Merge lp:~madnklo/mg5amcnlo/MadNkLO_MCPS into lp:~madnklo/mg5amcnlo/MadNkLO

Proposed by Valentin Hirschi on 2018-04-25
Status: Merged
Merged at revision: 552
Proposed branch: lp:~madnklo/mg5amcnlo/MadNkLO_MCPS
Merge into: lp:~madnklo/mg5amcnlo/MadNkLO
Diff against target: 5942 lines (+3201/-912)
12 files modified
madgraph/core/diagram_generation.py (+4/-5)
madgraph/integrator/ME7_integrands.py (+177/-116)
madgraph/integrator/integrators.py (+33/-6)
madgraph/integrator/phase_space_generators.py (+950/-17)
madgraph/integrator/vegas3_integrator.py (+442/-3)
madgraph/interface/ME7_interface.py (+37/-9)
madgraph/interface/madgraph_interface.py (+4/-3)
madgraph/iolibs/drawing_eps.py (+2/-2)
tests/parallel_tests/compare_ME7_with_ME6.py (+28/-10)
tests/parallel_tests/madevent_comparator.py (+7/-3)
tests/time_db (+348/-347)
tests/unit_tests/integrator/test_phase_space_generators.py (+1169/-391)
To merge this branch: bzr merge lp:~madnklo/mg5amcnlo/MadNkLO_MCPS
Reviewer Review Type Date Requested Status
Simone Lionetti 2018-04-25 Pending
Valentin Hirschi Pending
Review via email: mp+344221@code.launchpad.net

Commit message

This branch is the result of my work on implementing Monte-Carlo Multi-channeling and parametrisation technique for efficient numerical integration if inclusive cross-sections.

To post a comment you must log in.
Dario Kermanschah (kedario) wrote :

I still need to clean up the code and let you know when you can look at the details.

lp:~madnklo/mg5amcnlo/MadNkLO_MCPS updated on 2018-04-26
461. By Dario Kermanschah on 2018-04-26

1. Cleaned up code and added comments

462. By Dario Kermanschah on 2018-04-26

Added my thesis that documents how importance sampling
and multi-channeling are implemented.

Dario Kermanschah (kedario) wrote :
Download full text (3.4 KiB)

New techniques in the integration framework: importance sampling and multi-channeling.

CHANGES within phase_space_generators.py:

new class SingleChannelPhasespace (SCPS):
     Implements phase space factorization and importance sampling of a single topology.
     Returns the phase space point together with its importance sampling weight (that flattens the single topology peak structure).
--> can be used as an alternative to FlatInvertiblePhasespace (FLATPS) with single channel integrators NAIVE or VEGAS3.

new class MultiChannelPhasespace (MCPS):
     Similar to SCPS, implements phase space factorization and importance sampling of a single channel according to the topology it corresponds to.
     However, comes with a multi-channel weight (that flattens the peak structures of all topologies involved simultaneously).
     Returns the phase space point together with this multi-channel weight.
--> cannot be used as an alternative to FlatInvertiblePhasespace (FLATPS), since it does not work with single channel integrators NAIVE or VEGAS3. Can only be used in combination with the multi-channel integrator MC_VEGAS3

---> new option in MadEvent interface: --PS_generator, can choose from {FLATPS, SCPS, MCPS}.

CHANGES within vegas3_integrator.py:

new class MultiChannelVegas3Integrator (MC_VEGAS3):
     Implements multi-channeling according to the Jacobian method with channel weights (alpha).
     Specifies the various channels according to their topologies as an instance of the class Channel and compute their channel integral in parallel.
     Only works with MCPS.
     Iteratively adapts the vegas grid of each channel and the channel weights in a survey stage and just increases the number of points in a refinement stage until the desired accuracy is reached.
     A max and a min number of iterations can be set.
     The iterations stop before max if the vegas grid and the channel weights converge.
     Prints the changes after iterations and frequent (after every 10s) current estimates of the full integral.
     Returns the estimate of the integral and the Monte Carlo error.

new class Channel:
     Used by MC_VEGAS3.
     Specifies the integrand, the channel number and the vegas grid for this channel.
     Saves integration estimates, function evaluations, Monte Carlo error, ... for the course of one iteration in the adaptive multi-channel integrator MC_VEGAS3.

---> new integrator option in MadEvent interface: --integrator=MC_VEGAS3.

MORE DETAILED DESCRIPTIONS IN MY THESIS OR IN THE CODE.

EXAMPLE MULTI-CHANNELING:

generate e+ e- > mu+ mu- a --LO
output mytest
launch mytest
launch --integrator=MC_VEGAS3 --n_points=8000 --n_iterations=3 --PS_generator=MCPS

after 8 minutes (if you computer is as slow as mine) this should produce something like:
2.69383e-02 +/- 1.79e-04 [pb]

NOTE: if --integrator=MC_VEGAS3:
     n_points: specifies total number of points in first iteration, it is doubled with every iteration
     n_iterations: number of max iterations

EXAMPLE IMPORTANCE SAMPLING WITHOUT MULTI-CHANNELING:

generate e+ e- > mu+ mu- a --LO
output mytest
launch mytest
launch --integrator=NAIVE --n_points=1000 --PS_generator=SCPS

should produce ...

Read more...

lp:~madnklo/mg5amcnlo/MadNkLO_MCPS updated on 2018-04-26
463. By Dario Kermanschah on 2018-04-26

Forgot to delete something that is not used anymore.

464. By Dario Kermanschah on 2018-04-26

Fixed a minor bug.

Valentin Hirschi (valentin-hirschi) wrote :

Thank you a lot for the effort in this documentation.

Simone and I will go through the code in details and send comments.
As you will see we can be pretty pedantic :), but this is because there is no rush since you're going on holiday and also because it's good if we're all on the same page regarding coding standards.

Anyway, your work is a great addition to our project and I thank you for this and look forward to working again with you when you'll be back "for good" in November!

Enjoy your holiday

lp:~madnklo/mg5amcnlo/MadNkLO_MCPS updated on 2019-01-17
465. By Valentin Hirschi on 2019-01-16

1. Fixed test related to MCPS and added some minor features to the integration with SCPS so as to be able to select the channel.

466. By Valentin Hirschi on 2019-01-16

1. Merged with current trunk and fixed conflicts. Needs testing now and adding the option save_points_to_file to NAIVE integrator in function do_set_integrator_options

467. By Valentin Hirschi on 2019-01-17

1. Fixed generators to now also work at NLO

468. By Valentin Hirschi on 2019-01-17

1. Fixed tests.

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1=== added directory 'Documentation.moved'
2=== added file 'Documentation.moved/ThesisDarioKermanschah.pdf'
3Binary files Documentation.moved/ThesisDarioKermanschah.pdf 1970-01-01 00:00:00 +0000 and Documentation.moved/ThesisDarioKermanschah.pdf 2019-01-17 08:58:59 +0000 differ
4=== added file 'Documentation/ThesisDarioKermanschah.pdf'
5Binary files Documentation/ThesisDarioKermanschah.pdf 1970-01-01 00:00:00 +0000 and Documentation/ThesisDarioKermanschah.pdf 2019-01-17 08:58:59 +0000 differ
6=== modified file 'madgraph/core/diagram_generation.py'
7--- madgraph/core/diagram_generation.py 2017-10-04 17:43:35 +0000
8+++ madgraph/core/diagram_generation.py 2019-01-17 08:58:59 +0000
9@@ -911,8 +911,8 @@
10
11 res = diag_list.__class__()
12 nb_removed = 0
13- for diag in diag_list:
14- if remove_diag(diag):
15+ for i,diag in enumerate(diag_list):
16+ if remove_diag(diag) or (i != 0 and i != 1):
17 nb_removed +=1
18 else:
19 res.append(diag)
20@@ -1677,7 +1677,7 @@
21
22 processes = base_objects.ProcessList()
23 amplitudes = AmplitudeList()
24-
25+
26 # failed_procs and success_procs are sorted processes that have
27 # already failed/succeeded based on crossing symmetry
28 failed_procs = []
29@@ -1723,7 +1723,7 @@
30 for id in prod])
31
32 legs = base_objects.LegList(leg_list)
33-
34+
35 # Check for crossed processes
36 sorted_legs = sorted([(l,i+1) for (i,l) in \
37 enumerate(legs.get_outgoing_id_list(model))])
38@@ -1843,7 +1843,6 @@
39 "No amplitudes generated from process %s. Please enter a valid process" % \
40 process_definition.nice_string()
41
42-
43 # Return the produced amplitudes
44 return amplitudes
45
46
47=== modified file 'madgraph/integrator/ME7_integrands.py'
48--- madgraph/integrator/ME7_integrands.py 2019-01-14 15:14:44 +0000
49+++ madgraph/integrator/ME7_integrands.py 2019-01-17 08:58:59 +0000
50@@ -49,12 +49,12 @@
51 # useful shortcut
52 pjoin = os.path.join
53
54-#root_path = pjoin(os.path.dirname(os.path.realpath( __file__ )), *([os.path.pardir]*3))
55-#sys.path.insert(0, root_path)
56+# root_path = pjoin(os.path.dirname(os.path.realpath( __file__ )), *([os.path.pardir]*3))
57+# sys.path.insert(0, root_path)
58
59 # Special logger for the Cmd Interface
60-logger = logging.getLogger('madevent7') # -> stdout
61-logger_stderr = logging.getLogger('madevent7.stderr') # ->stderr
62+logger = logging.getLogger('madevent7') # -> stdout
63+logger_stderr = logging.getLogger('madevent7.stderr') # ->stderr
64
65 import madgraph.core.base_objects as base_objects
66 import madgraph.core.subtraction as subtraction
67@@ -639,6 +639,8 @@
68 class ME7Integrand(integrands.VirtualIntegrand):
69 """ Specialization for multi-purpose integration with ME7."""
70
71+ counter = 0
72+
73 # Maximum size of the cache for PDF calls
74 PDF_cache_max_size = 1000
75
76@@ -699,7 +701,7 @@
77 else:
78 return super(ME7Integrand, cls).__new__(cls, *all_args, **opt)
79
80- def __init__(self, model,
81+ def __init__(self, model,
82 run_card,
83 contribution_definition,
84 processes_map,
85@@ -730,17 +732,17 @@
86
87 # The process map of the Contribution instance at the origin of this integrand.
88 # The format is identical to the one generated from the function 'get_process_map' of a contribution.
89- self.processes_map = processes_map
90+ self.processes_map = processes_map
91
92 # Add information about the topology of the diagrams constituting the processes,
93 # so as to be able to build efficient phase-space parametrizations. The format of these dictionaries
94 # is specified in the function 'set_phase_space_topologies' of the class contributions.Contribution
95- self.topologies_to_processes = topologies_to_processes
96- self.processes_to_topologies = processes_to_topologies
97+ self.topologies_to_processes = topologies_to_processes
98+ self.processes_to_topologies = processes_to_topologies
99
100 # An instance of accessors.MEAccessorDict providing access to all ME available as part of this
101 # ME7 session.
102- self.all_MEAccessors = all_MEAccessors
103+ self.all_MEAccessors = all_MEAccessors
104
105 # Update and define many properties of self based on the provided run-card and model.
106 self.synchronize(model, run_card, ME7_configuration)
107@@ -771,8 +773,8 @@
108 contribution. Can be overloaded by daughter classes."""
109 GREEN = '\033[92m'
110 ENDC = '\033[0m'
111- return GREEN+' %s'%defining_process.nice_string(print_weighted=False).\
112- replace('Process: ','')+ENDC
113+ return GREEN + ' %s' % defining_process.nice_string(print_weighted=False).\
114+ replace('Process: ', '') + ENDC
115
116 def get_short_name(self):
117 """ Returns the short-name for this integrand, typically extracted from the one
118@@ -788,21 +790,21 @@
119 res.append('%-30s: %s'%('ME7Integrand_type',type(self)))
120 res.extend([self.contribution_definition.nice_string()])
121 if not self.topologies_to_processes is None:
122- res.append('%-30s: %d'%('Number of topologies',
123+ res.append('%-30s: %d' % ('Number of topologies',
124 len(self.topologies_to_processes.keys())))
125 res.extend(self.get_additional_nice_string_printout_lines())
126
127 if format < 1:
128- res.append('Generated and mapped processes for this contribution: %d (+%d mapped)'%
129- ( len(self.processes_map.keys()),
130- len(sum([v[1] for v in self.processes_map.values()],[])) ) )
131+ res.append('Generated and mapped processes for this contribution: %d (+%d mapped)' %
132+ (len(self.processes_map.keys()),
133+ len(sum([v[1] for v in self.processes_map.values()], []))))
134 else:
135 res.append('Generated and mapped processes for this contribution:')
136 for process_key, (defining_process, mapped_processes) in self.processes_map.items():
137 res.append(self.get_nice_string_process_line(process_key, defining_process, format=format))
138 for mapped_process in mapped_processes:
139- res.append(BLUE+u' \u21b3 '+mapped_process.nice_string(print_weighted=False)\
140- .replace('Process: ','')+ENDC)
141+ res.append(BLUE + u' \u21b3 ' + mapped_process.nice_string(print_weighted=False)\
142+ .replace('Process: ', '') + ENDC)
143
144 return '\n'.join(res).encode('utf-8')
145
146@@ -959,15 +961,15 @@
147 """ Synchronize this integrand with the most recent run_card and model."""
148
149 # The option dictionary of ME7
150- self.ME7_configuration = ME7_configuration
151+ self.ME7_configuration = ME7_configuration
152
153 # A ModelReader instance, initialized with the values of the param_card.dat of this run
154- self.model = model
155+ self.model = model
156 if not isinstance(self.model, model_reader.ModelReader):
157 raise MadGraph5Error("The ME7Integrand must be initialized with a ModelReader instance.")
158
159 # A RunCardME7 instance, properly initialized with the values of the run_card.dat of this run
160- self.run_card = run_card
161+ self.run_card = run_card
162
163 self.flavor_cut_function = ME7Integrand.build_flavor_cut_function(self.run_card['flavor_cuts'])
164
165@@ -978,17 +980,17 @@
166 this_proc_masses = proc.get_external_masses(self.model)
167 if this_proc_masses != self.masses:
168 raise MadGraph5Error("A contribution must entail processes with all the same external masses.\n"
169- "This is not the case; process\n%s\nhas masses '%s' while process\n%s\n has masses '%s'."%
170- (all_processes[0].nice_string(), self.masses, proc.nice_string(), this_proc_masses) )
171+ "This is not the case; process\n%s\nhas masses '%s' while process\n%s\n has masses '%s'." %
172+ (all_processes[0].nice_string(), self.masses, proc.nice_string(), this_proc_masses))
173 self.n_initial = len(self.masses[0])
174 self.n_final = len(self.masses[1])
175
176- if self.n_initial==1:
177+ if self.n_initial == 1:
178 raise InvalidCmd("MadEvent7 does not yet support decay processes.")
179
180- if not (self.run_card['lpp1']==self.run_card['lpp2']==1) and \
181- not (self.run_card['lpp1']==self.run_card['lpp2']==0):
182- raise InvalidCmd("MadEvent7 does not support the following collider mode yet (%d,%d)."%\
183+ if not (self.run_card['lpp1'] == self.run_card['lpp2'] == 1) and \
184+ not (self.run_card['lpp1'] == self.run_card['lpp2'] == 0):
185+ raise InvalidCmd("MadEvent7 does not support the following collider mode yet (%d,%d)." % \
186 (self.run_card['lpp1'], self.run_card['lpp2']))
187
188 # Always initialize the basic flat PS generator. It can be overwritten later if necessary.
189@@ -996,15 +998,57 @@
190 0 if self.contribution_definition.beam_factorization['beam_one'] is None else 1,
191 0 if self.contribution_definition.beam_factorization['beam_two'] is None else 1,
192 )
193- self.phase_space_generator = phase_space_generators.FlatInvertiblePhasespace(
194- self.masses[0], self.masses[1],
195- beam_Es = (self.run_card['ebeam1'], self.run_card['ebeam2']),
196- beam_types = simplified_beam_types,
197- is_beam_factorization_active =
198- ( self.contribution_definition.is_beam_active('beam_one'),
199- self.contribution_definition.is_beam_active('beam_two') ),
200- correlated_beam_convolution = self.contribution_definition.correlated_beam_convolution
201- )
202+
203+ # now choose the right PS_generator according to specified option
204+ if 'PS_generator' not in ME7_configuration.keys() or ME7_configuration['PS_generator'] is None:
205+ # If nothing is specified, we use FLATPS by default
206+ selected_PS_generator = "FLATPS"
207+ else:
208+ selected_PS_generator = ME7_configuration['PS_generator']
209+
210+ PS_generator_args = [self.masses[0], self.masses[1]]
211+ PS_generator_options = {
212+ 'beam_Es' : (self.run_card['ebeam1'], self.run_card['ebeam2']),
213+ 'beam_types' : simplified_beam_types,
214+ 'is_beam_factorization_active' : ( self.contribution_definition.is_beam_active('beam_one'),
215+ self.contribution_definition.is_beam_active('beam_two') ),
216+ 'correlated_beam_convolution' : self.contribution_definition.correlated_beam_convolution
217+ }
218+
219+ if selected_PS_generator.startswith('SCPS'):
220+ if '@' in ME7_configuration['PS_generator']:
221+ topology_number = int(ME7_configuration['PS_generator'].split('@')[1])-1
222+ else:
223+ topology_number = 0
224+ if topology_number >= len(self.topologies_to_processes):
225+ raise InvalidCmd('This process only has %d topologies. You cannot choose the #%dth one.'%(
226+ len(self.topologies_to_processes), topology_number+1))
227+ chosen_key = self.topologies_to_processes.keys()[topology_number]
228+ a_topology = self.topologies_to_processes[chosen_key]['s_and_t_channels']
229+ PS_generator_options['model'] = self.model
230+ PS_generator_options['topology'] = a_topology
231+ self.phase_space_generator = phase_space_generators.SingleChannelPhasespace(
232+ *PS_generator_args, **PS_generator_options)
233+ logger.debug('Integrand %s is using PS_generator=SCPS, with topology #%d/%d:%s'%(
234+ self.get_short_name(),
235+ topology_number+1, len(self.topologies_to_processes),
236+ self.phase_space_generator.get_topology_string(a_topology,
237+ path_to_print=self.phase_space_generator.path)
238+ ))
239+ elif selected_PS_generator == 'MCPS':
240+ all_topologies = [None] * len(self.topologies_to_processes.keys())
241+ for a_key in self.topologies_to_processes.keys():
242+ all_topologies[a_key[1]] = self.topologies_to_processes[a_key]['s_and_t_channels']
243+ PS_generator_options['model'] = self.model
244+ PS_generator_options['topologies'] = all_topologies
245+ self.phase_space_generator = phase_space_generators.MultiChannelPhasespace(
246+ *PS_generator_args, **PS_generator_options)
247+ elif selected_PS_generator == 'FLATPS':
248+ self.phase_space_generator = phase_space_generators.FlatInvertiblePhasespace(
249+ *PS_generator_args, **PS_generator_options)
250+ #logger.info('Using PS_generator=FLATPS')
251+ else:
252+ raise MadGraph5Error('Specified phase-space generator not reckognized: %s'%selected_PS_generator)
253
254 # Add a copy of the PS generator dimensions here.
255 # Notice however that we could add more dimensions pertaining to this integrand only, and PS generation.
256@@ -1016,8 +1060,8 @@
257 d for d in integrand_dimensions if d.name not in self.FROZEN_DIMENSIONS)
258 self.set_dimensions(integrand_dimensions)
259 self.dim_ordered_names = [d.name for d in self.get_dimensions()]
260- self.dim_name_to_position = dict((name,i) for i, name in enumerate(self.dim_ordered_names))
261- self.position_to_dim_name = dict((v,k) for (k,v) in self.dim_name_to_position.items())
262+ self.dim_name_to_position = dict((name, i) for i, name in enumerate(self.dim_ordered_names))
263+ self.position_to_dim_name = dict((v, k) for (k, v) in self.dim_name_to_position.items())
264
265 self.collider_energy = self.run_card['ebeam1'] + self.run_card['ebeam2']
266 # Set the seed
267@@ -1028,7 +1072,7 @@
268 # Setup the PDF cache
269 self.PDF_cache = {}
270 self.PDF_cache_entries = []
271- if self.run_card['lpp1']==0 and self.run_card['lpp2']==0:
272+ if self.run_card['lpp1'] == 0 and self.run_card['lpp2'] == 0:
273 self.pdf = None
274 self.pdfsets = None
275 else:
276@@ -1044,21 +1088,21 @@
277 raise MadGraph5Error("The python lhapdf API could not be loaded.")
278 # Adjust LHAPDF verbosity to current logger's verbosity
279 # Ask for logging.DEBUG-1 so as to only have lhapdf verbose if really desired.
280- lhapdf.setVerbosity(1 if logger.level<=(logging.DEBUG-1) else 0)
281+ lhapdf.setVerbosity(1 if logger.level <= (logging.DEBUG - 1) else 0)
282
283- pdfsets_dir = subprocess.Popen([lhapdf_config,'--datadir'],\
284+ pdfsets_dir = subprocess.Popen([lhapdf_config, '--datadir'], \
285 stdout=subprocess.PIPE).stdout.read().strip()
286 lhapdf.pathsPrepend(pdfsets_dir)
287- lhapdf_version = subprocess.Popen([lhapdf_config,'--version'],\
288+ lhapdf_version = subprocess.Popen([lhapdf_config, '--version'], \
289 stdout=subprocess.PIPE).stdout.read().strip()
290 pdf_info = common_run.CommonRunCmd.get_lhapdf_pdfsets_list_static(pdfsets_dir, lhapdf_version)
291 lhaid = self.run_card.get_lhapdf_id()
292 if lhaid not in pdf_info:
293- raise InvalidCmd("Could not find PDF set with lhaid #%d in %s."%(lhaid, pdfsets_dir))
294+ raise InvalidCmd("Could not find PDF set with lhaid #%d in %s." % (lhaid, pdfsets_dir))
295 pdf_set_name = pdf_info[lhaid]['filename']
296 if not os.path.isdir(pjoin(pdfsets_dir, pdf_set_name)):
297- raise InvalidCmd("Could not find PDF set directory named "+
298- "'%s' in '%s'.\n"%(pdf_set_name, pdfsets_dir)+
299+ raise InvalidCmd("Could not find PDF set directory named " +
300+ "'%s' in '%s'.\n" % (pdf_set_name, pdfsets_dir) +
301 "It can be downloaded from LHAPDF official online resources.")
302
303 self.pdfsets = lhapdf.getPDFSet(pdf_info[lhaid]['filename'])
304@@ -1079,13 +1123,13 @@
305 # Leave these parameters to their default if not specified in the model
306 continue
307 else:
308- raise InvalidCmd("When not using PDFsets, MadEvent7 requires a model with the"+
309- " parameter %s to be defined so as to be able to run alpha_S."%param)
310+ raise InvalidCmd("When not using PDFsets, MadEvent7 requires a model with the" +
311+ " parameter %s to be defined so as to be able to run alpha_S." % param)
312 if model_param_dict[param] != 0.:
313 as_running_params[param] = model_param_dict[param]
314 # For now always chose to run alpha_S at two loops.
315 n_loop_for_as_running = 2
316- self.alpha_s_runner = model_reader.Alphas_Runner(as_running_params['aS'], n_loop_for_as_running,
317+ self.alpha_s_runner = model_reader.Alphas_Runner(as_running_params['aS'], n_loop_for_as_running,
318 as_running_params['mdl_MZ'], as_running_params['mdl_MC'], as_running_params['mdl_MB'])
319
320 #Import the observables from the FO_analysis folder
321@@ -1128,8 +1172,8 @@
322 """ Initialize self from a dump and possibly other information necessary for reconstructing this
323 integrand."""
324
325- return cls( model,
326- run_card,
327+ return cls(model,
328+ run_card,
329 dump['contribution_definition'],
330 dump['processes_map'],
331 dump['topologies_to_processes'],
332@@ -1141,12 +1185,12 @@
333 def set_phase_space_generator(self, PS_generator):
334 """ Overwrites current phase-space generator."""
335 if not isinstance(PS_generator, phase_space_generators.VirtualPhaseSpaceGenerator):
336- raise MadGraph5Error("Cannot assign to a MadEvent7 integrand a phase-space generator that "+
337+ raise MadGraph5Error("Cannot assign to a MadEvent7 integrand a phase-space generator that " +
338 " does not inherit from VirtualPhaseSpaceGenerator.")
339 if PS_generator.nDimPhaseSpace() != self.phase_space_generator.nDimPhaseSpace():
340- raise MadGraph5Error("A MadEvent7 integrand was assigned a phase-space generator with the"+
341- " wrong number of integration dimensions: %d instead of %d"%
342- (PS_generator.nDimPhaseSpace(),self.phase_space_generator.nDimPhaseSpace()))
343+ raise MadGraph5Error("A MadEvent7 integrand was assigned a phase-space generator with the" +
344+ " wrong number of integration dimensions: %d instead of %d" %
345+ (PS_generator.nDimPhaseSpace(), self.phase_space_generator.nDimPhaseSpace()))
346 self.phase_space_generator = PS_generator
347
348 def is_part_of_process_selection(self, process_list, selection=None):
349@@ -1159,7 +1203,7 @@
350 def pdg_list_match(target_list, selection_list):
351 if len(target_list) != len(selection_list):
352 return False
353- targets = dict( (k, target_list.count(k)) for k in set(target_list) )
354+ targets = dict((k, target_list.count(k)) for k in set(target_list))
355 found_it = False
356 for sel in itertools.product(*selection_list):
357 found_it = True
358@@ -1292,19 +1336,19 @@
359
360 # These cuts are not allowed to resolve flavour, but only whether a particle is a jet or not
361 def is_a_jet(pdg):
362- return abs(pdg) in range(1,self.run_card['maxjetflavor']+1)+[21]
363+ return abs(pdg) in range(1, self.run_card['maxjetflavor'] + 1) + [21]
364
365 def is_a_lepton(pdg):
366- return abs(pdg) in [11,13,15]
367+ return abs(pdg) in [11, 13, 15]
368
369 def is_a_neutrino(pdg):
370- return abs(pdg) in [12,14,16]
371+ return abs(pdg) in [12, 14, 16]
372
373 def is_a_photon(pdg):
374- return pdg==22
375+ return pdg == 22
376
377- if debug_cuts: logger.debug( "Processing flavor-blind cuts for process %s and PS point:\n%s"%(
378- str(process_pdgs), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial) ))
379+ if debug_cuts: logger.debug("Processing flavor-blind cuts for process %s and PS point:\n%s" % (
380+ str(process_pdgs), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial)))
381
382 if n_jets_allowed_to_be_clustered is None:
383 n_jets_allowed_to_be_clustered = self.contribution_definition.n_unresolved_particles
384@@ -1336,8 +1380,8 @@
385 return True
386 else:
387 # If fastjet is needed but not found, make sure to stop
388- if (not PYJET_AVAILABLE) and n_jets_allowed_to_be_clustered>0:
389- raise MadEvent7Error("Fast-jet python bindings are necessary for integrating"+
390+ if (not PYJET_AVAILABLE) and n_jets_allowed_to_be_clustered > 0:
391+ raise MadEvent7Error("Fast-jet python bindings are necessary for integrating" +
392 " real-emission type of contributions. Please install pyjet.")
393
394 if PYJET_AVAILABLE and drjj_cut > 0.:
395@@ -1346,7 +1390,7 @@
396 jets_list = []
397 for i, p in enumerate(PS_point[self.n_initial:]):
398 if is_a_jet(process_pdgs[1][i]):
399- jets_list.append(tuple(list(p)+[i+self.n_initial+1,]))
400+ jets_list.append(tuple(list(p) + [i + self.n_initial + 1, ]))
401 # Count partonic jets
402 starting_n_jets = len(jets_list)
403
404@@ -1387,9 +1431,9 @@
405 # Make sure that the number of clustered jets is at least larger or equal to the
406 # starting list of jets minus the number of particles that are allowed to go
407 # unresolved in this contribution.
408- if debug_cuts: logger.debug("Number of identified jets: %d (min %d)"%
409- ( len(jets), (starting_n_jets-n_jets_allowed_to_be_clustered) ))
410- if len(jets) < (starting_n_jets-n_jets_allowed_to_be_clustered):
411+ if debug_cuts: logger.debug("Number of identified jets: %d (min %d)" %
412+ (len(jets), (starting_n_jets - n_jets_allowed_to_be_clustered)))
413+ if len(jets) < (starting_n_jets - n_jets_allowed_to_be_clustered):
414
415 return False
416
417@@ -1402,7 +1446,7 @@
418 if ptj_cut > 0.:
419 # Apply the Ptj cut first
420 for i, p in enumerate(all_jets):
421- if debug_cuts: logger.debug('pj_%i.pt()=%.5e'%((i+1),p.pt()))
422+ if debug_cuts: logger.debug('pj_%i.pt()=%.5e' % ((i + 1), p.pt()))
423 if p.pt() < ptj_cut:
424 return False
425
426@@ -1412,15 +1456,15 @@
427 for j, p2 in enumerate(all_jets):
428 if j <= i:
429 continue
430- if debug_cuts: logger.debug('deltaR(pj_%i,pj_%i)=%.5e'%(
431- i+1, j+1, p1.deltaR(p2)))
432+ if debug_cuts: logger.debug('deltaR(pj_%i,pj_%i)=%.5e' % (
433+ i + 1, j + 1, p1.deltaR(p2)))
434 if p1.deltaR(p2) < drjj_cut:
435 return False
436
437 # Now handle all other cuts
438 if etaj_cut > 0.:
439 for i, p_jet in enumerate(all_jets):
440- if debug_cuts: logger.debug('eta(pj_%i)=%.5e'%(i+1,p_jet.pseudoRap()))
441+ if debug_cuts: logger.debug('eta(pj_%i)=%.5e' % (i + 1, p_jet.pseudoRap()))
442 if abs(p_jet.pseudoRap()) > etaj_cut:
443 return False
444
445@@ -1470,8 +1514,7 @@
446 if debug_cuts: logger.debug('deltaR(pl_%i,pj_%i)=%.5e'%(i+1, j+1, p.deltaR(p_jet)))
447 if self.run_card['drjl'] > 0.0 and p.deltaR(p_jet) < self.run_card['drjl']:
448 return False
449-
450-
451+
452 # All cuts pass, therefore return True
453 return True
454
455@@ -1507,8 +1550,8 @@
456 elif not isinstance(PS_point, LorentzVectorList):
457 PS_point = LorentzVectorList(LorentzVector(v) for v in PS_point)
458
459- if debug_cuts: logger.debug( "Processing flavor-sensitive cuts for flavors %s and PS point:\n%s"%(
460- str(flavors), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial) ))
461+ if debug_cuts: logger.debug("Processing flavor-sensitive cuts for flavors %s and PS point:\n%s" % (
462+ str(flavors), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial)))
463
464 ###########################################
465 # User can define his own flavor cut below
466@@ -1524,10 +1567,10 @@
467 return True
468
469 @staticmethod
470- def Lambda(s,sqrMA,sqrMB):
471+ def Lambda(s, sqrMA, sqrMB):
472 """ Kahlen function."""
473
474- return s**2 + sqrMA**2 + sqrMB**2 - 2.*s*sqrMA - 2.*sqrMB*sqrMA - 2.*s*sqrMB
475+ return s ** 2 + sqrMA ** 2 + sqrMB ** 2 - 2.*s * sqrMA - 2.*sqrMB * sqrMA - 2.*s * sqrMB
476
477 def get_scales(self, PS_point):
478 """ Returns mu_r, mu_f1, mu_f2 for that PS point."""
479@@ -1547,7 +1590,7 @@
480 if pdf is None:
481 return 1.
482
483- if pdg not in [21,22] and abs(pdg) not in range(1,7):
484+ if pdg not in [21, 22] and abs(pdg) not in range(1, 7):
485 return 1.
486
487 if (pdf, pdg, x, scale2) in self.PDF_cache:
488@@ -1557,8 +1600,8 @@
489 f = pdf.xfxQ2(pdg, x, scale2)/x
490
491 # Update the PDF cache
492- self.PDF_cache[(pdf, pdg,x,scale2)] = f
493- self.PDF_cache_entries.append((pdf, pdg,x,scale2))
494+ self.PDF_cache[(pdf, pdg, x, scale2)] = f
495+ self.PDF_cache_entries.append((pdf, pdg, x, scale2))
496 if len(self.PDF_cache_entries) > self.PDF_cache_max_size:
497 del self.PDF_cache[self.PDF_cache_entries.pop(0)]
498
499@@ -1566,7 +1609,16 @@
500
501 def __call__(self, continuous_inputs, discrete_inputs, **opts):
502 """ Main function of the integrand, returning the weight to be passed to the integrator."""
503-
504+
505+ if 'adaptive_wgts' in opts:
506+ adaptive_wgts = opts.pop('adaptive_wgts')
507+ else:
508+ adaptive_wgts = None
509+ if 'channel_nr' in opts:
510+ channel_nr = opts.pop('channel_nr')
511+ else:
512+ channel_nr = None
513+
514 # A unique float must be returned
515 wgt = 1.0
516 # And the conversion from GeV^-2 to picobarns
517@@ -1596,8 +1648,8 @@
518 self.contribution_definition.nice_string())
519
520 # Random variables sent
521- random_variables = list(continuous_inputs)
522- if __debug__: logger.debug('Random variables received: %s',str(random_variables))
523+ random_variables = list(continuous_inputs)
524+ if __debug__: logger.debug('Random variables received: %s', str(random_variables))
525
526 # Now assign the variables pertaining to PS generations
527 PS_random_variables = [
528@@ -1605,7 +1657,8 @@
529 random_variables[self.dim_name_to_position[name]] )
530 for name in self.phase_space_generator.dim_ordered_names ]
531
532- PS_point, PS_weight, x1s, x2s = self.phase_space_generator.get_PS_point(PS_random_variables)
533+ PS_point, PS_weight, x1s, x2s = self.phase_space_generator.get_PS_point(PS_random_variables,
534+ adaptive_wgts=adaptive_wgts,channel_nr=channel_nr)
535
536 # Unpack the initial momenta rescalings (if present) so as to access both Bjorken
537 # rescalings xb_<i> and the ISR factorization convolution rescalings xi<i>.
538@@ -1632,6 +1685,7 @@
539
540 # Account for PS weight
541 wgt *= PS_weight
542+
543 if __debug__: logger.debug("PS_weight: %.5e"%PS_weight)
544
545 # The E_cm entering the flux factor is computed *without* including the xi<i> rescalings
546@@ -1644,10 +1698,10 @@
547 flux = 1. / (2.*math.sqrt(self.Lambda(E_cm**2, self.masses[0][0]**2, self.masses[0][1]**2)))
548 elif self.n_initial == 1:
549 flux = 1. / (2.*E_cm)
550- flux /= math.pow(2.*math.pi, 3*self.n_final - 4)
551+ flux /= math.pow(2.*math.pi, 3 * self.n_final - 4)
552 wgt *= flux
553- if __debug__: logger.debug("Flux factor: %.5e"%flux)
554-
555+ if __debug__: logger.debug("Flux factor: %.5e" % flux)
556+
557 # Recover scales to be used
558 mu_r, mu_f1, mu_f2 = self.get_scales(PS_point)
559
560@@ -1668,6 +1722,7 @@
561 # Now loop over processes
562 total_wgt = 0.
563 for process_key, (process, mapped_processes) in self.processes_map.items():
564+
565 # If one wishes to integrate only one particular subprocess, it can be done by uncommenting
566 # and modifying the lines below.
567 # if process.get_cached_initial_final_pdgs() in [((2,-2),(23,1,-1)), ((2,-2),(23,1,-1))] :
568@@ -1682,8 +1737,10 @@
569 all_flavor_configurations = []
570
571 # The process mirroring is accounted for at the very end only
572+
573 for proc in all_processes:
574 initial_final_pdgs = proc.get_cached_initial_final_pdgs()
575+
576 all_flavor_configurations.append(initial_final_pdgs)
577
578 # Compute the short distance cross-section. The 'events' returned is an instance
579@@ -1749,7 +1806,7 @@
580
581
582 # Now finally return the total weight for this contribution
583- if __debug__: logger.debug(misc.bcolors.GREEN + "Final weight returned: %.5e"%total_wgt + misc.bcolors.ENDC)
584+ if __debug__: logger.debug(misc.bcolors.GREEN + "Final weight returned: %.5e" % total_wgt + misc.bcolors.ENDC)
585 if __debug__: logger.debug("="*80)
586
587 return total_wgt
588@@ -1891,7 +1948,7 @@
589 # Combine the weights and flavor of the event obtained from this convolution
590 # with the ones obtained from the previous convolutions in this loop.
591 convolved_event += event_to_convolve
592-
593+
594 # Determine Bjorken scalings.
595 assert( all(bc[0] is None for bc in beam_factorization_currents) or
596 all(bc[0] is not None for bc in beam_factorization_currents) )
597@@ -1943,8 +2000,8 @@
598 """ Return additional information lines for the function nice_string of this contribution."""
599 res = []
600 if self.integrated_counterterms:
601- res.append('%-30s: %d'%('Nb. of integrated counterterms',
602- len(sum(self.integrated_counterterms.values(),[]))))
603+ res.append('%-30s: %d' % ('Nb. of integrated counterterms',
604+ len(sum(self.integrated_counterterms.values(), []))))
605 return res
606
607 def get_nice_string_process_line(self, process_key, defining_process, format=0):
608@@ -1953,15 +2010,15 @@
609
610 GREEN = '\033[92m'
611 ENDC = '\033[0m'
612- res = GREEN+' %s'%defining_process.nice_string(print_weighted=False).\
613- replace('Process: ','')+ENDC
614+ res = GREEN + ' %s' % defining_process.nice_string(print_weighted=False).\
615+ replace('Process: ', '') + ENDC
616
617 if not self.integrated_counterterms:
618 return res
619
620- if format<2:
621+ if format < 2:
622 if process_key in self.integrated_counterterms:
623- res += ' | %d integrated counterterms'%len(self.integrated_counterterms[process_key])
624+ res += ' | %d integrated counterterms' % len(self.integrated_counterterms[process_key])
625 else:
626 res += ' | 0 integrated counterterm'
627
628@@ -1969,19 +2026,19 @@
629 long_res = [' | with the following integrated counterterms:']
630 for CT_properties in self.integrated_counterterms[process_key]:
631 CT = CT_properties['integrated_counterterm']
632- if format==2:
633- long_res.append( ' | %s'%CT.__str__(
634- print_n=True, print_pdg=False, print_state=False ) )
635- elif format==3:
636- long_res.append( ' | %s'%CT.__str__(
637- print_n=True, print_pdg=True, print_state=True ) )
638- elif format==4:
639- long_res.append( ' | %s'%str(CT))
640- elif format>4:
641- long_res.append( ' | %s'%str(CT))
642+ if format == 2:
643+ long_res.append(' | %s' % CT.__str__(
644+ print_n=True, print_pdg=False, print_state=False))
645+ elif format == 3:
646+ long_res.append(' | %s' % CT.__str__(
647+ print_n=True, print_pdg=True, print_state=True))
648+ elif format == 4:
649+ long_res.append(' | %s' % str(CT))
650+ elif format > 4:
651+ long_res.append(' | %s' % str(CT))
652 for key, value in CT_properties.items():
653 if not key in ['integrated_counterterm', 'matching_process_key']:
654- long_res.append( ' + %s : %s'%(key, str(value)))
655+ long_res.append(' + %s : %s' % (key, str(value)))
656
657 res += '\n'.join(long_res)
658
659@@ -2086,15 +2143,16 @@
660 assert ((mapped_flavors not in all_mapped_flavors))
661 all_mapped_flavors.append(mapped_flavors)
662
663+
664 # Now map the momenta
665 if isinstance(PS_point,dict):
666 # Dictionary format LorentzVectorDict starts at 1
667- mapped_PS_point = phase_space_generators.LorentzVectorDict(
668- (i+1, PS_point[input_mapping[i]+1]) for i in range(n_initial+n_final) )
669+ mapped_PS_point = phase_space_generators.LorentzVectorDict(
670+ (i + 1, PS_point[input_mapping[i] + 1]) for i in range(n_initial + n_final))
671 else:
672 # List formatLorentzVectorList starts at 0
673- mapped_PS_point = phase_space_generators.LorentzVectorDict(
674- (i+1, PS_point[input_mapping[i]]) for i in range(n_initial+n_final) )
675+ mapped_PS_point = phase_space_generators.LorentzVectorDict(
676+ (i + 1, PS_point[input_mapping[i]]) for i in range(n_initial + n_final))
677
678 # We must also map the Bjorken x's and the xi rescalings
679 xi1, xi2 = [xi1, xi2][input_mapping[0]], [xi1, xi2][input_mapping[1]]
680@@ -2255,6 +2313,7 @@
681
682 # First generate a kinematic point
683 # Specifying None forces to use uniformly random generating variables.
684+
685 # Make sure to generate a point within the cuts if necessary:
686 max_attempts = 10000
687 n_attempts = 0
688@@ -2295,7 +2354,7 @@
689 defining_process.nice_string().replace('Process','process'))
690 # Make sure that the selected process satisfies the selected process
691 if not self.is_part_of_process_selection(
692- [defining_process,]+mapped_processes, selection = test_options['process'] ):
693+ [defining_process, ] + mapped_processes, selection=test_options['process']):
694 continue
695
696 all_processes = [defining_process,]+mapped_processes
697@@ -2531,7 +2590,7 @@
698 def analyze_IR_poles_check(self, all_evaluations, acceptance_threshold):
699 """ Analyze the results of the check_IR_pole_residues command. """
700
701- #TODO
702+ # TODO
703 # misc.sprint("----- SUMMARY -----")
704 # for key, evaluation in all_evaluations.items():
705 # misc.sprint("Result for test: %s | %s"%(str(dict(key[0])['PDGs']),key[1]))
706@@ -2650,8 +2709,8 @@
707 """ Return additional information lines for the function nice_string of this integrand."""
708 res = []
709 if self.counterterms:
710- res.append('%-30s: %d'%('Number of local counterterms',
711- len([1 for CT in sum(self.counterterms.values(),[]) if CT.is_singular()]) ))
712+ res.append('%-30s: %d' % ('Number of local counterterms',
713+ len([1 for CT in sum(self.counterterms.values(), []) if CT.is_singular()])))
714 return res
715
716 def get_nice_string_process_line(self, process_key, defining_process, format=0):
717@@ -3299,7 +3358,7 @@
718 This is typically what can happen when your process definition is not inclusive over all IR sensitive particles.
719 Make sure that your process definition is specified using the relevant multiparticle labels (typically 'p' and 'j').
720 Also make sure that there is no coupling order specification which receives corrections.
721-The missing process is: %s"""%ME_process.nice_string())
722+The missing process is: %s""" % ME_process.nice_string())
723 raise e
724
725 # Multiply the various pieces building the event weight
726@@ -3376,6 +3435,7 @@
727 for counterterm in self.counterterms[process_key]:
728 if not counterterm.is_singular():
729 continue
730+
731 CT_event = self.evaluate_counterterm(
732 counterterm, PS_point, base_weight, mu_r, mu_f1, mu_f2,
733 xb_1, xb_2, xi1, xi2,
734@@ -3418,6 +3478,7 @@
735 # Specifying None forces to use uniformly random generating variables.
736 # Make sure to generate a point within the cuts if necessary:
737 max_attempts = 10000
738+
739 n_attempts = 0
740 while n_attempts < max_attempts:
741 n_attempts += 1
742@@ -4229,7 +4290,7 @@
743 def get_integrands_of_order(self, correction_order):
744 """ Returns a list of all contributions of a certain correction_order in argument."""
745 return ME7IntegrandList([integrand for integrand in self if
746- integrand.contribution_definition.correction_order==correction_order])
747+ integrand.contribution_definition.correction_order == correction_order])
748
749 def get_integrands_of_type(self, correction_classes):
750 """ Returns a list of all contributions that are direct instances of certain classes."""
751@@ -4244,7 +4305,7 @@
752 def nice_string(self, format=0):
753 """ A nice representation of a list of contributions.
754 We can reuse the function from ContributionDefinitions."""
755- return base_objects.ContributionDefinitionList.contrib_list_string(self,
756+ return base_objects.ContributionDefinitionList.contrib_list_string(self,
757 format=format)
758
759 def sort_integrands(self):
760
761=== modified file 'madgraph/integrator/integrators.py'
762--- madgraph/integrator/integrators.py 2018-07-26 20:20:01 +0000
763+++ madgraph/integrator/integrators.py 2019-01-17 08:58:59 +0000
764@@ -13,6 +13,7 @@
765 #
766 ################################################################################
767
768+import traceback
769 import os
770 import logging
771 import math
772@@ -79,15 +80,19 @@
773 accuracy_target=0.01,
774 n_iterations=None,
775 n_points_per_iterations=100,
776- verbosity = 2, **opts):
777+ verbosity = 2,
778+ save_points_to_file = None,
779+ **opts):
780 """ Initialize the simplest MC integrator."""
781
782 self.accuracy_target = accuracy_target
783 self.n_iterations = n_iterations
784 self.n_points_per_iterations = n_points_per_iterations
785 self.verbosity = verbosity
786+ self.save_points_to_file = save_points_to_file
787
788 super(SimpleMonteCarloIntegrator, self).__init__(integrands, **opts)
789+ #misc.sprint(self.integrands)
790
791 def integrate(self):
792 """ Return the final integral and error estimates."""
793@@ -99,7 +104,17 @@
794 error_estimate = sys.maxint
795 integral_estimate = 0.0
796
797+ for i, integrand in enumerate(self.integrands):
798+ integrand.counter = 0
799+
800 phase_space_volumes = [integrand.get_dimensions().volume() for integrand in self.integrands]
801+
802+ if self.save_points_to_file is not None:
803+ logger.info("Saving all integration sample points to file '%s'."%self.save_points_to_file)
804+ out_stream = open(self.save_points_to_file, 'w')
805+ out_stream.write('IntegrandNumber, continuous_dimensions, discrete_dimension, complete_weight')
806+ else:
807+ out_stream = None
808
809 while (self.n_iterations is None or iteration_number < self.n_iterations ) and \
810 (self.accuracy_target is None or error_estimate/(1e-99+integral_estimate) > self.accuracy_target):
811@@ -109,15 +124,23 @@
812 while n_curr_points < self.n_points_per_iterations:
813 n_points += 1
814 n_curr_points += 1
815- # Compute phase-space volue
816+ # Compute phase-space volume
817 new_wgt = 0.0
818 for i, integrand in enumerate(self.integrands):
819 discrete_dimensions = integrand.discrete_dimensions.random_sample()
820 continuous_dimensions = integrand.continuous_dimensions.random_sample()
821- new_wgt += phase_space_volumes[i]*integrand(continuous_dimensions,discrete_dimensions)
822+ try:
823+ new_wgt += phase_space_volumes[i]*integrand(continuous_dimensions,discrete_dimensions)
824+ except AssertionError as err:
825+ traceback.print_tb(sys.exc_info()[-1])
826+ logger.warning('Assertion error encountered.')
827+ pass
828+ if out_stream is not None:
829+ out_stream.write('\n'+', '.join([str(i+1),str(list(continuous_dimensions)),str(list(discrete_dimensions)),'%.16e'%new_wgt]))
830+
831 sum_int += new_wgt
832 sum_squared += new_wgt**2
833-
834+
835 integral_estimate = sum_int / n_points
836 error_estimate = math.sqrt( ((sum_squared / n_points) - integral_estimate**2)/n_points)
837 msg = '%s :: iteration # %d / %s :: point #%d :: %.4e +/- %.2e'%(
838@@ -125,10 +148,14 @@
839 '%d'%self.n_iterations if self.n_iterations else 'inf' ,n_points,
840 integral_estimate, error_estimate)
841 if self.verbosity > 0:
842- print msg
843+ logger.info(msg)
844+
845+ if out_stream is not None:
846+ out_stream.close()
847+ logger.info("Saved all integration sample points to file '%s'."%self.save_points_to_file)
848
849 return integral_estimate, error_estimate
850-
851+
852
853 if __name__ == "__main__":
854
855
856=== modified file 'madgraph/integrator/phase_space_generators.py'
857--- madgraph/integrator/phase_space_generators.py 2019-01-07 14:33:05 +0000
858+++ madgraph/integrator/phase_space_generators.py 2019-01-17 08:58:59 +0000
859@@ -22,6 +22,7 @@
860
861 import logging
862 import math
863+import random
864 from madgraph.integrator.vectors import Vector, LorentzVector
865 from madgraph.integrator.vectors import LorentzVectorDict, LorentzVectorList
866
867@@ -87,7 +88,7 @@
868
869 raise NotImplementedError
870
871- def get_PS_point(self, random_variables):
872+ def get_PS_point(self, random_variables, **opts):
873 """Generate a complete PS point, including Bjorken x's,
874 dictating a specific choice of incoming particle's momenta."""
875
876@@ -150,10 +151,103 @@
877 return dims
878
879 class MultiChannelPhasespace(VirtualPhaseSpaceGenerator):
880+ """ A phase space generator for a channel in multi-channel integration."""
881+
882+ def __init__(self,*args,**opts):
883+
884+ if 'model' not in opts:
885+ raise PhaseSpaceGeneratorError("A model must be specified with the option 'model' when"+
886+ " instantiating the class %s."%self.__class__.__name__)
887+ self.model = opts.pop('model')
888+
889+ if 'topologies' not in opts:
890+ raise PhaseSpaceGeneratorError("A list of topologies must be specified with the "+
891+ "option 'topologies' when instantiating the class %s."%self.__class__.__name__)
892+ self.topologies = opts.pop('topologies')
893+
894+ super(MultiChannelPhasespace, self).__init__(*args, **opts)
895+
896+ self.channels = []
897+ for topology in self.topologies:
898+ opts = {'initial_masses': self.initial_masses, 'final_masses': self.masses,
899+ 'beam_Es': self.beam_Es, 'beam_types': self.beam_types, 'model': self.model}
900+ self.channels.append(SingleChannelPhasespace(topology=topology,**opts))
901+
902+ def get_PS_point(self, random_variables, adaptive_wgts=None,channel_nr = None):
903+ """Provides a momentum configuration according to the right phase space parameterization
904+ and the multi-channel weight (the Jacobians from all channels with their channel weights (alpha)). """
905+ """ adaptive_wgts = channel wgts (alpha) """
906+
907+ #TODO: flattening_strategy = {channel_id: {'flattening_technique': 'diagram_fuction to call'/'jacobians', 'use_alphas': True/False}}
908+ #TODO: get_flattener_for_channel(id)
909+
910+ if random_variables is None:
911+ random_variables = self.dimensions.random_sample()
912+ if adaptive_wgts is None:
913+ raise PhaseSpaceGeneratorError('Specify the channel weights.')
914+ #adaptive_wgts = [1./len(self.channels)]*len(self.channels)
915+ if channel_nr is None:
916+ raise PhaseSpaceGeneratorError('Specify the channel number.')
917+ #randomly pick a channel
918+ #channel_nr = random.randint(0,len(self.channels)-1)
919+ PS_random_variables = random_variables
920+
921+ channel_wgts = [None]*len(self.channels)
922+
923+ PS_point, channel_wgts[channel_nr], xb_1, xb_2 = self.channels[channel_nr].get_PS_point(PS_random_variables)#, path=None)
924+ if PS_point == None:
925+ return None, 0., 1., 1.
926+
927+ for i, channel in enumerate(self.channels):
928+ if i != channel_nr:
929+ variables, channel_wgts[i] = channel.get_PS_point(PS_point)
930+ if channel_wgts[i] == 0.:
931+ return None, 0, 1., 1.
932+
933+ multi_channel_wgt = 1./sum( alpha / wgt_jac for alpha, wgt_jac in zip(adaptive_wgts,channel_wgts))
934+
935+ #if channel_nr == 1:
936+ # multi_channel_wgt*= 1e-9
937+
938+ return PS_point, multi_channel_wgt, xb_1, xb_2
939+
940+ #matrix element multi-channeling idea
941+ """
942+ full_path = '/Users/Dario/Desktop/Thesis/HighEnergySoftwares/MadGraph5/MadEvent6/mytest_MCPS_full/SubProcesses/P1_epem_mupmuma'
943+ sys.path.append(full_path)
944+ import matrix6py as full_matrix_element
945+ full_matrix_element.initialisemodel(os.path.abspath(os.path.join(full_path,os.pardir,os.pardir,'Cards','param_card.dat')))
946+
947+ def invert_momenta(p):
948+ #fortran/C-python do not order table in the same order
949+ new_p = []
950+ for i in range(len(p[0])): new_p.append([0]*len(p))
951+ for i, onep in enumerate(p):
952+ for j, x in enumerate(onep):
953+ new_p[j][i] = x
954+ return new_p
955+
956+ P = invert_momenta(PS_point)
957+ matrix_elements = [None]*len(self.channels)
958+ for i in xrange(len(self.channels)):
959+ # diagram i might not correspond to function i+1, CHECK
960+ matrix_elements[i] = full_matrix_element.smatrix(P,i+1)
961+
962+ multi_channel_wgt = channel_wgts[channel_nr]*matrix_elements[channel_nr]
963+ multi_channel_wgt *= 1./sum( alpha*me for alpha, me in zip(alphas,matrix_elements))
964+ return PS_point,multi_channel_wgt, xb_1, xb_2
965+ """
966+
967+
968+class SingleChannelPhasespace(VirtualPhaseSpaceGenerator):
969 """Implementation of a phase-space generator that lines up integration variables
970- with s- and t-channels specifying a paticular provided topology.
971+ with s- and t-channels specifying a particular provided topology.
972 """
973
974+ # The lowest value that the center of mass energy can take.
975+ # Below 1 GeV non-perturbative effects dominate and factorization does not apply
976+ absolute_Ecm_min = 1.
977+
978 def __init__(self, *args, **opts):
979
980 if 'model' not in opts:
981@@ -166,23 +260,862 @@
982 "option 'topology' when instantiating the class %s."%self.__class__.__name__)
983 self.topology = opts.pop('topology')
984
985- super(MultiChannelPhasespace, self).__init__(*args, **opts)
986+ super(SingleChannelPhasespace, self).__init__(*args, **opts)
987+
988+ if 'path' not in opts:
989+ # In order to make resutls deterministic, force using the first available path as opposed
990+ # as a random one.
991+ path = self.get_random_path(select_first=True)
992+ else:
993+ path = opts.pop('path')
994+ self.path = path
995+
996+ ##topology_string = self.get_topology_string(self.topology,path_to_print=self.path)
997+ ##misc.sprint(topology_string)
998+
999+ """
1000+ N_t = len(self.topology[1]) - 1 # number of t-channels
1001+ N_s = len(self.topology[0]) # number of s-channels
1002+
1003+ min_index = 0
1004+ max_index = N_s
1005+ for i in range(min_index,max_index):
1006+ self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'distr_s_%d' % (i+1-min_index)
1007+ min_index = max_index
1008+ max_index += N_t -1
1009+ for i in range(min_index,max_index):
1010+ self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'unif_s_%d' % (i+1-min_index)
1011+ min_index = max_index
1012+ max_index += N_t
1013+ for i in range(min_index,max_index):
1014+ self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 't_%d' % (i+1-min_index)
1015+ min_index = max_index
1016+ max_index += N_s
1017+ for i in range(min_index,max_index):
1018+ self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'cos_theta_%d' % (i+1-min_index)
1019+ min_index = max_index
1020+ max_index += N_t + N_s
1021+ for i in range(min_index,max_index):
1022+ self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'phi_%d' % (i+1-min_index)
1023+ """
1024
1025 # One can do additional business here upon instantiating this PS generator, like renaming
1026 # the random variables to names describing the "propagators generated" with them.
1027-
1028- def get_PS_point(self, random_variables, **opts):
1029- """Generate a complete PS point, including Bjorken x's,
1030- dictating a specific choice of incoming particle's momenta.
1031- """
1032-
1033- #
1034- # TODO IMPLEMENTATION
1035- #
1036- # For now just return a random PS point from flat generation
1037- return FlatInvertiblePhasespace(self.initial_masses,self.masses,self.beam_Es,
1038- beam_types = self.beam_types).get_PS_point(random_variables)
1039- # raise NotImplementedError
1040+
1041+ def get_topology_string(self,topology_to_print,path_to_print=None):
1042+ """Example of a nice way to printout what these topologies are:"""
1043+ if topology_to_print[0] == None:
1044+ topology_string = '\n no s-channels \nand t-channels: %s'%\
1045+ (', '.join('%s > %d(%d)'%(
1046+ ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
1047+ vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[1]))
1048+ else:
1049+ topology_string = '\ns-channels: %s\nand t-channels: %s'%\
1050+ (', '.join('%s > %d(%d)'%(
1051+ ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
1052+ vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[0]),
1053+ ', '.join('%s > %d(%d)'%(
1054+ ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
1055+ vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[1]))
1056+ if path_to_print != None:
1057+ topology_string += '\nselected path: %s'%path_to_print
1058+ return topology_string
1059+
1060+ def uniform_distr(self,r,min,max):
1061+ """distributes r uniformly within (min, max), with jacobian dvariable"""
1062+ dvariable = (max-min)
1063+ variable = min + dvariable*r
1064+ return variable, dvariable
1065+
1066+ def inv_uniform_distr(self,variable,min,max):
1067+ """inverse of uniform_distr, obtain r and inverse jacobian dvariable from variable"""
1068+ assert(min<=variable<= max)
1069+ dvariable = (max-min)
1070+ r = (variable-min)/dvariable
1071+ return r, dvariable
1072+
1073+ def massless_distr(self,r,min,max,nu=1.1,m2 = 0):
1074+ """distributes r within (min, max), with jacobian dvariable \propto variable^(nu)
1075+ for cross-section \propto 1/(s-m)^2
1076+ m2 is a small parameter < 0, that fixes numerical problems when min=0,
1077+ instead of setting min=small number, this method still allows to map to min=0"""
1078+ if min == 0 and m2==0:
1079+ m2 = -self.absolute_Ecm_min**2
1080+ if nu != 1:
1081+ variable = ((max-m2)**(1.-nu)*r+(min-m2)**(1.-nu)*(1.-r))**((1.-nu)**(-1.))+m2
1082+ dvariable = ((variable-m2)**nu)*((max-m2)**(1.-nu)-(min-m2)**(1.-nu))/(1.-nu)
1083+ else:
1084+ variable = math.exp(r*math.log(max-m2)+(1.-r)*math.log(min-m2))+m2
1085+ dvariable = (math.log(max-m2)-math.log(min-m2))*(variable-m2)
1086+ assert(dvariable>=0)
1087+ return variable, dvariable
1088+
1089+ def inv_massless_distr(self,variable,min,max,nu=1.1,m2=0):
1090+ """inverse of massless_distr, obtain r and inverse jacobian dvariable from variable"""
1091+ assert(min<=variable<=max)
1092+ if min == 0 and m2==0:
1093+ m2 = -self.absolute_Ecm_min**2
1094+ if nu != 1:
1095+ r = ((variable-m2)**(1.-nu)-(min-m2)**(1.-nu))/((max-m2)**(1.-nu)-(min-m2)**(1.-nu))
1096+ dvariable = ((variable-m2)**nu)*((max-m2)**(1.-nu)-(min-m2)**(1.-nu))/(1.-nu)
1097+ else:
1098+ r = (math.log(variable-m2)-math.log(min-m2))/(math.log(max-m2)-math.log(min-m2))
1099+ dvariable = (math.log(max-m2)-math.log(min-m2))*(variable-m2)
1100+ assert(dvariable>=0)
1101+ return r, dvariable
1102+
1103+ def massive_distr(self,r,mass,width,min,max):
1104+ """distributes r within (min, max), with jacobian dvariable \propto 1/Breit-Winger
1105+ for cross-section \propto Breit-Wigner"""
1106+ mass = mass.real
1107+ width = width.real
1108+ y_1 = math.atan((min-mass**2)/(mass*width))
1109+ y_2 = math.atan((max-mass**2)/(mass*width))
1110+ variable = mass**2 + mass*width*math.tan(y_1+(y_2-y_1)*r)
1111+ dvariable = (y_2-y_1)*((variable-mass**2)**2 + (mass*width)**2)/(mass*width)
1112+ # equivalently: dvariable = mass*width*(y_2-y_1)*math.cos(y_1+(y_2-y_1)*r)**(-2)
1113+ assert(dvariable>=0)
1114+ return variable, dvariable
1115+
1116+ def inv_massive_distr(self,variable,mass,width,min,max):
1117+ """inverse of massive_distr, obtain r and inverse jacobian dvariable from variable"""
1118+ assert(min<=variable<=max)
1119+ mass = mass.real
1120+ width = width.real
1121+ y_1 = math.atan((min-mass**2)/(mass*width))
1122+ y_2 = math.atan((max-mass**2)/(mass*width))
1123+ r = (math.atan((variable-mass**2)/(mass*width))-y_1)/(y_2-y_1)
1124+ dvariable = (y_2-y_1)*((variable-mass**2)**2 + (mass*width)**2)/(mass*width)
1125+ assert(dvariable>=0)
1126+ return r, dvariable
1127+
1128+ def inv_t(self,p_2,p1_2,p2_2,p3_2,p4_2,cos_theta):
1129+ """Mandelstam invariant t=(p1-p3)^2 formula C21 in https://arxiv.org/pdf/hep-ph/0008033.pdf
1130+ p=p1+p2 is at rest;
1131+ p1, p2 are opposite along z-axis
1132+ p3, p4 are opposite along the direction defined by theta
1133+ theta is the angle in the center of mass frame between p1 & p3"""
1134+ nom = (p_2+p3_2-p4_2)*(p_2+p1_2-p2_2) - math.sqrt(self.Lambda(p_2,p3_2,p4_2))*math.sqrt(self.Lambda(p_2,p1_2,p2_2))*cos_theta
1135+ t = p3_2+p1_2 - nom/(2*p_2)
1136+ if t>0:
1137+ t = 0
1138+ assert(t<=0)
1139+ return t
1140+
1141+ def cos_theta_from_inv_t(self,p_2,p1_2,p2_2,p3_2,p4_2,t):
1142+ """https://arxiv.org/pdf/hep-ph/0008033.pdf forula C21
1143+ invert t=(p1-p3)^2 to cos_theta = ..."""
1144+ nom = (t-p3_2-p1_2)*2*p_2 + (p_2+p3_2-p4_2)*(p_2+p1_2-p2_2)
1145+ denom = math.sqrt(self.Lambda(p_2,p3_2,p4_2))*math.sqrt(self.Lambda(p_2,p1_2,p2_2))
1146+ cos_theta = nom/denom
1147+ assert(-1<=cos_theta<=1)
1148+ return cos_theta
1149+
1150+ def angles_to_rotate_along_z(self,p):
1151+ """gives the angles phi and theta,
1152+ so that \vec{p} can be parametrized as |\vec{p}|*(cos phi*sin theta, sin phi*sin theta, cos theta)"""
1153+ theta = math.acos(p[3]/p.rho())
1154+ if p[1]> 0:
1155+ phi = math.atan(p[2]/p[1])
1156+ elif p[1]< 0:
1157+ phi = math.atan(p[2]/p[1]) + math.pi
1158+ else:
1159+ phi = 0
1160+ return phi,theta
1161+
1162+ def rotate_along_z_inv(self,p,phi,theta):
1163+ """rotates a 4 vector p in space
1164+ the z-axis (0,0,1) rotates to (cos phi*sin theta, sin phi*sin theta, cos theta)"""
1165+ l = LorentzVector([p[0],0,0,0])
1166+ l[1] = math.cos(theta)*math.cos(phi)*p[1] -math.sin(phi)*p[2] +math.sin(theta)*math.cos(phi)*p[3]
1167+ l[2] = math.cos(theta)*math.sin(phi)*p[1] + math.cos(phi)*p[2] + math.sin(theta)*math.sin(phi)*p[3]
1168+ l[3] = -math.sin(theta)*p[1] +math.cos(theta)*p[3]
1169+ return l
1170+
1171+ def Lambda(self,x,y,z):
1172+ return x**2+y**2+z**2-2*x*y-2*x*z-2*y*z
1173+
1174+ def get_cm_momenta(self,s,p3_2,p4_2,cos_theta,phi):
1175+ """generates p3, p4 in the center of mass frame, with E_cm^2 = s
1176+ the direction is defined by the angles cos_theta and phi"""
1177+ assert(-1 <= cos_theta <= 1.)
1178+ assert(s>0)
1179+ q = 1./(2*math.sqrt(s))
1180+ sin_theta = math.sqrt(1-cos_theta**2)
1181+ p3 = LorentzVector([1.,sin_theta*math.cos(phi),sin_theta*math.sin(phi), cos_theta])
1182+ p4 = LorentzVector([1.,-sin_theta*math.cos(phi),-sin_theta*math.sin(phi), -cos_theta])
1183+ p3[0] *= (s+p3_2-p4_2)*q
1184+ p4[0] *= (s+p4_2-p3_2)*q
1185+ assert(self.Lambda(s,p3_2,p4_2)>0)
1186+ rho = math.sqrt(self.Lambda(s,p3_2,p4_2))
1187+ for i in xrange(1,4):
1188+ p3[i] *= rho*q
1189+ p4[i] *= rho*q
1190+ wgt_PS = rho/(8*s)
1191+ return p3,p4,wgt_PS
1192+
1193+ def get_two_body_PS_wgt(self,s,p3_2,p4_2):
1194+ """returns the two body PS_wgt that is also generated in generate_cm_momenta"""
1195+ assert(s>0)
1196+ assert(self.Lambda(s,p3_2,p4_2)>0)
1197+ rho = math.sqrt(self.Lambda(s,p3_2,p4_2))
1198+ wgt_PS = rho/(8*s)
1199+ return wgt_PS
1200+
1201+ def get_random_path(self, select_first=False):
1202+ """Generates a random path in which the (distributed and uniform) invariants are sampled.
1203+ For now, only paths are generated that go from two known outer legs to the inner leg.
1204+ Breit-Wigner competition (in distributed invariants) is not taken into account yet.
1205+ When setting the flag 'select_first' to True, this is made deterministic and the first possible
1206+ path is selected instead."""
1207+
1208+ max_leg_nr = self.n_initial+ self.n_final
1209+ min_leg_nr = self.topology[1][-1].get('legs')[-1].get('number')
1210+ numbers = range(min_leg_nr,0)+range(3,max_leg_nr+1)+[1]
1211+
1212+ """distr_inv stores vertices for distributed (=s-channel) invariants that
1213+ can be used to generate a next invariant (available)
1214+ were already used to generate an invariant (finished)
1215+ third category: vertices that haven't been used already but are not available yet to generate invariants
1216+ distr_inv['finished'] will eventually be ordered from outer (final) legs to inner legs"""
1217+
1218+ distr_inv = {'available': [],'finished': []}
1219+ # kinematics stores if a leg is available (True/False)
1220+ kinematics = dict((nr,{'is_available': False}) for nr in numbers)
1221+
1222+ # final legs are available
1223+ for nr in kinematics:
1224+ if nr > 2:
1225+ kinematics[nr]['is_available'] = True
1226+
1227+ # when no t-channels, first s-channel invariant is fixed
1228+ if len(self.topology[1]) == 1:
1229+ last_vertex = self.topology[0][-1]
1230+ leg1_nr = last_vertex.get('legs')[0].get('number')
1231+ leg2_nr = last_vertex.get('legs')[1].get('number')
1232+ kinematics[leg1_nr]['is_available'] = False
1233+ kinematics[leg2_nr]['is_available'] = False
1234+
1235+ # find out which s-channel vertices are available
1236+ for i, vertex in enumerate(self.topology[0]):
1237+ leg1_nr = vertex.get('legs')[0].get('number')
1238+ leg2_nr = vertex.get('legs')[1].get('number')
1239+ if kinematics[leg1_nr]['is_available'] and kinematics[leg2_nr]['is_available']:
1240+ distr_inv['available'].append(i)
1241+
1242+ # generate a path in which distributed (=s-channel) invariants are generated
1243+ # in general the ordering is relevant since it changes the boundaries
1244+ # TODO: Breit-Wigner competition, for now, (final) outer to inner vertices
1245+ if len(self.topology[0]) > 0:
1246+ nr_available_vertices = len(distr_inv['available'])
1247+ while (nr_available_vertices > 0):
1248+ if not select_first:
1249+ i = random.choice(distr_inv['available'])
1250+ else:
1251+ i = distr_inv['available'][0]
1252+ vertex = self.topology[0][i]
1253+ leg1_nr = vertex.get('legs')[0].get('number')
1254+ leg2_nr = vertex.get('legs')[1].get('number')
1255+ leg3_nr = vertex.get('legs')[-1].get('number')
1256+ kinematics[leg1_nr]['is_available'] = False
1257+ kinematics[leg2_nr]['is_available'] = False
1258+ kinematics[leg3_nr]['is_available'] = True
1259+ # distr_inv['finished'] is ordered from outer (final) legs to inner legs
1260+ distr_inv['finished'].append(i)
1261+ distr_inv['available'].remove(i)
1262+
1263+ # check if another vertex is available now
1264+ # if no t-channels (i.e. first s-channel invariant fixed), skip the fixed (=last) vertex
1265+ if len(self.topology[1])==1:
1266+ s_channel_range= len(self.topology[0][:-1])
1267+ else:
1268+ s_channel_range = len(self.topology[0])
1269+ for m in xrange(i,s_channel_range):
1270+ if m in distr_inv['finished']:
1271+ continue
1272+ vertex = self.topology[0][m]
1273+ leg1_nr = vertex.get('legs')[0].get('number')
1274+ leg2_nr = vertex.get('legs')[1].get('number')
1275+ if leg3_nr == leg1_nr or leg3_nr == leg2_nr:
1276+ if kinematics[leg2_nr+leg1_nr-leg3_nr]['is_available']:
1277+ distr_inv['available'].append(m)
1278+ break
1279+ nr_available_vertices= len(distr_inv['available'])
1280+
1281+ t_channel_path = []
1282+
1283+ """find a path to generate the uniformly sampled invariants
1284+ idea: save pairs of vertices that correspond to a uniform invariant
1285+ randomly pick a vertex i<last, save in t_channel_path [(0,i),(i+1,last)]
1286+ for pair in t_channel_path pick random vertex j<last, do the same thing (-> i.e. (0,j) (j+1,i))
1287+ save in t_channel_path except either j+1 = i, or 0=j
1288+ to find possible pairs of vertices, one needs to go from outermost to innermost t-channels."""
1289+
1290+ if len(self.topology[1]) > 2:
1291+ start = 0
1292+ end = len(self.topology[1])-1
1293+ if not select_first:
1294+ i = random.choice(range(start,end))
1295+ else:
1296+ i = range(start,end)[0]
1297+ if i != 0:
1298+ t_channel_path.append((0,i))
1299+ if i+1 != len(self.topology[1])-1:
1300+ t_channel_path.append((i+1,len(self.topology[1])-1))
1301+ for tuple in t_channel_path:
1302+ start = tuple[0]
1303+ end = tuple[1]
1304+ if not select_first:
1305+ i = random.choice(range(start,end))
1306+ else:
1307+ i = range(start,end)[0]
1308+ if i != start:
1309+ t_channel_path.append((start,i))
1310+ if i+1 != end:
1311+ t_channel_path.append((i+1,end))
1312+
1313+ """paths are ordered lists, their order will determine in what order the invariants will be generated
1314+ elements of s_channel_path are the indices of the s-channel vertices
1315+ e.g. s_channel_path = [0,4,1,2,...]
1316+ elements of t_channel_path are tuples of indices of the t-channel vertices
1317+ e.g. t_channel_path = [(0,4),(5,6),(0,2),(3,4),...])"""
1318+ s_channel_path = distr_inv['finished']
1319+ t_channel_path.reverse()
1320+
1321+ return [s_channel_path,t_channel_path]
1322+
1323+
1324+ def get_PS_point(self, input_variables, path=None, **opts):
1325+ """ Generates a complete PS point, including Bjorken x's, dictating a specific choice
1326+ of incoming particle's momenta,"""
1327+
1328+ # PROBLEMS:
1329+ # 0) line 644, tolerance=...
1330+ # 1) line 1067, s-channel momentum. pt_cut, boost to c
1331+ # 2) line 1044,1092,1120, momentum conservation, t-channel, s-channel, tot
1332+ # 3) line 795, 961, no_phase_space
1333+ # 4) line 632, randomly pick one path every time, or stick with one
1334+
1335+ if path is None:
1336+ path = self.path
1337+ #path = self.get_random_path()
1338+
1339+ # kinematics is the object with all important info it it
1340+ max_leg_nr = self.n_initial+ self.n_final
1341+ min_leg_nr = self.topology[1][-1].get('legs')[-1].get('number')
1342+ numbers = range(min_leg_nr,0)+range(3,max_leg_nr+1)+[1]
1343+ kinematics = dict((nr,{'inv_mass': None, 'momentum': None, 'is_available': False, 'inv_mass_limits': None}) for nr in numbers)
1344+
1345+ wgt = 1. #store the weight, coming from the importance sampling and reparameterization (reconstruct mode)
1346+ tolerance = 1e-3
1347+
1348+ if isinstance(input_variables, LorentzVectorList):
1349+ reconstruct = True
1350+ PS_point = input_variables
1351+ output_variables = self.get_dimensions()
1352+ kinematics[min_leg_nr]['momentum'] = PS_point[1]
1353+ kinematics[1]['momentum'] = PS_point[0]
1354+ for nr in xrange(3,max_leg_nr+1):
1355+ kinematics[nr]['momentum'] = PS_point[nr-1]
1356+ else:
1357+ reconstruct = False
1358+ random_variables = input_variables
1359+ if random_variables is None:
1360+ random_variables = self.dimensions.random_sample()
1361+ output_momenta = [LorentzVector()]*max_leg_nr
1362+ variable_index = 0 #index to pick random variable, there might be a nicer way to do it
1363+
1364+ # fill final and initial mass info into kinematics
1365+ kinematics[min_leg_nr]['inv_mass'] = self.initial_masses[1]**2
1366+ kinematics[1]['inv_mass'] = self.initial_masses[0]**2
1367+ for nr in xrange(3,max_leg_nr+1):
1368+ kinematics[nr]['inv_mass'] = self.masses[nr-3]**2
1369+ kinematics[nr]['is_available'] = True #flag is necessary to calculate limits
1370+
1371+ # TODO: Breit-Wigner competition: tau is always generated first for now: can lead to problems in BW comp.
1372+ # get the bjorken x's in case of pp-collison
1373+ if self.beam_types[0]==self.beam_types[1]==1:
1374+ # definition: ycm = 1/2*log(xb_1/xb_2)
1375+ # definition: tau = xb_1*xb_2
1376+ if reconstruct:
1377+ E_cm = math.sqrt((kinematics[1]['momentum']+kinematics[min_leg_nr]['momentum']).square())
1378+ tau = E_cm**2/self.collider_energy**2
1379+ ycm = None # need Bjorken x's as inputs for that
1380+ else:
1381+ x_ycm = random_variables[self.dim_name_to_position['ycms']]
1382+ x_tau = random_variables[self.dim_name_to_position['tau']]
1383+ tot_final_state_masses = sum(self.masses)
1384+ tau_min = (max(tot_final_state_masses, self.absolute_Ecm_min)/self.collider_energy)**2
1385+ tau_max = 1.
1386+ if len(self.topology[1]) == 1: #if there are no t-channels, tau is distributed
1387+ last_vertex = self.topology[0][-1]
1388+ id = last_vertex.get('legs')[-1].get('id')
1389+ particle = self.model.get_particle(id)
1390+ mass_param = particle.get('mass')
1391+ if mass_param.lower() == 'zero':
1392+ if reconstruct:
1393+ x_tau, wgt_jac = self.inv_massless_distr(tau, tau_min, tau_max)
1394+ else:
1395+ tau, wgt_jac = self.massless_distr(x_tau,tau_min,tau_max)
1396+ else:
1397+ mass = self.model.get('parameter_dict')[mass_param]/self.collider_energy
1398+ width = self.model.get('parameter_dict')[particle.get('width')]/self.collider_energy
1399+ if reconstruct:
1400+ x_tau, wgt_jac = self.inv_massive_distr(tau, mass, width, tau_min, tau_max)
1401+ else:
1402+ tau, wgt_jac = self.massive_distr(x_tau,mass,width, tau_min, tau_max)
1403+ else: #if there are t-channels, tau is uniform
1404+ if reconstruct:
1405+ x_tau, wgt_jac = self.inv_uniform_distr(tau,tau_min,tau_max)
1406+ else:
1407+ tau, wgt_jac = self.uniform_distr(x_tau,tau_min,tau_max)
1408+ wgt *= wgt_jac
1409+ # ycm always sampled uniformly
1410+ ycm_min = 0.5 * math.log(tau)
1411+ ycm_max = -ycm_min
1412+ if reconstruct:
1413+ x_ycm, wgt_jac = None, (ycm_max-ycm_min)
1414+ output_variables[self.dim_name_to_position['tau']] = x_tau
1415+ output_variables[self.dim_name_to_position['ycms']] = x_ycm
1416+ else:
1417+ ycm, wgt_jac = self.uniform_distr(x_ycm,ycm_min,ycm_max)
1418+ xb_1 = math.sqrt(tau)*math.exp(ycm)
1419+ xb_2 = math.sqrt(tau)*math.exp(-ycm)
1420+ E_cm = math.sqrt(tau)*self.collider_energy
1421+ wgt *= wgt_jac
1422+ elif self.beam_types[0]==self.beam_types[1]==0: # set default in case of ll-collision
1423+ xb_1 = 1.
1424+ xb_2 = 1.
1425+ E_cm = self.collider_energy
1426+ else:
1427+ raise InvalidCmd("This basic PS generator does not yet support collider mode (%d,%d)."%self.beam_types)
1428+
1429+ # Also generate the ISR collinear factorization convolutoin variables xi<i> if
1430+ # necessary. In order for the + distributions of the PDF counterterms and integrated
1431+ # collinear ISR counterterms to hit the PDF only (and not the matrix elements or
1432+ # observables functions), a change of variable is necessary: xb_1' = xb_1 * xi1
1433+ if self.correlated_beam_convolution:
1434+ # Both xi1 and xi2 must be set equal then
1435+ xi1 = random_variables[self.dim_name_to_position['xi']]
1436+ xi2 = random_variables[self.dim_name_to_position['xi']]
1437+ else:
1438+ if self.is_beam_factorization_active[0]:
1439+ xi1 = random_variables[self.dim_name_to_position['xi1']]
1440+ else:
1441+ xi1 = None
1442+ if self.is_beam_factorization_active[1]:
1443+ xi2 = random_variables[self.dim_name_to_position['xi2']]
1444+ else:
1445+ xi2 = None
1446+
1447+ if not reconstruct:
1448+ """generate initial momenta in CENTER OF MASS frame, along z-axis
1449+ -> ALL output_momenta are going to be in this frame
1450+ BUT: cuts are dependent on lab frame, therefore return Bjorken x's too"""
1451+ leg1_nr = 1
1452+ leg2_nr = min_leg_nr
1453+ kinematics[leg1_nr]['momentum'] = E_cm/2.*LorentzVector([1.,0,0, 1.])
1454+ kinematics[leg2_nr]['momentum'] = E_cm/2.*LorentzVector([1.,0,0, -1.])
1455+
1456+ # if there are no t-channels do the first s-channel, its invariant mass is fixed
1457+ if len(self.topology[1])==1:
1458+ # set the momentum of the very first s-channel propagator (p_initial1+p_initial2)
1459+ last_vertex = self.topology[0][-1]
1460+ leg1_nr = 1
1461+ leg2_nr = min_leg_nr
1462+ leg3_nr = last_vertex.get('legs')[-1].get('number')
1463+ kinematics[leg3_nr]['inv_mass'] = E_cm**2
1464+ kinematics[leg3_nr]['momentum'] = kinematics[leg1_nr]['momentum'] + kinematics[leg2_nr]['momentum']
1465+
1466+ # set the invariant masses for the s-channels, all distributed
1467+ # remember: if there is only one s-channel, there is no invariant to generate
1468+ if len(self.topology[0]) > 0:
1469+ for i in path[0]:
1470+ vertex = self.topology[0][i]
1471+ leg1_nr = vertex.get('legs')[0].get('number')
1472+ leg2_nr = vertex.get('legs')[1].get('number')
1473+ leg3_nr = vertex.get('legs')[-1].get('number')
1474+ kinematics[leg1_nr]['is_available'] = False #flag is necessary to calculate limits
1475+ kinematics[leg2_nr]['is_available'] = False
1476+ sum_masses = sum([math.sqrt(kinematics[nr]['inv_mass']) for nr in kinematics.keys() if kinematics[nr]['is_available']])
1477+ s_max = (E_cm - sum_masses)**2
1478+ m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
1479+ m2 = math.sqrt(kinematics[leg2_nr]['inv_mass'])
1480+ s_min = (m1 + m2)**2
1481+ kinematics[leg3_nr]['inv_mass_limits'] = (s_min,s_max)
1482+ #use importance sampling
1483+ id = vertex.get('legs')[-1].get('id')
1484+ particle = self.model.get_particle(id)
1485+ mass_param = particle.get('mass')
1486+ variable_index += 1
1487+ if reconstruct:
1488+ assert(kinematics[leg3_nr]['momentum']==None)
1489+ kinematics[leg3_nr]['momentum'] = kinematics[leg1_nr]['momentum']+kinematics[leg2_nr]['momentum']
1490+ s = kinematics[leg3_nr]['momentum'].square()
1491+ if 0. < s_min-s < tolerance**2*self.absolute_Ecm_min**2:
1492+ s = s_min
1493+ if 0. < s-s_max < tolerance**2*s_max:
1494+ s = s_max
1495+ kinematics[leg3_nr]['inv_mass'] = s
1496+ if mass_param.lower() == 'zero':
1497+ x_s, wgt_jac = self.inv_massless_distr(s,s_min,s_max)
1498+ else:
1499+ mass = self.model.get('parameter_dict')[mass_param]
1500+ width = self.model.get('parameter_dict')[particle.get('width')]
1501+ if width == 0:
1502+ x_s, wgt_jac = self.inv_massless_distr(s, s_min, s_max, m2=mass**2)
1503+ else:
1504+ x_s, wgt_jac = self.inv_massive_distr(s,mass,width, s_min, s_max)
1505+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_s
1506+ else:
1507+ assert(kinematics[leg3_nr]['inv_mass'] == None)
1508+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1509+ if mass_param.lower() == 'zero':
1510+ s, wgt_jac = self.massless_distr(random_variable,s_min,s_max)
1511+ else:
1512+ mass = self.model.get('parameter_dict')[mass_param]
1513+ width = self.model.get('parameter_dict')[particle.get('width')]
1514+ if width == 0:
1515+ s, wgt_jac = self.massless_distr(random_variable, s_min, s_max, m2=mass**2)
1516+ else:
1517+ s, wgt_jac = self.massive_distr(random_variable,mass,width, s_min, s_max)
1518+ # think about a better solution to handle if particle is produced at rest
1519+ no_phase_space = 0.*max([s,m1**2,m2**2])**2 >= self.Lambda(s,m1**2,m2**2) >= -tolerance**2*s**2
1520+ if no_phase_space:
1521+ misc.sprint('This kinematic configuration leaves no phase space ', self.Lambda(s,m1**2,m2**2))
1522+ return None,0.,(xb_1, xi1) , (xb_2, xi2)
1523+ kinematics[leg3_nr]['inv_mass'] = s
1524+ wgt *= wgt_jac
1525+ kinematics[leg3_nr]['is_available'] = True
1526+
1527+ # if there is more than one t-channel, there are uniform invariants to be generated
1528+ uniform_inv = []
1529+ if len(self.topology[1]) > 2:
1530+ # go from innermost to outermost vertex pair in t-channel path
1531+ for item in path[1]:
1532+ # extract the path-info into uniform_inv dictionary
1533+ uniform_inv.append({'vertices': (item[0],item[1]), 'inv_mass': None, 'is_available': False, 'inv_mass_limits': None, 'momentum': None})
1534+ for k,item in enumerate(uniform_inv):
1535+ start = item['vertices'][0]
1536+ finish = item['vertices'][1]
1537+ if (start+1) == finish:
1538+ vertex1 = self.topology[1][start]
1539+ vertex2 = self.topology[1][finish]
1540+ leg1_nr = vertex1.get('legs')[1].get('number')
1541+ leg2_nr = vertex2.get('legs')[1].get('number')
1542+ m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
1543+ m2 = math.sqrt(kinematics[leg2_nr]['inv_mass'])
1544+ kinematics[leg1_nr]['is_available'] = False #flag is necessary to calculate limits
1545+ kinematics[leg2_nr]['is_available'] = False
1546+ if reconstruct:
1547+ p1 = kinematics[leg1_nr]['momentum']
1548+ p2 = kinematics[leg2_nr]['momentum']
1549+ else:
1550+ for l in reversed(xrange(0,k)):
1551+ if uniform_inv[l]['vertices'][0] == start:
1552+ m1 = math.sqrt(uniform_inv[l]['inv_mass'])
1553+ if reconstruct:
1554+ p1 = uniform_inv[l]['momentum']
1555+ uniform_inv[l]['is_available'] = False
1556+ break
1557+ else:
1558+ vertex1 = self.topology[1][start]
1559+ leg1_nr = vertex1.get('legs')[1].get('number')
1560+ m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
1561+ if reconstruct:
1562+ p1 = kinematics[leg1_nr]['momentum']
1563+ kinematics[leg1_nr]['is_available'] = False
1564+ for l in reversed(xrange(0,k)):
1565+ if uniform_inv[l]['vertices'][1] == finish:
1566+ m2 = math.sqrt(uniform_inv[l]['inv_mass'])
1567+ if reconstruct:
1568+ p2 = uniform_inv[l]['momentum']
1569+ uniform_inv[l]['is_available'] = False
1570+ break
1571+ else:
1572+ vertex2 = self.topology[1][finish]
1573+ leg2_nr = vertex1.get('legs')[1].get('number')
1574+ m2 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
1575+ if reconstruct:
1576+ p2 = kinematics[leg2_nr]['momentum']
1577+ kinematics[leg2_nr]['is_available'] = False
1578+ sum_masses = sum([math.sqrt(kinematics[nr]['inv_mass']) for nr in kinematics.keys() if kinematics[nr]['is_available']])
1579+ sum_masses += sum([math.sqrt(inv['inv_mass']) for inv in uniform_inv if inv['is_available']])
1580+ s_max = (E_cm - sum_masses)**2
1581+ s_min = (m1 + m2)**2
1582+ item['inv_mass_limits']= (s_min,s_max)
1583+ variable_index += 1
1584+ if reconstruct:
1585+ item['momentum']=p1+p2
1586+ s = item['momentum'].square()
1587+ if 0. < s_min-s < tolerance**2*self.absolute_Ecm_min**2:
1588+ s = s_min
1589+ if 0. < s-s_max < tolerance**2*s_max:
1590+ s = s_max
1591+ item['inv_mass'] = s
1592+ x_s, wgt_jac = self.inv_uniform_distr(s,s_min,s_max)
1593+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_s
1594+ else:
1595+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1596+ s, wgt_jac = self.uniform_distr(random_variable,s_min,s_max)
1597+ item['inv_mass'] = s
1598+ wgt *= wgt_jac
1599+ item['is_available'] = True
1600+
1601+ # all invariant masses are generated, so for consistency set all available-flags to False
1602+ # REMARK: there are some that say True but are not available i.e.
1603+ # 1) if no t-channels and more than one s-channel -> last vertex final leg 1,2 say True
1604+ # 2) if there's t-channels:
1605+ # one single: final leg 1,2 say True;
1606+ # more than one: either final leg 1 or 2 say True
1607+ for item in uniform_inv:
1608+ item['is_available'] = False
1609+ for nr in kinematics.keys():
1610+ kinematics[nr]['is_available'] = False
1611+
1612+ # sample the t-variables (uniform if massive, 1/x^nu if massless)
1613+ # go from outermost to innermost t-channel
1614+ # structure: leg1 + leg2 -> leg3 + leg4
1615+ # if there are t-channels
1616+ if len(self.topology[1]) > 1:
1617+ uniform_inv.append({'vertices': (0,len(self.topology[1])-1), 'inv_mass': E_cm**2, 'is_available': False, 'momentum': None})
1618+ uniform_inv.reverse()
1619+ for k,item in enumerate(uniform_inv):
1620+ start = item['vertices'][0]
1621+ finish = item['vertices'][1]
1622+ leg1_nr = self.topology[1][start].get('legs')[0].get('number')
1623+ leg2_nr = self.topology[1][finish].get('legs')[-1].get('number')
1624+ p1 = kinematics[leg1_nr]['momentum'].copy()
1625+ p2 = kinematics[leg2_nr]['momentum'].copy()
1626+ if leg1_nr < 0:
1627+ # t-channel propagator momenta always point upwards
1628+ # this code procedure requires p1 to point downwards
1629+ p1 = -p1.copy()
1630+ # p is the sum of incoming momenta
1631+ p = p1+p2
1632+ if (start + 1) ==finish:
1633+ leg3_nr = self.topology[1][start].get('legs')[1].get('number')
1634+ leg4_nr = self.topology[1][finish].get('legs')[1].get('number')
1635+ if reconstruct:
1636+ p3 = kinematics[leg3_nr]['momentum']
1637+ p4 = kinematics[leg4_nr]['momentum']
1638+ p3_2 = kinematics[leg3_nr]['inv_mass']
1639+ p4_2 = kinematics[leg4_nr]['inv_mass']
1640+ prop_leg = self.topology[1][start].get('legs')[-1]
1641+ else:
1642+ for inv in uniform_inv[k+1:]:
1643+ if inv['vertices'][0] == start:
1644+ leg3_nr = None
1645+ if reconstruct:
1646+ p3 = inv['momentum']
1647+ p3_2 = inv['inv_mass']
1648+ prop_leg = self.topology[1][inv['vertices'][1]].get('legs')[-1]
1649+ break
1650+ elif inv['vertices'][0] == start + 1:
1651+ leg3_nr = self.topology[1][start].get('legs')[1].get('number')
1652+ if reconstruct:
1653+ p3 = kinematics[leg3_nr]['momentum']
1654+ p3_2 = kinematics[leg3_nr]['inv_mass']
1655+ prop_leg = self.topology[1][inv['vertices'][0]].get('legs')[0]
1656+ break
1657+ for inv in uniform_inv[k+1:]:
1658+ if inv['vertices'][1] == finish:
1659+ leg4_nr = None
1660+ if reconstruct:
1661+ p4 = inv['momentum']
1662+ p4_2 = inv['inv_mass']
1663+ #not needed because already determined above
1664+ #prop_leg = self.topology[1][inv['vertices'][0]].get('legs')[0]
1665+ break
1666+ elif inv['vertices'][1] == finish - 1:
1667+ leg4_nr = self.topology[1][finish].get('legs')[1].get('number')
1668+ if reconstruct:
1669+ p4 = kinematics[leg4_nr]['momentum']
1670+ p4_2 = kinematics[leg4_nr]['inv_mass']
1671+ #not needed because already determined above
1672+ #prop_leg = self.topology[1][inv['vertices'][1]].get('legs')[-1]
1673+ break
1674+ prop_id = prop_leg.get('id')
1675+ s = p.square()
1676+ assert(s >= 0)
1677+ if not reconstruct:
1678+ # think about a better solution to handle if particle is produced at rest
1679+ no_phase_space = 0.*max([s,p3_2,p4_2])**2 >= self.Lambda(s,p3_2,p4_2) >= -tolerance**4*max([s,p3_2,p4_2])**2
1680+ if no_phase_space:
1681+ misc.sprint('This kinematic configuration leaves no phase space ', self.Lambda(s,p3_2,p4_2))
1682+ return None,0.,(xb_1, xi1) , (xb_2, xi2)
1683+ t_max = self.inv_t(s, p1.square(), p2.square(), p3_2, p4_2, 1.)
1684+ if abs(t_max) < tolerance**2*self.absolute_Ecm_min**2:
1685+ t_max = 0.
1686+ t_min = self.inv_t(s, p1.square(), p2.square(), p3_2, p4_2, -1.)
1687+ assert(t_min<=t_max<=0.)
1688+ particle = self.model.get_particle(prop_id)
1689+ mass_param = particle.get('mass')
1690+ prop_nr = prop_leg.get('number')
1691+ assert(prop_nr<0)
1692+ variable_index += 1
1693+ if reconstruct:
1694+ kinematics[prop_nr]['momentum'] = p3-p1
1695+ t = kinematics[prop_nr]['momentum'].square()
1696+ if abs(t) < tolerance**2*self.absolute_Ecm_min**2:
1697+ t = t_max
1698+ if 0. < t_min-t < tolerance**2*t_min:
1699+ t = t_min
1700+ kinematics[prop_nr]['inv_mass'] = t
1701+ if mass_param.lower() == 'zero':
1702+ # t is always negative, distribute abs(t)
1703+ x_abs_t,wgt_jac = self.inv_massless_distr(abs(t), abs(t_max), abs(t_min))
1704+ else:
1705+ x_abs_t, wgt_jac = self.inv_uniform_distr(abs(t), abs(t_max), abs(t_min))
1706+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_abs_t
1707+ else:
1708+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1709+ if mass_param.lower() == 'zero':
1710+ # t is always negative, distribute abs(t)
1711+ abs_t,wgt_jac = self.massless_distr(random_variable, abs(t_max), abs(t_min))
1712+ t = -abs_t
1713+ else:
1714+ abs_t, wgt_jac = self.uniform_distr(random_variable, abs(t_max), abs(t_min))
1715+ t = -abs_t
1716+ wgt *= wgt_jac
1717+ cos_theta = self.cos_theta_from_inv_t(s, p1.square(), p2.square(), p3_2, p4_2, t)
1718+ variable_index += 1
1719+ if reconstruct:
1720+ x_phi, wgt_jac = None, 2*math.pi
1721+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_phi
1722+ else:
1723+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1724+ phi, wgt_jac = self.uniform_distr(random_variable, 0, 2*math.pi)
1725+ wgt *= wgt_jac
1726+ if reconstruct:
1727+ wgt_PS = self.get_two_body_PS_wgt(s, p3_2, p4_2)
1728+ else:
1729+ p3, p4, wgt_PS = self.get_cm_momenta(s,p3_2,p4_2,cos_theta,phi)
1730+ #t-channel only factor:
1731+ wgt_PS *= 2.*s/(math.sqrt(self.Lambda(s,p3_2,p4_2))*math.sqrt(self.Lambda(s,p1.square(),p2.square())))
1732+ wgt *= wgt_PS
1733+
1734+ if not reconstruct:
1735+ p1_com = p1.copy()
1736+ p1_com.boost(-p.boostVector())
1737+
1738+ # test if boost is correct
1739+ """
1740+ p2_com = p2.copy()
1741+ p2_com.boost(-p.boostVector())
1742+ assert(all(abs(x) < tolerance for x in (p1_com+p2_com)[1:]))
1743+ """
1744+
1745+ phi, theta = self.angles_to_rotate_along_z(p1_com)
1746+
1747+ # this is to test if rotation works as expected
1748+ """
1749+ phi1,theta1 = (math.pi/2,math.pi/6)
1750+ ptest = LorentzVector([10,math.cos(phi1)*math.sin(theta1),math.sin(phi1)*math.sin(theta1),math.cos(theta1)])
1751+ ptest_z = LorentzVector([10,0,0,1])
1752+ misc.sprint('\n',ptest)
1753+ phi2,theta2 = self.angles_to_rotate_along_z(ptest)
1754+ misc.sprint(phi1-phi2,theta1-theta2) #should be 0,0
1755+ misc.sprint('\n',self.rotate_along_z_inv(ptest_z,phi2, theta2)-ptest) #should be [0,0,0,0]
1756+ """
1757+
1758+ p3 = self.rotate_along_z_inv(p3,phi, theta)
1759+ p4 = self.rotate_along_z_inv(p4,phi, theta)
1760+ assert(abs((p3-p1_com).square()-t) < tolerance**2*s)
1761+
1762+ p3.boost(p.boostVector())
1763+ p4.boost(p.boostVector())
1764+
1765+ if leg3_nr != None:
1766+ assert(kinematics[leg3_nr]['momentum'] == None)
1767+ kinematics[leg3_nr]['momentum'] = p3
1768+ if leg4_nr != None:
1769+ assert(kinematics[leg4_nr]['momentum'] == None)
1770+ kinematics[leg4_nr]['momentum'] = p4
1771+
1772+ assert(kinematics[prop_nr]['momentum'] == None)
1773+ # t-propagator-momentum always points upwards
1774+ kinematics[prop_nr]['momentum'] = p3-p1
1775+ assert(abs(kinematics[prop_nr]['momentum'].square()-t) < tolerance**2*s)
1776+ assert(all(abs(x) < tolerance*math.sqrt(s) for x in (p-p3-p4)))
1777+
1778+ # s-channel momentum generation
1779+ # start at the very end of topology[0] and go upwards, ordering doesn't matter
1780+ # structure leg0 -> leg1 + leg2
1781+ for vertex in reversed(self.topology[0]):
1782+ leg0_nr = vertex.get('legs')[-1].get('number')
1783+ leg1_nr = vertex.get('legs')[0].get('number')
1784+ leg2_nr = vertex.get('legs')[1].get('number')
1785+ s = kinematics[leg0_nr]['inv_mass']
1786+ p = kinematics[leg0_nr]['momentum']
1787+ p1_2 = kinematics[leg1_nr]['inv_mass']
1788+ p2_2 = kinematics[leg2_nr]['inv_mass']
1789+ variable_index += 1
1790+ if reconstruct:
1791+ x_cos_theta, wgt_jac = None, 2
1792+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_cos_theta
1793+ else:
1794+ # this is for the case where a massless propagator is produced almost on shell, this leads
1795+ # to a boost to velocity c which is numerically unstable
1796+ # it's not an actual problem since it can only happen when massless splits into 2 massless,
1797+ # e.g. e- > e- a, photon or gluon radiation. This would eventually be treated by pt_cuts anyways
1798+ if s < tolerance**2*self.absolute_Ecm_min**2:
1799+ #misc.sprint(s)
1800+ return None,0.,(xb_1, xi1) , (xb_2, xi2)
1801+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1802+ cos_theta, wgt_jac = self.uniform_distr(random_variable, -1, 1)
1803+ wgt *= wgt_jac
1804+ variable_index += 1
1805+ if reconstruct:
1806+ x_phi, wgt_jac = None, 2*math.pi
1807+ output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_phi
1808+ else:
1809+ random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
1810+ phi, wgt_jac = self.uniform_distr(random_variable, 0, 2*math.pi)
1811+ wgt *= wgt_jac
1812+ if reconstruct:
1813+ wgt_PS = self.get_two_body_PS_wgt(s, p1_2, p2_2)
1814+ else:
1815+ p1,p2,wgt_PS = self.get_cm_momenta(s, p1_2, p2_2, cos_theta, phi)
1816+ wgt *= wgt_PS
1817+ if not reconstruct:
1818+ p1.boost(p.boostVector())
1819+ p2.boost(p.boostVector())
1820+ assert(kinematics[leg1_nr]['momentum'] == None and kinematics[leg2_nr]['momentum'] == None)
1821+ kinematics[leg1_nr]['momentum'] = p1
1822+ kinematics[leg2_nr]['momentum'] = p2
1823+
1824+ if not (all(abs(x) < tolerance*math.sqrt(s) for x in (p-p1-p2))):
1825+ logger.critical('Possible loss of precision with tolerance %.2e: %s'%(tolerance*math.sqrt(s), str(p-p1-p2)))
1826+ #assert(all(abs(x) < tolerance*math.sqrt(s) for x in (p-p1-p2)))
1827+
1828+ if not reconstruct:
1829+ # sanity check if all random variables were actually used
1830+ if self.beam_types == (1,1):
1831+ assert(variable_index +2 == len(random_variables))
1832+ if self.beam_types == (0,0):
1833+ assert(variable_index== len(random_variables))
1834+
1835+ if not reconstruct:
1836+ # save final momenta into output_momenta -> PS_point
1837+ for nr in kinematics:
1838+ if nr > 0:
1839+ output_momenta[nr-1] = kinematics[nr]['momentum']
1840+ if nr == min_leg_nr:
1841+ output_momenta[1] = kinematics[nr]['momentum']
1842+ PS_point = LorentzVectorList(output_momenta)
1843+
1844+ # test if total momentum is conserved
1845+ p_start = sum(PS_point[0:2])
1846+ p_finish = sum(PS_point[2:])
1847+ assert(all(abs(x) < tolerance*self.collider_energy for x in (p_start-p_finish)))
1848+
1849+ assert(wgt>0)
1850+
1851+ if reconstruct:
1852+ return output_variables, wgt
1853+ else:
1854+ return LorentzVectorList(PS_point), wgt, (xb_1, xi1) , (xb_2, xi2)
1855
1856 class FlatInvertiblePhasespace(VirtualPhaseSpaceGenerator):
1857 """Implementation following S. Platzer, arxiv:1308.2922"""
1858@@ -310,7 +1243,7 @@
1859 output_momenta[1] = LorentzVector([E2/2.0 , 0., 0., -Z/2.0])
1860 return
1861
1862- def get_PS_point(self, random_variables):
1863+ def get_PS_point(self, random_variables, **opts):
1864 """Generate a complete PS point, including Bjorken x's,
1865 dictating a specific choice of incoming particle's momenta.
1866 """
1867
1868=== modified file 'madgraph/integrator/vegas3_integrator.py'
1869--- madgraph/integrator/vegas3_integrator.py 2018-09-21 18:30:26 +0000
1870+++ madgraph/integrator/vegas3_integrator.py 2019-01-17 08:58:59 +0000
1871@@ -13,12 +13,14 @@
1872 #
1873 ################################################################################
1874
1875+import traceback
1876 import sys
1877 import time
1878 import os
1879 import logging
1880 import random
1881 from multiprocessing import Process
1882+from aetypes import Enum
1883
1884 if __name__ == '__main__':
1885 sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.path.pardir, os.path.pardir))
1886@@ -216,7 +218,13 @@
1887
1888 all_results = []
1889 for integrand in self.integrands:
1890- res = integrand(fct_inputs, np.array([], dtype=int), integrator_jacobian=jacobian)
1891+ try:
1892+ res = integrand(fct_inputs, np.array([], dtype=int), integrator_jacobian=jacobian)
1893+ except AssertionError, err:
1894+ traceback.print_tb(sys.exc_info()[-1])
1895+ logger.warning('Assertion error encountered.')
1896+ res = 0.
1897+
1898 res *= integrand.get_dimensions().volume()
1899 all_results.append(res)
1900
1901@@ -395,7 +403,13 @@
1902 (self.tot_func_evals,self.cluster.nb_core))
1903 logger.debug('Vegas3 returned final results : %s'%summed_result)
1904 logger.debug("===============================================================")
1905-
1906+
1907+ #out_data = open('0result.dat','a')
1908+ #out_data.write('%.16e, '%(summed_result.mean)+'%.16e, '%(summed_result.sdev)+'%i\n'%(self.integrands[0].counter))
1909+ #out_data.close()
1910+ ##misc.sprint('\n%.16e, '%(summed_result.mean)+'%.16e, '%(summed_result.sdev)+'%i\n'%(self.integrands[0].counter))
1911+ self.integrands[0].counter = 0
1912+
1913 return summed_result.mean, summed_result.sdev
1914
1915 def show_grid(self, n_grid=40, shrink=False, axes=None):
1916@@ -413,6 +427,431 @@
1917 self.vegas3_integrator.map.show_grid(n_grid, shrink, axes=axes)
1918
1919
1920+class Channel():
1921+ """ class that specifies a channel and its parameters when using the multi-channel integration technique."""
1922+ def __init__(self,i,integrand,adaptive_wgt,selector_wgt):
1923+ self.nr = i
1924+ self.full_integrand = integrand
1925+ self.adaptive_wgt = adaptive_wgt
1926+ self.selector_wgt = selector_wgt
1927+ self.dimension = len(integrand.continuous_dimensions)
1928+ # TODO: Hypercubes of vegas not used here (see add_point)
1929+ self.integrator = vegas.Integrator(self.dimension*[[0.,1.]])
1930+ self.reset()
1931+ return
1932+
1933+ def integrand(self,random_variable,adaptive_wgts):
1934+ """ Current integrand of channel. Adaptive_wgts change over course of integration. """
1935+ res = self.full_integrand(random_variable, np.array([], dtype=int),
1936+ adaptive_wgts=adaptive_wgts,
1937+ channel_nr=self.nr)
1938+ return res
1939+
1940+ def update(self):
1941+ """ Compute estimates of integral, integral^2, sdev, rel variance. """
1942+ self.results_squared = [res**2 for res in self.results]
1943+ assert(self.n == len(self.results))
1944+ self.n = len(self.results)
1945+ self.result = np.mean(self.results)
1946+ self.result_squared = np.mean(self.results_squared)
1947+ self.sdev = np.sqrt((self.result_squared-self.result**2)/self.n)
1948+ self.rel_variance = (self.result_squared-self.result**2)/self.result**2
1949+ return
1950+
1951+ def reset(self):
1952+ """ (Re)sets all data gathered while integrating to zero/empty. """
1953+ self.results = [] # each evaluation of integrand*wgt
1954+ self.random_variables = [] # = y in vegas manual
1955+ self.grid_variables = [] # = x in vegas manual
1956+ self.results_squared = [] # each evaluation of (integrand*wgt)^2
1957+ self.wgts = [] # = wgt in vegas manual
1958+ self.result = 0. # current estimate of integrand (calculated in update)
1959+ self.result_squared = 0. # current estimate of integrand^2 (calculated in update)
1960+ self.sdev = 0. # current estimate of sdev (calculated in update)
1961+ self.n = 0 # number of total evaluation (==len(results))
1962+ self.rel_variance = 0. # current estimate of variance/result^2 (calculated in update)
1963+ self.n_passed_cuts = 0 # number of evaluations that passed cuts
1964+ return
1965+
1966+ def add_point(self,adaptive_wgts):
1967+ """ Adds an integration point sampled according to vegas3 grid and evaluates the integrand. """
1968+ y = np.random.uniform(0., 1., (1, self.dimension))
1969+ x = np.empty(y.shape, float)
1970+ wgt = np.empty(y.shape[0], float)
1971+ self.integrator.map.map(y,x,wgt)
1972+ y = y[0]
1973+ x= x[0]
1974+ wgt = wgt[0]
1975+ #misc.sprint('\n%s \n%s \n%e'%(x,y,wgt))
1976+ self.random_variables += [y]
1977+ self.grid_variables += [x]
1978+ self.wgts += [wgt]
1979+ try:
1980+ res = self.integrand(x,adaptive_wgts)*wgt
1981+ except AssertionError, err:
1982+ traceback.print_tb(sys.exc_info()[-1])
1983+ misc.sprint('Assertion error encountered.')
1984+ res = 0.
1985+ self.results += [res]
1986+ if res != 0.:
1987+ self.n_passed_cuts += 1
1988+ self.n += 1
1989+ return
1990+
1991+ def adjust_to_new_grid(self):
1992+ # currently not used!
1993+ """Integrand values can be reused during vegas3 grid updates.
1994+ For a given x, there's an old y & old wgt from the old grid that is known
1995+ and a new y & new wgt from the new grid that is unknown, yet.
1996+ The xs are invariant by construction.
1997+ Here the new y & new wgt are found and the integrand updated as integrand*=new_wgt/old_wgt."""
1998+ grid = self.integrator.map.grid
1999+ # collect new ys from old xs
2000+ for j,x in enumerate(self.grid_variables):
2001+ y_new = np.empty_like(x)
2002+ for d in xrange(len(grid[:,0])):
2003+ for i in xrange(len(grid[0,:])-1):
2004+ if grid[d,i]<=x[d]<grid[d,i+1]:
2005+ delta = (x[d]-grid[d,i])/(grid[d,i+1]-grid[d,i])
2006+ y_new[d] = (delta + i)*1./self.integrator.map.ninc#[d]
2007+ # overwrite old ys with new ones
2008+ #misc.sprint('\n',self.random_variables[j],'\n',y_new)
2009+ self.random_variables[j]=y_new
2010+ # check with built in function if I inverted correctly:
2011+ check_x = np.empty_like(self.random_variables)
2012+ new_wgt = np.empty(check_x.shape[0],np.float_)
2013+ self.integrator.map.map(np.array(self.random_variables),check_x,new_wgt)
2014+ #misc.sprint(check_x[0],self.grid_variables[0],self.integrator.map(np.array(self.random_variables[0])))
2015+ assert(np.all(x==x_alt for x,x_alt in zip(self.grid_variables,check_x)))
2016+ # adjust all saved results with the new wgts, so that they are reusable
2017+ for i in xrange(len(self.results)):
2018+ self.results[i] *= new_wgt[i]/self.wgts[i]
2019+ #misc.sprint(new_wgt[i]/self.wgts[i])
2020+ self.wgts[i] = new_wgt[i]
2021+ return
2022+
2023+ def header_string(self):
2024+ msg = '{:<7} {:>14} {:>12} {:>7} {:<22}'.format('channel','#passed / tot',
2025+ 'adaptive wgt','rel var','estimate')
2026+ return msg
2027+
2028+ def summary_string(self):
2029+ """ Returns a string with the current state of the integration. Formatted according to header_string. """
2030+ msg = '{:<7} {:>14} {:>12} {:>7} {:>28}'.format(self.nr,'%4d / %4d'%(self.n_passed_cuts,self.n),
2031+ '%.10f'%self.adaptive_wgt,
2032+ '%3.2f'%self.rel_variance,
2033+ '%.6e +- %.6e'%(self.result,self.sdev))
2034+ return msg
2035+
2036+ def get_n_points(self,abs_acc):
2037+ """ estimates the number of Monte Carlo points needed to arrive at an accuracy abs_acc of THIS CHANNEL. """
2038+ if self.sdev < abs_acc:
2039+ n_points = 0
2040+ else:
2041+ #var = self.sdev**2*self.n
2042+ #n_improve = var/abs_target_accuracy**2 - self.n
2043+ n_points = int((self.sdev**2/abs_acc**2-1.)*self.n)
2044+ return n_points
2045+
2046+class MultiChannelVegas3Integrator(integrators.VirtualIntegrator):
2047+ """ Mutli-Channel integrator: Class that handles channels specified as instances of class Channel."""
2048+
2049+ def __init__(self, integrands, adaptive_wgts=None, selector_wgts=None, **opts):
2050+ """ specifies:
2051+ adaptive_wgts: channel weights (usually denoted by \alpha), sum up to one
2052+ selector_wgts: channel more often sampled if its selector wgt is large, sum up to one """
2053+
2054+ super(MultiChannelVegas3Integrator,self).__init__(integrands, **opts)
2055+
2056+ # TODO: #flattening_strategy = {
2057+ # channel_id: {'flattening_technique': 'diagram_fuction to call'/'jacobians',
2058+ # 'use_alphas': True/False}}
2059+
2060+ #np.random.seed(0)
2061+
2062+ self.start_time = time.time()
2063+ self.time = time.time()
2064+
2065+ self.neval = opts.pop('n_points') #number of evaluations of first iteration, doubled after every itn
2066+ self.rel_ch_acc = 0.1 #relative target accuracy of each channel
2067+ self.v_acc = 0.01 #relative change of vegas grid
2068+ self.a_acc = 0.01 #relative change of channel weights (alpha)
2069+ self.min_nitn = 3
2070+ self.max_nitn = opts.pop('n_iterations')
2071+ assert(self.min_nitn<=self.max_nitn)
2072+ self.rel_acc = 0.01 #relative target accuracy of final integral
2073+
2074+ # channel properties
2075+ if len(integrands)>1:
2076+ misc.sprint('This multi channel integrator works with only one process/integrand for now.')
2077+ raise
2078+ self.n_channels = len(self.integrands[0].topologies_to_processes)
2079+ if adaptive_wgts is None: #uniform
2080+ adaptive_wgts = [1./self.n_channels]*self.n_channels
2081+ if selector_wgts is None: #uniform
2082+ selector_wgts = [1./self.n_channels]*self.n_channels
2083+ self.adaptive_wgts = adaptive_wgts
2084+ self.selector_wgts = selector_wgts
2085+
2086+
2087+ # initialize channels
2088+ integrand = self.integrands[0]
2089+ self.channels = []
2090+ for i in xrange(self.n_channels):
2091+ self.channels += [Channel(i,integrand,self.adaptive_wgts[i],self.selector_wgts[i])]
2092+
2093+ # variables for the full integrand
2094+ self.result = 0.
2095+ self.sdev = 0.
2096+ self.n = 0
2097+ self.n_passed_cuts = 0
2098+ self.rel_variance = 0.
2099+ return
2100+
2101+ def reset(self):
2102+ """ Deletes all integration data, except for grid and channel weights (alpha) """
2103+ for channel in self.channels:
2104+ channel.reset()
2105+ return
2106+
2107+ def update(self):
2108+ """ Combine the estimates from all the channels to give an estimate of the full integral. """
2109+ for channel in self.channels:
2110+ channel.update()
2111+ self.result = sum([channel.result*channel.adaptive_wgt for channel in self.channels])
2112+ self.n = sum([channel.n for channel in self.channels])
2113+ self.n_passed_cuts = sum([channel.n_passed_cuts for channel in self.channels])
2114+ W = sum([channel.result_squared*channel.adaptive_wgt for channel in self.channels])
2115+ self.sdev = np.sqrt((W-self.result**2)/self.n)
2116+ self.rel_variance = (W-self.result**2)/self.result**2
2117+ return
2118+
2119+ def adapt_vegas(self,alpha):
2120+ """ Adapts vegas grid of each channel with saved function evaluations
2121+ and returns a measure for the change of the grid. """
2122+ ch_wgt_change = []
2123+ for channel in self.channels:
2124+ old_wgts = np.array(channel.integrator.map.inc)
2125+ channel.integrator.map.add_training_data(np.array(channel.random_variables),np.array(channel.results))
2126+ channel.integrator.map.adapt(alpha=alpha)
2127+ new_wgts = np.array(channel.integrator.map.inc)
2128+ wgt_change = []
2129+ wgt_change = [abs(new-old)/old for new,old in zip(new_wgts,old_wgts)]
2130+ wgt_change = np.mean(wgt_change,axis=1) # sum within dimension (already normalized wgts)
2131+ wgt_change = np.mean(wgt_change) #mean along each PS dimension
2132+ ch_wgt_change += [wgt_change]
2133+ return np.array(ch_wgt_change)
2134+
2135+
2136+ def get_channels_with_points(self,n_points=None,rel_target_accuracy=None,training=-1):
2137+ if n_points == None and rel_target_accuracy != None:
2138+ self.update()
2139+ if training == True:
2140+ abs_target_accuracies = [rel_target_accuracy*channel.result for channel in self.channels]
2141+ n_points = [channel.get_n_points(abs_target_accuracies[channel.nr]) for channel in self.channels]
2142+ #if n_points > self.max_refine_neval:
2143+ # n_points = self.max_refine_neval
2144+ elif training == False:
2145+ abs_target_accuracy = rel_target_accuracy*self.result
2146+ n_points = self.get_n_points(abs_target_accuracy)
2147+ n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
2148+ abs_target_accuracy_alt = rel_target_accuracy*self.result/np.sqrt(self.n_channels)
2149+ n_points_alt = [channel.get_n_points(abs_target_accuracy_alt) for channel in self.channels]
2150+ if np.mean(n_points)>np.mean(n_points_alt):
2151+ n_points = n_points_alt
2152+ else:
2153+ logger.info('Set training either True or False.')
2154+ raise
2155+ elif n_points != None and rel_target_accuracy == None:
2156+ n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
2157+ else:
2158+ self.selector_wgts = self.adaptive_wgts
2159+ for channel in self.channels:
2160+ channel.selector_wgt = channel.adaptive_wgt
2161+ logger.info('Either choose n_points or rel_target_accuracy.')
2162+ raise
2163+ channels_with_points = {channel.nr: n_points[channel.nr] for channel in self.channels}
2164+ return channels_with_points
2165+
2166+
2167+ def get_n_points(self,abs_target_accuracy):
2168+ """ estimates the number of Monte Carlo points needed to arrive at an accuracy abs_target_accuracy of FULL INTEGRAL. """
2169+ if self.sdev < abs_target_accuracy:
2170+ n_points = 0
2171+ else:
2172+ #var = self.sdev**2*self.n
2173+ #n_improve = var/abs_target_accuracy**2 - self.n
2174+ n_points = int((self.sdev**2/abs_target_accuracy**2-1.)*self.n)
2175+ return n_points
2176+
2177+
2178+ def adapt_wgts(self):
2179+ """ One adaptation of adaptive_wgts. Per iteration there are n_vegas_adapt adaptations of the vegas grid.
2180+ There are at least adapt_min_n_points points evaluations. If rel_target_accuracy is not reached,
2181+ increase max n_max_improve_itns of n_improve_steps."""
2182+ #now update the adaptive_wgts and normalize
2183+ old_wgts = self.adaptive_wgts
2184+ #misc.sprint(old_adaptive_wgts)
2185+ all_adaptive_wgts = []
2186+ for channel in self.channels:
2187+ channel.adaptive_wgt *= np.sqrt(channel.result_squared)#**alpha
2188+ all_adaptive_wgts += [channel.adaptive_wgt]
2189+ adaptive_norm = sum(all_adaptive_wgts)
2190+ for channel in self.channels:
2191+ channel.adaptive_wgt *= 1./adaptive_norm
2192+ all_adaptive_wgts[channel.nr] = channel.adaptive_wgt
2193+ self.adaptive_wgts = all_adaptive_wgts
2194+ new_wgts = self.adaptive_wgts
2195+ wgt_change = [abs(new-old)/old for new,old in zip(new_wgts,old_wgts)]
2196+ wgt_change = np.mean(wgt_change) #(already normalized wgts)
2197+ return (wgt_change)
2198+
2199+ def summary_string(self):
2200+ """ Returns a string with the current state of the integration of all channels and their sum. """
2201+ msg = '{:<7} {:>14} {:>12} {:>7} {:>28}'.format('full','%4d / %4d'%(self.n_passed_cuts,self.n),'-',
2202+ '%3.2f'%self.rel_variance,
2203+ '%.6e +- %.6e'%(self.result,self.sdev))
2204+ return msg
2205+
2206+ def print_summary(self):
2207+ for channel in self.channels:
2208+ channel.update()
2209+ logger.info(channel.summary_string())
2210+ logger.info('-'*70)
2211+ self.update()
2212+ logger.info(self.summary_string())
2213+ return
2214+
2215+ def get_channel_points(self,n_points):
2216+ """ distributes a total of n_points on channels according to selector_wgts.
2217+ returns a dict: {channel_nr: n_pts} """
2218+ n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
2219+ pts_per_channels = {channel.nr: n_points[channel.nr] for channel in self.channels}
2220+ return pts_per_channels
2221+
2222+ def get_refine_channel_points(self,rel_acc):
2223+ """ estimates number of points needed to reach rel_acc. returns a dict: {channel_nr: n_pts} """
2224+ abs_acc = rel_acc*self.result/np.sqrt(self.n_channels)
2225+ abs_ch_acc = [abs_acc/channel.adaptive_wgt for channel in self.channels]
2226+ n_points = [channel.get_n_points(abs_ch_acc[channel.nr]) for channel in self.channels]
2227+ refine_pts_per_channels = {channel.nr: n_points[channel.nr] for channel in self.channels}
2228+ return refine_pts_per_channels
2229+
2230+ def survey(self, neval, max_nitn, min_nitn=5, a_acc=0.01, v_acc=0.01):
2231+ """ survey stage of integration: girds and channel weights (alpha) are trained.
2232+ while both the vegas grid and the channel weights have not stabilized (reach a_acc,v_acc),
2233+ do an iteration with double the number of points neval.
2234+ When adding points in parallel, print a full estimate after every "update_time" seconds.
2235+ run a minimum and a maximum of iterations. """
2236+ msg = '='*27 + ' training stage '+'='*27
2237+ logger.info(msg,'$MG:color:GREEN')
2238+ logger.info(self.channels[0].header_string())
2239+
2240+ update_time = 10
2241+ tot_it = 0
2242+ a_it = 0
2243+ v_it = 0
2244+ while not (a_acc == -1 and v_acc == -1):
2245+ self.reset()
2246+ channel_points = self.get_channel_points(neval*2**(tot_it)) #double every time
2247+ t0 = time.time()
2248+ while sum(channel_points.values()) > 0:
2249+ for nr in channel_points.keys():
2250+ if channel_points[nr] == 0:
2251+ channel_points.pop(nr)
2252+ else:
2253+ self.channels[nr].add_point(self.adaptive_wgts)
2254+ channel_points[nr] -= 1
2255+ if (time.time()-t0) > update_time:
2256+ total_time = time.time()-self.start_time
2257+ minutes = int(total_time/60)
2258+ time_string = '%d min %.2f sec'%(minutes,total_time-minutes*60)
2259+ self.update()
2260+ logger.info(self.summary_string()+' '+'\033[94m'+time_string+'\033[0m')
2261+ t0 = time.time()
2262+ self.print_summary()
2263+ # if max is reached stop
2264+ if tot_it == max_nitn:
2265+ v_acc = -1
2266+ a_acc = -1
2267+ elif tot_it > 0 and tot_it >= min_nitn:
2268+ if v_acc == -1 and a_acc != -1:
2269+ if a_change < a_acc:
2270+ a_acc = -1
2271+ elif v_acc != -1 and a_acc == -1:
2272+ if mean_v_change < v_acc:
2273+ v_acc = -1
2274+ else:
2275+ if mean_v_change < v_acc and a_change < a_acc:
2276+ v_acc = -1
2277+ a_acc = -1
2278+ if v_acc != -1:
2279+ v_change = self.adapt_vegas(alpha=.5)
2280+ v_it += 1
2281+ mean_v_change = np.mean(v_change)
2282+ logger.info('#%d vegas grid changed by %.2f%%'%(v_it,mean_v_change*1e2),'$MG:color:GREEN')
2283+ #logger.info('#%d vegas adapted: %s'%(v_it,' '.join(['(Ch. %d: %.4e)'%(nr,change) for nr,change in zip(xrange(self.n_channels),v_change)])),'$MG:color:GREEN')
2284+ if a_acc != -1:
2285+ a_change = self.adapt_wgts()
2286+ a_it += 1
2287+ logger.info('#%d: adaptive weights changed by %.2f%%'%(a_it,a_change*1e2),'$MG:color:GREEN')
2288+ tot_it += 1
2289+ return
2290+
2291+ def refine(self,rel_acc):
2292+ """ refine stage of integration.
2293+ estimates number of points needed to reach rel_acc.
2294+ after every "update_time" seconds, prints out current estimate of full integrand
2295+ and restimates number of points needed to reach rel_acc. """
2296+
2297+ msg = '='*26 + ' refinement stage '+'='*26
2298+ logger.info(msg,'$MG:color:GREEN')
2299+ logger.info(self.channels[0].header_string())
2300+
2301+ update_time = 10
2302+ refine_channel_points = self.get_refine_channel_points(rel_acc=rel_acc)
2303+ for nr,pts in refine_channel_points.items():
2304+ if pts != 0:
2305+ logger.info('ESTIMATE: Refining channel %d with %d additional points.'%(nr,pts))
2306+ t0 = time.time()
2307+ while sum(refine_channel_points.values()) > 0:
2308+ for nr in refine_channel_points.keys():
2309+ if refine_channel_points[nr] == 0:
2310+ refine_channel_points.pop(nr)
2311+ else:
2312+ self.channels[nr].add_point(self.adaptive_wgts)
2313+ refine_channel_points[nr] -= 1
2314+ if (time.time()-t0) > update_time:
2315+ total_time = time.time()-self.start_time
2316+ minutes = int(total_time/60)
2317+ time_string = '%d min %.2f sec'%(int(total_time/60),total_time-minutes*60)
2318+ self.update()
2319+ logger.info(self.summary_string()+' '+'\033[94m'+time_string+'\033[0m')
2320+ t0 = time.time()
2321+ refine_channel_points = self.get_refine_channel_points(rel_acc=self.rel_acc)
2322+ for nr,pts in refine_channel_points.items():
2323+ if pts != 0:
2324+ logger.info('ESTIMATE: Refining channel %d with %d additional points.'%(nr,pts))
2325+ self.print_summary()
2326+ return
2327+
2328+ def integrate(self):
2329+ """ combines survey and refine stage. and returns final result and Monte Carlo error. """
2330+
2331+ self.survey(neval=self.neval,
2332+ max_nitn=self.max_nitn, min_nitn=self.min_nitn,
2333+ a_acc=self.a_acc, v_acc=self.v_acc)#,rel_ch_acc=self.rel_ch_acc)
2334+
2335+ if self.result == 0:
2336+ logger.info('Do training first.','$MG:color:BLUE')
2337+ stop
2338+
2339+ self.refine(rel_acc=self.rel_acc)
2340+
2341+ misc.sprint('\n%.16e, '%(self.result)+'%.16e, '%(self.sdev)+'%i\n'%(self.n_passed_cuts))
2342+ return self.result, self.sdev
2343+
2344+
2345 # Wrapper around the original Vegas.Integrator which adds the jacobian of the point as
2346 # the last input of the 1-dimensional array passed to the integrand function in the
2347 # __call__ function of the integrator.
2348@@ -576,7 +1015,7 @@
2349 sum_wf2[s, t] * neval - sum_wf[s] * sum_wf[t]
2350 ) / (neval - 1.)
2351 if var[s, s] <= 0:
2352- var[s, s] = mean[s] ** 2 * 1e-15 + TINY
2353+ var[s, s] = mean[s] ** 2 * 1e-15
2354 sigf2 = abs(sum_wf2[0, 0] * neval - sum_wf[0] * sum_wf[0])
2355 if self.beta > 0 and self.adapt:
2356 if not self.minimize_mem:
2357
2358=== modified file 'madgraph/interface/ME7_interface.py'
2359--- madgraph/interface/ME7_interface.py 2018-11-27 18:23:17 +0000
2360+++ madgraph/interface/ME7_interface.py 2019-01-17 08:58:59 +0000
2361@@ -12,6 +12,7 @@
2362 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
2363 #
2364 ################################################################################
2365+from gvar.ode import Integrator
2366 """A user friendly command line interface to steer ME7 integration.
2367 Uses the cmd package for command interpretation and tab completion.
2368 """
2369@@ -630,6 +631,7 @@
2370 'refresh_filters' : 'auto',
2371 'compile' : 'auto',
2372 'seed' : None,
2373+ 'PS_generator' : None,
2374 # Here we store a list of lambda function to apply as filters
2375 # to the integrand we must consider
2376 'integrands' : [lambda integrand: True],
2377@@ -669,11 +671,20 @@
2378
2379 elif key in ['--veto_integrands', '--veto_itg']:
2380 launch_options['integrands'].extend(self.get_integrand_filters(value, 'reject'))
2381+
2382+ elif key == '--PS_generator':
2383+ launch_options['PS_generator'] = value
2384+
2385 elif key=='--run_name':
2386 launch_options['run_name'] = value
2387 else:
2388 raise InvalidCmd("Option '%s' for the launch command not recognized."%key)
2389
2390+ # Automatically adjust the default value of the PS_generator depending on the selected integrator
2391+ if launch_options['PS_generator'] is None:
2392+ if launch_options['integrator'] == 'MC_VEGAS3':
2393+ launch_options['PS_generator'] = 'MCPS'
2394+
2395 return new_args, launch_options
2396
2397 def parse_display_integrands(self, args):
2398@@ -791,10 +802,15 @@
2399 # parameter controllable by user commands, eventually.
2400 integrator_verbosity = 1 if logger.level > logging.DEBUG else 2
2401 _integrators = {
2402+ 'MC_VEGAS3' : (vegas3_integrator.MultiChannelVegas3Integrator,
2403+ { 'n_iterations' : 10,
2404+ 'n_points' : 1000 } ),
2405+
2406 'NAIVE' : (integrators.SimpleMonteCarloIntegrator,
2407 { 'n_iterations' : 10,
2408 'n_points_per_iterations' : 100,
2409 'accuracy_target' : None,
2410+ 'save_points_to_file' : None, #'./TEST.dat',
2411 'verbosity' : integrator_verbosity } ),
2412
2413 'VEGAS3' : (vegas3_integrator.Vegas3Integrator,
2414@@ -803,7 +819,7 @@
2415 'n_iterations_refine' : 10,
2416 'n_points_refine' : 2000,
2417 'verbosity' : integrator_verbosity } ),
2418-
2419+
2420 'VEGAS' : (pyCubaIntegrator.pyCubaIntegrator,
2421 { 'algorithm' : 'Vegas',
2422 'verbosity' : integrator_verbosity,
2423@@ -925,11 +941,16 @@
2424 max_order = max_integrand_order
2425 return max_integrand_order
2426
2427- def synchronize(self, **opts):
2428- """ Re-compile all necessary resources and sync integrands with the cards and model"""
2429+ def synchronize(self, integrands_to_synchronize=None, **opts):
2430+ """ Re-compile all necessary resources and sync integrands with the cards and model.
2431+ The option integrands_to_synchronize offers the possibility of specifying which integrands
2432+ must be specified. If None, then all will be synchronised."""
2433
2434 logger.info("Synchronizing MadEvent7 internal status with cards and matrix elements source codes...")
2435
2436+ if integrands_to_synchronize is None:
2437+ integrands_to_synchronize = self.all_integrands
2438+
2439 self.run_card = banner_mod.RunCardME7(pjoin(self.me_dir,'Cards','run_card.dat'))
2440 # Check if run_card values are supported.
2441 self.run_card.check_validity()
2442@@ -938,8 +959,12 @@
2443 param_card = pjoin(self.me_dir,'Cards','param_card.dat'),
2444 scale=self.run_card['scale'],
2445 complex_mass_scheme=self.complex_mass_scheme)
2446-
2447- for integrand in self.all_integrands:
2448+
2449+ self.options['PS_generator'] = opts['PS_generator']
2450+
2451+ #misc.sprint(self.options)
2452+
2453+ for integrand in integrands_to_synchronize:
2454 integrand.synchronize(self.model, self.run_card, self.options)
2455
2456 # Try and import some options from those provided to this function
2457@@ -997,11 +1022,16 @@
2458 This is super naive and only for illustrative purposes for now."""
2459
2460 args = self.split_arg(line)
2461+
2462 new_args, launch_options = self.parse_launch(args)
2463
2464+ integrands_to_consider = ME7_integrands.ME7IntegrandList([
2465+ itg for itg in self.all_integrands
2466+ if all(filter(itg) for filter in launch_options['integrands']) ])
2467+
2468 # In principle we want to start by recompiling the process output
2469 # to make sure that everything is up to date.
2470- self.synchronize(**launch_options)
2471+ self.synchronize(integrands_to_synchronize=integrands_to_consider, **launch_options)
2472
2473 # Setup parallelization
2474 self.configure_run_mode(self.options['run_mode'])
2475@@ -1011,14 +1041,12 @@
2476 integrator_options = self._integrators[integrator_name][1]
2477
2478 integrator_options['verbosity'] = launch_options['verbosity']
2479+
2480 integrator_options['cluster'] = self.cluster
2481
2482 if integrator_name=='VEGAS3':
2483 integrator_options['parallelization'] = self.cluster
2484
2485- integrands_to_consider = ME7_integrands.ME7IntegrandList([
2486- itg for itg in self.all_integrands
2487- if all(filter(itg) for filter in launch_options['integrands']) ])
2488 self.integrator = self._integrators[integrator_name][0](
2489 integrands_to_consider, **integrator_options)
2490
2491
2492=== modified file 'madgraph/interface/madgraph_interface.py'
2493--- madgraph/interface/madgraph_interface.py 2019-01-14 20:52:22 +0000
2494+++ madgraph/interface/madgraph_interface.py 2019-01-17 08:58:59 +0000
2495@@ -4586,8 +4586,9 @@
2496 warn = 'WARNING: You try to draw decay chain diagrams without first running output.\n'
2497 warn += '\t The decay processes will be drawn separately'
2498 logger.warning(warn)
2499-
2500+
2501 (options, args) = _draw_parser.parse_args(args)
2502+
2503 options = draw_lib.DrawOption(options)
2504 start = time.time()
2505
2506@@ -4600,7 +4601,7 @@
2507 for amp in amplitudes:
2508 filename = pjoin(args[0], 'diagrams_' + \
2509 amp.get('process').shell_string() + ".eps")
2510-
2511+
2512 if selection=='all' and type != 'loop':
2513 diags=amp.get('diagrams')
2514 elif selection=='born':
2515@@ -4611,7 +4612,7 @@
2516 if len(diags) > 5000:
2517 logger.warning('Displaying only the first 5000 diagrams')
2518 diags = base_objects.DiagramList(diags[:5000])
2519-
2520+
2521 plot = draw.MultiEpsDiagramDrawer(diags,
2522 filename,
2523 model=self._curr_model,
2524
2525=== modified file 'madgraph/iolibs/drawing_eps.py'
2526--- madgraph/iolibs/drawing_eps.py 2016-09-03 13:26:00 +0000
2527+++ madgraph/iolibs/drawing_eps.py 2019-01-17 08:58:59 +0000
2528@@ -674,7 +674,7 @@
2529 #compute the number of pages
2530 self.npage = 1
2531 self.diagram_type = diagram_type
2532-
2533+
2534 diagramlist = [d for d in diagramlist if not (isinstance(d, loop_objects.LoopUVCTDiagram) or \
2535 (isinstance(d, loop_objects.LoopDiagram) and d.get('type') < 0))]
2536 diagramlist = base_objects.DiagramList(diagramlist)
2537@@ -686,7 +686,7 @@
2538 add = (len(diagramlist) - limit -1) // \
2539 (self.second_scale['nb_col'] * self.second_scale['nb_line'])
2540 self.npage += self.lower_scale + add
2541-
2542+
2543 if diagramlist:
2544 # diagramlist Argument should be a DiagramList object
2545 assert(isinstance(diagramlist, base_objects.DiagramList))
2546
2547=== modified file 'tests/parallel_tests/compare_ME7_with_ME6.py'
2548--- tests/parallel_tests/compare_ME7_with_ME6.py 2018-07-18 10:53:48 +0000
2549+++ tests/parallel_tests/compare_ME7_with_ME6.py 2019-01-17 08:58:59 +0000
2550@@ -84,16 +84,24 @@
2551
2552 def compare_cross_section(self, my_proc_list = [], orders = {}, model = 'sm',
2553 filename = "", print_result = False, append_output=False,
2554- tolerance = 1e-02, ME6_options={}, ME7_options={}):
2555+ tolerance = 1e-01, ME6_options={}, ME7_options={}):
2556 """ """
2557
2558 ME6_runner = madevent_comparator.ME6Runner(**ME6_options)
2559 ME6_runner.setup(MG5DIR)
2560 ME6_runner.store_proc_card = True
2561
2562- ME7_runner = madevent_comparator.ME7Runner(**ME7_options)
2563- ME7_runner.setup(MG5DIR)
2564- ME7_runner.store_proc_card = True
2565+ ME7_options['PS_generator'] = 'MCPS'
2566+
2567+ ME7_runner_MCPS = madevent_comparator.ME7Runner(**ME7_options)
2568+ ME7_runner_MCPS.setup(MG5DIR)
2569+ ME7_runner_MCPS.store_proc_card = True
2570+
2571+ ME7_options['PS_generator'] = 'FLATPS'
2572+
2573+ ME7_runner_FLATPS = madevent_comparator.ME7Runner(**ME7_options)
2574+ ME7_runner_FLATPS.setup(MG5DIR)
2575+ ME7_runner_FLATPS.store_proc_card = True
2576
2577 self.nb_test +=1
2578 if os.path.exists(pjoin(MG5DIR,'models','paralel_test_model_%s' % model)):
2579@@ -103,11 +111,11 @@
2580
2581 # Create and setup a comparator
2582 my_comp = madevent_comparator.MadEventComparator(allow_no_present=True)
2583- my_comp.set_me_runners(ME6_runner,ME7_runner)
2584+ my_comp.set_me_runners(ME6_runner,ME7_runner_MCPS,ME7_runner_FLATPS)
2585
2586 # Run the actual comparison
2587 my_comp.run_comparison(my_proc_list,
2588- ['paralel_test_model_%s' % model, model], orders)
2589+ ['paralel_test_model_%s' % model, model, model], orders)
2590
2591 # Print the output
2592 if filename:
2593@@ -115,7 +123,7 @@
2594 mystream = open(filename, 'a')
2595 else:
2596 mystream = open(filename, 'w')
2597- my_comp.output_result(filename=mystream)
2598+ my_comp.output_result(filename=mystream,tolerance=tolerance)
2599 mystream.close()
2600
2601 if print_result:
2602@@ -172,18 +180,28 @@
2603 # An example of a comparison against values recomputed live with ME6
2604 def test_ME7_paralel_cross_sm(self):
2605 """Test a short list of sm processes"""
2606+
2607+ #print(opts)
2608+
2609+ #if 'PS_generator' in opts:
2610+ # misc.sprint('hello')
2611+ #self.PS_gen = opts['PS_generator']
2612+
2613 # Create a list of processes to check automatically
2614- proc_lists = [['p p > t t~'], ['u d~ > W+ j'], ['u d~ > W+ j j']]
2615+ #proc_lists = [['p p > t t~'], ['u d~ > W+ j'], ['u d~ > W+ j j']]
2616+ proc_lists= [(['u c > h > u c e+ e- mu+ mu- $$ c u / a s d s~ d~'],0,99)]
2617+ #proc_lists = [['u d > u d mu+ mu-']]
2618+ #proc_lists = [['u d~ > W+ j j']]
2619 #proc_lists = [['p p > t t~']]
2620 #proc_lists = [['u d~ > W+ j', 'u d~ > W+ j j']]
2621 # Store list of non-zero processes and results in file
2622 pickle_file = os.path.join(_pickle_path, "short_ME7_parraleltest_cross_sm.pkl")
2623 if os.path.isfile('short_ME7_cs_sm.log'):
2624 os.remove('short_ME7_cs_sm.log')
2625- for my_proc_list in proc_lists:
2626+ for my_proc_list, QCDorder, QEDorder in proc_lists:
2627 print 'Now running process(es) %s ...'%str(my_proc_list)
2628 self.compare_cross_section(my_proc_list,
2629- orders = {'QED':99, 'QCD':99},
2630+ orders = {'QED':QEDorder, 'QCD':QCDorder},
2631 filename = "short_ME7_cs_sm.log",
2632 append_output = True,
2633 ME6_options={'accuracy':0.01},
2634
2635=== modified file 'tests/parallel_tests/madevent_comparator.py'
2636--- tests/parallel_tests/madevent_comparator.py 2017-12-01 19:13:40 +0000
2637+++ tests/parallel_tests/madevent_comparator.py 2019-01-17 08:58:59 +0000
2638@@ -81,6 +81,7 @@
2639 self.results.append(runner.run(proc_list, model[i], orders))
2640 cpu_time2 = time.time()
2641 logging.info(" Done in %0.3f s" % (cpu_time2 - cpu_time1))
2642+ misc.sprint(" Done in %0.3f s" % (cpu_time2 - cpu_time1))
2643 # logging.info(" (%i/%i with zero ME)" % \
2644 # (len([res for res in self.results[-1] if res[0][0] == 0.0]),
2645 # len(proc_list)))
2646@@ -510,7 +511,7 @@
2647
2648 for i, proc in enumerate(proc_list):
2649 v5_string += 'add process ' + proc + ' ' + couplings + \
2650- '@%i' % i + '\n'
2651+ '@%i' % i + ' --diagram_filter' + '\n'
2652 v5_string += "output %s -f\n" % \
2653 os.path.join(self.mg5_path, self.temp_dir_name)
2654 v5_string += "launch -i --multicore\n"
2655@@ -635,6 +636,9 @@
2656 def __init__(self, *args, **opts):
2657
2658 self.n_points = 1000
2659+ if 'PS_generator' in opts:
2660+ self.PS_generator = opts.pop('PS_generator')
2661+ self.name = 'ME7 %s' %self.PS_generator
2662 if 'n_points' in opts:
2663 self.n_points = opts.pop('n_points')
2664 self.integrator = 'VEGAS3'
2665@@ -660,7 +664,7 @@
2666 def format_ME7_proc_card(self, proc_list, model, orders):
2667 """Create a proc_card.dat string following v5 conventions."""
2668
2669- perturbation_orders = '--LO'
2670+ perturbation_orders = '--LO --diagram_filter'
2671 v5_string = "import model %s\n" % os.path.join(self.model_dir, model)
2672 v5_string += "set automatic_html_opening False\n"
2673 couplings = me_comparator.MERunner.get_coupling_definitions(orders)
2674@@ -670,7 +674,7 @@
2675 v5_string += "output %s -f\n" % \
2676 os.path.join(self.mg5_path, self.temp_dir_name)
2677 v5_string += "launch %s \n"%(os.path.join(self.mg5_path, self.temp_dir_name))
2678- v5_string += "launch --integrator=%s --n_points=%d\n"%(self.integrator, self.n_points)
2679+ v5_string += "launch --integrator=%s --n_points=%d --PS_generator=%s\n"%(self.integrator, self.n_points,self.PS_generator)
2680 v5_string += "exit\n"
2681 v5_string += "exit\n"
2682 return v5_string
2683
2684=== modified file 'tests/time_db'
2685--- tests/time_db 2019-01-14 20:52:22 +0000
2686+++ tests/time_db 2019-01-17 08:58:59 +0000
2687@@ -6,29 +6,28 @@
2688 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.LegTest testMethod=test_values_for_prop>]> 0.0001220703125
2689 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayModel testMethod=test_find_full_sm_decay_groups_advance>]> 2.03565192223
2690 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayAmplitude testMethod=test_get_amplitude_givenfinal>]> 0.241197824478
2691-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_drawing_eps.TestDrawingOption testMethod=test_option_multi_type>]> 0.0388491153717
2692 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_SoftVsFinalMapping_invertible>]> 0.0732071399689
2693 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_import_v4.ProcCardV4ReaderTest testMethod=test_line_creation>]> 0.0172848701477
2694 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_color_algebra.ColorFactorTest testMethod=test_f_product>]> 0.00337600708008
2695 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasMatrixElementTest testMethod=test_generate_helas_diagrams_uux_gepem>]> 0.0183539390564
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2721 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_drawing_eps.TestDrawingS_EPS testMethod=test_schedular>]> 0.184823036194
2722@@ -38,7 +37,7 @@
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2731@@ -49,30 +48,30 @@
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2768@@ -82,42 +81,42 @@
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2822@@ -125,64 +124,64 @@
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2902@@ -190,9 +189,10 @@
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2914@@ -203,7 +203,7 @@
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2923@@ -215,11 +215,10 @@
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2938@@ -236,20 +235,20 @@
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3030@@ -314,11 +315,11 @@
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3043@@ -332,12 +333,11 @@
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3057@@ -351,40 +351,33 @@
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3103@@ -398,26 +391,26 @@
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3138@@ -425,75 +418,75 @@
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3320@@ -568,6 +563,7 @@
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3328@@ -580,7 +576,6 @@
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3336@@ -595,38 +590,34 @@
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3384@@ -634,27 +625,26 @@
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3589@@ -802,12 +790,10 @@
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3603@@ -817,10 +803,10 @@
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3616@@ -828,7 +814,7 @@
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3656@@ -869,56 +854,60 @@
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3733@@ -926,84 +915,89 @@
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3970@@ -1111,21 +1107,22 @@
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4060@@ -1180,41 +1180,41 @@
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4174-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks_EW.IOExportFKSEWTest testMethod=test_write_real_me_wrapper_EW>]> 5.69820404053e-05
4175+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAddVariable testMethod=testsumaddvar>]> 0.000104904174805
4176+<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestAddVariable testMethod=testsumaddadd>]> 0.000117063522339
4177+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.testLorentzObject testMethod=test_spin2propagator5>]> 0.182165145874
4178 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_files.TestFilesGestion testMethod=test_is_uptodate>]> 1.0137488842
4179-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestAddVariable testMethod=test_short_factorization5>]> 0.00028395652771
4180+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAlohaWriter testMethod=test_aloha_get_name>]> 0.030855178833
4181 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_forbidden_particles_uux_uuxng>]> 0.0401759147644
4182-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_drawing.TestFeynmanDiagram testMethod=test_find_initial_vertex_position>]> 0.000370979309082
4183+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_rambo.test_wavefunctions testMethod=test_I>]> 0.000235080718994
4184+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_helas_call_writers.UFOHELASCallWriterTest testMethod=test_UFO_Python_helas_call_writer>]> 0.0118069648743
4185 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_phase_space_generators.SimpleNLOWalkerTest testMethod=test_FlatCollinearWalker_invertible>]> 0.00349998474121
4186 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestFKSDiagramTag testMethod=test_reorder_permutation>]> 5.41210174561e-05
4187 <__main__.TestSuiteModified tests=[<tests.parallel_tests.compare_with_old_mg5_version.OLDMG5Comparator testMethod=test_short_cross_loop_induced>]> 50.1276230812
4188-<__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestLinkRBConfSM testMethod=test_link_duxhuduxux_guxhuuxux>]> 1.03614902496
4189-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_shower_card.TestShowerCard testMethod=test_shower_card_hw6_analyse>]> 0.000184059143066
4190+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.ProcessDefinitionTest testMethod=test_representation>]> 0.000728130340576
4191+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_subtraction.NLOSubtractionTest testMethod=test_count_unresolved>]> 0.0163149833679
4192 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestVariable testMethod=testrsumvarvar>]> 9.29832458496e-05
4193 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.ParticleTest testMethod=test_get_helicity_states>]> 0.000174045562744
4194 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_output_files.IOExportMadLoopAcceptanceTest testMethod=test_IOTests>]> 22.2488629818
4195@@ -1276,24 +1277,23 @@
4196 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputIOTest testMethod=testIO_sqso_uux_uuxuuxx>]> 1.1301419735
4197 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestBlock testMethod=test_block_append_remove>]> 0.000144958496094
4198 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_subtraction.NLOSubtractionTest testMethod=test_generation_of_elementary_operators>]> 0.000527858734131
4199-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_subtraction.NLOSubtractionTest testMethod=test_parent_PDGs>]> 0.00149607658386
4200+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_import_ufo.TestRestrictModel testMethod=test_detect_special_parameters>]> 0.0848360061646
4201 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCard testMethod=test_mod_param>]> 0.000418901443481
4202 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_FinalLorentzMapping_invertible>]> 0.229403018951
4203 <__main__.TestSuiteModified tests=[<tests.parallel_tests.compare_with_old_mg5_version.OLDMG5Comparator testMethod=test_paralel_cross_sm>]> 865.433312893
4204-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.ProcessTest testMethod=test_nice_string>]> 0.000377178192139
4205-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_diagram_symmetry.TestDiagramSymmetry testMethod=test_find_symmetry_qq_qqg_with_subprocess_group>]> 0.406945943832
4206+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_generate_helas_diagrams_enu_enu>]> 0.008131980896
4207+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_IOTest_examples.IOTestExampleWithSetUp testMethod=testIO_MyHandwrittenIOTest>]> 0.000919103622437
4208 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_banner.TESTMadLoopParam testMethod=test_initMadLoopParam>]> 0.00100302696228
4209-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.testLorentzObject testMethod=testexpand>]> 0.00219416618347
4210-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCardRule testMethod=test_check_param>]> 0.0894939899445
4211-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestSomeObjectProperty testMethod=test_other>]> 4.31537628174e-05
4212-<__main__.TestSuiteModified tests=[<tests.unit_tests.interface.test_edit_card.TestEditCardCmd testMethod=test_modif_run_card>]> 0.0163931846619
4213-<__main__.TestSuiteModified tests=[<tests.unit_tests.interface.test_madevent.TestMadEventCmd testMethod=test_card_type_recognition>]> 0.0509171485901
4214-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks_EW.IOExportFKSEWTest testMethod=test_write_born_nhel_file_EW>]> 0.125450849533
4215+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayModel testMethod=test_find_mssm_decay_groups_modified_mssm>]> 7.05039405823
4216+<__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd_amcatnlo.TestMECmdShell testMethod=test_check_html_long_process_strings>]> 20.4391908646
4217+<__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_ME7.TestME7_NLO_colorful_epem_jjj testMethod=test_ME7_real_integrand_call>]> 0.00147199630737
4218+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_vector_clash_majorana_process>]> 0.012188911438
4219 <__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_diagram_generation.LoopEWDiagramGenerationTest testMethod=test_diagram_generation_uux_ga_EW>]> 15.2475500107
4220 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5.ML5Test testMethod=test_long_sm_vs_stored_ML4_dxd_zzg>]> 32.0563452244
4221-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayModel testMethod=test_find_full_sm_decay_groups>]> 5.82679080963
4222+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.MultiLegTest testMethod=test_representation>]> 5.91278076172e-05
4223 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_output_files.IOExportMadLoopAcceptanceTest testMethod=testIO_ProcOutputIOTests>]> 28.318707943
4224-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.DiagramTest testMethod=test_setget_diagram_correct>]> 9.48905944824e-05
4225+<__main__.TestSuiteModified tests=[<tests.unit_tests.madspin.test_madspin.TestEvent testMethod=test_madspin_event>]> 0.00227999687195
4226+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.testLorentzObject testMethod=test_expand_veto>]> 0.00188112258911
4227 <__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_exporters.IOTestMadLoopOutputFromInterface testMethod=testIO_TIR_output>]> 190.159698009
4228 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_required_s_channel_uux_uuxng>]> 0.146993160248
4229 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_SoftVsInitialMapping_invertible>]> 0.0726389884949
4230@@ -1301,47 +1301,48 @@
4231 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestLorentzObjectRepresentation testMethod=testspinsum>]> 0.00303506851196
4232 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_export_majorana_decay_chain>]> 0.290829896927
4233 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_import_ufo.TestRestrictModel testMethod=test_remove_couplings>]> 0.0872521400452
4234-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.test_aloha_creation testMethod=test_short_use_of_library_spin2>]> 0.116204977036
4235+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_drawing_eps.TestDrawingOption testMethod=test_option_6g_second>]> 0.0498158931732
4236 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_file_writers.CPPWriterTest testMethod=test_write_cplusplus_line>]> 0.0056209564209
4237 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.TestDiagramTag testMethod=test_diagram_tag_to_diagram_uux_nglue>]> 0.129276990891
4238 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_group_subprocs.SubProcessGroupTest testMethod=test_single_decay_combinations>]> 0.0449919700623
4239-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayParticle testMethod=test_setgetinit_exceptions>]> 0.111714839935
4240+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_drawing.TestFeynmanLine testMethod=test_line_orientation>]> 0.000136137008667
4241 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestLinkRBConfSM testMethod=test_link_udxwpg_udxwp>]> 0.0118889808655
4242 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAddVariable testMethod=testmultaddvar_legacy>]> 0.00015115737915
4243 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasWavefunctionTest testMethod=test_values_for_prop>]> 0.000183820724487
4244-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5EW.ML5EWTest testMethod=test_ML5EW_sm_vs_stored_ML5EW_sqso>]> 0.00940799713135
4245-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.IOExportFKSTest testMethod=test_get_color_data_lines_from_color_matrix>]> 1.95838093758
4246-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5.ML5Test testMethod=test_long_sm_vs_stored_ML4_epem_ddx>]> 25.6196660995
4247+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_ufo_parsers.UFOParserTest testMethod=test_parse_info_str_error>]> 0.0325899124146
4248+<__main__.TestSuiteModified tests=[<tests.unit_tests.madspin.test_madspin.TestBanner testMethod=test_extract_info>]> 0.00151705741882
4249+<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestSimplify testMethod=testsimplifyMultLorentz>]> 0.0103709697723
4250+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.IOExportFKSTest testMethod=test_write_nexternal_file>]> 0.000472784042358
4251 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.DiagramTest testMethod=test_diagram_list_nice_string>]> 0.00311493873596
4252-<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.WalkersTest testMethod=test_FinalLorentzNLOWalker_invertible>]> 0.122242212296
4253-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_ufo_parsers.UFOParserTest testMethod=test_parse_info_str_error>]> 0.0325899124146
4254+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAddVariable testMethod=test_factorization5>]> 0.000308036804199
4255+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_FinalLorentz_reduces_to_FinalLorentzOne>]> 0.0485739707947
4256 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_shower_card.TestShowerCard testMethod=test_shower_card_py6>]> 0.000173807144165
4257-<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.testLorentzObject testMethod=test_short_expand_veto>]> 0.00168204307556
4258-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayModel testMethod=test_setget>]> 2.0447640419
4259-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_python.IOExportPythonTest testMethod=test_get_python_matrix_methods>]> 0.0204730033875
4260-<__main__.TestSuiteModified tests=[<tests.unit_tests.madspin.test_madspin.TestEvent testMethod=test_madspin_event>]> 0.00227999687195
4261-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasMatrixElementTest testMethod=test_fermionfactor_epem_sepsemepem>]> 0.074609041214
4262+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.SomogyietalSoftTest testMethod=test_SomogyietalSoft_invertible>]> 0.0936510562897
4263+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.testLorentzObject testMethod=test_mass_overmass>]> 0.00163412094116
4264+<__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_drawing.TestLoopDrawer testMethod=test_level_with_flipping_triangle>]> 0.00130605697632
4265+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_import_ufo.TestRestrictModel testMethod=test_restrict_from_a_param_card>]> 0.106390953064
4266+<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.testLorentzObject testMethod=testexpand>]> 0.00219416618347
4267 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestAlohaWriter testMethod=test_short_pythonwriter_Plorentz>]> 0.0777571201324
4268 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_import_ufo.TestRestrictModel testMethod=test_put_parameters_to_zero>]> 0.0871658325195
4269-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestSomeObjectProperty testMethod=testGammaAlgebraDefinition>]> 1.01560497284
4270+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_phase_space_generators.CollinearVariablesTest testMethod=test_collinear_variables_away_from_limit>]> 0.000630855560303
4271 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_color_amp.ColorAmpTest testMethod=test_color_flow_string_epsilon>]> 0.00355815887451
4272-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_drawing.TestVertexPoint testMethod=test_add_line>]> 0.000107049942017
4273+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.ProcessDefinitionTest testMethod=test_values_for_prop>]> 0.000351905822754
4274 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_InitialLorentzOneMapping_invertible>]> 0.0667169094086
4275 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasAmplitudeTest testMethod=test_setget_amplitude_exceptions>]> 0.000211000442505
4276 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.NLOWalkerTest testMethod=test_NLOWalker_approach_limit>]> 0.083899974823
4277-<__main__.TestSuiteModified tests=[<tests.parallel_tests.compare_with_old_mg5_version.OLDMG5Comparator testMethod=test_short_cross_sm1>]> 38.1165001392
4278-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_forbidden_s_channel_uux_uuxng>]> 0.0782489776611
4279+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_diagram_generation_uux_gg>]> 0.00705409049988
4280+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DecayChainAmplitudeTest testMethod=test_setget_process_exceptions>]> 0.000777006149292
4281 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestLinkRBConfSM testMethod=test_link_uuuug_guug>]> 0.0394339561462
4282 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestMultVariable testMethod=testsummulvar>]> 0.000197887420654
4283-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_AbstractModel testMethod=test_help_generate_ab_amplitude>]> 0.233986139297
4284+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCardRule testMethod=test_read_write_param_card>]> 0.00176000595093
4285 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_configs_4f_decay>]> 0.0363750457764
4286-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.TestFKSOutput testMethod=test_w_nlo_gen_gosam>]> 0.396389007568
4287+<__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_diagram_generation.LoopEWDiagramGenerationTest testMethod=test_diagram_generation_uux_epem_EW>]> 7.10565686226
4288 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5.ML5Test testMethod=test_long_sm_vs_stored_ML5_gs_emvexc>]> 13.0895609856
4289-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_misc.IOMiscTest testMethod=test_parse_info_str_correct>]> 0.000116109848022
4290+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCardRule testMethod=test_check_param>]> 0.0894939899445
4291 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_madweight.TestMadWeight testMethod=test_short_mw_wa>]> 11.9575219154
4292 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.LegTest testMethod=test_setget_leg_exceptions>]> 0.000114917755127
4293-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.IOExportFKSTest testMethod=test_write_nexternal_file>]> 0.000472784042358
4294-<__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd_amcatnlo.TestMECmdShell testMethod=test_check_ppzjj>]> 59.9299731255
4295+<__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5.ML5Test testMethod=test_long_sm_vs_stored_ML4_epem_ddx>]> 25.6196660995
4296+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_python.IOExportPythonTest testMethod=test_export_matrix_element_python_madevent_group>]> 0.0395710468292
4297 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_cmd_amcatnlo.MECmdShell testMethod=test_short_check_generate_events_nlo_py6pt_fsr>]> 34.7858700752
4298 <__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_helas_objects.LoopHelasMatrixElementTest testMethod=test_helas_diagrams_dxd_gz>]> 2.18089485168
4299 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestAlohaWriter testMethod=test_short_aloha_MP_mode>]> 0.0324759483337
4300\ No newline at end of file
4301
4302=== modified file 'tests/unit_tests/integrator/test_phase_space_generators.py'
4303--- tests/unit_tests/integrator/test_phase_space_generators.py 2019-01-14 20:52:22 +0000
4304+++ tests/unit_tests/integrator/test_phase_space_generators.py 2019-01-17 08:58:59 +0000
4305@@ -12,24 +12,124 @@
4306 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
4307 #
4308 ##########################################################################################
4309-"""Unit test library for phase-space generation."""
4310+"""Unit test library for the various phase-space generation/handling features."""
4311
4312 import madgraph.integrator.phase_space_generators as PS
4313+import madgraph.core.subtraction as subtraction
4314 import madgraph.core.base_objects as base_objects
4315+import madgraph.integrator.vectors as vectors
4316 import madgraph.various.misc as misc
4317 import models.import_ufo as import_ufo
4318 from madgraph import MG5DIR
4319
4320+import copy
4321+import math
4322 import random
4323 import os
4324
4325 import tests.unit_tests as unittest
4326+import tests.input_files.simple_qcd as simple_qcd
4327
4328 pjoin = os.path.join
4329
4330 #=========================================================================================
4331+# Shorthands for initial and final state
4332+#=========================================================================================
4333+
4334+INITIAL = base_objects.Leg.INITIAL
4335+FINAL = base_objects.Leg.FINAL
4336+
4337+assert subtraction.SubtractionLeg.INITIAL == INITIAL
4338+assert subtraction.SubtractionLeg.FINAL == FINAL
4339+
4340+#=========================================================================================
4341+# Test Vectors
4342+#=========================================================================================
4343+
4344+class VectorsTest(unittest.TestCase):
4345+ """Test class for BaseVector, Vector and LorentzVector."""
4346+
4347+ def setUp(self):
4348+ pass
4349+
4350+ def test_Vector_basic(self):
4351+ """Test the basic operations for vectors."""
4352+
4353+ # Component-wise addition and subtraction
4354+ v1 = PS.Vector([1., 0., 2., 3.])
4355+ v2 = PS.Vector(4*[0.5, ])
4356+ self.assertEqual(v1 + v2, PS.Vector([1.5, 0.5, 2.5, 3.5]))
4357+ v3 = PS.Vector([1., 2., 0.5, -1.])
4358+ self.assertEqual(v1 - v3, PS.Vector([0., -2., 1.5, 4.]))
4359+ v3 += PS.Vector([1., 0., 0., 0.])
4360+ self.assertEqual(v3, PS.Vector([2., 2., 0.5, -1.]))
4361+ v3 -= PS.Vector([0.5, 1.5, 0., 0.])
4362+ self.assertEqual(v3, PS.Vector([1.5, 0.5, 0.5, -1.]))
4363+
4364+ # Multiplication and division by scalars
4365+ self.assertEqual(v2 * 2., PS.Vector(4*[1., ]))
4366+ self.assertEqual(v1 / 4., PS.Vector([0.25, 0., 0.5, 0.75]))
4367+ v3 *= 3
4368+ self.assertEqual(v3, PS.Vector([4.5, 1.5, 1.5, -3.]))
4369+ v3 /= 1.5
4370+ self.assertEqual(v3, PS.Vector([3., 1., 1., -2.]))
4371+ self.assertEqual(3 * v1, v1 * 3)
4372+
4373+ # Negative
4374+ self.assertEqual(-v3, PS.Vector([-3., -1., -1., 2.]))
4375+
4376+ def test_Vector_Euclid(self):
4377+ """Test scalar products and related functions for the class Vector."""
4378+
4379+ # Square and norm
4380+ v1 = PS.Vector([0., 1., -2.])
4381+ self.assertEqual(v1.square(), 5.)
4382+ v2 = PS.Vector([3., 4., 0.])
4383+ self.assertEqual(abs(v2), 5.)
4384+ v2n = 0.2 * v2
4385+ v2.normalize()
4386+ self.assertEqual(v2, v2n)
4387+ self.assertEqual(PS.Vector.dot(v1, v2), 0.8)
4388+ v3 = PS.Vector([random.random() for _ in range(3)])
4389+ w = PS.Vector([random.random() for _ in range(3)])
4390+ v3p = v3.project_onto(w)
4391+ v3t = v3.component_orthogonal_to(w)
4392+ self.assertEqual(v3, v3p + v3t)
4393+ self.assertAlmostEqual(PS.Vector.dot(v3t, w), 0.)
4394+ self.assertAlmostEqual(abs(v3p+w), abs(v3p)+abs(w))
4395+
4396+ def test_Vector_Minkowski(self):
4397+ """Test the relativistic norm."""
4398+
4399+ # Square and norm
4400+ v1 = PS.LorentzVector([1, 0, 0, +1])
4401+ v2 = PS.LorentzVector([1, 0, 0, -1])
4402+ self.assertAlmostEqual(v1.square(), 0, 10)
4403+ self.assertAlmostEqual(v2.square(), 0, 10)
4404+ self.assertAlmostEqual(v1.dot(v2), 2, 10)
4405+
4406+ # Test the rotoboost
4407+ # Definition
4408+ v1 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
4409+ v2 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
4410+ m = random.random()
4411+ v1.set_square(m**2)
4412+ v2.set_square(m**2)
4413+ for x, y in zip(v2.rotoboost(v2, v1), v1):
4414+ self.assertAlmostEqual(x,y,10)
4415+ # Inverse
4416+ v3 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
4417+ v3.set_square(m**2)
4418+ v4 = PS.LorentzVector([random.random() for _ in range(4)])
4419+ v4_old = v4.get_copy()
4420+ v4.rotoboost(v1, v3)
4421+ v4.rotoboost(v3, v1)
4422+ for x, y in zip(v4, v4_old):
4423+ self.assertAlmostEqual(x,y,10)
4424+
4425+#===============================================================================
4426 # Test the phase-space generators
4427-#=========================================================================================
4428+#===============================================================================
4429
4430 class PhaseSpaceGeneratorsTest(unittest.TestCase):
4431 """ Test various phase-space generators."""
4432@@ -48,6 +148,670 @@
4433 complex_mass_scheme=False)
4434 self.model = model_with_params_set
4435
4436+ # A specific sets of s- and t-channels for this test:
4437+
4438+ ####################################################################
4439+ # a) A simple unique massless photon s-channel from u u~ > d d~ / z w+ QCD=0
4440+ ####################################################################
4441+
4442+ self.massless_photon_schannel_specifier = (
4443+ # s-channels first:
4444+ base_objects.VertexList([
4445+ base_objects.Vertex({
4446+ 'id': 15,
4447+ 'legs': base_objects.LegList([
4448+ base_objects.Leg({
4449+ 'id': -1,
4450+ 'number': 4,
4451+ 'state': True,
4452+ 'from_group': True,
4453+ 'loop_line': False,
4454+ 'onshell': None
4455+ }),
4456+ base_objects.Leg({
4457+ 'id': 1,
4458+ 'number': 3,
4459+ 'state': True,
4460+ 'from_group': True,
4461+ 'loop_line': False,
4462+ 'onshell': None
4463+ }),
4464+ base_objects.Leg({
4465+ 'id': 22,
4466+ 'number': -1,
4467+ 'state': True,
4468+ 'from_group': True,
4469+ 'loop_line': False,
4470+ 'onshell': None
4471+ })
4472+ ])
4473+ }),
4474+ ]),
4475+ # t-channels then:
4476+ base_objects.VertexList([
4477+ base_objects.Vertex({
4478+ 'id': 34,
4479+ 'legs': base_objects.LegList([
4480+ base_objects.Leg({
4481+ 'id': 2,
4482+ 'number': 1,
4483+ 'state': False,
4484+ 'from_group': True,
4485+ 'loop_line': False,
4486+ 'onshell': None
4487+ }),
4488+ base_objects.Leg({
4489+ 'id': 22,
4490+ 'number': -1,
4491+ 'state': True,
4492+ 'from_group': True,
4493+ 'loop_line': False,
4494+ 'onshell': None
4495+ }),
4496+ base_objects.Leg({
4497+ 'id': -2,
4498+ 'number': -2,
4499+ 'state': False,
4500+ 'from_group': True,
4501+ 'loop_line': False,
4502+ 'onshell': None
4503+ })
4504+ ])
4505+ }),
4506+ ])
4507+ )
4508+
4509+ ####################################################################
4510+ # a) A simple unique massive Z-boson s-channel from u u~ > d d~ / a w+ QCD = 0
4511+ ####################################################################
4512+
4513+ self.massive_zboson_schannel_specifier = (
4514+ # s-channels first:
4515+ base_objects.VertexList([
4516+ base_objects.Vertex({
4517+ 'id': 22,
4518+ 'legs': base_objects.LegList([
4519+ base_objects.Leg({
4520+ 'id': -1,
4521+ 'number': 4,
4522+ 'state': True,
4523+ 'from_group': True,
4524+ 'loop_line': False,
4525+ 'onshell': None
4526+ }),
4527+ base_objects.Leg({
4528+ 'id': 1,
4529+ 'number': 3,
4530+ 'state': True,
4531+ 'from_group': True,
4532+ 'loop_line': False,
4533+ 'onshell': None
4534+ }),
4535+ base_objects.Leg({
4536+ 'id': 23,
4537+ 'number': -1,
4538+ 'state': True,
4539+ 'from_group': True,
4540+ 'loop_line': False,
4541+ 'onshell': None
4542+ }),
4543+ ])
4544+ }),
4545+ ]),
4546+ # t-channels then:
4547+ base_objects.VertexList([
4548+ base_objects.Vertex({
4549+ 'id': 40,
4550+ 'legs': base_objects.LegList([
4551+ base_objects.Leg({
4552+ 'id': 2,
4553+ 'number': 1,
4554+ 'state': False,
4555+ 'from_group': True,
4556+ 'loop_line': False,
4557+ 'onshell': None
4558+ }),
4559+ base_objects.Leg({
4560+ 'id': 23,
4561+ 'number': -1,
4562+ 'state': True,
4563+ 'from_group': True,
4564+ 'loop_line': False,
4565+ 'onshell': None
4566+ }),
4567+ base_objects.Leg({
4568+ 'id': -2,
4569+ 'number': -2,
4570+ 'state': False,
4571+ 'from_group': True,
4572+ 'loop_line': False,
4573+ 'onshell': None
4574+ }),
4575+ ])
4576+ }),
4577+ ]),
4578+ )
4579+
4580+ ####################################################################
4581+ # b) A simple unique massive Z-boson t-channel from generate u d > u d /a w+ w- QCD=0 --LO
4582+ ####################################################################
4583+
4584+ self.massive_zboson_tchannel_specifier = (
4585+ # s-channels first:
4586+ base_objects.VertexList([]),
4587+ # t-channels then:
4588+ base_objects.VertexList([
4589+ base_objects.Vertex({
4590+ 'id': 63,
4591+ 'legs': base_objects.LegList([
4592+ base_objects.Leg({
4593+ 'id': -2,
4594+ 'number': 1,
4595+ 'state': False,
4596+ 'from_group': True,
4597+ 'loop_line': False,
4598+ 'onshell': None
4599+ }),
4600+ base_objects.Leg({
4601+ 'id': 2,
4602+ 'number': 3,
4603+ 'state': True,
4604+ 'from_group': True,
4605+ 'loop_line': False,
4606+ 'onshell': None
4607+ }),
4608+ base_objects.Leg({
4609+ 'id': 23,
4610+ 'number': -1,
4611+ 'state': False,
4612+ 'from_group': True,
4613+ 'loop_line': False,
4614+ 'onshell': None
4615+ }),
4616+ ])
4617+ }),
4618+ base_objects.Vertex({
4619+ 'id': 22,
4620+ 'legs': base_objects.LegList([
4621+ base_objects.Leg({
4622+ 'id': 23,
4623+ 'number': -1,
4624+ 'state': False,
4625+ 'from_group': True,
4626+ 'loop_line': False,
4627+ 'onshell': None
4628+ }),
4629+ base_objects.Leg({
4630+ 'id': 1,
4631+ 'number': 4,
4632+ 'state': True,
4633+ 'from_group': True,
4634+ 'loop_line': False,
4635+ 'onshell': None
4636+ }),
4637+ base_objects.Leg({
4638+ 'id': -1,
4639+ 'number': -2,
4640+ 'state': False,
4641+ 'from_group': True,
4642+ 'loop_line': False,
4643+ 'onshell': None
4644+ }),
4645+ ])
4646+ }),
4647+ ]),
4648+ )
4649+
4650+ ####################################################################
4651+ # b) A simple unique massless photon t-channel from u d > u d / z
4652+ ####################################################################
4653+
4654+ self.massless_photon_tchannel_specifier = (
4655+ # s-channels first:
4656+ base_objects.VertexList([]),
4657+ # t-channels then:
4658+ base_objects.VertexList([
4659+ base_objects.Vertex({
4660+ 'id': 63,
4661+ 'legs': base_objects.LegList([
4662+ base_objects.Leg({
4663+ 'id': -2,
4664+ 'number': 1,
4665+ 'state': False,
4666+ 'from_group': True,
4667+ 'loop_line': False,
4668+ 'onshell': None
4669+ }),
4670+ base_objects.Leg({
4671+ 'id': 2,
4672+ 'number': 3,
4673+ 'state': True,
4674+ 'from_group': True,
4675+ 'loop_line': False,
4676+ 'onshell': None
4677+ }),
4678+ base_objects.Leg({
4679+ 'id': 22,
4680+ 'number': -1,
4681+ 'state': True,
4682+ 'from_group': True,
4683+ 'loop_line': False,
4684+ 'onshell': None
4685+ }),
4686+ ])
4687+ }),
4688+ base_objects.Vertex({
4689+ 'id': 22,
4690+ 'legs': base_objects.LegList([
4691+ base_objects.Leg({
4692+ 'id': 22,
4693+ 'number': -1,
4694+ 'state': True,
4695+ 'from_group': True,
4696+ 'loop_line': False,
4697+ 'onshell': None
4698+ }),
4699+ base_objects.Leg({
4700+ 'id': 1,
4701+ 'number': 4,
4702+ 'state': True,
4703+ 'from_group': True,
4704+ 'loop_line': False,
4705+ 'onshell': None
4706+ }),
4707+ base_objects.Leg({
4708+ 'id': -1,
4709+ 'number': -2,
4710+ 'state': False,
4711+ 'from_group': True,
4712+ 'loop_line': False,
4713+ 'onshell': None
4714+ }),
4715+ ])
4716+ }),
4717+ ]),
4718+ )
4719+
4720+
4721+
4722+ ###############################################################################
4723+ # c) A complicated fully decayed VBF topology:
4724+ # from: generate u c > h > u c e+ e- mu+ mu- $$ c u / a s d s~ d~ QCD=0 --LO
4725+ ###############################################################################
4726+ self.vbf_topology_s_and_t_channel_specifier = (
4727+ # s-channels first:
4728+ base_objects.VertexList([
4729+ base_objects.Vertex({
4730+ 'id': 41,
4731+ 'legs': base_objects.LegList([
4732+ base_objects.Leg({
4733+ 'id': 13,
4734+ 'number': 8,
4735+ 'state': True,
4736+ 'from_group': True,
4737+ 'loop_line': False,
4738+ 'onshell': None
4739+ }),
4740+ base_objects.Leg({
4741+ 'id': -13,
4742+ 'number': 7,
4743+ 'state': True,
4744+ 'from_group': True,
4745+ 'loop_line': False,
4746+ 'onshell': None
4747+ }),
4748+ base_objects.Leg({
4749+ 'id': 23,
4750+ 'number': -1,
4751+ 'state': True,
4752+ 'from_group': True,
4753+ 'loop_line': False,
4754+ 'onshell': None
4755+ }),
4756+ ])
4757+ }),
4758+ base_objects.Vertex({
4759+ 'id': 40,
4760+ 'legs': base_objects.LegList([
4761+ base_objects.Leg({
4762+ 'id': 11,
4763+ 'number': 6,
4764+ 'state': True,
4765+ 'from_group': True,
4766+ 'loop_line': False,
4767+ 'onshell': None
4768+ }),
4769+ base_objects.Leg({
4770+ 'id': -11,
4771+ 'number': 5,
4772+ 'state': True,
4773+ 'from_group': True,
4774+ 'loop_line': False,
4775+ 'onshell': None
4776+ }),
4777+ base_objects.Leg({
4778+ 'id': 23,
4779+ 'number': -2,
4780+ 'state': True,
4781+ 'from_group': True,
4782+ 'loop_line': False,
4783+ 'onshell': None
4784+ }),
4785+ ])
4786+ }),
4787+ base_objects.Vertex({
4788+ 'id': 13,
4789+ 'legs': base_objects.LegList([
4790+ base_objects.Leg({
4791+ 'id': 23,
4792+ 'number': -1,
4793+ 'state': True,
4794+ 'from_group': True,
4795+ 'loop_line': False,
4796+ 'onshell': None
4797+ }),
4798+ base_objects.Leg({
4799+ 'id': 23,
4800+ 'number': -2,
4801+ 'state': True,
4802+ 'from_group': True,
4803+ 'loop_line': False,
4804+ 'onshell': None
4805+ }),
4806+ base_objects.Leg({
4807+ 'id': 25,
4808+ 'number': -3,
4809+ 'state': True,
4810+ 'from_group': True,
4811+ 'loop_line': False,
4812+ 'onshell': None
4813+ }),
4814+ ])
4815+ })
4816+ ]),
4817+ # t-channels then:
4818+ base_objects.VertexList([
4819+ base_objects.Vertex({
4820+ 'id': 63,
4821+ 'legs': base_objects.LegList([
4822+ base_objects.Leg({
4823+ 'id': -2,
4824+ 'number': 1,
4825+ 'state': False,
4826+ 'from_group': True,
4827+ 'loop_line': False,
4828+ 'onshell': None
4829+ }),
4830+ base_objects.Leg({
4831+ 'id': 2,
4832+ 'number': 3,
4833+ 'state': True,
4834+ 'from_group': True,
4835+ 'loop_line': False,
4836+ 'onshell': None
4837+ }),
4838+ base_objects.Leg({
4839+ 'id': 23,
4840+ 'number': -4,
4841+ 'state': False,
4842+ 'from_group': True,
4843+ 'loop_line': False,
4844+ 'onshell': None
4845+ }),
4846+ ])
4847+ }),
4848+ base_objects.Vertex({
4849+ 'id': 13,
4850+ 'legs': base_objects.LegList([
4851+ base_objects.Leg({
4852+ 'id': 23,
4853+ 'number': -4,
4854+ 'state': False,
4855+ 'from_group': True,
4856+ 'loop_line': False,
4857+ 'onshell': None
4858+ }),
4859+ base_objects.Leg({
4860+ 'id': 25,
4861+ 'number': -3,
4862+ 'state': True,
4863+ 'from_group': True,
4864+ 'loop_line': False,
4865+ 'onshell': None
4866+ }),
4867+ base_objects.Leg({
4868+ 'id': 23,
4869+ 'number': -5,
4870+ 'state': False,
4871+ 'from_group': True,
4872+ 'loop_line': False,
4873+ 'onshell': None
4874+ }),
4875+ ])
4876+ }),
4877+ base_objects.Vertex({
4878+ 'id': 64,
4879+ 'legs': base_objects.LegList([
4880+ base_objects.Leg({
4881+ 'id': 23,
4882+ 'number': -5,
4883+ 'state': False,
4884+ 'from_group': True,
4885+ 'loop_line': False,
4886+ 'onshell': None
4887+ }),
4888+ base_objects.Leg({
4889+ 'id': 4,
4890+ 'number': 4,
4891+ 'state': True,
4892+ 'from_group': True,
4893+ 'loop_line': False,
4894+ 'onshell': None
4895+ }),
4896+ base_objects.Leg({
4897+ 'id': -4,
4898+ 'number': -6,
4899+ 'state': False,
4900+ 'from_group': True,
4901+ 'loop_line': False,
4902+ 'onshell': None
4903+ }),
4904+ ])
4905+ }),
4906+ ]),
4907+ )
4908+
4909+
4910+ ###############################################################################
4911+ # d) A complicated fully decayed VBF topology:
4912+ # from: generate e- e+ > h > e+ e- mu+ mu- ta+ ta- $$ e+ e- \ a QCD=0 --diagram_filter --LO
4913+ ###############################################################################
4914+ # where diagram filter removes the first three diagrams
4915+ # import model sm-dario
4916+ self.vbf_topology_s_and_t_channel_specifier2 = (
4917+ # s-channels first:
4918+ base_objects.VertexList([
4919+ base_objects.Vertex({
4920+ 'id': 42,
4921+ 'legs': base_objects.LegList([
4922+ base_objects.Leg({
4923+ 'id': 15,
4924+ 'number': 8,
4925+ 'state': True,
4926+ 'from_group': True,
4927+ 'loop_line': False,
4928+ 'onshell': None
4929+ }),
4930+ base_objects.Leg({
4931+ 'id': -15,
4932+ 'number': 7,
4933+ 'state': True,
4934+ 'from_group': True,
4935+ 'loop_line': False,
4936+ 'onshell': None
4937+ }),
4938+ base_objects.Leg({
4939+ 'id': 23,
4940+ 'number': -1,
4941+ 'state': True,
4942+ 'from_group': True,
4943+ 'loop_line': False,
4944+ 'onshell': None
4945+ }),
4946+ ])
4947+ }),
4948+ base_objects.Vertex({
4949+ 'id': 41,
4950+ 'legs': base_objects.LegList([
4951+ base_objects.Leg({
4952+ 'id': 13,
4953+ 'number': 6,
4954+ 'state': True,
4955+ 'from_group': True,
4956+ 'loop_line': False,
4957+ 'onshell': None
4958+ }),
4959+ base_objects.Leg({
4960+ 'id': -13,
4961+ 'number': 5,
4962+ 'state': True,
4963+ 'from_group': True,
4964+ 'loop_line': False,
4965+ 'onshell': None
4966+ }),
4967+ base_objects.Leg({
4968+ 'id': 23,
4969+ 'number': -2,
4970+ 'state': True,
4971+ 'from_group': True,
4972+ 'loop_line': False,
4973+ 'onshell': None
4974+ }),
4975+ ])
4976+ }),
4977+ base_objects.Vertex({
4978+ 'id': 13,
4979+ 'legs': base_objects.LegList([
4980+ base_objects.Leg({
4981+ 'id': 23,
4982+ 'number': -1,
4983+ 'state': True,
4984+ 'from_group': True,
4985+ 'loop_line': False,
4986+ 'onshell': None
4987+ }),
4988+ base_objects.Leg({
4989+ 'id': 23,
4990+ 'number': -2,
4991+ 'state': True,
4992+ 'from_group': True,
4993+ 'loop_line': False,
4994+ 'onshell': None
4995+ }),
4996+ base_objects.Leg({
4997+ 'id': 25,
4998+ 'number': -3,
4999+ 'state': True,
5000+ 'from_group': True,
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