Package madgraph :: Package core :: Module diagram_generation
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Source Code for Module madgraph.core.diagram_generation

   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. 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') 
32 33 34 -class NoDiagramException(InvalidCmd): pass
35
36 #=============================================================================== 37 # DiagramTag mother class 38 #=============================================================================== 39 40 -class DiagramTag(object):
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
62 - class DiagramTagError(Exception):
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 # wf_dict keeps track of the intermediate particles 71 leg_dict = {} 72 # Create the chain which will be the diagram tag 73 for vertex in diagram.get('vertices'): 74 # Only add incoming legs 75 legs = vertex.get('legs')[:-1] 76 lastvx = vertex == diagram.get('vertices')[-1] 77 if lastvx: 78 # If last vertex, all legs are incoming 79 legs = vertex.get('legs') 80 # Add links corresponding to the relevant legs 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 # Add vertex to leg_dict if not last one 89 if not lastvx: 90 leg_dict[vertex.get('legs')[-1].get('number')] = link 91 92 # The resulting link is the hypothetical result 93 self.tag = link 94 95 # Now make sure to find the central vertex in the diagram, 96 # defined by the longest leg being as short as possible 97 done = max([l.depth for l in self.tag.links]) == 0 98 while not done: 99 # Identify the longest chain in the tag 100 longest_chain = self.tag.links[0] 101 # Create a new link corresponding to moving one step 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 # Create a new final vertex in the direction of the longest link 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 # Switch to new tag, continue search 117 self.tag = other_link 118 else: 119 # We have found the central vertex 120 done = True
121
122 - def get_external_numbers(self):
123 """Get the order of external particles in this tag""" 124 125 return self.tag.get_external_numbers()
126
127 - def diagram_from_tag(self, model):
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 # Create the vertices, starting from the final vertex 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 177 178 @classmethod
179 - def legPDGs_from_vertex_id(cls, vertex_id,model):
180 """Returns the list of external PDGs of the interaction corresponding 181 to this vertex_id.""" 182 183 # In case we have to deal with a regular vertex, we return the list 184 # external PDGs as given by the model information on that integer 185 # vertex id. 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
193 - def leg_from_legs(cls,legs, vertex_id, model):
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 # Extract the resulting pdg code from the interaction pdgs 199 for pdg in [leg.get('id') for leg in legs]: 200 pdgs.remove(pdg) 201 202 assert len(pdgs) == 1 203 # Prepare the new leg properties 204 pdg = model.get_particle(pdgs[0]).get_anti_pdg_code() 205 number = min([l.get('number') for l in legs]) 206 # State is False for t-channel, True for s-channel 207 state = (len([l for l in legs if l.get('state') == False]) != 1) 208 # Note that this needs to be done before combining decay chains 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 243 244 @staticmethod
245 - def id_from_vertex_id(vertex_id):
246 """Return the numerical vertex id from a link.vertex_id""" 247 248 return vertex_id[0][0]
249 250 @staticmethod
251 - def loop_info_from_vertex_id(vertex_id):
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
259 - def reorder_permutation(perm, start_perm):
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 279 280 @staticmethod
281 - def vertex_id_from_vertex(vertex, last_vertex, model, ninitial):
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 # return (vertex.get('id'),(),{'PDGs':vertex.get('PDGs')}) 290 return ((vertex.get('id'),vertex.get('loop_tag')),(), 291 {'PDGs':vertex.get('PDGs')}) 292 else: 293 return ((vertex.get('id'),()),(),{})
294 295 @staticmethod
296 - def flip_vertex(new_vertex, old_vertex, links):
297 """Returns the default vertex flip: just the new_vertex""" 298 return new_vertex
299
300 - def __eq__(self, other):
301 """Equal if same tag""" 302 if type(self) != type(other): 303 return False 304 return self.tag == other.tag
305
306 - def __ne__(self, other):
307 return not self.__eq__(other)
308
309 - def __str__(self):
310 return str(self.tag)
311
312 - def __lt__(self, other):
313 return self.tag < other.tag
314
315 - def __gt__(self, other):
316 return self.tag > other.tag
317 318 __repr__ = __str__
319 405
406 #=============================================================================== 407 # Amplitude 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
415 - def default_setup(self):
416 """Default values for all properties""" 417 418 self['process'] = base_objects.Process() 419 self['diagrams'] = None 420 # has_mirror_process is True if the same process but with the 421 # two incoming particles interchanged has been generated 422 self['has_mirror_process'] = False
423
424 - def __init__(self, argument=None):
425 """Allow initialization with Process""" 426 if isinstance(argument, base_objects.Process): 427 super(Amplitude, self).__init__() 428 self.set('process', argument) 429 self.generate_diagrams() 430 elif argument != None: 431 # call the mother routine 432 super(Amplitude, self).__init__(argument) 433 else: 434 # call the mother routine 435 super(Amplitude, self).__init__()
436
437 - def filter(self, name, value):
438 """Filter for valid amplitude property values.""" 439 440 if name == 'process': 441 if not isinstance(value, base_objects.Process): 442 raise self.PhysicsObjectError, \ 443 "%s is not a valid Process object" % str(value) 444 if name == 'diagrams': 445 if not isinstance(value, base_objects.DiagramList): 446 raise self.PhysicsObjectError, \ 447 "%s is not a valid DiagramList object" % str(value) 448 if name == 'has_mirror_process': 449 if not isinstance(value, bool): 450 raise self.PhysicsObjectError, \ 451 "%s is not a valid boolean" % str(value) 452 return True
453
454 - def get(self, name):
455 """Get the value of the property name.""" 456 457 if name == 'diagrams' and self[name] == None: 458 # Have not yet generated diagrams for this process 459 if self['process']: 460 self.generate_diagrams() 461 462 return super(Amplitude, self).get(name)
463 # return Amplitude.__bases__[0].get(self, name) #return the mother routine 464 465
466 - def get_sorted_keys(self):
467 """Return diagram property names as a nicely sorted list.""" 468 469 return ['process', 'diagrams', 'has_mirror_process']
470
471 - def get_number_of_diagrams(self):
472 """Returns number of diagrams for this amplitude""" 473 return len(self.get('diagrams'))
474
475 - def get_amplitudes(self):
476 """Return an AmplitudeList with just this amplitude. 477 Needed for DecayChainAmplitude.""" 478 479 return AmplitudeList([self])
480
481 - def nice_string(self, indent=0):
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
486 - def nice_string_processes(self, indent=0):
487 """Returns a nicely formatted string of the amplitude process.""" 488 return self.get('process').nice_string(indent)
489
490 - def get_ninitial(self):
491 """Returns the number of initial state particles in the process.""" 492 return self.get('process').get_ninitial()
493
494 - def has_loop_process(self):
495 """ Returns wether this amplitude has a loop process.""" 496 497 return self.get('process').get('perturbation_couplings')
498
499 - def generate_diagrams(self, returndiag=False, diagram_filter=False):
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 # Make sure orders is the minimum of orders and overall_orders 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 # First check that the number of fermions is even 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 # Then check same number of incoming and outgoing fermions (if 576 # no Majorana particles in model) 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 # Finally check that charge (conserve by all interactions) of the process 587 #is globally conserve for this process. 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 # Give numbers to legs in process 618 for i in range(0, len(process.get('legs'))): 619 # Make sure legs are unique 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 # Copy leglist from process, so we can flip leg identities 626 # without affecting the original process 627 leglist = self.copy_leglist(process.get('legs')) 628 629 for leg in leglist: 630 631 # For the first step, ensure the tag from_group 632 # is true for all legs 633 leg.set('from_group', True) 634 635 # Need to flip part-antipart for incoming particles, 636 # so they are all outgoing 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 # Calculate the maximal multiplicity of n-1>1 configurations 642 # to restrict possible leg combinations 643 max_multi_to1 = max([len(key) for key in \ 644 model.get('ref_dict_to1').keys()]) 645 646 647 # Reduce the leg list and return the corresponding 648 # list of vertices 649 650 # For decay processes, generate starting from final-state 651 # combined only as the last particle. This allows to use these 652 # in decay chains later on. 653 is_decay_proc = process.get_ninitial() == 1 654 if is_decay_proc: 655 part = model.get('particle_dict')[leglist[0].get('id')] 656 # For decay chain legs, we want everything to combine to 657 # the initial leg. This is done by only allowing the 658 # initial leg to combine as a final identity. 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 # Need to set initial leg from_group to None, to make sure 662 # it can only be combined at the end. 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 #In LoopAmplitude the function below is overloaded such that it 677 #converts back all DGLoopLegs to Legs. In the default tree-level 678 #diagram generation, this does nothing. 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 # Record whether or not we failed generation before required 686 # s-channel propagators are taken into account 687 failed_crossing = not res 688 689 # Required s-channels is a list of id-lists. Select the 690 # diagrams where all required s-channel propagators in any of 691 # the lists are present (i.e., the different lists correspond 692 # to "or", while the elements of the list correspond to 693 # "and"). 694 if process.get('required_s_channels') and \ 695 process.get('required_s_channels')[0]: 696 # We shouldn't look at the last vertex in each diagram, 697 # since that is the n->0 vertex 698 lastvx = -1 699 # For decay chain processes, there is an "artificial" 700 # extra vertex corresponding to particle 1=1, so we need 701 # to exclude the two last vertexes. 702 if is_decay_proc: lastvx = -2 703 ninitial = len(filter(lambda leg: leg.get('state') == False, 704 process.get('legs'))) 705 # Check required s-channels for each list in required_s_channels 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 # Add diagrams only if not already in res 717 res.extend([diag for diag in res_diags if diag not in res]) 718 719 # Remove all diagrams with a "double" forbidden s-channel propagator 720 # is present. 721 # Note that we shouldn't look at the last vertex in each 722 # diagram, since that is the n->0 vertex 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 # split since we need to avoid that the initial particle is forbidden 736 # as well. 737 newres= [] 738 for diagram in res: 739 leg1 = 1 740 #check the latest vertex to see if the leg 1 is inside if it 741 #is we need to inverse the look-up and allow the first s-channel 742 # of the associate particles. 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 # Mark forbidden (onshell) s-channel propagators, to forbid onshell 764 # generation. 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 # Use onshell = False to indicate that this s-channel is forbidden 777 newleg = copy.copy(vert.get('legs').pop(-1)) 778 newleg.set('onshell', False) 779 vert.get('legs').append(newleg) 780 781 # Set actual coupling orders for each diagram 782 for diagram in res: 783 diagram.calculate_orders(model) 784 785 # Filter the diagrams according to the squared coupling order 786 # constraints and possible the negative one. Remember that OrderName=-n 787 # means that the user wants to include everything up to the N^(n+1)LO 788 # contribution in that order and at most one order can be restricted 789 # in this way. We shall do this only if the diagrams are not asked to 790 # be returned, as it is the case for NLO because it this case the 791 # interference are not necessarily among the diagrams generated here only. 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 # Replace final id=0 vertex if necessary 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 # Need to "glue together" last and next-to-last 804 # vertex, by replacing the (incoming) last leg of the 805 # next-to-last vertex with the (outgoing) leg in the 806 # last vertex 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 # Reset onshell in case we have forbidden s-channels 815 if lastleg.get('onshell') == False: 816 lastleg.set('onshell', None) 817 # Replace the last leg of nexttolastvertex 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 # Trim down number of legs and vertices used to save memory 827 self.trim_diagrams(diaglist=res) 828 829 # Sort process legs according to leg number 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 # Set diagrams to res if not asked to be returned 836 if not returndiag: 837 self['diagrams'] = res 838 return not failed_crossing 839 else: 840 return not failed_crossing, res
841
842 - def apply_squared_order_constraints(self, diag_list):
843 """Applies the user specified squared order constraints on the diagram 844 list in argument.""" 845 846 res = copy.copy(diag_list) 847 848 # Apply the filtering on constrained amplitude (== and >) 849 # No need to iterate on this one 850 for name, (value, operator) in self['process'].get('constrained_orders').items(): 851 res.filter_constrained_orders(name, value, operator) 852 853 # Iterate the filtering since the applying the constraint on one 854 # type of coupling order can impact what the filtering on a previous 855 # one (relevant for the '==' type of constraint). 856 while True: 857 new_res = res.apply_positive_sq_orders(res, 858 self['process'].get('squared_orders'), 859 self['process']['sqorders_types']) 860 # Exit condition 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 # Actualizing the list of diagram for the next iteration 867 res = new_res 868 869 870 871 # Now treat the negative squared order constraint (at most one) 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 # Now check any negative order constraint 877 res, target_order = res.apply_negative_sq_order(res, neg_order,\ 878 neg_value, self['process']['sqorders_types'][neg_order]) 879 # Substitute the negative value to this positive one so that 880 # the resulting computed constraints appears in the print out 881 # and at the output stage we no longer have to deal with 882 # negative valued target orders 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
890 - def apply_user_filter(self, diag_list):
891 """Applies the user specified squared order constraints on the diagram 892 list in argument.""" 893 894 if True: 895 try: 896 from PLUGIN.user_filter import remove_diag 897 except ImportError: 898 raise MadGraph5Error, 'user filter required to be defined in PLUGIN/user_filter.py with the function remove_diag(ONEDIAG) which returns True if the daigram has to be removed' 899 else: 900 #example and simple tests 901 def remove_diag(diag): 902 for vertex in diag['vertices']: #last 903 if vertex['id'] == 0: #special final vertex 904 continue 905 if vertex['legs'][-1]['number'] < 3: #this means T-channel 906 if abs(vertex['legs'][-1]['id']) <6: 907 return True 908 return False
909 910 res = diag_list.__class__() 911 nb_removed = 0 912 for diag in diag_list: 913 if remove_diag(diag): 914 nb_removed +=1 915 else: 916 res.append(diag) 917 918 if nb_removed: 919 logger.warning('Diagram filter is ON and removed %s diagrams for this subprocess.' % nb_removed) 920 921 return res
922 923 924
925 - def create_diagram(self, vertexlist):
926 """ Return a Diagram created from the vertex list. This function can be 927 overloaded by daughter classes.""" 928 return base_objects.Diagram({'vertices':vertexlist})
929
930 - def convert_dgleg_to_leg(self, vertexdoublelist):
931 """ In LoopAmplitude, it converts back all DGLoopLegs into Legs. 932 In Amplitude, there is nothing to do. """ 933 934 return True
935
936 - def copy_leglist(self, legs):
937 """ Simply returns a copy of the leg list. This function is 938 overloaded in LoopAmplitude so that a DGLoopLeg list is returned. 939 The DGLoopLeg has some additional parameters only useful during 940 loop diagram generation""" 941 942 return base_objects.LegList(\ 943 [ copy.copy(leg) for leg in legs ])
944
945 - def reduce_leglist(self, curr_leglist, max_multi_to1, ref_dict_to0, 946 is_decay_proc = False, coupling_orders = None):
947 """Recursive function to reduce N LegList to N-1 948 For algorithm, see doc for generate_diagrams. 949 """ 950 951 # Result variable which is a list of lists of vertices 952 # to be added 953 res = [] 954 955 # Stop condition. If LegList is None, that means that this 956 # diagram must be discarded 957 if curr_leglist is None: 958 return None 959 960 # Extract ref dict information 961 model = self.get('process').get('model') 962 ref_dict_to1 = self.get('process').get('model').get('ref_dict_to1') 963 964 965 # If all legs can be combined in one single vertex, add this 966 # vertex to res and continue. 967 # Special treatment for decay chain legs 968 969 if curr_leglist.can_combine_to_0(ref_dict_to0, is_decay_proc): 970 # Extract the interaction id associated to the vertex 971 972 vertex_ids = self.get_combined_vertices(curr_leglist, 973 copy.copy(ref_dict_to0[tuple(sorted([leg.get('id') for \ 974 leg in curr_leglist]))])) 975 976 final_vertices = [base_objects.Vertex({'legs':curr_leglist, 977 'id':vertex_id}) for \ 978 vertex_id in vertex_ids] 979 # Check for coupling orders. If orders < 0, skip vertex 980 for final_vertex in final_vertices: 981 if self.reduce_orders(coupling_orders, model, 982 [final_vertex.get('id')]) != False: 983 res.append([final_vertex]) 984 # Stop condition 2: if the leglist contained exactly two particles, 985 # return the result, if any, and stop. 986 if len(curr_leglist) == 2: 987 if res: 988 return res 989 else: 990 return None 991 992 # Create a list of all valid combinations of legs 993 comb_lists = self.combine_legs(curr_leglist, 994 ref_dict_to1, max_multi_to1) 995 996 # Create a list of leglists/vertices by merging combinations 997 leg_vertex_list = self.merge_comb_legs(comb_lists, ref_dict_to1) 998 999 # Consider all the pairs 1000 for leg_vertex_tuple in leg_vertex_list: 1001 1002 # Remove forbidden particles 1003 if self.get('process').get('forbidden_particles') and \ 1004 any([abs(vertex.get('legs')[-1].get('id')) in \ 1005 self.get('process').get('forbidden_particles') \ 1006 for vertex in leg_vertex_tuple[1]]): 1007 continue 1008 1009 # Check for coupling orders. If couplings < 0, skip recursion. 1010 new_coupling_orders = self.reduce_orders(coupling_orders, 1011 model, 1012 [vertex.get('id') for vertex in \ 1013 leg_vertex_tuple[1]]) 1014 if new_coupling_orders == False: 1015 # Some coupling order < 0 1016 continue 1017 1018 # This is where recursion happens 1019 # First, reduce again the leg part 1020 reduced_diagram = self.reduce_leglist(leg_vertex_tuple[0], 1021 max_multi_to1, 1022 ref_dict_to0, 1023 is_decay_proc, 1024 new_coupling_orders) 1025 # If there is a reduced diagram 1026 if reduced_diagram: 1027 vertex_list_list = [list(leg_vertex_tuple[1])] 1028 vertex_list_list.append(reduced_diagram) 1029 expanded_list = expand_list_list(vertex_list_list) 1030 res.extend(expanded_list) 1031 1032 return res
1033
1034 - def reduce_orders(self, coupling_orders, model, vertex_id_list):
1035 """Return False if the coupling orders for any coupling is < 1036 0, otherwise return the new coupling orders with the vertex 1037 orders subtracted. If coupling_orders is not given, return 1038 None (which counts as success). 1039 WEIGHTED is a special order, which corresponds to the sum of 1040 order hierarchies for the couplings. 1041 We ignore negative constraints as these cannot be taken into 1042 account on the fly but only after generation.""" 1043 1044 if not coupling_orders: 1045 return None 1046 1047 present_couplings = copy.copy(coupling_orders) 1048 for id in vertex_id_list: 1049 # Don't check for identity vertex (id = 0) 1050 if not id: 1051 continue 1052 inter = model.get("interaction_dict")[id] 1053 for coupling in inter.get('orders').keys(): 1054 # Note that we don't consider a missing coupling as a 1055 # constraint 1056 if coupling in present_couplings and \ 1057 present_couplings[coupling]>=0: 1058 # Reduce the number of couplings that are left 1059 present_couplings[coupling] -= \ 1060 inter.get('orders')[coupling] 1061 if present_couplings[coupling] < 0: 1062 # We have too many couplings of this type 1063 return False 1064 # Now check for WEIGHTED, i.e. the sum of coupling hierarchy values 1065 if 'WEIGHTED' in present_couplings and \ 1066 present_couplings['WEIGHTED']>=0: 1067 weight = sum([model.get('order_hierarchy')[c]*n for \ 1068 (c,n) in inter.get('orders').items()]) 1069 present_couplings['WEIGHTED'] -= weight 1070 if present_couplings['WEIGHTED'] < 0: 1071 # Total coupling weight too large 1072 return False 1073 1074 return present_couplings
1075
1076 - def combine_legs(self, list_legs, ref_dict_to1, max_multi_to1):
1077 """Recursive function. Take a list of legs as an input, with 1078 the reference dictionary n-1->1, and output a list of list of 1079 tuples of Legs (allowed combinations) and Legs (rest). Algorithm: 1080 1081 1. Get all n-combinations from list [123456]: [12],..,[23],..,[123],.. 1082 1083 2. For each combination, say [34]. Check if combination is valid. 1084 If so: 1085 1086 a. Append [12[34]56] to result array 1087 1088 b. Split [123456] at index(first element in combination+1), 1089 i.e. [12],[456] and subtract combination from second half, 1090 i.e.: [456]-[34]=[56]. Repeat from 1. with this array 1091 1092 3. Take result array from call to 1. (here, [[56]]) and append 1093 (first half in step b - combination) + combination + (result 1094 from 1.) = [12[34][56]] to result array 1095 1096 4. After appending results from all n-combinations, return 1097 resulting array. Example, if [13] and [45] are valid 1098 combinations: 1099 [[[13]2456],[[13]2[45]6],[123[45]6]] 1100 """ 1101 1102 res = [] 1103 1104 # loop over possible combination lengths (+1 is for range convention!) 1105 for comb_length in range(2, max_multi_to1 + 1): 1106 1107 # Check the considered length is not longer than the list length 1108 if comb_length > len(list_legs): 1109 return res 1110 1111 # itertools.combinations returns all possible combinations 1112 # of comb_length elements from list_legs 1113 for comb in itertools.combinations(list_legs, comb_length): 1114 1115 # Check if the combination is valid 1116 if base_objects.LegList(comb).can_combine_to_1(ref_dict_to1): 1117 1118 # Identify the rest, create a list [comb,rest] and 1119 # add it to res 1120 res_list = copy.copy(list_legs) 1121 for leg in comb: 1122 res_list.remove(leg) 1123 res_list.insert(list_legs.index(comb[0]), comb) 1124 res.append(res_list) 1125 1126 # Now, deal with cases with more than 1 combination 1127 1128 # First, split the list into two, according to the 1129 # position of the first element in comb, and remove 1130 # all elements form comb 1131 res_list1 = list_legs[0:list_legs.index(comb[0])] 1132 res_list2 = list_legs[list_legs.index(comb[0]) + 1:] 1133 for leg in comb[1:]: 1134 res_list2.remove(leg) 1135 1136 # Create a list of type [comb,rest1,rest2(combined)] 1137 res_list = res_list1 1138 res_list.append(comb) 1139 # This is where recursion actually happens, 1140 # on the second part 1141 for item in self.combine_legs(res_list2, 1142 ref_dict_to1, 1143 max_multi_to1): 1144 final_res_list = copy.copy(res_list) 1145 final_res_list.extend(item) 1146 res.append(final_res_list) 1147 1148 return res
1149 1150
1151 - def merge_comb_legs(self, comb_lists, ref_dict_to1):
1152 """Takes a list of allowed leg combinations as an input and returns 1153 a set of lists where combinations have been properly replaced 1154 (one list per element in the ref_dict, so that all possible intermediate 1155 particles are included). For each list, give the list of vertices 1156 corresponding to the executed merging, group the two as a tuple. 1157 """ 1158 1159 res = [] 1160 1161 for comb_list in comb_lists: 1162 1163 reduced_list = [] 1164 vertex_list = [] 1165 1166 for entry in comb_list: 1167 1168 # Act on all leg combinations 1169 if isinstance(entry, tuple): 1170 1171 # Build the leg object which will replace the combination: 1172 # 1) leg ids is as given in the ref_dict 1173 leg_vert_ids = copy.copy(ref_dict_to1[\ 1174 tuple(sorted([leg.get('id') for leg in entry]))]) 1175 # 2) number is the minimum of leg numbers involved in the 1176 # combination 1177 number = min([leg.get('number') for leg in entry]) 1178 # 3) state is final, unless there is exactly one initial 1179 # state particle involved in the combination -> t-channel 1180 if len(filter(lambda leg: leg.get('state') == False, 1181 entry)) == 1: 1182 state = False 1183 else: 1184 state = True 1185 # 4) from_group is True, by definition 1186 1187 # Create and add the object. This is done by a 1188 # separate routine, to allow overloading by 1189 # daughter classes 1190 new_leg_vert_ids = [] 1191 if leg_vert_ids: 1192 new_leg_vert_ids = self.get_combined_legs(entry, 1193 leg_vert_ids, 1194 number, 1195 state) 1196 1197 reduced_list.append([l[0] for l in new_leg_vert_ids]) 1198 1199 1200 # Create and add the corresponding vertex 1201 # Extract vertex ids corresponding to the various legs 1202 # in mylegs 1203 vlist = base_objects.VertexList() 1204 for (myleg, vert_id) in new_leg_vert_ids: 1205 # Start with the considered combination... 1206 myleglist = base_objects.LegList(list(entry)) 1207 # ... and complete with legs after reducing 1208 myleglist.append(myleg) 1209 # ... and consider the correct vertex id 1210 vlist.append(base_objects.Vertex( 1211 {'legs':myleglist, 1212 'id':vert_id})) 1213 1214 vertex_list.append(vlist) 1215 1216 # If entry is not a combination, switch the from_group flag 1217 # and add it 1218 else: 1219 cp_entry = copy.copy(entry) 1220 # Need special case for from_group == None; this 1221 # is for initial state leg of decay chain process 1222 # (see Leg.can_combine_to_0) 1223 if cp_entry.get('from_group') != None: 1224 cp_entry.set('from_group', False) 1225 reduced_list.append(cp_entry) 1226 1227 # Flatten the obtained leg and vertex lists 1228 flat_red_lists = expand_list(reduced_list) 1229 flat_vx_lists = expand_list(vertex_list) 1230 1231 # Combine the two lists in a list of tuple 1232 for i in range(0, len(flat_vx_lists)): 1233 res.append((base_objects.LegList(flat_red_lists[i]), \ 1234 base_objects.VertexList(flat_vx_lists[i]))) 1235 1236 return res
1237
1238 - def get_combined_legs(self, legs, leg_vert_ids, number, state):
1239 """Create a set of new legs from the info given. This can be 1240 overloaded by daughter classes.""" 1241 1242 mylegs = [(base_objects.Leg({'id':leg_id, 1243 'number':number, 1244 'state':state, 1245 'from_group':True}), 1246 vert_id)\ 1247 for leg_id, vert_id in leg_vert_ids] 1248 1249 return mylegs
1250
1251 - def get_combined_vertices(self, legs, vert_ids):
1252 """Allow for selection of vertex ids. This can be 1253 overloaded by daughter classes.""" 1254 1255 return vert_ids
1256
1257 - def trim_diagrams(self, decay_ids=[], diaglist=None):
1258 """Reduce the number of legs and vertices used in memory. 1259 When called by a diagram generation initiated by LoopAmplitude, 1260 this function should not trim the diagrams in the attribute 'diagrams' 1261 but rather a given list in the 'diaglist' argument.""" 1262 1263 legs = [] 1264 vertices = [] 1265 1266 if diaglist is None: 1267 diaglist=self.get('diagrams') 1268 1269 # Flag decaying legs in the core process by onshell = True 1270 process = self.get('process') 1271 for leg in process.get('legs'): 1272 if leg.get('state') and leg.get('id') in decay_ids: 1273 leg.set('onshell', True) 1274 1275 for diagram in diaglist: 1276 # Keep track of external legs (leg numbers already used) 1277 leg_external = set() 1278 for ivx, vertex in enumerate(diagram.get('vertices')): 1279 for ileg, leg in enumerate(vertex.get('legs')): 1280 # Ensure that only external legs get decay flag 1281 if leg.get('state') and leg.get('id') in decay_ids and \ 1282 leg.get('number') not in leg_external: 1283 # Use onshell to indicate decaying legs, 1284 # i.e. legs that have decay chains 1285 leg = copy.copy(leg) 1286 leg.set('onshell', True) 1287 try: 1288 index = legs.index(leg) 1289 except ValueError: 1290 vertex.get('legs')[ileg] = leg 1291 legs.append(leg) 1292 else: # Found a leg 1293 vertex.get('legs')[ileg] = legs[index] 1294 leg_external.add(leg.get('number')) 1295 try: 1296 index = vertices.index(vertex) 1297 diagram.get('vertices')[ivx] = vertices[index] 1298 except ValueError: 1299 vertices.append(vertex)
1300
1301 #=============================================================================== 1302 # AmplitudeList 1303 #=============================================================================== 1304 -class AmplitudeList(base_objects.PhysicsObjectList):
1305 """List of Amplitude objects 1306 """ 1307
1308 - def has_any_loop_process(self):
1309 """ Check the content of all processes of the amplitudes in this list to 1310 see if there is any which defines perturbation couplings. """ 1311 1312 for amp in self: 1313 if amp.has_loop_process(): 1314 return True
1315
1316 - def is_valid_element(self, obj):
1317 """Test if object obj is a valid Amplitude for the list.""" 1318 1319 return isinstance(obj, Amplitude)
1320
1321 #=============================================================================== 1322 # DecayChainAmplitude 1323 #=============================================================================== 1324 -class DecayChainAmplitude(Amplitude):
1325 """A list of amplitudes + a list of decay chain amplitude lists; 1326 corresponding to a ProcessDefinition with a list of decay chains 1327 """ 1328
1329 - def default_setup(self):
1330 """Default values for all properties""" 1331 1332 self['amplitudes'] = AmplitudeList() 1333 self['decay_chains'] = DecayChainAmplitudeList()
1334
1335 - def __init__(self, argument = None, collect_mirror_procs = False, 1336 ignore_six_quark_processes = False, loop_filter=None, diagram_filter=False):
1337 """Allow initialization with Process and with ProcessDefinition""" 1338 1339 if isinstance(argument, base_objects.Process): 1340 super(DecayChainAmplitude, self).__init__() 1341 from madgraph.loop.loop_diagram_generation import LoopMultiProcess 1342 if argument['perturbation_couplings']: 1343 MultiProcessClass=LoopMultiProcess 1344 else: 1345 MultiProcessClass=MultiProcess 1346 if isinstance(argument, base_objects.ProcessDefinition): 1347 self['amplitudes'].extend(\ 1348 MultiProcessClass.generate_multi_amplitudes(argument, 1349 collect_mirror_procs, 1350 ignore_six_quark_processes, 1351 loop_filter=loop_filter, 1352 diagram_filter=diagram_filter)) 1353 else: 1354 self['amplitudes'].append(\ 1355 MultiProcessClass.get_amplitude_from_proc(argument, 1356 loop_filter=loop_filter, 1357 diagram_filter=diagram_filter)) 1358 # Clean decay chains from process, since we haven't 1359 # combined processes with decay chains yet 1360 process = copy.copy(self.get('amplitudes')[0].get('process')) 1361 process.set('decay_chains', base_objects.ProcessList()) 1362 self['amplitudes'][0].set('process', process) 1363 1364 for process in argument.get('decay_chains'): 1365 if process.get('perturbation_couplings'): 1366 raise MadGraph5Error,\ 1367 "Decay processes can not be perturbed" 1368 process.set('overall_orders', argument.get('overall_orders')) 1369 if not process.get('is_decay_chain'): 1370 process.set('is_decay_chain',True) 1371 if not process.get_ninitial() == 1: 1372 raise InvalidCmd,\ 1373 "Decay chain process must have exactly one" + \ 1374 " incoming particle" 1375 self['decay_chains'].append(\ 1376 DecayChainAmplitude(process, collect_mirror_procs, 1377 ignore_six_quark_processes)) 1378 1379 # Flag decaying legs in the core diagrams by onshell = True 1380 decay_ids = sum([[a.get('process').get('legs')[0].get('id') \ 1381 for a in dec.get('amplitudes')] for dec in \ 1382 self['decay_chains']], []) 1383 decay_ids = set(decay_ids) 1384 for amp in self['amplitudes']: 1385 amp.trim_diagrams(decay_ids) 1386 1387 # Check that all decay ids are present in at least some process 1388 for amp in self['amplitudes']: 1389 for l in amp.get('process').get('legs'): 1390 if l.get('id') in decay_ids: 1391 decay_ids.remove(l.get('id')) 1392 1393 if decay_ids: 1394 model = amp.get('process').get('model') 1395 names = [model.get_particle(id).get('name') for id in decay_ids] 1396 1397 logger.warning( 1398 "$RED Decay without corresponding particle in core process found.\n" + \ 1399 "Decay information for particle(s) %s is discarded.\n" % ','.join(names) + \ 1400 "Please check your process definition carefully. \n" + \ 1401 "This warning usually means that you forgot parentheses in presence of subdecay.\n" + \ 1402 "Example of correct syntax: p p > t t~, ( t > w+ b, w+ > l+ vl)") 1403 1404 # Remove unused decays from the process list 1405 for dc in reversed(self['decay_chains']): 1406 for a in reversed(dc.get('amplitudes')): 1407 # Remove the amplitudes from this decay chain 1408 if a.get('process').get('legs')[0].get('id') in decay_ids: 1409 dc.get('amplitudes').remove(a) 1410 if not dc.get('amplitudes'): 1411 # If no amplitudes left, remove the decay chain 1412 self['decay_chains'].remove(dc) 1413 1414 # Finally, write a fat warning if any decay process has 1415 # the decaying particle (or its antiparticle) in the final state 1416 bad_procs = [] 1417 for dc in self['decay_chains']: 1418 for amp in dc.get('amplitudes'): 1419 legs = amp.get('process').get('legs') 1420 fs_parts = [abs(l.get('id')) for l in legs if 1421 l.get('state')] 1422 is_part = [l.get('id') for l in legs if not 1423 l.get('state')][0] 1424 if abs(is_part) in fs_parts: 1425 bad_procs.append(amp.get('process')) 1426 1427 if bad_procs: 1428 logger.warning( 1429 "$RED Decay(s) with particle decaying to itself:\n" + \ 1430 '\n'.join([p.nice_string() for p in bad_procs]) + \ 1431 "\nPlease check your process definition carefully. \n") 1432 1433 1434 elif argument != None: 1435 # call the mother routine 1436 super(DecayChainAmplitude, self).__init__(argument) 1437 else: 1438 # call the mother routine 1439 super(DecayChainAmplitude, self).__init__()
1440
1441 - def filter(self, name, value):
1442 """Filter for valid amplitude property values.""" 1443 1444 if name == 'amplitudes': 1445 if not isinstance(value, AmplitudeList): 1446 raise self.PhysicsObjectError, \ 1447 "%s is not a valid AmplitudeList" % str(value) 1448 if name == 'decay_chains': 1449 if not isinstance(value, DecayChainAmplitudeList): 1450 raise self.PhysicsObjectError, \ 1451 "%s is not a valid DecayChainAmplitudeList object" % \ 1452 str(value) 1453 return True
1454
1455 - def get_sorted_keys(self):
1456 """Return diagram property names as a nicely sorted list.""" 1457 1458 return ['amplitudes', 'decay_chains']
1459 1460 # Helper functions 1461
1462 - def get_number_of_diagrams(self):
1463 """Returns number of diagrams for this amplitude""" 1464 return sum(len(a.get('diagrams')) for a in self.get('amplitudes')) \ 1465 + sum(d.get_number_of_diagrams() for d in \ 1466 self.get('decay_chains'))
1467
1468 - def nice_string(self, indent = 0):
1469 """Returns a nicely formatted string of the amplitude content.""" 1470 mystr = "" 1471 for amplitude in self.get('amplitudes'): 1472 mystr = mystr + amplitude.nice_string(indent) + "\n" 1473 1474 if self.get('decay_chains'): 1475 mystr = mystr + " " * indent + "Decays:\n" 1476 for dec in self.get('decay_chains'): 1477 mystr = mystr + dec.nice_string(indent + 2) + "\n" 1478 1479 return mystr[:-1]
1480
1481 - def nice_string_processes(self, indent = 0):
1482 """Returns a nicely formatted string of the amplitude processes.""" 1483 mystr = "" 1484 for amplitude in self.get('amplitudes'): 1485 mystr = mystr + amplitude.nice_string_processes(indent) + "\n" 1486 1487 if self.get('decay_chains'): 1488 mystr = mystr + " " * indent + "Decays:\n" 1489 for dec in self.get('decay_chains'): 1490 mystr = mystr + dec.nice_string_processes(indent + 2) + "\n" 1491 1492 return mystr[:-1]
1493
1494 - def get_ninitial(self):
1495 """Returns the number of initial state particles in the process.""" 1496 return self.get('amplitudes')[0].get('process').get_ninitial()
1497
1498 - def get_decay_ids(self):
1499 """Returns a set of all particle ids for which a decay is defined""" 1500 1501 decay_ids = [] 1502 1503 # Get all amplitudes for the decay processes 1504 for amp in sum([dc.get('amplitudes') for dc \ 1505 in self['decay_chains']], []): 1506 # For each amplitude, find the initial state leg 1507 decay_ids.append(amp.get('process').get_initial_ids()[0]) 1508 1509 # Return a list with unique ids 1510 return list(set(decay_ids))
1511
1512 - def has_loop_process(self):
1513 """ Returns wether this amplitude has a loop process.""" 1514 return self['amplitudes'].has_any_loop_process()
1515
1516 - def get_amplitudes(self):
1517 """Recursive function to extract all amplitudes for this process""" 1518 1519 amplitudes = AmplitudeList() 1520 1521 amplitudes.extend(self.get('amplitudes')) 1522 for decay in self.get('decay_chains'): 1523 amplitudes.extend(decay.get_amplitudes()) 1524 1525 return amplitudes
1526
1527 1528 #=============================================================================== 1529 # DecayChainAmplitudeList 1530 #=============================================================================== 1531 -class DecayChainAmplitudeList(base_objects.PhysicsObjectList):
1532 """List of DecayChainAmplitude objects 1533 """ 1534
1535 - def is_valid_element(self, obj):
1536 """Test if object obj is a valid DecayChainAmplitude for the list.""" 1537 1538 return isinstance(obj, DecayChainAmplitude)
1539
1540 1541 #=============================================================================== 1542 # MultiProcess 1543 #=============================================================================== 1544 -class MultiProcess(base_objects.PhysicsObject):
1545 """MultiProcess: list of process definitions 1546 list of processes (after cleaning) 1547 list of amplitudes (after generation) 1548 """ 1549
1550 - def default_setup(self):
1551 """Default values for all properties""" 1552 1553 self['process_definitions'] = base_objects.ProcessDefinitionList() 1554 # self['amplitudes'] can be an AmplitudeList or a 1555 # DecayChainAmplitudeList, depending on whether there are 1556 # decay chains in the process definitions or not. 1557 self['amplitudes'] = AmplitudeList() 1558 # Flag for whether to combine IS mirror processes together 1559 self['collect_mirror_procs'] = False 1560 # List of quark flavors where we ignore processes with at 1561 # least 6 quarks (three quark lines) 1562 self['ignore_six_quark_processes'] = [] 1563 # Allow to use the model parameter numerical value for optimization. 1564 #This is currently use for 1->N generation(check mass). 1565 self['use_numerical'] = False
1566
1567 - def __init__(self, argument=None, collect_mirror_procs = False, 1568 ignore_six_quark_processes = [], optimize=False, 1569 loop_filter=None, diagram_filter=None):
1570 """Allow initialization with ProcessDefinition or 1571 ProcessDefinitionList 1572 optimize allows to use param_card information. (usefull for 1-.N)""" 1573 1574 if isinstance(argument, base_objects.ProcessDefinition): 1575 super(MultiProcess, self).__init__() 1576 self['process_definitions'].append(argument) 1577 elif isinstance(argument, base_objects.ProcessDefinitionList): 1578 super(MultiProcess, self).__init__() 1579 self['process_definitions'] = argument 1580 elif argument != None: 1581 # call the mother routine 1582 super(MultiProcess, self).__init__(argument) 1583 else: 1584 # call the mother routine 1585 super(MultiProcess, self).__init__() 1586 1587 self['collect_mirror_procs'] = collect_mirror_procs 1588 self['ignore_six_quark_processes'] = ignore_six_quark_processes 1589 self['use_numerical'] = optimize 1590 self['loop_filter'] = loop_filter 1591 self['diagram_filter'] = diagram_filter # only True/False so far 1592 1593 if isinstance(argument, base_objects.ProcessDefinition) or \ 1594 isinstance(argument, base_objects.ProcessDefinitionList): 1595 # Generate the diagrams 1596 self.get('amplitudes')
1597 1598
1599 - def filter(self, name, value):
1600 """Filter for valid process property values.""" 1601 1602 if name == 'process_definitions': 1603 if not isinstance(value, base_objects.ProcessDefinitionList): 1604 raise self.PhysicsObjectError, \ 1605 "%s is not a valid ProcessDefinitionList object" % str(value) 1606 1607 if name == 'amplitudes': 1608 if not isinstance(value, AmplitudeList): 1609 raise self.PhysicsObjectError, \ 1610 "%s is not a valid AmplitudeList object" % str(value) 1611 1612 if name in ['collect_mirror_procs']: 1613 if not isinstance(value, bool): 1614 raise self.PhysicsObjectError, \ 1615 "%s is not a valid boolean" % str(value) 1616 1617 if name == 'ignore_six_quark_processes': 1618 if not isinstance(value, list): 1619 raise self.PhysicsObjectError, \ 1620 "%s is not a valid list" % str(value) 1621 1622 return True
1623
1624 - def get(self, name):
1625 """Get the value of the property name.""" 1626 1627 if (name == 'amplitudes') and not self[name]: 1628 for process_def in self.get('process_definitions'): 1629 if process_def.get('decay_chains'): 1630 # This is a decay chain process 1631 # Store amplitude(s) as DecayChainAmplitude 1632 self['amplitudes'].append(\ 1633 DecayChainAmplitude(process_def, 1634 self.get('collect_mirror_procs'), 1635 self.get('ignore_six_quark_processes'), 1636 diagram_filter=self['diagram_filter'])) 1637 else: 1638 self['amplitudes'].extend(\ 1639 self.generate_multi_amplitudes(process_def, 1640 self.get('collect_mirror_procs'), 1641 self.get('ignore_six_quark_processes'), 1642 self['use_numerical'], 1643 loop_filter=self['loop_filter'], 1644 diagram_filter=self['diagram_filter'])) 1645 1646 return MultiProcess.__bases__[0].get(self, name) # call the mother routine
1647
1648 - def get_sorted_keys(self):
1649 """Return process property names as a nicely sorted list.""" 1650 1651 return ['process_definitions', 'amplitudes']
1652 1653 @classmethod
1654 - def generate_multi_amplitudes(cls,process_definition, 1655 collect_mirror_procs = False, 1656 ignore_six_quark_processes = [], 1657 use_numerical=False, 1658 loop_filter=None, 1659 diagram_filter=False):
1660 """Generate amplitudes in a semi-efficient way. 1661 Make use of crossing symmetry for processes that fail diagram 1662 generation, but not for processes that succeed diagram 1663 generation. Doing so will risk making it impossible to 1664 identify processes with identical amplitudes. 1665 """ 1666 assert isinstance(process_definition, base_objects.ProcessDefinition), \ 1667 "%s not valid ProcessDefinition object" % \ 1668 repr(process_definition) 1669 1670 # Set automatic coupling orders 1671 process_definition.set('orders', MultiProcess.\ 1672 find_optimal_process_orders(process_definition, 1673 diagram_filter)) 1674 # Check for maximum orders from the model 1675 process_definition.check_expansion_orders() 1676 1677 processes = base_objects.ProcessList() 1678 amplitudes = AmplitudeList() 1679 1680 # failed_procs and success_procs are sorted processes that have 1681 # already failed/succeeded based on crossing symmetry 1682 failed_procs = [] 1683 success_procs = [] 1684 # Complete processes, for identification of mirror processes 1685 non_permuted_procs = [] 1686 # permutations keeps the permutations of the crossed processes 1687 permutations = [] 1688 1689 # Store the diagram tags for processes, to allow for 1690 # identifying identical matrix elements already at this stage. 1691 model = process_definition['model'] 1692 1693 isids = [leg['ids'] for leg in process_definition['legs'] \ 1694 if leg['state'] == False] 1695 fsids = [leg['ids'] for leg in process_definition['legs'] \ 1696 if leg['state'] == True] 1697 # Generate all combinations for the initial state 1698 1699 for prod in itertools.product(*isids): 1700 islegs = [\ 1701 base_objects.Leg({'id':id, 'state': False}) \ 1702 for id in prod] 1703 1704 # Generate all combinations for the final state, and make 1705 # sure to remove double counting 1706 1707 red_fsidlist = [] 1708 1709 for prod in itertools.product(*fsids): 1710 1711 # Remove double counting between final states 1712 if tuple(sorted(prod)) in red_fsidlist: 1713 continue 1714 1715 red_fsidlist.append(tuple(sorted(prod))); 1716 1717 # Generate leg list for process 1718 leg_list = [copy.copy(leg) for leg in islegs] 1719 1720 leg_list.extend([\ 1721 base_objects.Leg({'id':id, 'state': True}) \ 1722 for id in prod]) 1723 1724 legs = base_objects.LegList(leg_list) 1725 1726 # Check for crossed processes 1727 sorted_legs = sorted([(l,i+1) for (i,l) in \ 1728 enumerate(legs.get_outgoing_id_list(model))]) 1729 permutation = [l[1] for l in sorted_legs] 1730 1731 sorted_legs = array.array('i', [l[0] for l in sorted_legs]) 1732 1733 # Check for six-quark processes 1734 if ignore_six_quark_processes and \ 1735 len([i for i in sorted_legs if abs(i) in \ 1736 ignore_six_quark_processes]) >= 6: 1737 continue 1738 1739 # Check if crossed process has already failed, 1740 # in that case don't check process 1741 if sorted_legs in failed_procs: 1742 continue 1743 1744 # If allowed check mass validity [assume 1->N] 1745 if use_numerical: 1746 # check that final state has lower mass than initial state 1747 initial_mass = abs(model['parameter_dict'][model.get_particle(legs[0].get('id')).get('mass')]) 1748 if initial_mass == 0: 1749 continue 1750 for leg in legs[1:]: 1751 m = model['parameter_dict'][model.get_particle(leg.get('id')).get('mass')] 1752 initial_mass -= abs(m) 1753 if initial_mass.real <= 0: 1754 continue 1755 1756 # Setup process 1757 process = process_definition.get_process_with_legs(legs) 1758 1759 fast_proc = \ 1760 array.array('i',[leg.get('id') for leg in legs]) 1761 if collect_mirror_procs and \ 1762 process_definition.get_ninitial() == 2: 1763 # Check if mirrored process is already generated 1764 mirror_proc = \ 1765 array.array('i', [fast_proc[1], fast_proc[0]] + \ 1766 list(fast_proc[2:])) 1767 try: 1768 mirror_amp = \ 1769 amplitudes[non_permuted_procs.index(mirror_proc)] 1770 except Exception: 1771 # Didn't find any mirror process 1772 pass 1773 else: 1774 # Mirror process found 1775 mirror_amp.set('has_mirror_process', True) 1776 logger.info("Process %s added to mirror process %s" % \ 1777 (process.base_string(), 1778 mirror_amp.get('process').base_string())) 1779 continue 1780 1781 # Check for successful crossings, unless we have specified 1782 # properties that break crossing symmetry 1783 if not process.get('required_s_channels') and \ 1784 not process.get('forbidden_onsh_s_channels') and \ 1785 not process.get('forbidden_s_channels') and \ 1786 not process.get('is_decay_chain'): 1787 try: 1788 crossed_index = success_procs.index(sorted_legs) 1789 # The relabeling of legs for loop amplitudes is cumbersome 1790 # and does not save so much time. It is disable here and 1791 # we use the key 'loop_diagrams' to decide whether 1792 # it is an instance of LoopAmplitude. 1793 if 'loop_diagrams' in amplitudes[crossed_index]: 1794 raise ValueError 1795 except ValueError: 1796 # No crossing found, just continue 1797 pass 1798 else: 1799 # Found crossing - reuse amplitude 1800 amplitude = MultiProcess.cross_amplitude(\ 1801 amplitudes[crossed_index], 1802 process, 1803 permutations[crossed_index], 1804 permutation) 1805 amplitudes.append(amplitude) 1806 success_procs.append(sorted_legs) 1807 permutations.append(permutation) 1808 non_permuted_procs.append(fast_proc) 1809 logger.info("Crossed process found for %s, reuse diagrams." % \ 1810 process.base_string()) 1811 continue 1812 1813 # Create new amplitude 1814 amplitude = cls.get_amplitude_from_proc(process, 1815 loop_filter=loop_filter) 1816 1817 try: 1818 result = amplitude.generate_diagrams(diagram_filter=diagram_filter) 1819 except InvalidCmd as error: 1820 failed_procs.append(sorted_legs) 1821 else: 1822 # Succeeded in generating diagrams 1823 if amplitude.get('diagrams'): 1824 amplitudes.append(amplitude) 1825 success_procs.append(sorted_legs) 1826 permutations.append(permutation) 1827 non_permuted_procs.append(fast_proc) 1828 elif not result: 1829 # Diagram generation failed for all crossings 1830 failed_procs.append(sorted_legs) 1831 1832 # Raise exception if there are no amplitudes for this process 1833 if not amplitudes: 1834 if len(failed_procs) == 1 and 'error' in locals(): 1835 raise error 1836 else: 1837 raise NoDiagramException, \ 1838 "No amplitudes generated from process %s. Please enter a valid process" % \ 1839 process_definition.nice_string() 1840 1841 1842 # Return the produced amplitudes 1843 return amplitudes
1844 1845 @classmethod
1846 - def get_amplitude_from_proc(cls,proc,**opts):
1847 """ Return the correct amplitude type according to the characteristics of 1848 the process proc. The only option that could be specified here is 1849 loop_filter and it is of course not relevant for a tree amplitude.""" 1850 1851 return Amplitude({"process": proc})
1852 1853 1854 @staticmethod
1855 - def find_optimal_process_orders(process_definition, diagram_filter=False):
1856 """Find the minimal WEIGHTED order for this set of processes. 1857 1858 The algorithm: 1859 1860 1) Check the coupling hierarchy of the model. Assign all 1861 particles to the different coupling hierarchies so that a 1862 particle is considered to be in the highest hierarchy (i.e., 1863 with lowest value) where it has an interaction. 1864 1865 2) Pick out the legs in the multiprocess according to the 1866 highest hierarchy represented (so don't mix particles from 1867 different hierarchy classes in the same multiparticles!) 1868 1869 3) Find the starting maximum WEIGHTED order as the sum of the 1870 highest n-2 weighted orders 1871 1872 4) Pick out required s-channel particle hierarchies, and use 1873 the highest of the maximum WEIGHTED order from the legs and 1874 the minimum WEIGHTED order extracted from 2*s-channel 1875 hierarchys plus the n-2-2*(number of s-channels) lowest 1876 leg weighted orders. 1877 1878 5) Run process generation with the WEIGHTED order determined 1879 in 3)-4) - # final state gluons, with all gluons removed from 1880 the final state 1881 1882 6) If no process is found, increase WEIGHTED order by 1 and go 1883 back to 5), until we find a process which passes. Return that 1884 order. 1885 1886 7) Continue 5)-6) until we reach (n-2)*(highest hierarchy)-1. 1887 If still no process has passed, return 1888 WEIGHTED = (n-2)*(highest hierarchy) 1889 """ 1890 1891 assert isinstance(process_definition, base_objects.ProcessDefinition), \ 1892 "%s not valid ProcessDefinition object" % \ 1893 repr(process_definition) 1894 1895 processes = base_objects.ProcessList() 1896 amplitudes = AmplitudeList() 1897 1898 # If there are already couplings defined, return 1899 if process_definition.get('orders') or \ 1900 process_definition.get('overall_orders') or \ 1901 process_definition.get('NLO_mode')=='virt': 1902 return process_definition.get('orders') 1903 1904 # If this is a decay process (and not a decay chain), return 1905 if process_definition.get_ninitial() == 1 and not \ 1906 process_definition.get('is_decay_chain'): 1907 return process_definition.get('orders') 1908 1909 logger.info("Checking for minimal orders which gives processes.") 1910 logger.info("Please specify coupling orders to bypass this step.") 1911 1912 # Calculate minimum starting guess for WEIGHTED order 1913 max_order_now, particles, hierarchy = \ 1914 process_definition.get_minimum_WEIGHTED() 1915 coupling = 'WEIGHTED' 1916 1917 model = process_definition.get('model') 1918 1919 # Extract the initial and final leg ids 1920 isids = [leg['ids'] for leg in \ 1921 filter(lambda leg: leg['state'] == False, process_definition['legs'])] 1922 fsids = [leg['ids'] for leg in \ 1923 filter(lambda leg: leg['state'] == True, process_definition['legs'])] 1924 1925 max_WEIGHTED_order = \ 1926 (len(fsids + isids) - 2)*int(model.get_max_WEIGHTED()) 1927 1928 # get the definition of the WEIGHTED 1929 hierarchydef = process_definition['model'].get('order_hierarchy') 1930 tmp = [] 1931 hierarchy = hierarchydef.items() 1932 hierarchy.sort() 1933 for key, value in hierarchydef.items(): 1934 if value>1: 1935 tmp.append('%s*%s' % (value,key)) 1936 else: 1937 tmp.append('%s' % key) 1938 wgtdef = '+'.join(tmp) 1939 # Run diagram generation with increasing max_order_now until 1940 # we manage to get diagrams 1941 while max_order_now < max_WEIGHTED_order: 1942 logger.info("Trying coupling order WEIGHTED<=%d: WEIGTHED IS %s" % (max_order_now, wgtdef)) 1943 1944 oldloglevel = logger.level 1945 logger.setLevel(logging.WARNING) 1946 1947 # failed_procs are processes that have already failed 1948 # based on crossing symmetry 1949 failed_procs = [] 1950 1951 # Generate all combinations for the initial state 1952 for prod in apply(itertools.product, isids): 1953 islegs = [ base_objects.Leg({'id':id, 'state': False}) \ 1954 for id in prod] 1955 1956 # Generate all combinations for the final state, and make 1957 # sure to remove double counting 1958 1959 red_fsidlist = [] 1960 1961 for prod in apply(itertools.product, fsids): 1962 1963 # Remove double counting between final states 1964 if tuple(sorted(prod)) in red_fsidlist: 1965 continue 1966 1967 red_fsidlist.append(tuple(sorted(prod))); 1968 1969 # Remove gluons from final state if QCD is among 1970 # the highest coupling hierarchy 1971 nglue = 0 1972 if 21 in particles[0]: 1973 nglue = len([id for id in prod if id == 21]) 1974 prod = [id for id in prod if id != 21] 1975 1976 # Generate leg list for process 1977 leg_list = [copy.copy(leg) for leg in islegs] 1978 1979 leg_list.extend([\ 1980 base_objects.Leg({'id':id, 'state': True}) \ 1981 for id in prod]) 1982 1983 legs = base_objects.LegList(leg_list) 1984 1985 # Set summed coupling order according to max_order_now 1986 # subtracting the removed gluons 1987 coupling_orders_now = {coupling: max_order_now - \ 1988 nglue * model['order_hierarchy']['QCD']} 1989 1990 # Setup process 1991 process = base_objects.Process({\ 1992 'legs':legs, 1993 'model':model, 1994 'id': process_definition.get('id'), 1995 'orders': coupling_orders_now, 1996 'required_s_channels': \ 1997 process_definition.get('required_s_channels'), 1998 'forbidden_onsh_s_channels': \ 1999 process_definition.get('forbidden_onsh_s_channels'), 2000 'sqorders_types': \ 2001 process_definition.get('sqorders_types'), 2002 'squared_orders': \ 2003 process_definition.get('squared_orders'), 2004 'split_orders': \ 2005 process_definition.get('split_orders'), 2006 'forbidden_s_channels': \ 2007 process_definition.get('forbidden_s_channels'), 2008 'forbidden_particles': \ 2009 process_definition.get('forbidden_particles'), 2010 'is_decay_chain': \ 2011 process_definition.get('is_decay_chain'), 2012 'overall_orders': \ 2013 process_definition.get('overall_orders'), 2014 'split_orders': \ 2015 process_definition.get('split_orders')}) 2016 2017 # Check for couplings with given expansion orders 2018 process.check_expansion_orders() 2019 2020 # Check for crossed processes 2021 sorted_legs = sorted(legs.get_outgoing_id_list(model)) 2022 # Check if crossed process has already failed 2023 # In that case don't check process 2024 if tuple(sorted_legs) in failed_procs: 2025 continue 2026 2027 amplitude = Amplitude({'process': process}) 2028 try: 2029 amplitude.generate_diagrams(diagram_filter=diagram_filter) 2030 except InvalidCmd: 2031 failed_procs.append(tuple(sorted_legs)) 2032 else: 2033 if amplitude.get('diagrams'): 2034 # We found a valid amplitude. Return this order number 2035 logger.setLevel(oldloglevel) 2036 return {coupling: max_order_now} 2037 else: 2038 failed_procs.append(tuple(sorted_legs)) 2039 2040 # No processes found, increase max_order_now 2041 max_order_now += 1 2042 logger.setLevel(oldloglevel) 2043 2044 # If no valid processes found with nfinal-1 couplings, return maximal 2045 return {coupling: max_order_now}
2046 2047 @staticmethod
2048 - def cross_amplitude(amplitude, process, org_perm, new_perm):
2049 """Return the amplitude crossed with the permutation new_perm""" 2050 # Create dict from original leg numbers to new leg numbers 2051 perm_map = dict(zip(org_perm, new_perm)) 2052 # Initiate new amplitude 2053 new_amp = copy.copy(amplitude) 2054 # Number legs 2055 for i, leg in enumerate(process.get('legs')): 2056 leg.set('number', i+1) 2057 # Set process 2058 new_amp.set('process', process) 2059 # Now replace the leg numbers in the diagrams 2060 diagrams = base_objects.DiagramList([d.renumber_legs(perm_map, 2061 process.get('legs'),) for \ 2062 d in new_amp.get('diagrams')]) 2063 new_amp.set('diagrams', diagrams) 2064 new_amp.trim_diagrams() 2065 2066 # Make sure to reset mirror process 2067 new_amp.set('has_mirror_process', False) 2068 2069 return new_amp
2070
2071 #=============================================================================== 2072 # Global helper methods 2073 #=============================================================================== 2074 2075 -def expand_list(mylist):
2076 """Takes a list of lists and elements and returns a list of flat lists. 2077 Example: [[1,2], 3, [4,5]] -> [[1,3,4], [1,3,5], [2,3,4], [2,3,5]] 2078 """ 2079 2080 # Check that argument is a list 2081 assert isinstance(mylist, list), "Expand_list argument must be a list" 2082 2083 res = [] 2084 2085 tmplist = [] 2086 for item in mylist: 2087 if isinstance(item, list): 2088 tmplist.append(item) 2089 else: 2090 tmplist.append([item]) 2091 2092 for item in apply(itertools.product, tmplist): 2093 res.append(list(item)) 2094 2095 return res
2096
2097 -def expand_list_list(mylist):
2098 """Recursive function. Takes a list of lists and lists of lists 2099 and returns a list of flat lists. 2100 Example: [[1,2],[[4,5],[6,7]]] -> [[1,2,4,5], [1,2,6,7]] 2101 """ 2102 2103 res = [] 2104 2105 if not mylist or len(mylist) == 1 and not mylist[0]: 2106 return [[]] 2107 2108 # Check the first element is at least a list 2109 assert isinstance(mylist[0], list), \ 2110 "Expand_list_list needs a list of lists and lists of lists" 2111 2112 # Recursion stop condition, one single element 2113 if len(mylist) == 1: 2114 if isinstance(mylist[0][0], list): 2115 return mylist[0] 2116 else: 2117 return mylist 2118 2119 if isinstance(mylist[0][0], list): 2120 for item in mylist[0]: 2121 # Here the recursion happens, create lists starting with 2122 # each element of the first item and completed with 2123 # the rest expanded 2124 for rest in expand_list_list(mylist[1:]): 2125 reslist = copy.copy(item) 2126 reslist.extend(rest) 2127 res.append(reslist) 2128 else: 2129 for rest in expand_list_list(mylist[1:]): 2130 reslist = copy.copy(mylist[0]) 2131 reslist.extend(rest) 2132 res.append(reslist) 2133 2134 2135 return res
2136