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 # For the first step, ensure the tag from_group 631 # is true for all legs 632 leg.set('from_group', True) 633 634 # Need to flip part-antipart for incoming particles, 635 # so they are all outgoing 636 if leg.get('state') == False: 637 part = model.get('particle_dict')[leg.get('id')] 638 leg.set('id', part.get_anti_pdg_code()) 639 640 # Calculate the maximal multiplicity of n-1>1 configurations 641 # to restrict possible leg combinations 642 max_multi_to1 = max([len(key) for key in \ 643 model.get('ref_dict_to1').keys()]) 644 645 646 # Reduce the leg list and return the corresponding 647 # list of vertices 648 649 # For decay processes, generate starting from final-state 650 # combined only as the last particle. This allows to use these 651 # in decay chains later on. 652 is_decay_proc = process.get_ninitial() == 1 653 if is_decay_proc: 654 part = model.get('particle_dict')[leglist[0].get('id')] 655 # For decay chain legs, we want everything to combine to 656 # the initial leg. This is done by only allowing the 657 # initial leg to combine as a final identity. 658 ref_dict_to0 = {(part.get_pdg_code(),part.get_anti_pdg_code()):[0], 659 (part.get_anti_pdg_code(),part.get_pdg_code()):[0]} 660 # Need to set initial leg from_group to None, to make sure 661 # it can only be combined at the end. 662 leglist[0].set('from_group', None) 663 reduced_leglist = self.reduce_leglist(leglist, 664 max_multi_to1, 665 ref_dict_to0, 666 is_decay_proc, 667 process.get('orders')) 668 else: 669 reduced_leglist = self.reduce_leglist(leglist, 670 max_multi_to1, 671 model.get('ref_dict_to0'), 672 is_decay_proc, 673 process.get('orders')) 674 675 #In LoopAmplitude the function below is overloaded such that it 676 #converts back all DGLoopLegs to Legs. In the default tree-level 677 #diagram generation, this does nothing. 678 self.convert_dgleg_to_leg(reduced_leglist) 679 680 if reduced_leglist: 681 for vertex_list in reduced_leglist: 682 res.append(self.create_diagram(base_objects.VertexList(vertex_list))) 683 684 # Record whether or not we failed generation before required 685 # s-channel propagators are taken into account 686 failed_crossing = not res 687 688 # Required s-channels is a list of id-lists. Select the 689 # diagrams where all required s-channel propagators in any of 690 # the lists are present (i.e., the different lists correspond 691 # to "or", while the elements of the list correspond to 692 # "and"). 693 if process.get('required_s_channels') and \ 694 process.get('required_s_channels')[0]: 695 # We shouldn't look at the last vertex in each diagram, 696 # since that is the n->0 vertex 697 lastvx = -1 698 # For decay chain processes, there is an "artificial" 699 # extra vertex corresponding to particle 1=1, so we need 700 # to exclude the two last vertexes. 701 if is_decay_proc: lastvx = -2 702 ninitial = len(filter(lambda leg: leg.get('state') == False, 703 process.get('legs'))) 704 # Check required s-channels for each list in required_s_channels 705 old_res = res 706 res = base_objects.DiagramList() 707 for id_list in process.get('required_s_channels'): 708 res_diags = filter(lambda diagram: \ 709 all([req_s_channel in \ 710 [vertex.get_s_channel_id(\ 711 process.get('model'), ninitial) \ 712 for vertex in diagram.get('vertices')[:lastvx]] \ 713 for req_s_channel in \ 714 id_list]), old_res) 715 # Add diagrams only if not already in res 716 res.extend([diag for diag in res_diags if diag not in res]) 717 718 # Remove all diagrams with a "double" forbidden s-channel propagator 719 # is present. 720 # Note that we shouldn't look at the last vertex in each 721 # diagram, since that is the n->0 vertex 722 if process.get('forbidden_s_channels'): 723 ninitial = len(filter(lambda leg: leg.get('state') == False, 724 process.get('legs'))) 725 if ninitial == 2: 726 res = base_objects.DiagramList(\ 727 filter(lambda diagram: \ 728 not any([vertex.get_s_channel_id(\ 729 process.get('model'), ninitial) \ 730 in process.get('forbidden_s_channels') 731 for vertex in diagram.get('vertices')[:-1]]), 732 res)) 733 else: 734 # split since we need to avoid that the initial particle is forbidden 735 # as well. 736 newres= [] 737 for diagram in res: 738 leg1 = 1 739 #check the latest vertex to see if the leg 1 is inside if it 740 #is we need to inverse the look-up and allow the first s-channel 741 # of the associate particles. 742 vertex = diagram.get('vertices')[-1] 743 if any([l['number'] ==1 for l in vertex.get('legs')]): 744 leg1 = [l['number'] for l in vertex.get('legs') if l['number'] !=1][0] 745 to_loop = range(len(diagram.get('vertices'))-1) 746 if leg1 >1: 747 to_loop.reverse() 748 for i in to_loop: 749 vertex = diagram.get('vertices')[i] 750 if leg1: 751 if any([l['number'] ==leg1 for l in vertex.get('legs')]): 752 leg1 = 0 753 continue 754 if vertex.get_s_channel_id(process.get('model'), ninitial)\ 755 in process.get('forbidden_s_channels'): 756 break 757 else: 758 newres.append(diagram) 759 res = base_objects.DiagramList(newres) 760 761 762 # Mark forbidden (onshell) s-channel propagators, to forbid onshell 763 # generation. 764 if process.get('forbidden_onsh_s_channels'): 765 ninitial = len(filter(lambda leg: leg.get('state') == False, 766 process.get('legs'))) 767 768 verts = base_objects.VertexList(sum([[vertex for vertex \ 769 in diagram.get('vertices')[:-1] 770 if vertex.get_s_channel_id(\ 771 process.get('model'), ninitial) \ 772 in process.get('forbidden_onsh_s_channels')] \ 773 for diagram in res], [])) 774 for vert in verts: 775 # Use onshell = False to indicate that this s-channel is forbidden 776 newleg = copy.copy(vert.get('legs').pop(-1)) 777 newleg.set('onshell', False) 778 vert.get('legs').append(newleg) 779 780 # Set actual coupling orders for each diagram 781 for diagram in res: 782 diagram.calculate_orders(model) 783 784 # Filter the diagrams according to the squared coupling order 785 # constraints and possible the negative one. Remember that OrderName=-n 786 # means that the user wants to include everything up to the N^(n+1)LO 787 # contribution in that order and at most one order can be restricted 788 # in this way. We shall do this only if the diagrams are not asked to 789 # be returned, as it is the case for NLO because it this case the 790 # interference are not necessarily among the diagrams generated here only. 791 if not returndiag and len(res)>0: 792 res = self.apply_squared_order_constraints(res) 793 794 if diagram_filter: 795 res = self.apply_user_filter(res) 796 797 # Replace final id=0 vertex if necessary 798 if not process.get('is_decay_chain'): 799 for diagram in res: 800 vertices = diagram.get('vertices') 801 if len(vertices) > 1 and vertices[-1].get('id') == 0: 802 # Need to "glue together" last and next-to-last 803 # vertex, by replacing the (incoming) last leg of the 804 # next-to-last vertex with the (outgoing) leg in the 805 # last vertex 806 vertices = copy.copy(vertices) 807 lastvx = vertices.pop() 808 nexttolastvertex = copy.copy(vertices.pop()) 809 legs = copy.copy(nexttolastvertex.get('legs')) 810 ntlnumber = legs[-1].get('number') 811 lastleg = filter(lambda leg: leg.get('number') != ntlnumber, 812 lastvx.get('legs'))[0] 813 # Reset onshell in case we have forbidden s-channels 814 if lastleg.get('onshell') == False: 815 lastleg.set('onshell', None) 816 # Replace the last leg of nexttolastvertex 817 legs[-1] = lastleg 818 nexttolastvertex.set('legs', legs) 819 vertices.append(nexttolastvertex) 820 diagram.set('vertices', vertices) 821 822 if res and not returndiag: 823 logger.info("Process has %d diagrams" % len(res)) 824 825 # Trim down number of legs and vertices used to save memory 826 self.trim_diagrams(diaglist=res) 827 828 # Sort process legs according to leg number 829 pertur = 'QCD' 830 if self.get('process')['perturbation_couplings']: 831 pertur = sorted(self.get('process')['perturbation_couplings'])[0] 832 self.get('process').get('legs').sort(pert=pertur) 833 834 # Set diagrams to res if not asked to be returned 835 if not returndiag: 836 self['diagrams'] = res 837 return not failed_crossing 838 else: 839 return not failed_crossing, res
840
841 - def apply_squared_order_constraints(self, diag_list):
842 """Applies the user specified squared order constraints on the diagram 843 list in argument.""" 844 845 res = copy.copy(diag_list) 846 847 # Apply the filtering on constrained amplitude (== and >) 848 # No need to iterate on this one 849 for name, (value, operator) in self['process'].get('constrained_orders').items(): 850 res.filter_constrained_orders(name, value, operator) 851 852 # Iterate the filtering since the applying the constraint on one 853 # type of coupling order can impact what the filtering on a previous 854 # one (relevant for the '==' type of constraint). 855 while True: 856 new_res = res.apply_positive_sq_orders(res, 857 self['process'].get('squared_orders'), 858 self['process']['sqorders_types']) 859 # Exit condition 860 if len(res)==len(new_res): 861 break 862 elif (len(new_res)>len(res)): 863 raise MadGraph5Error( 864 'Inconsistency in function apply_squared_order_constraints().') 865 # Actualizing the list of diagram for the next iteration 866 res = new_res 867 868 869 870 # Now treat the negative squared order constraint (at most one) 871 neg_orders = [(order, value) for order, value in \ 872 self['process'].get('squared_orders').items() if value<0] 873 if len(neg_orders)==1: 874 neg_order, neg_value = neg_orders[0] 875 # Now check any negative order constraint 876 res, target_order = res.apply_negative_sq_order(res, neg_order,\ 877 neg_value, self['process']['sqorders_types'][neg_order]) 878 # Substitute the negative value to this positive one so that 879 # the resulting computed constraints appears in the print out 880 # and at the output stage we no longer have to deal with 881 # negative valued target orders 882 self['process']['squared_orders'][neg_order]=target_order 883 elif len(neg_orders)>1: 884 raise InvalidCmd('At most one negative squared order constraint'+\ 885 ' can be specified, not %s.'%str(neg_orders)) 886 887 return res
888
889 - def apply_user_filter(self, diag_list):
890 """Applies the user specified squared order constraints on the diagram 891 list in argument.""" 892 893 if True: 894 remove_diag = misc.plugin_import('user_filter', 895 'user filter required to be defined in PLUGIN/user_filter.py with the function remove_diag(ONEDIAG) which returns True if the diagram has to be removed', 896 fcts=['remove_diag']) 897 else: 898 #example and simple tests 899 def remove_diag(diag, model=None): 900 for vertex in diag['vertices']: #last 901 if vertex['id'] == 0: #special final vertex 902 continue 903 if vertex['legs'][-1]['number'] < 3: #this means T-channel 904 if abs(vertex['legs'][-1]['id']) <6: 905 return True 906 return False
907 908 res = diag_list.__class__() 909 nb_removed = 0 910 model = self['process']['model'] 911 for diag in diag_list: 912 if remove_diag(diag, model): 913 nb_removed +=1 914 else: 915 res.append(diag) 916 917 if nb_removed: 918 logger.warning('Diagram filter is ON and removed %s diagrams for this subprocess.' % nb_removed) 919 920 return res
921 922 923
924 - def create_diagram(self, vertexlist):
925 """ Return a Diagram created from the vertex list. This function can be 926 overloaded by daughter classes.""" 927 return base_objects.Diagram({'vertices':vertexlist})
928
929 - def convert_dgleg_to_leg(self, vertexdoublelist):
930 """ In LoopAmplitude, it converts back all DGLoopLegs into Legs. 931 In Amplitude, there is nothing to do. """ 932 933 return True
934
935 - def copy_leglist(self, legs):
936 """ Simply returns a copy of the leg list. This function is 937 overloaded in LoopAmplitude so that a DGLoopLeg list is returned. 938 The DGLoopLeg has some additional parameters only useful during 939 loop diagram generation""" 940 941 return base_objects.LegList(\ 942 [ copy.copy(leg) for leg in legs ])
943
944 - def reduce_leglist(self, curr_leglist, max_multi_to1, ref_dict_to0, 945 is_decay_proc = False, coupling_orders = None):
946 """Recursive function to reduce N LegList to N-1 947 For algorithm, see doc for generate_diagrams. 948 """ 949 950 # Result variable which is a list of lists of vertices 951 # to be added 952 res = [] 953 954 # Stop condition. If LegList is None, that means that this 955 # diagram must be discarded 956 if curr_leglist is None: 957 return None 958 959 # Extract ref dict information 960 model = self.get('process').get('model') 961 ref_dict_to1 = self.get('process').get('model').get('ref_dict_to1') 962 963 964 # If all legs can be combined in one single vertex, add this 965 # vertex to res and continue. 966 # Special treatment for decay chain legs 967 968 if curr_leglist.can_combine_to_0(ref_dict_to0, is_decay_proc): 969 # Extract the interaction id associated to the vertex 970 971 vertex_ids = self.get_combined_vertices(curr_leglist, 972 copy.copy(ref_dict_to0[tuple(sorted([leg.get('id') for \ 973 leg in curr_leglist]))])) 974 975 final_vertices = [base_objects.Vertex({'legs':curr_leglist, 976 'id':vertex_id}) for \ 977 vertex_id in vertex_ids] 978 # Check for coupling orders. If orders < 0, skip vertex 979 for final_vertex in final_vertices: 980 if self.reduce_orders(coupling_orders, model, 981 [final_vertex.get('id')]) != False: 982 res.append([final_vertex]) 983 # Stop condition 2: if the leglist contained exactly two particles, 984 # return the result, if any, and stop. 985 if len(curr_leglist) == 2: 986 if res: 987 return res 988 else: 989 return None 990 991 # Create a list of all valid combinations of legs 992 comb_lists = self.combine_legs(curr_leglist, 993 ref_dict_to1, max_multi_to1) 994 995 # Create a list of leglists/vertices by merging combinations 996 leg_vertex_list = self.merge_comb_legs(comb_lists, ref_dict_to1) 997 998 # Consider all the pairs 999 for leg_vertex_tuple in leg_vertex_list: 1000 1001 # Remove forbidden particles 1002 if self.get('process').get('forbidden_particles') and \ 1003 any([abs(vertex.get('legs')[-1].get('id')) in \ 1004 self.get('process').get('forbidden_particles') \ 1005 for vertex in leg_vertex_tuple[1]]): 1006 continue 1007 1008 # Check for coupling orders. If couplings < 0, skip recursion. 1009 new_coupling_orders = self.reduce_orders(coupling_orders, 1010 model, 1011 [vertex.get('id') for vertex in \ 1012 leg_vertex_tuple[1]]) 1013 if new_coupling_orders == False: 1014 # Some coupling order < 0 1015 continue 1016 1017 # This is where recursion happens 1018 # First, reduce again the leg part 1019 reduced_diagram = self.reduce_leglist(leg_vertex_tuple[0], 1020 max_multi_to1, 1021 ref_dict_to0, 1022 is_decay_proc, 1023 new_coupling_orders) 1024 # If there is a reduced diagram 1025 if reduced_diagram: 1026 vertex_list_list = [list(leg_vertex_tuple[1])] 1027 vertex_list_list.append(reduced_diagram) 1028 expanded_list = expand_list_list(vertex_list_list) 1029 res.extend(expanded_list) 1030 1031 return res
1032
1033 - def reduce_orders(self, coupling_orders, model, vertex_id_list):
1034 """Return False if the coupling orders for any coupling is < 1035 0, otherwise return the new coupling orders with the vertex 1036 orders subtracted. If coupling_orders is not given, return 1037 None (which counts as success). 1038 WEIGHTED is a special order, which corresponds to the sum of 1039 order hierarchies for the couplings. 1040 We ignore negative constraints as these cannot be taken into 1041 account on the fly but only after generation.""" 1042 1043 if not coupling_orders: 1044 return None 1045 1046 present_couplings = copy.copy(coupling_orders) 1047 for id in vertex_id_list: 1048 # Don't check for identity vertex (id = 0) 1049 if not id: 1050 continue 1051 inter = model.get("interaction_dict")[id] 1052 for coupling in inter.get('orders').keys(): 1053 # Note that we don't consider a missing coupling as a 1054 # constraint 1055 if coupling in present_couplings and \ 1056 present_couplings[coupling]>=0: 1057 # Reduce the number of couplings that are left 1058 present_couplings[coupling] -= \ 1059 inter.get('orders')[coupling] 1060 if present_couplings[coupling] < 0: 1061 # We have too many couplings of this type 1062 return False 1063 # Now check for WEIGHTED, i.e. the sum of coupling hierarchy values 1064 if 'WEIGHTED' in present_couplings and \ 1065 present_couplings['WEIGHTED']>=0: 1066 weight = sum([model.get('order_hierarchy')[c]*n for \ 1067 (c,n) in inter.get('orders').items()]) 1068 present_couplings['WEIGHTED'] -= weight 1069 if present_couplings['WEIGHTED'] < 0: 1070 # Total coupling weight too large 1071 return False 1072 1073 return present_couplings
1074
1075 - def combine_legs(self, list_legs, ref_dict_to1, max_multi_to1):
1076 """Recursive function. Take a list of legs as an input, with 1077 the reference dictionary n-1->1, and output a list of list of 1078 tuples of Legs (allowed combinations) and Legs (rest). Algorithm: 1079 1080 1. Get all n-combinations from list [123456]: [12],..,[23],..,[123],.. 1081 1082 2. For each combination, say [34]. Check if combination is valid. 1083 If so: 1084 1085 a. Append [12[34]56] to result array 1086 1087 b. Split [123456] at index(first element in combination+1), 1088 i.e. [12],[456] and subtract combination from second half, 1089 i.e.: [456]-[34]=[56]. Repeat from 1. with this array 1090 1091 3. Take result array from call to 1. (here, [[56]]) and append 1092 (first half in step b - combination) + combination + (result 1093 from 1.) = [12[34][56]] to result array 1094 1095 4. After appending results from all n-combinations, return 1096 resulting array. Example, if [13] and [45] are valid 1097 combinations: 1098 [[[13]2456],[[13]2[45]6],[123[45]6]] 1099 """ 1100 1101 res = [] 1102 1103 # loop over possible combination lengths (+1 is for range convention!) 1104 for comb_length in range(2, max_multi_to1 + 1): 1105 1106 # Check the considered length is not longer than the list length 1107 if comb_length > len(list_legs): 1108 return res 1109 1110 # itertools.combinations returns all possible combinations 1111 # of comb_length elements from list_legs 1112 for comb in itertools.combinations(list_legs, comb_length): 1113 1114 # Check if the combination is valid 1115 if base_objects.LegList(comb).can_combine_to_1(ref_dict_to1): 1116 1117 # Identify the rest, create a list [comb,rest] and 1118 # add it to res 1119 res_list = copy.copy(list_legs) 1120 for leg in comb: 1121 res_list.remove(leg) 1122 res_list.insert(list_legs.index(comb[0]), comb) 1123 res.append(res_list) 1124 1125 # Now, deal with cases with more than 1 combination 1126 1127 # First, split the list into two, according to the 1128 # position of the first element in comb, and remove 1129 # all elements form comb 1130 res_list1 = list_legs[0:list_legs.index(comb[0])] 1131 res_list2 = list_legs[list_legs.index(comb[0]) + 1:] 1132 for leg in comb[1:]: 1133 res_list2.remove(leg) 1134 1135 # Create a list of type [comb,rest1,rest2(combined)] 1136 res_list = res_list1 1137 res_list.append(comb) 1138 # This is where recursion actually happens, 1139 # on the second part 1140 for item in self.combine_legs(res_list2, 1141 ref_dict_to1, 1142 max_multi_to1): 1143 final_res_list = copy.copy(res_list) 1144 final_res_list.extend(item) 1145 res.append(final_res_list) 1146 1147 return res
1148 1149
1150 - def merge_comb_legs(self, comb_lists, ref_dict_to1):
1151 """Takes a list of allowed leg combinations as an input and returns 1152 a set of lists where combinations have been properly replaced 1153 (one list per element in the ref_dict, so that all possible intermediate 1154 particles are included). For each list, give the list of vertices 1155 corresponding to the executed merging, group the two as a tuple. 1156 """ 1157 1158 res = [] 1159 1160 for comb_list in comb_lists: 1161 1162 reduced_list = [] 1163 vertex_list = [] 1164 1165 for entry in comb_list: 1166 1167 # Act on all leg combinations 1168 if isinstance(entry, tuple): 1169 1170 # Build the leg object which will replace the combination: 1171 # 1) leg ids is as given in the ref_dict 1172 leg_vert_ids = copy.copy(ref_dict_to1[\ 1173 tuple(sorted([leg.get('id') for leg in entry]))]) 1174 # 2) number is the minimum of leg numbers involved in the 1175 # combination 1176 number = min([leg.get('number') for leg in entry]) 1177 # 3) state is final, unless there is exactly one initial 1178 # state particle involved in the combination -> t-channel 1179 if len(filter(lambda leg: leg.get('state') == False, 1180 entry)) == 1: 1181 state = False 1182 else: 1183 state = True 1184 # 4) from_group is True, by definition 1185 1186 # Create and add the object. This is done by a 1187 # separate routine, to allow overloading by 1188 # daughter classes 1189 new_leg_vert_ids = [] 1190 if leg_vert_ids: 1191 new_leg_vert_ids = self.get_combined_legs(entry, 1192 leg_vert_ids, 1193 number, 1194 state) 1195 1196 reduced_list.append([l[0] for l in new_leg_vert_ids]) 1197 1198 1199 # Create and add the corresponding vertex 1200 # Extract vertex ids corresponding to the various legs 1201 # in mylegs 1202 vlist = base_objects.VertexList() 1203 for (myleg, vert_id) in new_leg_vert_ids: 1204 # Start with the considered combination... 1205 myleglist = base_objects.LegList(list(entry)) 1206 # ... and complete with legs after reducing 1207 myleglist.append(myleg) 1208 # ... and consider the correct vertex id 1209 vlist.append(base_objects.Vertex( 1210 {'legs':myleglist, 1211 'id':vert_id})) 1212 1213 vertex_list.append(vlist) 1214 1215 # If entry is not a combination, switch the from_group flag 1216 # and add it 1217 else: 1218 cp_entry = copy.copy(entry) 1219 # Need special case for from_group == None; this 1220 # is for initial state leg of decay chain process 1221 # (see Leg.can_combine_to_0) 1222 if cp_entry.get('from_group') != None: 1223 cp_entry.set('from_group', False) 1224 reduced_list.append(cp_entry) 1225 1226 # Flatten the obtained leg and vertex lists 1227 flat_red_lists = expand_list(reduced_list) 1228 flat_vx_lists = expand_list(vertex_list) 1229 1230 # Combine the two lists in a list of tuple 1231 for i in range(0, len(flat_vx_lists)): 1232 res.append((base_objects.LegList(flat_red_lists[i]), \ 1233 base_objects.VertexList(flat_vx_lists[i]))) 1234 1235 return res
1236
1237 - def get_combined_legs(self, legs, leg_vert_ids, number, state):
1238 """Create a set of new legs from the info given. This can be 1239 overloaded by daughter classes.""" 1240 1241 mylegs = [(base_objects.Leg({'id':leg_id, 1242 'number':number, 1243 'state':state, 1244 'from_group':True}), 1245 vert_id)\ 1246 for leg_id, vert_id in leg_vert_ids] 1247 1248 return mylegs
1249
1250 - def get_combined_vertices(self, legs, vert_ids):
1251 """Allow for selection of vertex ids. This can be 1252 overloaded by daughter classes.""" 1253 1254 return vert_ids
1255
1256 - def trim_diagrams(self, decay_ids=[], diaglist=None):
1257 """Reduce the number of legs and vertices used in memory. 1258 When called by a diagram generation initiated by LoopAmplitude, 1259 this function should not trim the diagrams in the attribute 'diagrams' 1260 but rather a given list in the 'diaglist' argument.""" 1261 1262 legs = [] 1263 vertices = [] 1264 1265 if diaglist is None: 1266 diaglist=self.get('diagrams') 1267 1268 # Flag decaying legs in the core process by onshell = True 1269 process = self.get('process') 1270 for leg in process.get('legs'): 1271 if leg.get('state') and leg.get('id') in decay_ids: 1272 leg.set('onshell', True) 1273 1274 for diagram in diaglist: 1275 # Keep track of external legs (leg numbers already used) 1276 leg_external = set() 1277 for ivx, vertex in enumerate(diagram.get('vertices')): 1278 for ileg, leg in enumerate(vertex.get('legs')): 1279 # Ensure that only external legs get decay flag 1280 if leg.get('state') and leg.get('id') in decay_ids and \ 1281 leg.get('number') not in leg_external: 1282 # Use onshell to indicate decaying legs, 1283 # i.e. legs that have decay chains 1284 leg = copy.copy(leg) 1285 leg.set('onshell', True) 1286 try: 1287 index = legs.index(leg) 1288 except ValueError: 1289 vertex.get('legs')[ileg] = leg 1290 legs.append(leg) 1291 else: # Found a leg 1292 vertex.get('legs')[ileg] = legs[index] 1293 leg_external.add(leg.get('number')) 1294 try: 1295 index = vertices.index(vertex) 1296 diagram.get('vertices')[ivx] = vertices[index] 1297 except ValueError: 1298 vertices.append(vertex)
1299
1300 #=============================================================================== 1301 # AmplitudeList 1302 #=============================================================================== 1303 -class AmplitudeList(base_objects.PhysicsObjectList):
1304 """List of Amplitude objects 1305 """ 1306
1307 - def has_any_loop_process(self):
1308 """ Check the content of all processes of the amplitudes in this list to 1309 see if there is any which defines perturbation couplings. """ 1310 1311 for amp in self: 1312 if amp.has_loop_process(): 1313 return True
1314
1315 - def is_valid_element(self, obj):
1316 """Test if object obj is a valid Amplitude for the list.""" 1317 1318 return isinstance(obj, Amplitude)
1319
1320 #=============================================================================== 1321 # DecayChainAmplitude 1322 #=============================================================================== 1323 -class DecayChainAmplitude(Amplitude):
1324 """A list of amplitudes + a list of decay chain amplitude lists; 1325 corresponding to a ProcessDefinition with a list of decay chains 1326 """ 1327
1328 - def default_setup(self):
1329 """Default values for all properties""" 1330 1331 self['amplitudes'] = AmplitudeList() 1332 self['decay_chains'] = DecayChainAmplitudeList()
1333
1334 - def __init__(self, argument = None, collect_mirror_procs = False, 1335 ignore_six_quark_processes = False, loop_filter=None, diagram_filter=False):
1336 """Allow initialization with Process and with ProcessDefinition""" 1337 1338 if isinstance(argument, base_objects.Process): 1339 super(DecayChainAmplitude, self).__init__() 1340 from madgraph.loop.loop_diagram_generation import LoopMultiProcess 1341 if argument['perturbation_couplings']: 1342 MultiProcessClass=LoopMultiProcess 1343 else: 1344 MultiProcessClass=MultiProcess 1345 if isinstance(argument, base_objects.ProcessDefinition): 1346 self['amplitudes'].extend(\ 1347 MultiProcessClass.generate_multi_amplitudes(argument, 1348 collect_mirror_procs, 1349 ignore_six_quark_processes, 1350 loop_filter=loop_filter, 1351 diagram_filter=diagram_filter)) 1352 else: 1353 self['amplitudes'].append(\ 1354 MultiProcessClass.get_amplitude_from_proc(argument, 1355 loop_filter=loop_filter, 1356 diagram_filter=diagram_filter)) 1357 # Clean decay chains from process, since we haven't 1358 # combined processes with decay chains yet 1359 process = copy.copy(self.get('amplitudes')[0].get('process')) 1360 process.set('decay_chains', base_objects.ProcessList()) 1361 self['amplitudes'][0].set('process', process) 1362 1363 for process in argument.get('decay_chains'): 1364 if process.get('perturbation_couplings'): 1365 raise MadGraph5Error,\ 1366 "Decay processes can not be perturbed" 1367 process.set('overall_orders', argument.get('overall_orders')) 1368 if not process.get('is_decay_chain'): 1369 process.set('is_decay_chain',True) 1370 if not process.get_ninitial() == 1: 1371 raise InvalidCmd,\ 1372 "Decay chain process must have exactly one" + \ 1373 " incoming particle" 1374 self['decay_chains'].append(\ 1375 DecayChainAmplitude(process, collect_mirror_procs, 1376 ignore_six_quark_processes, 1377 diagram_filter=diagram_filter)) 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 islegs = [leg for leg in process_definition['legs'] \ 1694 if leg['state'] == False] 1695 fslegs = [leg for leg in process_definition['legs'] \ 1696 if leg['state'] == True] 1697 1698 isids = [leg['ids'] for leg in process_definition['legs'] \ 1699 if leg['state'] == False] 1700 fsids = [leg['ids'] for leg in process_definition['legs'] \ 1701 if leg['state'] == True] 1702 polids = [tuple(leg['polarization']) for leg in process_definition['legs'] \ 1703 if leg['state'] == True] 1704 # Generate all combinations for the initial state 1705 for prod in itertools.product(*isids): 1706 islegs = [\ 1707 base_objects.Leg({'id':id, 'state': False, 1708 'polarization': islegs[i]['polarization']}) 1709 for i,id in enumerate(prod)] 1710 1711 # Generate all combinations for the final state, and make 1712 # sure to remove double counting 1713 1714 red_fsidlist = set() 1715 1716 for prod in itertools.product(*fsids): 1717 tag = zip(prod, polids) 1718 tag = sorted(tag) 1719 # Remove double counting between final states 1720 if tuple(tag) in red_fsidlist: 1721 continue 1722 1723 red_fsidlist.add(tuple(tag)) 1724 # Generate leg list for process 1725 leg_list = [copy.copy(leg) for leg in islegs] 1726 leg_list.extend([\ 1727 base_objects.Leg({'id':id, 'state': True, 'polarization': fslegs[i]['polarization']}) \ 1728 for i,id in enumerate(prod)]) 1729 1730 legs = base_objects.LegList(leg_list) 1731 1732 # Check for crossed processes 1733 sorted_legs = sorted([(l,i+1) for (i,l) in \ 1734 enumerate(legs.get_outgoing_id_list(model))]) 1735 permutation = [l[1] for l in sorted_legs] 1736 1737 sorted_legs = array.array('i', [l[0] for l in sorted_legs]) 1738 1739 # Check for six-quark processes 1740 if ignore_six_quark_processes and \ 1741 len([i for i in sorted_legs if abs(i) in \ 1742 ignore_six_quark_processes]) >= 6: 1743 continue 1744 1745 # Check if crossed process has already failed, 1746 # in that case don't check process 1747 if sorted_legs in failed_procs: 1748 continue 1749 1750 # If allowed check mass validity [assume 1->N] 1751 if use_numerical: 1752 # check that final state has lower mass than initial state 1753 initial_mass = abs(model['parameter_dict'][model.get_particle(legs[0].get('id')).get('mass')]) 1754 if initial_mass == 0: 1755 continue 1756 for leg in legs[1:]: 1757 m = model['parameter_dict'][model.get_particle(leg.get('id')).get('mass')] 1758 initial_mass -= abs(m) 1759 if initial_mass.real <= 0: 1760 continue 1761 1762 # Setup process 1763 process = process_definition.get_process_with_legs(legs) 1764 1765 fast_proc = \ 1766 array.array('i',[leg.get('id') for leg in legs]) 1767 if collect_mirror_procs and \ 1768 process_definition.get_ninitial() == 2: 1769 # Check if mirrored process is already generated 1770 mirror_proc = \ 1771 array.array('i', [fast_proc[1], fast_proc[0]] + \ 1772 list(fast_proc[2:])) 1773 try: 1774 mirror_amp = \ 1775 amplitudes[non_permuted_procs.index(mirror_proc)] 1776 except Exception: 1777 # Didn't find any mirror process 1778 pass 1779 else: 1780 # Mirror process found 1781 mirror_amp.set('has_mirror_process', True) 1782 logger.info("Process %s added to mirror process %s" % \ 1783 (process.base_string(), 1784 mirror_amp.get('process').base_string())) 1785 continue 1786 1787 # Check for successful crossings, unless we have specified 1788 # properties that break crossing symmetry 1789 if not process.get('required_s_channels') and \ 1790 not process.get('forbidden_onsh_s_channels') and \ 1791 not process.get('forbidden_s_channels') and \ 1792 not process.get('is_decay_chain') and not diagram_filter: 1793 try: 1794 crossed_index = success_procs.index(sorted_legs) 1795 # The relabeling of legs for loop amplitudes is cumbersome 1796 # and does not save so much time. It is disable here and 1797 # we use the key 'loop_diagrams' to decide whether 1798 # it is an instance of LoopAmplitude. 1799 if 'loop_diagrams' in amplitudes[crossed_index]: 1800 raise ValueError 1801 except ValueError: 1802 # No crossing found, just continue 1803 pass 1804 else: 1805 # Found crossing - reuse amplitude 1806 amplitude = MultiProcess.cross_amplitude(\ 1807 amplitudes[crossed_index], 1808 process, 1809 permutations[crossed_index], 1810 permutation) 1811 amplitudes.append(amplitude) 1812 success_procs.append(sorted_legs) 1813 permutations.append(permutation) 1814 non_permuted_procs.append(fast_proc) 1815 logger.info("Crossed process found for %s, reuse diagrams." % \ 1816 process.base_string()) 1817 continue 1818 1819 # Create new amplitude 1820 amplitude = cls.get_amplitude_from_proc(process, 1821 loop_filter=loop_filter) 1822 1823 try: 1824 result = amplitude.generate_diagrams(diagram_filter=diagram_filter) 1825 except InvalidCmd as error: 1826 failed_procs.append(sorted_legs) 1827 else: 1828 # Succeeded in generating diagrams 1829 if amplitude.get('diagrams'): 1830 amplitudes.append(amplitude) 1831 success_procs.append(sorted_legs) 1832 permutations.append(permutation) 1833 non_permuted_procs.append(fast_proc) 1834 elif not result: 1835 # Diagram generation failed for all crossings 1836 failed_procs.append(sorted_legs) 1837 1838 # Raise exception if there are no amplitudes for this process 1839 if not amplitudes: 1840 if len(failed_procs) == 1 and 'error' in locals(): 1841 raise error 1842 else: 1843 raise NoDiagramException, \ 1844 "No amplitudes generated from process %s. Please enter a valid process" % \ 1845 process_definition.nice_string() 1846 1847 1848 # Return the produced amplitudes 1849 return amplitudes
1850 1851 @classmethod
1852 - def get_amplitude_from_proc(cls,proc,**opts):
1853 """ Return the correct amplitude type according to the characteristics of 1854 the process proc. The only option that could be specified here is 1855 loop_filter and it is of course not relevant for a tree amplitude.""" 1856 1857 return Amplitude({"process": proc})
1858 1859 1860 @staticmethod
1861 - def find_optimal_process_orders(process_definition, diagram_filter=False):
1862 """Find the minimal WEIGHTED order for this set of processes. 1863 1864 The algorithm: 1865 1866 1) Check the coupling hierarchy of the model. Assign all 1867 particles to the different coupling hierarchies so that a 1868 particle is considered to be in the highest hierarchy (i.e., 1869 with lowest value) where it has an interaction. 1870 1871 2) Pick out the legs in the multiprocess according to the 1872 highest hierarchy represented (so don't mix particles from 1873 different hierarchy classes in the same multiparticles!) 1874 1875 3) Find the starting maximum WEIGHTED order as the sum of the 1876 highest n-2 weighted orders 1877 1878 4) Pick out required s-channel particle hierarchies, and use 1879 the highest of the maximum WEIGHTED order from the legs and 1880 the minimum WEIGHTED order extracted from 2*s-channel 1881 hierarchys plus the n-2-2*(number of s-channels) lowest 1882 leg weighted orders. 1883 1884 5) Run process generation with the WEIGHTED order determined 1885 in 3)-4) - # final state gluons, with all gluons removed from 1886 the final state 1887 1888 6) If no process is found, increase WEIGHTED order by 1 and go 1889 back to 5), until we find a process which passes. Return that 1890 order. 1891 1892 7) Continue 5)-6) until we reach (n-2)*(highest hierarchy)-1. 1893 If still no process has passed, return 1894 WEIGHTED = (n-2)*(highest hierarchy) 1895 """ 1896 1897 assert isinstance(process_definition, base_objects.ProcessDefinition), \ 1898 "%s not valid ProcessDefinition object" % \ 1899 repr(process_definition) 1900 1901 processes = base_objects.ProcessList() 1902 amplitudes = AmplitudeList() 1903 1904 # If there are already couplings defined, return 1905 if process_definition.get('orders') or \ 1906 process_definition.get('overall_orders') or \ 1907 process_definition.get('NLO_mode')=='virt': 1908 return process_definition.get('orders') 1909 1910 # If this is a decay process (and not a decay chain), return 1911 if process_definition.get_ninitial() == 1 and not \ 1912 process_definition.get('is_decay_chain'): 1913 return process_definition.get('orders') 1914 1915 logger.info("Checking for minimal orders which gives processes.") 1916 logger.info("Please specify coupling orders to bypass this step.") 1917 1918 # Calculate minimum starting guess for WEIGHTED order 1919 max_order_now, particles, hierarchy = \ 1920 process_definition.get_minimum_WEIGHTED() 1921 coupling = 'WEIGHTED' 1922 1923 model = process_definition.get('model') 1924 1925 # Extract the initial and final leg ids 1926 isids = [leg['ids'] for leg in \ 1927 filter(lambda leg: leg['state'] == False, process_definition['legs'])] 1928 fsids = [leg['ids'] for leg in \ 1929 filter(lambda leg: leg['state'] == True, process_definition['legs'])] 1930 1931 max_WEIGHTED_order = \ 1932 (len(fsids + isids) - 2)*int(model.get_max_WEIGHTED()) 1933 # get the definition of the WEIGHTED 1934 hierarchydef = process_definition['model'].get('order_hierarchy') 1935 tmp = [] 1936 hierarchy = hierarchydef.items() 1937 hierarchy.sort() 1938 for key, value in hierarchydef.items(): 1939 if value>1: 1940 tmp.append('%s*%s' % (value,key)) 1941 else: 1942 tmp.append('%s' % key) 1943 wgtdef = '+'.join(tmp) 1944 # Run diagram generation with increasing max_order_now until 1945 # we manage to get diagrams 1946 while max_order_now < max_WEIGHTED_order: 1947 logger.info("Trying coupling order WEIGHTED<=%d: WEIGTHED IS %s" % (max_order_now, wgtdef)) 1948 1949 oldloglevel = logger.level 1950 logger.setLevel(logging.WARNING) 1951 1952 # failed_procs are processes that have already failed 1953 # based on crossing symmetry 1954 failed_procs = [] 1955 # Generate all combinations for the initial state 1956 for prod in apply(itertools.product, isids): 1957 islegs = [ base_objects.Leg({'id':id, 'state': False}) \ 1958 for id in prod] 1959 1960 # Generate all combinations for the final state, and make 1961 # sure to remove double counting 1962 1963 red_fsidlist = [] 1964 1965 for prod in apply(itertools.product, fsids): 1966 1967 # Remove double counting between final states 1968 if tuple(sorted(prod)) in red_fsidlist: 1969 continue 1970 1971 red_fsidlist.append(tuple(sorted(prod))); 1972 1973 # Remove gluons from final state if QCD is among 1974 # the highest coupling hierarchy 1975 nglue = 0 1976 if 21 in particles[0]: 1977 nglue = len([id for id in prod if id == 21]) 1978 prod = [id for id in prod if id != 21] 1979 1980 # Generate leg list for process 1981 leg_list = [copy.copy(leg) for leg in islegs] 1982 1983 leg_list.extend([\ 1984 base_objects.Leg({'id':id, 'state': True}) \ 1985 for id in prod]) 1986 1987 legs = base_objects.LegList(leg_list) 1988 1989 # Set summed coupling order according to max_order_now 1990 # subtracting the removed gluons 1991 coupling_orders_now = {coupling: max_order_now - \ 1992 nglue * model['order_hierarchy']['QCD']} 1993 1994 # Setup process 1995 process = base_objects.Process({\ 1996 'legs':legs, 1997 'model':model, 1998 'id': process_definition.get('id'), 1999 'orders': coupling_orders_now, 2000 'required_s_channels': \ 2001 process_definition.get('required_s_channels'), 2002 'forbidden_onsh_s_channels': \ 2003 process_definition.get('forbidden_onsh_s_channels'), 2004 'sqorders_types': \ 2005 process_definition.get('sqorders_types'), 2006 'squared_orders': \ 2007 process_definition.get('squared_orders'), 2008 'split_orders': \ 2009 process_definition.get('split_orders'), 2010 'forbidden_s_channels': \ 2011 process_definition.get('forbidden_s_channels'), 2012 'forbidden_particles': \ 2013 process_definition.get('forbidden_particles'), 2014 'is_decay_chain': \ 2015 process_definition.get('is_decay_chain'), 2016 'overall_orders': \ 2017 process_definition.get('overall_orders'), 2018 'split_orders': \ 2019 process_definition.get('split_orders')}) 2020 2021 # Check for couplings with given expansion orders 2022 process.check_expansion_orders() 2023 2024 # Check for crossed processes 2025 sorted_legs = sorted(legs.get_outgoing_id_list(model)) 2026 # Check if crossed process has already failed 2027 # In that case don't check process 2028 if tuple(sorted_legs) in failed_procs and not process_definition.get('forbidden_s_channels'): 2029 continue 2030 2031 amplitude = Amplitude({'process': process}) 2032 try: 2033 amplitude.generate_diagrams(diagram_filter=diagram_filter) 2034 except InvalidCmd, error: 2035 failed_procs.append(tuple(sorted_legs)) 2036 else: 2037 if amplitude.get('diagrams'): 2038 # We found a valid amplitude. Return this order number 2039 logger.setLevel(oldloglevel) 2040 return {coupling: max_order_now} 2041 else: 2042 failed_procs.append(tuple(sorted_legs)) 2043 # No processes found, increase max_order_now 2044 max_order_now += 1 2045 logger.setLevel(oldloglevel) 2046 2047 # If no valid processes found with nfinal-1 couplings, return maximal 2048 return {coupling: max_order_now}
2049 2050 @staticmethod
2051 - def cross_amplitude(amplitude, process, org_perm, new_perm):
2052 """Return the amplitude crossed with the permutation new_perm""" 2053 # Create dict from original leg numbers to new leg numbers 2054 perm_map = dict(zip(org_perm, new_perm)) 2055 # Initiate new amplitude 2056 new_amp = copy.copy(amplitude) 2057 # Number legs 2058 for i, leg in enumerate(process.get('legs')): 2059 leg.set('number', i+1) 2060 # Set process 2061 new_amp.set('process', process) 2062 # Now replace the leg numbers in the diagrams 2063 diagrams = base_objects.DiagramList([d.renumber_legs(perm_map, 2064 process.get('legs'),) for \ 2065 d in new_amp.get('diagrams')]) 2066 new_amp.set('diagrams', diagrams) 2067 new_amp.trim_diagrams() 2068 2069 # Make sure to reset mirror process 2070 new_amp.set('has_mirror_process', False) 2071 2072 return new_amp
2073
2074 #=============================================================================== 2075 # Global helper methods 2076 #=============================================================================== 2077 2078 -def expand_list(mylist):
2079 """Takes a list of lists and elements and returns a list of flat lists. 2080 Example: [[1,2], 3, [4,5]] -> [[1,3,4], [1,3,5], [2,3,4], [2,3,5]] 2081 """ 2082 2083 # Check that argument is a list 2084 assert isinstance(mylist, list), "Expand_list argument must be a list" 2085 2086 res = [] 2087 2088 tmplist = [] 2089 for item in mylist: 2090 if isinstance(item, list): 2091 tmplist.append(item) 2092 else: 2093 tmplist.append([item]) 2094 2095 for item in apply(itertools.product, tmplist): 2096 res.append(list(item)) 2097 2098 return res
2099
2100 -def expand_list_list(mylist):
2101 """Recursive function. Takes a list of lists and lists of lists 2102 and returns a list of flat lists. 2103 Example: [[1,2],[[4,5],[6,7]]] -> [[1,2,4,5], [1,2,6,7]] 2104 """ 2105 2106 res = [] 2107 2108 if not mylist or len(mylist) == 1 and not mylist[0]: 2109 return [[]] 2110 2111 # Check the first element is at least a list 2112 assert isinstance(mylist[0], list), \ 2113 "Expand_list_list needs a list of lists and lists of lists" 2114 2115 # Recursion stop condition, one single element 2116 if len(mylist) == 1: 2117 if isinstance(mylist[0][0], list): 2118 return mylist[0] 2119 else: 2120 return mylist 2121 2122 if isinstance(mylist[0][0], list): 2123 for item in mylist[0]: 2124 # Here the recursion happens, create lists starting with 2125 # each element of the first item and completed with 2126 # the rest expanded 2127 for rest in expand_list_list(mylist[1:]): 2128 reslist = copy.copy(item) 2129 reslist.extend(rest) 2130 res.append(reslist) 2131 else: 2132 for rest in expand_list_list(mylist[1:]): 2133 reslist = copy.copy(mylist[0]) 2134 reslist.extend(rest) 2135 res.append(reslist) 2136 2137 2138 return res
2139