort()
1320 if inter.is_UVloop():
1321 # If it is a CT of type UVloop, then do not specify the
1322 # keya (leave it empty) but make sure the particles
1323 # specified as loop particles are not forbidden before
1324 # adding this CT to CT_interactions
1325 if (set(self['process']['forbidden_particles']) & \
1326 set(lparts)) != set([]):
1327 continue
1328 else:
1329 keya=[]
1330 keyb=[part.get_pdg_code() for part in inter['particles']]
1331 keyb.sort()
1332 key=(tuple(keyb),tuple(keya))
1333 # We keep track of 'i' (i.e. the position of the
1334 # loop_particle list in the inter['loop_particles']) so
1335 # that each coupling in a vertex of type 'UVloop' is
1336 # correctly accounted for since the keya is always replaced
1337 # by an empty list since the constraint on the loop particles
1338 # is simply that there is not corresponding forbidden
1339 # particles in the process definition and not that the
1340 # actual particle content of the loop generate matches.
1341 #
1342 # This can also happen with the type 'UVmass' or 'R2'
1343 # CTvertex ex1(
1344 # type='UVmass'
1345 # [...]
1346 # loop_particles=[[[d,g],[d,g]]])
1347 # Which is a bit silly but can happen and would mean that
1348 # we must account twice for the coupling associated to each
1349 # of these loop_particles.
1350 # One might imagine someone doing it with
1351 # loop_particles=[[[],[]]], for example, because he wanted
1352 # to get rid of the loop particle constraint for some reason.
1353 try:
1354 CT_interactions[key].append((inter['id'],i))
1355 except KeyError:
1356 CT_interactions[key]=[(inter['id'],i),]
1357
1358 # The dictionary CTmass_added keeps track of what are the CounterTerms of
1359 # type UVmass or R2 already added and prevents us from adding them again.
1360 # For instance, the fermion boxes with four external gluons exists in 6 copies
1361 # (with different crossings of the external legs each time) and the
1362 # corresponding R2 must be added only once. The key of this dictionary
1363 # characterizing the loop is (tupleA,tupleB). Tuple A is made from the
1364 # list of the ID of the external structures attached to this loop and
1365 # tuple B from list of the pdg of the particles building this loop.
1366
1367 # Notice that when a CT of type UVmass is specified with an empty
1368 # loop_particles attribute, then it means it must be added once for each
1369 # particle with a matching topology, irrespectively of the loop content.
1370 # Whenever added, such a CT is put in the dictionary CT_added with a key
1371 # having an empty tupleB.
1372 # Finally, because CT interactions of type UVloop do specify a
1373 # loop_particles attribute, but which serves only to be filtered against
1374 # particles forbidden in the process definition, they will also be added
1375 # with an empty tupleB.
1376 CT_added = {}
1377
1378 for diag in self['loop_diagrams']:
1379 # First build the key from this loop for the CT_interaction dictionary
1380 # (Searching Key) and the key for the CT_added dictionary (tracking Key)
1381 searchingKeyA=[]
1382 # Notice that searchingKeyB below also serves as trackingKeyB
1383 searchingKeyB=[]
1384 trackingKeyA=[]
1385 for tagElement in diag['canonical_tag']:
1386 for structID in tagElement[1]:
1387 trackingKeyA.append(structID)
1388 searchingKeyA.append(self['process']['model'].get_particle(\
1389 self['structure_repository'][structID]['binding_leg']['id']).\
1390 get_pdg_code())
1391 searchingKeyB.append(self['process']['model'].get_particle(\
1392 tagElement[0]).get('pdg_code'))
1393 searchingKeyA.sort()
1394 # We do not repeat particles present many times in the loop
1395 searchingKeyB=list(set(searchingKeyB))
1396 searchingKeyB.sort()
1397 trackingKeyA.sort()
1398 # I repeat, they are two kinds of keys:
1399 # searchingKey:
1400 # This serves to scan the CT interactions defined and then find
1401 # which ones match a given loop topology and particle.
1402 # trackingKey:
1403 # Once some CT vertices are identified to be a match for a loop,
1404 # the trackingKey is used in conjunction with the dictionary
1405 # CT_added to make sure that this CT has not already been included.
1406
1407 # Each of these two keys above, has the format
1408 # (tupleA, tupleB)
1409 # with tupleB being the loop_content and either contains the set of
1410 # loop particles PDGs of the interaction (for the searchingKey)
1411 # or of the loops already scanned (trackingKey). It can also be
1412 # empty when considering interactions of type UVmass or R2 which
1413 # have an empty loop_particle attribute or those of type UVloop.
1414 # TupleA is the set of external particle PDG (for the searchingKey)
1415 # and the unordered list of structID attached to the loop (for the
1416 # trackingKey)
1417 searchingKeySimple=(tuple(searchingKeyA),())
1418 searchingKeyLoopPart=(tuple(searchingKeyA),tuple(searchingKeyB))
1419 trackingKeySimple=(tuple(trackingKeyA),())
1420 trackingKeyLoopPart=(tuple(trackingKeyA),tuple(searchingKeyB))
1421 # Now we look for a CT which might correspond to this loop by looking
1422 # for its searchingKey in CT_interactions
1423
1424 # misc.sprint("I have the following CT_interactions=",CT_interactions)
1425 try:
1426 CTIDs=copy.copy(CT_interactions[searchingKeySimple])
1427 except KeyError:
1428 CTIDs=[]
1429 try:
1430 CTIDs.extend(copy.copy(CT_interactions[searchingKeyLoopPart]))
1431 except KeyError:
1432 pass
1433 if not CTIDs:
1434 continue
1435 # We have found some CT interactions corresponding to this loop
1436 # so we must make sure we have not included them already
1437 try:
1438 usedIDs=copy.copy(CT_added[trackingKeySimple])
1439 except KeyError:
1440 usedIDs=[]
1441 try:
1442 usedIDs.extend(copy.copy(CT_added[trackingKeyLoopPart]))
1443 except KeyError:
1444 pass
1445
1446 for CTID in CTIDs:
1447 # Make sure it has not been considered yet and that the loop
1448 # orders match
1449 if CTID not in usedIDs and diag.get_loop_orders(\
1450 self['process']['model'])==\
1451 self['process']['model']['interaction_dict'][CTID[0]]['orders']:
1452 # Create the amplitude vertex corresponding to this CT
1453 # and add it to the LoopDiagram treated.
1454 CTleglist = base_objects.LegList()
1455 for tagElement in diag['canonical_tag']:
1456 for structID in tagElement[1]:
1457 CTleglist.append(\
1458 self['structure_repository'][structID]['binding_leg'])
1459 CTVertex = base_objects.Vertex({'id':CTID[0], \
1460 'legs':CTleglist})
1461 diag['CT_vertices'].append(CTVertex)
1462 # Now add this CT vertex to the CT_added dictionary so that
1463 # we are sure it will not be double counted
1464 if self['process']['model']['interaction_dict'][CTID[0]]\
1465 ['loop_particles'][CTID[1]]==[] or \
1466 self['process']['model']['interaction_dict'][CTID[0]].\
1467 is_UVloop():
1468 try:
1469 CT_added[trackingKeySimple].append(CTID)
1470 except KeyError:
1471 CT_added[trackingKeySimple] = [CTID, ]
1472 else:
1473 try:
1474 CT_added[trackingKeyLoopPart].append(CTID)
1475 except KeyError:
1476 CT_added[trackingKeyLoopPart] = [CTID, ]
1477
1479 """ Return a LoopDiagram created."""
1480 return loop_base_objects.LoopDiagram({'vertices':vertexlist})
1481
1483 """ Returns a DGLoopLeg list instead of the default copy_leglist
1484 defined in base_objects.Amplitude """
1485
1486 dgloopleglist=base_objects.LegList()
1487 for leg in leglist:
1488 dgloopleglist.append(loop_base_objects.DGLoopLeg(leg))
1489
1490 return dgloopleglist
1491
1493 """ Overloaded here to convert back all DGLoopLegs into Legs. """
1494 for vertexlist in vertexdoublelist:
1495 for vertex in vertexlist:
1496 if not isinstance(vertex['legs'][0],loop_base_objects.DGLoopLeg):
1497 continue
1498 vertex['legs'][:]=[leg.convert_to_leg() for leg in \
1499 vertex['legs']]
1500 return True
1501
1503 """Create a set of new legs from the info given."""
1504
1505 looplegs=[leg for leg in legs if leg['loop_line']]
1506
1507 # Get rid of all vanishing tadpoles
1508 #Ease the access to the model
1509 model=self['process']['model']
1510 exlegs=[leg for leg in looplegs if leg['depth']==0]
1511 if(len(exlegs)==2):
1512 if(any([part['mass'].lower()=='zero' for pdg,part in model.get('particle_dict').items() if pdg==abs(exlegs[0]['id'])])):
1513 return []
1514
1515 # Correctly propagate the loopflow
1516 loopline=(len(looplegs)==1)
1517 mylegs = []
1518 for i, (leg_id, vert_id) in enumerate(leg_vert_ids):
1519 # We can now create the set of possible merged legs.
1520 # However, we make sure that its PDG is not in the list of
1521 # L-cut particles we already explored. If it is, we simply reject
1522 # the diagram.
1523 if not loopline or not (leg_id in self.lcutpartemployed):
1524 # Reminder: The only purpose of the "depth" flag is to get rid
1525 # of (some, not all) of the wave-function renormalization
1526 # already during diagram generation. We reckognize a wf
1527 # renormalization diagram as follows:
1528 if len(legs)==2 and len(looplegs)==2:
1529 # We have candidate
1530 depths=(looplegs[0]['depth'],looplegs[1]['depth'])
1531 if (0 in depths) and (-1 not in depths) and depths!=(0,0):
1532 # Check that the PDG of the outter particle in the
1533 # wavefunction renormalization bubble is equal to the
1534 # one of the inner particle.
1535 continue
1536
1537 # If depth is not 0 because of being an external leg and not
1538 # the propagated PDG, then we set it to -1 so that from that
1539 # point we are sure the diagram will not be reckognized as a
1540 # wave-function renormalization.
1541 depth=-1
1542 # When creating a loop leg from exactly two external legs, we
1543 # set the depth to the PDG of the external non-loop line.
1544 if len(legs)==2 and loopline and (legs[0]['depth'],\
1545 legs[1]['depth'])==(0,0):
1546 if not legs[0]['loop_line']:
1547 depth=legs[0]['id']
1548 else:
1549 depth=legs[1]['id']
1550 # In case of two point interactions among two same particle
1551 # we propagate the existing depth
1552 if len(legs)==1 and legs[0]['id']==leg_id:
1553 depth=legs[0]['depth']
1554 # In all other cases we set the depth to -1 since no
1555 # wave-function renormalization diagram can arise from this
1556 # side of the diagram construction.
1557
1558 mylegs.append((loop_base_objects.DGLoopLeg({'id':leg_id,
1559 'number':number,
1560 'state':state,
1561 'from_group':True,
1562 'depth': depth,
1563 'loop_line': loopline}),
1564 vert_id))
1565 return mylegs
1566
1568 """Allow for selection of vertex ids."""
1569
1570 looplegs=[leg for leg in legs if leg['loop_line']]
1571 nonlooplegs=[leg for leg in legs if not leg['loop_line']]
1572
1573 # Get rid of all vanishing tadpoles
1574 model=self['process']['model']
1575 exlegs=[leg for leg in looplegs if leg['depth']==0]
1576 if(len(exlegs)==2):
1577 if(any([part['mass'].lower()=='zero' for pdg,part in \
1578 model.get('particle_dict').items() if pdg==abs(exlegs[0]['id'])])):
1579 return []
1580
1581
1582 # Get rid of some wave-function renormalization diagrams already during
1583 # diagram generation already.In a similar manner as in get_combined_legs.
1584 if(len(legs)==3 and len(looplegs)==2):
1585 depths=(looplegs[0]['depth'],looplegs[1]['depth'])
1586 if (0 in depths) and (-1 not in depths) and depths!=(0,0):
1587 return []
1588
1589 return vert_ids
1590
1591 # Helper function
1592
1594 """ Filters the diagrams according to the constraints on the squared
1595 orders in argument and wether the process has a born or not. """
1596
1597 diagRef=base_objects.DiagramList()
1598 AllLoopDiagrams=base_objects.DiagramList(self['loop_diagrams']+\
1599 self['loop_UVCT_diagrams'])
1600
1601 AllBornDiagrams=base_objects.DiagramList(self['born_diagrams'])
1602 if self['process']['has_born']:
1603 diagRef=AllBornDiagrams
1604 else:
1605 diagRef=AllLoopDiagrams
1606
1607 sqorders_types=copy.copy(self['process'].get('sqorders_types'))
1608
1609 # The WEIGHTED order might have been automatically assigned to the
1610 # squared order constraints, so we must assign it a type if not specified
1611 if 'WEIGHTED' not in sqorders_types:
1612 sqorders_types['WEIGHTED']='<='
1613
1614 if len(diagRef)==0:
1615 # If no born contributes but they were supposed to ( in the
1616 # case of self['process']['has_born']=True) then it means that
1617 # the loop cannot be squared against anything and none should
1618 # contribute either. The squared order constraints are just too
1619 # tight for anything to contribute.
1620 AllLoopDiagrams = base_objects.DiagramList()
1621
1622
1623 # Start by filtering the loop diagrams
1624 AllLoopDiagrams = AllLoopDiagrams.apply_positive_sq_orders(diagRef,
1625 sq_order_constrains, sqorders_types)
1626 # And now the Born ones if there are any
1627 if self['process']['has_born']:
1628 # We consider both the Born*Born and Born*Loop squared terms here
1629 AllBornDiagrams = AllBornDiagrams.apply_positive_sq_orders(
1630 AllLoopDiagrams+AllBornDiagrams, sq_order_constrains, sqorders_types)
1631
1632 # Now treat the negative squared order constraint (at most one)
1633 neg_orders = [(order, value) for order, value in \
1634 sq_order_constrains.items() if value<0]
1635 if len(neg_orders)==1:
1636 neg_order, neg_value = neg_orders[0]
1637 # If there is a Born contribution, then the target order will
1638 # be computed over all Born*Born and Born*loop contributions
1639 if self['process']['has_born']:
1640 AllBornDiagrams, target_order =\
1641 AllBornDiagrams.apply_negative_sq_order(
1642 base_objects.DiagramList(AllLoopDiagrams+AllBornDiagrams),
1643 neg_order,neg_value,sqorders_types[neg_order])
1644 # Now we must filter the loop diagrams using to the target_order
1645 # computed above from the LO and NLO contributions
1646 AllLoopDiagrams = AllLoopDiagrams.apply_positive_sq_orders(
1647 diagRef,{neg_order:target_order},
1648 {neg_order:sqorders_types[neg_order]})
1649
1650 # If there is no Born, then the situation is completely analoguous
1651 # to the tree level case since it is simply Loop*Loop
1652 else:
1653 AllLoopDiagrams, target_order = \
1654 AllLoopDiagrams.apply_negative_sq_order(
1655 diagRef,neg_order,neg_value,sqorders_types[neg_order])
1656
1657 # Substitute the negative value to this positive one
1658 # (also in the backed up values in user_squared_orders so that
1659 # this change is permanent and we will still have access to
1660 # it at the output stage)
1661 self['process']['squared_orders'][neg_order]=target_order
1662 user_squared_orders[neg_order]=target_order
1663
1664 elif len(neg_orders)>1:
1665 raise MadGraph5Error('At most one negative squared order constraint'+\
1666 ' can be specified, not %s.'%str(neg_orders))
1667
1668 if self['process']['has_born']:
1669 self['born_diagrams'] = AllBornDiagrams
1670 self['loop_diagrams']=[diag for diag in AllLoopDiagrams if not \
1671 isinstance(diag,loop_base_objects.LoopUVCTDiagram)]
1672 self['loop_UVCT_diagrams']=[diag for diag in AllLoopDiagrams if \
1673 isinstance(diag,loop_base_objects.LoopUVCTDiagram)]
1674
1676 """ This is a helper function for order_diagrams_according_to_split_orders
1677 and intended to be used from LoopHelasAmplitude only"""
1678
1679 # The dictionary below has keys being the tuple (split_order<i>_values)
1680 # and values being diagram lists sharing the same split orders.
1681 diag_by_so = {}
1682
1683 for diag in diag_set:
1684 so_key = tuple([diag.get_order(order) for order in split_orders])
1685 try:
1686 diag_by_so[so_key].append(diag)
1687 except KeyError:
1688 diag_by_so[so_key]=base_objects.DiagramList([diag,])
1689
1690 so_keys = diag_by_so.keys()
1691 # Complete the order hierarchy by possibly missing defined order for
1692 # which we set the weight to zero by default (so that they are ignored).
1693 order_hierarchy = self.get('process').get('model').get('order_hierarchy')
1694 order_weights = copy.copy(order_hierarchy)
1695 for so in split_orders:
1696 if so not in order_hierarchy.keys():
1697 order_weights[so]=0
1698
1699 # Now order the keys of diag_by_so by the WEIGHT of the split_orders
1700 # (and only those, the orders not included in the split_orders do not
1701 # count for this ordering as they could be mixed in any given group).
1702 so_keys = sorted(so_keys, key = lambda elem: (sum([power*order_weights[\
1703 split_orders[i]] for i,power in enumerate(elem)])))
1704
1705 # Now put the diagram back, ordered this time, in diag_set
1706 diag_set[:] = []
1707 for so_key in so_keys:
1708 diag_set.extend(diag_by_so[so_key])
1709
1710
1712 """ Reorder the loop and Born diagrams (if any) in group of diagrams
1713 sharing the same coupling orders are put together and these groups are
1714 order in decreasing WEIGHTED orders.
1715 Notice that this function is only called for now by the
1716 LoopHelasMatrixElement instances at the output stage.
1717 """
1718
1719 # If no split order is present (unlikely since the 'corrected order'
1720 # normally is a split_order by default, then do nothing
1721 if len(split_orders)==0:
1722 return
1723
1724 self.order_diagram_set(self['born_diagrams'], split_orders)
1725 self.order_diagram_set(self['loop_diagrams'], split_orders)
1726 self.order_diagram_set(self['loop_UVCT_diagrams'], split_orders)
1727
1728 #===============================================================================
1729 # LoopMultiProcess
1730 #===============================================================================
1731 -class LoopMultiProcess(diagram_generation.MultiProcess):
1732 """LoopMultiProcess: MultiProcess with loop features.
1733 """
1734
1735 @classmethod
1737 """ Return the correct amplitude type according to the characteristics
1738 of the process proc """
1739 return LoopAmplitude({"process": proc},**opts)
1740
1741 #===============================================================================
1742 # LoopInducedMultiProcess
1743 #===============================================================================
1744 -class LoopInducedMultiProcess(diagram_generation.MultiProcess):
1745 """Special mode for the LoopInduced."""
1746
1747 @classmethod
1749 """ Return the correct amplitude type according to the characteristics of
1750 the process proc """
1751 return LoopAmplitude({"process": proc, 'has_born':False},**opts)
1752
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