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

[Valentin Hirschi]

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.

[Dario Kermanschah (kedario)]

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

[lp:~madnklo/mg5amcnlo/MadNkLO_MCPS]

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)]

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 ...

[lp:~madnklo/mg5amcnlo/MadNkLO_MCPS]

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)]

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]

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.

=== added directory 'Documentation.moved'
=== added file 'Documentation.moved/ThesisDarioKermanschah.pdf'
Binary 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
=== added file 'Documentation/ThesisDarioKermanschah.pdf'
Binary files Documentation/ThesisDarioKermanschah.pdf	1970-01-01 00:00:00 +0000 and Documentation/ThesisDarioKermanschah.pdf	2019-01-17 08:58:59 +0000 differ
=== modified file 'madgraph/core/diagram_generation.py'
--- madgraph/core/diagram_generation.py	2017-10-04 17:43:35 +0000
+++ madgraph/core/diagram_generation.py	2019-01-17 08:58:59 +0000
@@ -911,8 +911,8 @@
 
         res = diag_list.__class__()                
         nb_removed = 0 
-        for diag in diag_list:
-            if remove_diag(diag):
+        for i,diag in enumerate(diag_list):
+            if remove_diag(diag) or (i != 0 and i != 1):
                 nb_removed +=1
             else:
                 res.append(diag)
@@ -1677,7 +1677,7 @@
 
         processes = base_objects.ProcessList()
         amplitudes = AmplitudeList()
-
+        
         # failed_procs and success_procs are sorted processes that have
         # already failed/succeeded based on crossing symmetry
         failed_procs = []
@@ -1723,7 +1723,7 @@
                         for id in prod])
                 
                 legs = base_objects.LegList(leg_list)
-
+                
                 # Check for crossed processes
                 sorted_legs = sorted([(l,i+1) for (i,l) in \
                                    enumerate(legs.get_outgoing_id_list(model))])
@@ -1843,7 +1843,6 @@
             "No amplitudes generated from process %s. Please enter a valid process" % \
                   process_definition.nice_string()
         
-
         # Return the produced amplitudes
         return amplitudes
 

=== modified file 'madgraph/integrator/ME7_integrands.py'
--- madgraph/integrator/ME7_integrands.py	2019-01-14 15:14:44 +0000
+++ madgraph/integrator/ME7_integrands.py	2019-01-17 08:58:59 +0000
@@ -49,12 +49,12 @@
 # useful shortcut
 pjoin = os.path.join
 
-#root_path = pjoin(os.path.dirname(os.path.realpath( __file__ )), *([os.path.pardir]*3))
-#sys.path.insert(0, root_path)
+# root_path = pjoin(os.path.dirname(os.path.realpath( __file__ )), *([os.path.pardir]*3))
+# sys.path.insert(0, root_path)
 
 # Special logger for the Cmd Interface
-logger = logging.getLogger('madevent7') # -> stdout
-logger_stderr = logging.getLogger('madevent7.stderr') # ->stderr
+logger = logging.getLogger('madevent7')  # -> stdout
+logger_stderr = logging.getLogger('madevent7.stderr')  # ->stderr
 
 import madgraph.core.base_objects as base_objects
 import madgraph.core.subtraction as subtraction
@@ -639,6 +639,8 @@
 class ME7Integrand(integrands.VirtualIntegrand):
     """ Specialization for multi-purpose integration with ME7."""
     
+    counter = 0
+    
     # Maximum size of the cache for PDF calls
     PDF_cache_max_size = 1000
     
@@ -699,7 +701,7 @@
         else:
             return super(ME7Integrand, cls).__new__(cls, *all_args, **opt)
     
-    def __init__(self, model, 
+    def __init__(self, model,
                        run_card,
                        contribution_definition,
                        processes_map,
@@ -730,17 +732,17 @@
 
         # The process map of the Contribution instance at the origin of this integrand.
         # The format is identical to the one generated from the function 'get_process_map' of a contribution.
-        self.processes_map              = processes_map
+        self.processes_map = processes_map
         
         # Add information about the topology of the diagrams constituting the processes,
         # so as to be able to build efficient phase-space parametrizations. The format of these dictionaries
         # is specified in the function 'set_phase_space_topologies' of the class contributions.Contribution
-        self.topologies_to_processes    = topologies_to_processes
-        self.processes_to_topologies    = processes_to_topologies
+        self.topologies_to_processes = topologies_to_processes
+        self.processes_to_topologies = processes_to_topologies
         
         # An instance of accessors.MEAccessorDict providing access to all ME available as part of this
         # ME7 session.
-        self.all_MEAccessors            = all_MEAccessors
+        self.all_MEAccessors = all_MEAccessors
 
         # Update and define many properties of self based on the provided run-card and model.
         self.synchronize(model, run_card, ME7_configuration)
@@ -771,8 +773,8 @@
         contribution. Can be overloaded by daughter classes."""
         GREEN = '\033[92m'
         ENDC = '\033[0m'        
-        return GREEN+'  %s'%defining_process.nice_string(print_weighted=False).\
-                                                               replace('Process: ','')+ENDC
+        return GREEN + '  %s' % defining_process.nice_string(print_weighted=False).\
+                                                               replace('Process: ', '') + ENDC
 
     def get_short_name(self):
         """ Returns the short-name for this integrand, typically extracted from the one
@@ -788,21 +790,21 @@
         res.append('%-30s:   %s'%('ME7Integrand_type',type(self)))
         res.extend([self.contribution_definition.nice_string()])
         if not self.topologies_to_processes is None:
-            res.append('%-30s:   %d'%('Number of topologies', 
+            res.append('%-30s:   %d' % ('Number of topologies',
                                                     len(self.topologies_to_processes.keys())))
         res.extend(self.get_additional_nice_string_printout_lines())
 
         if format < 1:
-            res.append('Generated and mapped processes for this contribution: %d (+%d mapped)'%
-                       ( len(self.processes_map.keys()),
-                         len(sum([v[1] for v in self.processes_map.values()],[])) ) )
+            res.append('Generated and mapped processes for this contribution: %d (+%d mapped)' % 
+                       (len(self.processes_map.keys()),
+                         len(sum([v[1] for v in self.processes_map.values()], []))))
         else:
             res.append('Generated and mapped processes for this contribution:')
             for process_key, (defining_process, mapped_processes) in self.processes_map.items():
                 res.append(self.get_nice_string_process_line(process_key, defining_process, format=format))                
                 for mapped_process in mapped_processes:
-                    res.append(BLUE+u'   \u21b3  '+mapped_process.nice_string(print_weighted=False)\
-                                                                        .replace('Process: ','')+ENDC)
+                    res.append(BLUE + u'   \u21b3  ' + mapped_process.nice_string(print_weighted=False)\
+                                                                        .replace('Process: ', '') + ENDC)
             
         return '\n'.join(res).encode('utf-8')
 
@@ -959,15 +961,15 @@
         """ Synchronize this integrand with the most recent run_card and model."""
 
         # The option dictionary of ME7
-        self.ME7_configuration          = ME7_configuration
+        self.ME7_configuration = ME7_configuration
         
         # A ModelReader instance, initialized with the values of the param_card.dat of this run
-        self.model                      = model
+        self.model = model
         if not isinstance(self.model, model_reader.ModelReader):
             raise MadGraph5Error("The ME7Integrand must be initialized with a ModelReader instance.")
 
         # A RunCardME7 instance, properly initialized with the values of the run_card.dat of this run
-        self.run_card                   = run_card
+        self.run_card = run_card
         
         self.flavor_cut_function = ME7Integrand.build_flavor_cut_function(self.run_card['flavor_cuts'])
         
@@ -978,17 +980,17 @@
             this_proc_masses = proc.get_external_masses(self.model)
             if this_proc_masses != self.masses:
                 raise MadGraph5Error("A contribution must entail processes with all the same external masses.\n"
-                 "This is not the case; process\n%s\nhas masses '%s' while process\n%s\n has masses '%s'."%
-                 (all_processes[0].nice_string(), self.masses, proc.nice_string(), this_proc_masses) )
+                 "This is not the case; process\n%s\nhas masses '%s' while process\n%s\n has masses '%s'." % 
+                 (all_processes[0].nice_string(), self.masses, proc.nice_string(), this_proc_masses))
         self.n_initial = len(self.masses[0])
         self.n_final = len(self.masses[1])
         
-        if self.n_initial==1:
+        if self.n_initial == 1:
             raise InvalidCmd("MadEvent7 does not yet support decay processes.")
         
-        if not (self.run_card['lpp1']==self.run_card['lpp2']==1) and \
-           not (self.run_card['lpp1']==self.run_card['lpp2']==0):
-            raise InvalidCmd("MadEvent7 does not support the following collider mode yet (%d,%d)."%\
+        if not (self.run_card['lpp1'] == self.run_card['lpp2'] == 1) and \
+           not (self.run_card['lpp1'] == self.run_card['lpp2'] == 0):
+            raise InvalidCmd("MadEvent7 does not support the following collider mode yet (%d,%d)." % \
                                                             (self.run_card['lpp1'], self.run_card['lpp2']))
         
         # Always initialize the basic flat PS generator. It can be overwritten later if necessary.
@@ -996,15 +998,57 @@
             0 if self.contribution_definition.beam_factorization['beam_one'] is None else 1,
             0 if self.contribution_definition.beam_factorization['beam_two'] is None else 1,
         )
-        self.phase_space_generator = phase_space_generators.FlatInvertiblePhasespace(
-            self.masses[0], self.masses[1],
-            beam_Es             = (self.run_card['ebeam1'], self.run_card['ebeam2']),
-            beam_types          = simplified_beam_types,
-            is_beam_factorization_active = 
-                        ( self.contribution_definition.is_beam_active('beam_one'),
-                          self.contribution_definition.is_beam_active('beam_two') ),
-            correlated_beam_convolution = self.contribution_definition.correlated_beam_convolution
-        )
+        
+	   # now choose the right PS_generator according to specified option
+        if 'PS_generator' not in ME7_configuration.keys() or ME7_configuration['PS_generator'] is None:
+            # If nothing is specified, we use FLATPS by default
+            selected_PS_generator = "FLATPS"
+        else:
+            selected_PS_generator = ME7_configuration['PS_generator']
+        
+        PS_generator_args    = [self.masses[0], self.masses[1]]
+        PS_generator_options = {
+            'beam_Es' : (self.run_card['ebeam1'], self.run_card['ebeam2']),
+            'beam_types' : simplified_beam_types,
+            'is_beam_factorization_active' : ( self.contribution_definition.is_beam_active('beam_one'),
+                                               self.contribution_definition.is_beam_active('beam_two') ),
+            'correlated_beam_convolution' : self.contribution_definition.correlated_beam_convolution
+        }
+        
+        if selected_PS_generator.startswith('SCPS'):
+            if '@' in ME7_configuration['PS_generator']:
+                topology_number = int(ME7_configuration['PS_generator'].split('@')[1])-1
+            else:
+                topology_number = 0
+            if topology_number >= len(self.topologies_to_processes):
+                raise InvalidCmd('This process only has %d topologies. You cannot choose the #%dth one.'%(
+                    len(self.topologies_to_processes), topology_number+1))
+            chosen_key = self.topologies_to_processes.keys()[topology_number]
+            a_topology = self.topologies_to_processes[chosen_key]['s_and_t_channels']
+            PS_generator_options['model'] = self.model
+            PS_generator_options['topology'] = a_topology
+            self.phase_space_generator = phase_space_generators.SingleChannelPhasespace(
+                                                         *PS_generator_args, **PS_generator_options)
+            logger.debug('Integrand %s is using PS_generator=SCPS, with topology #%d/%d:%s'%(
+                self.get_short_name(),
+                topology_number+1, len(self.topologies_to_processes),
+                self.phase_space_generator.get_topology_string(a_topology,
+                                                     path_to_print=self.phase_space_generator.path)
+            ))
+        elif selected_PS_generator == 'MCPS':
+            all_topologies = [None] * len(self.topologies_to_processes.keys())
+            for a_key in self.topologies_to_processes.keys():
+                all_topologies[a_key[1]] = self.topologies_to_processes[a_key]['s_and_t_channels']
+            PS_generator_options['model'] = self.model
+            PS_generator_options['topologies'] = all_topologies
+            self.phase_space_generator = phase_space_generators.MultiChannelPhasespace(
+                                                        *PS_generator_args, **PS_generator_options)
+        elif selected_PS_generator == 'FLATPS':
+            self.phase_space_generator = phase_space_generators.FlatInvertiblePhasespace(
+                                                        *PS_generator_args, **PS_generator_options)
+            #logger.info('Using PS_generator=FLATPS')
+        else:
+            raise MadGraph5Error('Specified phase-space generator not reckognized: %s'%selected_PS_generator)
 
         # Add a copy of the PS generator dimensions here.
         # Notice however that we could add more dimensions pertaining to this integrand only, and PS generation.
@@ -1016,8 +1060,8 @@
                             d for d in integrand_dimensions if d.name not in self.FROZEN_DIMENSIONS)
         self.set_dimensions(integrand_dimensions)
         self.dim_ordered_names = [d.name for d in self.get_dimensions()]
-        self.dim_name_to_position = dict((name,i) for i, name in enumerate(self.dim_ordered_names))
-        self.position_to_dim_name = dict((v,k) for (k,v) in self.dim_name_to_position.items())
+        self.dim_name_to_position = dict((name, i) for i, name in enumerate(self.dim_ordered_names))
+        self.position_to_dim_name = dict((v, k) for (k, v) in self.dim_name_to_position.items())
 
         self.collider_energy = self.run_card['ebeam1'] + self.run_card['ebeam2']
         # Set the seed
@@ -1028,7 +1072,7 @@
         # Setup the PDF cache
         self.PDF_cache = {}
         self.PDF_cache_entries = []
-        if self.run_card['lpp1']==0 and self.run_card['lpp2']==0:
+        if self.run_card['lpp1'] == 0 and self.run_card['lpp2'] == 0:
             self.pdf = None 
             self.pdfsets = None
         else:
@@ -1044,21 +1088,21 @@
                 raise MadGraph5Error("The python lhapdf API could not be loaded.")
             # Adjust LHAPDF verbosity to current logger's verbosity
             # Ask for logging.DEBUG-1 so as to only have lhapdf verbose if really desired.
-            lhapdf.setVerbosity(1 if logger.level<=(logging.DEBUG-1) else 0)
+            lhapdf.setVerbosity(1 if logger.level <= (logging.DEBUG - 1) else 0)
 
-            pdfsets_dir = subprocess.Popen([lhapdf_config,'--datadir'],\
+            pdfsets_dir = subprocess.Popen([lhapdf_config, '--datadir'], \
                                            stdout=subprocess.PIPE).stdout.read().strip()
             lhapdf.pathsPrepend(pdfsets_dir)
-            lhapdf_version = subprocess.Popen([lhapdf_config,'--version'],\
+            lhapdf_version = subprocess.Popen([lhapdf_config, '--version'], \
                                            stdout=subprocess.PIPE).stdout.read().strip()
             pdf_info = common_run.CommonRunCmd.get_lhapdf_pdfsets_list_static(pdfsets_dir, lhapdf_version)
             lhaid = self.run_card.get_lhapdf_id()
             if lhaid not in pdf_info:
-                raise InvalidCmd("Could not find PDF set with lhaid #%d in %s."%(lhaid, pdfsets_dir))
+                raise InvalidCmd("Could not find PDF set with lhaid #%d in %s." % (lhaid, pdfsets_dir))
             pdf_set_name = pdf_info[lhaid]['filename']
             if not os.path.isdir(pjoin(pdfsets_dir, pdf_set_name)):
-                raise InvalidCmd("Could not find PDF set directory named "+
-                    "'%s' in '%s'.\n"%(pdf_set_name, pdfsets_dir)+
+                raise InvalidCmd("Could not find PDF set directory named " + 
+                    "'%s' in '%s'.\n" % (pdf_set_name, pdfsets_dir) + 
                     "It can be downloaded from LHAPDF official online resources.")
 
             self.pdfsets = lhapdf.getPDFSet(pdf_info[lhaid]['filename'])
@@ -1079,13 +1123,13 @@
                         # Leave these parameters to their default if not specified in the model
                         continue
                     else:
-                        raise InvalidCmd("When not using PDFsets, MadEvent7 requires a model with the"+
-                                    " parameter %s to be defined so as to be able to run alpha_S."%param)
+                        raise InvalidCmd("When not using PDFsets, MadEvent7 requires a model with the" + 
+                                    " parameter %s to be defined so as to be able to run alpha_S." % param)
                 if model_param_dict[param] != 0.:
                     as_running_params[param] = model_param_dict[param]
             # For now always chose to run alpha_S at two loops.
             n_loop_for_as_running = 2
-            self.alpha_s_runner = model_reader.Alphas_Runner(as_running_params['aS'], n_loop_for_as_running, 
+            self.alpha_s_runner = model_reader.Alphas_Runner(as_running_params['aS'], n_loop_for_as_running,
                       as_running_params['mdl_MZ'], as_running_params['mdl_MC'], as_running_params['mdl_MB'])
 
         #Import the observables from the FO_analysis folder
@@ -1128,8 +1172,8 @@
         """ Initialize self from a dump and possibly other information necessary for reconstructing this
         integrand."""
         
-        return cls(  model, 
-                     run_card,                             
+        return cls(model,
+                     run_card,
                      dump['contribution_definition'],
                      dump['processes_map'],
                      dump['topologies_to_processes'],
@@ -1141,12 +1185,12 @@
     def set_phase_space_generator(self, PS_generator):
         """ Overwrites current phase-space generator."""
         if not isinstance(PS_generator, phase_space_generators.VirtualPhaseSpaceGenerator):
-            raise MadGraph5Error("Cannot assign to a MadEvent7 integrand a phase-space generator that "+
+            raise MadGraph5Error("Cannot assign to a MadEvent7 integrand a phase-space generator that " + 
                                  " does not inherit from VirtualPhaseSpaceGenerator.")
         if PS_generator.nDimPhaseSpace() != self.phase_space_generator.nDimPhaseSpace():
-            raise MadGraph5Error("A MadEvent7 integrand was assigned a phase-space generator with the"+
-                                 " wrong number of integration dimensions: %d instead of %d"%
-                (PS_generator.nDimPhaseSpace(),self.phase_space_generator.nDimPhaseSpace()))
+            raise MadGraph5Error("A MadEvent7 integrand was assigned a phase-space generator with the" + 
+                                 " wrong number of integration dimensions: %d instead of %d" % 
+                (PS_generator.nDimPhaseSpace(), self.phase_space_generator.nDimPhaseSpace()))
         self.phase_space_generator = PS_generator
 
     def is_part_of_process_selection(self, process_list, selection=None):
@@ -1159,7 +1203,7 @@
         def pdg_list_match(target_list, selection_list):
             if len(target_list) != len(selection_list):
                 return False
-            targets = dict( (k, target_list.count(k)) for k in set(target_list) )
+            targets = dict((k, target_list.count(k)) for k in set(target_list))
             found_it = False
             for sel in itertools.product(*selection_list):
                 found_it = True
@@ -1292,19 +1336,19 @@
         
         # These cuts are not allowed to resolve flavour, but only whether a particle is a jet or not
         def is_a_jet(pdg):
-            return abs(pdg) in range(1,self.run_card['maxjetflavor']+1)+[21]
+            return abs(pdg) in range(1, self.run_card['maxjetflavor'] + 1) + [21]
         
         def is_a_lepton(pdg):
-            return abs(pdg) in [11,13,15]
+            return abs(pdg) in [11, 13, 15]
         
         def is_a_neutrino(pdg):
-            return abs(pdg) in [12,14,16]
+            return abs(pdg) in [12, 14, 16]
         
         def is_a_photon(pdg):
-            return pdg==22
+            return pdg == 22
         
-        if debug_cuts: logger.debug( "Processing flavor-blind cuts for process %s and PS point:\n%s"%(
-            str(process_pdgs), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial) ))
+        if debug_cuts: logger.debug("Processing flavor-blind cuts for process %s and PS point:\n%s" % (
+            str(process_pdgs), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial)))
 
         if n_jets_allowed_to_be_clustered is None:
             n_jets_allowed_to_be_clustered = self.contribution_definition.n_unresolved_particles
@@ -1336,8 +1380,8 @@
             return True
         else:
             # If fastjet is needed but not found, make sure to stop
-            if (not PYJET_AVAILABLE) and n_jets_allowed_to_be_clustered>0:
-                raise MadEvent7Error("Fast-jet python bindings are necessary for integrating"+
+            if (not PYJET_AVAILABLE) and n_jets_allowed_to_be_clustered > 0:
+                raise MadEvent7Error("Fast-jet python bindings are necessary for integrating" + 
                              " real-emission type of contributions. Please install pyjet.")
 
         if PYJET_AVAILABLE and drjj_cut > 0.:
@@ -1346,7 +1390,7 @@
             jets_list = []
             for i, p in enumerate(PS_point[self.n_initial:]):
                 if is_a_jet(process_pdgs[1][i]):
-                    jets_list.append(tuple(list(p)+[i+self.n_initial+1,]))
+                    jets_list.append(tuple(list(p) + [i + self.n_initial + 1, ]))
             # Count partonic jets
             starting_n_jets = len(jets_list)
 
@@ -1387,9 +1431,9 @@
             # Make sure that the number of clustered jets is at least larger or equal to the
             # starting list of jets minus the number of particles that are allowed to go
             # unresolved in this contribution.
-            if debug_cuts: logger.debug("Number of identified jets: %d (min %d)"%
-                           ( len(jets), (starting_n_jets-n_jets_allowed_to_be_clustered) ))
-            if len(jets) < (starting_n_jets-n_jets_allowed_to_be_clustered):
+            if debug_cuts: logger.debug("Number of identified jets: %d (min %d)" % 
+                           (len(jets), (starting_n_jets - n_jets_allowed_to_be_clustered)))
+            if len(jets) < (starting_n_jets - n_jets_allowed_to_be_clustered):
 
                 return False
             
@@ -1402,7 +1446,7 @@
             if ptj_cut > 0.:
                 # Apply the Ptj cut first
                 for i, p in enumerate(all_jets):
-                    if debug_cuts: logger.debug('pj_%i.pt()=%.5e'%((i+1),p.pt()))
+                    if debug_cuts: logger.debug('pj_%i.pt()=%.5e' % ((i + 1), p.pt()))
                     if p.pt() < ptj_cut:
                         return False
     
@@ -1412,15 +1456,15 @@
                     for j, p2 in enumerate(all_jets):
                         if j <= i:
                             continue
-                        if debug_cuts: logger.debug('deltaR(pj_%i,pj_%i)=%.5e'%(
-                             i+1, j+1, p1.deltaR(p2)))
+                        if debug_cuts: logger.debug('deltaR(pj_%i,pj_%i)=%.5e' % (
+                             i + 1, j + 1, p1.deltaR(p2)))
                         if p1.deltaR(p2) < drjj_cut:
                             return False
     
         # Now handle all other cuts
         if etaj_cut > 0.:
             for i, p_jet in enumerate(all_jets):
-                if debug_cuts: logger.debug('eta(pj_%i)=%.5e'%(i+1,p_jet.pseudoRap()))
+                if debug_cuts: logger.debug('eta(pj_%i)=%.5e' % (i + 1, p_jet.pseudoRap()))
                 if abs(p_jet.pseudoRap()) > etaj_cut:
                     return False
 
@@ -1470,8 +1514,7 @@
                     if debug_cuts: logger.debug('deltaR(pl_%i,pj_%i)=%.5e'%(i+1, j+1, p.deltaR(p_jet)))
                     if self.run_card['drjl'] > 0.0 and p.deltaR(p_jet) < self.run_card['drjl']:
                         return False  
-            
-
+        
         # All cuts pass, therefore return True
         return True
 
@@ -1507,8 +1550,8 @@
         elif not isinstance(PS_point, LorentzVectorList):
             PS_point = LorentzVectorList(LorentzVector(v) for v in PS_point)   
 
-        if debug_cuts: logger.debug( "Processing flavor-sensitive cuts for flavors %s and PS point:\n%s"%(
-            str(flavors), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial) ))
+        if debug_cuts: logger.debug("Processing flavor-sensitive cuts for flavors %s and PS point:\n%s" % (
+            str(flavors), LorentzVectorList(PS_point).__str__(n_initial=self.phase_space_generator.n_initial)))
         
         ###########################################
         # User can define his own flavor cut below
@@ -1524,10 +1567,10 @@
         return True
 
     @staticmethod
-    def Lambda(s,sqrMA,sqrMB):
+    def Lambda(s, sqrMA, sqrMB):
         """ Kahlen function."""
 
-        return s**2 + sqrMA**2 + sqrMB**2 - 2.*s*sqrMA - 2.*sqrMB*sqrMA - 2.*s*sqrMB
+        return s ** 2 + sqrMA ** 2 + sqrMB ** 2 - 2.*s * sqrMA - 2.*sqrMB * sqrMA - 2.*s * sqrMB
 
     def get_scales(self, PS_point):
         """ Returns mu_r, mu_f1, mu_f2 for that PS point."""
@@ -1547,7 +1590,7 @@
         if pdf is None:
             return 1.
        
-        if pdg not in [21,22] and abs(pdg) not in range(1,7):
+        if pdg not in [21, 22] and abs(pdg) not in range(1, 7):
             return 1.
                 
         if (pdf, pdg, x, scale2) in self.PDF_cache:
@@ -1557,8 +1600,8 @@
         f = pdf.xfxQ2(pdg, x, scale2)/x
 
         # Update the PDF cache
-        self.PDF_cache[(pdf, pdg,x,scale2)] = f
-        self.PDF_cache_entries.append((pdf, pdg,x,scale2)) 
+        self.PDF_cache[(pdf, pdg, x, scale2)] = f
+        self.PDF_cache_entries.append((pdf, pdg, x, scale2)) 
         if len(self.PDF_cache_entries) > self.PDF_cache_max_size:
             del self.PDF_cache[self.PDF_cache_entries.pop(0)]
 
@@ -1566,7 +1609,16 @@
 
     def __call__(self, continuous_inputs, discrete_inputs, **opts):
         """ Main function of the integrand, returning the weight to be passed to the integrator."""
-
+        
+        if 'adaptive_wgts' in opts:
+            adaptive_wgts = opts.pop('adaptive_wgts')
+        else:
+            adaptive_wgts = None
+        if 'channel_nr' in opts:
+            channel_nr = opts.pop('channel_nr')
+        else:
+            channel_nr = None
+        
         # A unique float must be returned
         wgt = 1.0
         # And the conversion from GeV^-2 to picobarns
@@ -1596,8 +1648,8 @@
                                                     self.contribution_definition.nice_string())
         
         # Random variables sent
-        random_variables    = list(continuous_inputs)
-        if __debug__: logger.debug('Random variables received: %s',str(random_variables))        
+        random_variables = list(continuous_inputs)
+        if __debug__: logger.debug('Random variables received: %s', str(random_variables))        
     
         # Now assign the variables pertaining to PS generations
         PS_random_variables = [
@@ -1605,7 +1657,8 @@
                   random_variables[self.dim_name_to_position[name]] )
                                           for name in self.phase_space_generator.dim_ordered_names ]
         
-        PS_point, PS_weight, x1s, x2s = self.phase_space_generator.get_PS_point(PS_random_variables)
+        PS_point, PS_weight, x1s, x2s = self.phase_space_generator.get_PS_point(PS_random_variables,
+                                                  adaptive_wgts=adaptive_wgts,channel_nr=channel_nr)
 
         # Unpack the initial momenta rescalings (if present) so as to access both Bjorken
         # rescalings xb_<i> and the ISR factorization convolution rescalings xi<i>.
@@ -1632,6 +1685,7 @@
 
         # Account for PS weight
         wgt *= PS_weight
+
         if __debug__: logger.debug("PS_weight: %.5e"%PS_weight)
 
         # The E_cm entering the flux factor is computed *without* including the xi<i> rescalings
@@ -1644,10 +1698,10 @@
             flux = 1. / (2.*math.sqrt(self.Lambda(E_cm**2, self.masses[0][0]**2, self.masses[0][1]**2)))
         elif self.n_initial == 1:
             flux = 1. / (2.*E_cm)
-        flux /= math.pow(2.*math.pi, 3*self.n_final - 4)
+        flux /= math.pow(2.*math.pi, 3 * self.n_final - 4)
         wgt *= flux
-        if __debug__: logger.debug("Flux factor: %.5e"%flux)
-
+        if __debug__: logger.debug("Flux factor: %.5e" % flux)
+        
         # Recover scales to be used
         mu_r, mu_f1, mu_f2 = self.get_scales(PS_point)
 
@@ -1668,6 +1722,7 @@
         # Now loop over processes
         total_wgt = 0.
         for process_key, (process, mapped_processes) in self.processes_map.items():
+
             # If one wishes to integrate only one particular subprocess, it can be done by uncommenting
             # and modifying the lines below.
 #            if process.get_cached_initial_final_pdgs() in [((2,-2),(23,1,-1)), ((2,-2),(23,1,-1))] :
@@ -1682,8 +1737,10 @@
             all_flavor_configurations = []
 
             # The process mirroring is accounted for at the very end only
+
             for proc in all_processes:
                 initial_final_pdgs = proc.get_cached_initial_final_pdgs()
+                
                 all_flavor_configurations.append(initial_final_pdgs)
 
             # Compute the short distance cross-section. The 'events' returned is an instance
@@ -1749,7 +1806,7 @@
 
 
         # Now finally return the total weight for this contribution
-        if __debug__: logger.debug(misc.bcolors.GREEN + "Final weight returned: %.5e"%total_wgt + misc.bcolors.ENDC)
+        if __debug__: logger.debug(misc.bcolors.GREEN + "Final weight returned: %.5e" % total_wgt + misc.bcolors.ENDC)
         if __debug__: logger.debug("="*80)
         
         return total_wgt
@@ -1891,7 +1948,7 @@
                 # Combine the weights and flavor of the event obtained from this convolution
                 # with the ones obtained from the previous convolutions in this loop.
                 convolved_event += event_to_convolve
-        
+
         # Determine Bjorken scalings.
         assert( all(bc[0] is None for bc in beam_factorization_currents) or
                 all(bc[0] is not None for bc in beam_factorization_currents) )
@@ -1943,8 +2000,8 @@
         """ Return additional information lines for the function nice_string of this contribution."""
         res = []
         if self.integrated_counterterms:
-            res.append('%-30s:   %d'%('Nb. of integrated counterterms', 
-                                      len(sum(self.integrated_counterterms.values(),[]))))
+            res.append('%-30s:   %d' % ('Nb. of integrated counterterms',
+                                      len(sum(self.integrated_counterterms.values(), []))))
         return res
     
     def get_nice_string_process_line(self, process_key, defining_process, format=0):
@@ -1953,15 +2010,15 @@
 
         GREEN = '\033[92m'
         ENDC = '\033[0m'        
-        res = GREEN+'  %s'%defining_process.nice_string(print_weighted=False).\
-                                                               replace('Process: ','')+ENDC
+        res = GREEN + '  %s' % defining_process.nice_string(print_weighted=False).\
+                                                               replace('Process: ', '') + ENDC
 
         if not self.integrated_counterterms:
             return res
 
-        if format<2:
+        if format < 2:
             if process_key in self.integrated_counterterms:
-                res += ' | %d integrated counterterms'%len(self.integrated_counterterms[process_key])
+                res += ' | %d integrated counterterms' % len(self.integrated_counterterms[process_key])
             else:
                 res += ' | 0 integrated counterterm'
                 
@@ -1969,19 +2026,19 @@
             long_res = [' | with the following integrated counterterms:']
             for CT_properties in self.integrated_counterterms[process_key]:
                 CT = CT_properties['integrated_counterterm']
-                if format==2:
-                    long_res.append( '   | %s'%CT.__str__(
-                                        print_n=True, print_pdg=False, print_state=False )  )
-                elif format==3:
-                    long_res.append( '   | %s'%CT.__str__(
-                                        print_n=True, print_pdg=True, print_state=True )  )
-                elif format==4:
-                    long_res.append( '   | %s'%str(CT))
-                elif format>4:
-                    long_res.append( '   | %s'%str(CT))
+                if format == 2:
+                    long_res.append('   | %s' % CT.__str__(
+                                        print_n=True, print_pdg=False, print_state=False))
+                elif format == 3:
+                    long_res.append('   | %s' % CT.__str__(
+                                        print_n=True, print_pdg=True, print_state=True))
+                elif format == 4:
+                    long_res.append('   | %s' % str(CT))
+                elif format > 4:
+                    long_res.append('   | %s' % str(CT))
                     for key, value in CT_properties.items():
                         if not key in ['integrated_counterterm', 'matching_process_key']:
-                            long_res.append( '     + %s : %s'%(key, str(value)))
+                            long_res.append('     + %s : %s' % (key, str(value)))
 
             res += '\n'.join(long_res)
 
@@ -2086,15 +2143,16 @@
                 assert ((mapped_flavors not in all_mapped_flavors))
                 all_mapped_flavors.append(mapped_flavors)
 
+
         # Now map the momenta
         if isinstance(PS_point,dict):
             # Dictionary format LorentzVectorDict starts at 1
-            mapped_PS_point = phase_space_generators.LorentzVectorDict(   
-                (i+1, PS_point[input_mapping[i]+1]) for i in range(n_initial+n_final) )
+            mapped_PS_point = phase_space_generators.LorentzVectorDict(
+                (i + 1, PS_point[input_mapping[i] + 1]) for i in range(n_initial + n_final))
         else:
             # List formatLorentzVectorList starts at 0
-            mapped_PS_point = phase_space_generators.LorentzVectorDict(   
-                (i+1, PS_point[input_mapping[i]]) for i in range(n_initial+n_final) )
+            mapped_PS_point = phase_space_generators.LorentzVectorDict(
+                (i + 1, PS_point[input_mapping[i]]) for i in range(n_initial + n_final))
         
         # We must also map the Bjorken x's and the xi rescalings
         xi1, xi2 = [xi1, xi2][input_mapping[0]], [xi1, xi2][input_mapping[1]]
@@ -2255,6 +2313,7 @@
 
         # First generate a kinematic point
         # Specifying None forces to use uniformly random generating variables.
+
         # Make sure to generate a point within the cuts if necessary:
         max_attempts = 10000
         n_attempts   = 0
@@ -2295,7 +2354,7 @@
                               defining_process.nice_string().replace('Process','process'))
             # Make sure that the selected process satisfies the selected process
             if not self.is_part_of_process_selection(
-                [defining_process,]+mapped_processes, selection = test_options['process'] ):
+                [defining_process, ] + mapped_processes, selection=test_options['process']):
                 continue
 
             all_processes = [defining_process,]+mapped_processes
@@ -2531,7 +2590,7 @@
     def analyze_IR_poles_check(self, all_evaluations, acceptance_threshold):
         """ Analyze the results of the check_IR_pole_residues command. """
         
-        #TODO
+        # TODO
 #        misc.sprint("----- SUMMARY -----")
 #        for key, evaluation in all_evaluations.items():
 #            misc.sprint("Result for test: %s | %s"%(str(dict(key[0])['PDGs']),key[1]))
@@ -2650,8 +2709,8 @@
         """ Return additional information lines for the function nice_string of this integrand."""
         res = []
         if self.counterterms:
-            res.append('%-30s:   %d'%('Number of local counterterms', 
-               len([1 for CT in sum(self.counterterms.values(),[]) if CT.is_singular()]) ))
+            res.append('%-30s:   %d' % ('Number of local counterterms',
+               len([1 for CT in sum(self.counterterms.values(), []) if CT.is_singular()])))
         return res
         
     def get_nice_string_process_line(self, process_key, defining_process, format=0):
@@ -3299,7 +3358,7 @@
 This is typically what can happen when your process definition is not inclusive over all IR sensitive particles.
 Make sure that your process definition is specified using the relevant multiparticle labels (typically 'p' and 'j').
 Also make sure that there is no coupling order specification which receives corrections.
-The missing process is: %s"""%ME_process.nice_string())
+The missing process is: %s""" % ME_process.nice_string())
                 raise e
 
             # Multiply the various pieces building the event weight
@@ -3376,6 +3435,7 @@
         for counterterm in self.counterterms[process_key]:
             if not counterterm.is_singular():
                 continue
+            
             CT_event = self.evaluate_counterterm(
                 counterterm, PS_point, base_weight, mu_r, mu_f1, mu_f2,
                 xb_1, xb_2, xi1, xi2,
@@ -3418,6 +3478,7 @@
         # Specifying None forces to use uniformly random generating variables.
         # Make sure to generate a point within the cuts if necessary:
         max_attempts = 10000
+
         n_attempts   = 0
         while n_attempts < max_attempts:
             n_attempts += 1
@@ -4229,7 +4290,7 @@
     def get_integrands_of_order(self, correction_order):
         """ Returns a list of all contributions of a certain correction_order in argument."""
         return ME7IntegrandList([integrand for integrand in self if
-                integrand.contribution_definition.correction_order==correction_order])
+                integrand.contribution_definition.correction_order == correction_order])
 
     def get_integrands_of_type(self, correction_classes):
         """ Returns a list of all contributions that are direct instances of certain classes."""
@@ -4244,7 +4305,7 @@
     def nice_string(self, format=0):
         """ A nice representation of a list of contributions. 
         We can reuse the function from ContributionDefinitions."""
-        return base_objects.ContributionDefinitionList.contrib_list_string(self, 
+        return base_objects.ContributionDefinitionList.contrib_list_string(self,
                                                                             format=format)
 
     def sort_integrands(self):

=== modified file 'madgraph/integrator/integrators.py'
--- madgraph/integrator/integrators.py	2018-07-26 20:20:01 +0000
+++ madgraph/integrator/integrators.py	2019-01-17 08:58:59 +0000
@@ -13,6 +13,7 @@
 #
 ################################################################################
 
+import traceback
 import os
 import logging
 import math
@@ -79,15 +80,19 @@
                  accuracy_target=0.01,
                  n_iterations=None,
                  n_points_per_iterations=100,
-                 verbosity = 2, **opts):
+                 verbosity = 2,
+                 save_points_to_file = None,
+                 **opts):
         """ Initialize the simplest MC integrator."""
         
         self.accuracy_target = accuracy_target
         self.n_iterations = n_iterations
         self.n_points_per_iterations = n_points_per_iterations
         self.verbosity = verbosity
+        self.save_points_to_file = save_points_to_file
         
         super(SimpleMonteCarloIntegrator, self).__init__(integrands, **opts)
+        #misc.sprint(self.integrands)
         
     def integrate(self):
         """ Return the final integral and error estimates."""
@@ -99,7 +104,17 @@
         error_estimate     = sys.maxint
         integral_estimate  = 0.0
         
+        for i, integrand in enumerate(self.integrands):
+            integrand.counter = 0
+
         phase_space_volumes = [integrand.get_dimensions().volume() for integrand in self.integrands]
+    
+        if self.save_points_to_file is not None:
+            logger.info("Saving all integration sample points to file '%s'."%self.save_points_to_file)
+            out_stream = open(self.save_points_to_file, 'w')
+            out_stream.write('IntegrandNumber, continuous_dimensions, discrete_dimension, complete_weight')
+        else:
+            out_stream = None
 
         while (self.n_iterations is None or iteration_number < self.n_iterations ) and \
               (self.accuracy_target is None or error_estimate/(1e-99+integral_estimate) > self.accuracy_target):   
@@ -109,15 +124,23 @@
             while n_curr_points < self.n_points_per_iterations:
                 n_points += 1
                 n_curr_points += 1
-                # Compute phase-space volue
+                # Compute phase-space volume
                 new_wgt = 0.0
                 for i, integrand in enumerate(self.integrands):
                     discrete_dimensions = integrand.discrete_dimensions.random_sample()
                     continuous_dimensions = integrand.continuous_dimensions.random_sample()
-                    new_wgt += phase_space_volumes[i]*integrand(continuous_dimensions,discrete_dimensions)
+                    try:
+                        new_wgt += phase_space_volumes[i]*integrand(continuous_dimensions,discrete_dimensions)
+                    except AssertionError as err:
+                        traceback.print_tb(sys.exc_info()[-1])
+                        logger.warning('Assertion error encountered.')
+                        pass
+                    if out_stream is not None:
+                        out_stream.write('\n'+', '.join([str(i+1),str(list(continuous_dimensions)),str(list(discrete_dimensions)),'%.16e'%new_wgt]))
+
                 sum_int += new_wgt
                 sum_squared += new_wgt**2
-            
+
             integral_estimate = sum_int / n_points
             error_estimate =  math.sqrt( ((sum_squared / n_points) - integral_estimate**2)/n_points)
             msg = '%s :: iteration # %d / %s :: point #%d :: %.4e +/- %.2e'%(
@@ -125,10 +148,14 @@
                 '%d'%self.n_iterations if self.n_iterations else 'inf' ,n_points, 
                 integral_estimate, error_estimate)
             if self.verbosity > 0:
-                print msg
+                logger.info(msg)
+        
+        if out_stream is not None:
+            out_stream.close()
+            logger.info("Saved all integration sample points to file '%s'."%self.save_points_to_file)
 
         return integral_estimate, error_estimate
-            
+   
                 
 if __name__ == "__main__":
 

=== modified file 'madgraph/integrator/phase_space_generators.py'
--- madgraph/integrator/phase_space_generators.py	2019-01-07 14:33:05 +0000
+++ madgraph/integrator/phase_space_generators.py	2019-01-17 08:58:59 +0000
@@ -22,6 +22,7 @@
 
 import logging
 import math
+import random
 from madgraph.integrator.vectors import Vector, LorentzVector
 from madgraph.integrator.vectors import LorentzVectorDict, LorentzVectorList
 
@@ -87,7 +88,7 @@
 
         raise NotImplementedError
     
-    def get_PS_point(self, random_variables):
+    def get_PS_point(self, random_variables, **opts):
         """Generate a complete PS point, including Bjorken x's,
         dictating a specific choice of incoming particle's momenta."""
 
@@ -150,10 +151,103 @@
         return dims
 
 class MultiChannelPhasespace(VirtualPhaseSpaceGenerator):
+    """ A phase space generator for a channel in multi-channel integration."""
+        
+    def __init__(self,*args,**opts):
+        
+        if 'model' not in opts:
+            raise PhaseSpaceGeneratorError("A model must be specified with the option 'model' when"+
+                " instantiating the class %s."%self.__class__.__name__)
+        self.model = opts.pop('model')
+        
+        if 'topologies' not in opts:
+            raise PhaseSpaceGeneratorError("A list of topologies must be specified with the "+
+                "option 'topologies' when instantiating the class %s."%self.__class__.__name__)
+        self.topologies = opts.pop('topologies')
+        
+        super(MultiChannelPhasespace, self).__init__(*args, **opts)
+        
+        self.channels = []
+        for topology in self.topologies:
+            opts = {'initial_masses': self.initial_masses, 'final_masses': self.masses,
+                    'beam_Es': self.beam_Es, 'beam_types': self.beam_types, 'model': self.model}
+            self.channels.append(SingleChannelPhasespace(topology=topology,**opts))
+    
+    def get_PS_point(self, random_variables, adaptive_wgts=None,channel_nr = None):
+        """Provides a momentum configuration according to the right phase space parameterization
+        and the multi-channel weight (the Jacobians from all channels with their channel weights (alpha)). """
+        """ adaptive_wgts = channel wgts (alpha) """
+    
+        #TODO: flattening_strategy = {channel_id: {'flattening_technique': 'diagram_fuction to call'/'jacobians', 'use_alphas': True/False}}
+        #TODO: get_flattener_for_channel(id)
+                                
+        if random_variables is None:
+            random_variables = self.dimensions.random_sample()
+        if adaptive_wgts is None:
+            raise PhaseSpaceGeneratorError('Specify the channel weights.')
+            #adaptive_wgts = [1./len(self.channels)]*len(self.channels)
+        if channel_nr is None:
+            raise PhaseSpaceGeneratorError('Specify the channel number.')
+            #randomly pick a channel
+            #channel_nr = random.randint(0,len(self.channels)-1)
+        PS_random_variables = random_variables
+        
+        channel_wgts = [None]*len(self.channels)
+
+        PS_point, channel_wgts[channel_nr], xb_1, xb_2 = self.channels[channel_nr].get_PS_point(PS_random_variables)#, path=None)
+        if PS_point == None:
+            return None, 0., 1., 1.
+        
+        for i, channel in enumerate(self.channels):
+            if i != channel_nr:
+                variables, channel_wgts[i] = channel.get_PS_point(PS_point)
+                if channel_wgts[i] == 0.:
+                    return None, 0, 1., 1.
+            
+        multi_channel_wgt = 1./sum( alpha / wgt_jac for alpha, wgt_jac in zip(adaptive_wgts,channel_wgts))
+        
+        #if channel_nr == 1:
+        #    multi_channel_wgt*= 1e-9
+    
+        return PS_point, multi_channel_wgt, xb_1, xb_2
+        
+        #matrix element multi-channeling idea
+        """
+            full_path = '/Users/Dario/Desktop/Thesis/HighEnergySoftwares/MadGraph5/MadEvent6/mytest_MCPS_full/SubProcesses/P1_epem_mupmuma'
+            sys.path.append(full_path)
+            import matrix6py as full_matrix_element
+            full_matrix_element.initialisemodel(os.path.abspath(os.path.join(full_path,os.pardir,os.pardir,'Cards','param_card.dat')))
+
+            def invert_momenta(p):
+                #fortran/C-python do not order table in the same order
+                new_p = []
+                for i in range(len(p[0])):  new_p.append([0]*len(p))
+                for i, onep in enumerate(p):
+                    for j, x in enumerate(onep):
+                        new_p[j][i] = x
+                return new_p
+            
+            P = invert_momenta(PS_point)
+            matrix_elements = [None]*len(self.channels)
+            for i in xrange(len(self.channels)):
+                # diagram i might not correspond to function i+1, CHECK
+                matrix_elements[i] = full_matrix_element.smatrix(P,i+1)
+            
+            multi_channel_wgt = channel_wgts[channel_nr]*matrix_elements[channel_nr]
+            multi_channel_wgt *= 1./sum( alpha*me for alpha, me in zip(alphas,matrix_elements))
+            return PS_point,multi_channel_wgt, xb_1, xb_2
+        """
+            
+
+class SingleChannelPhasespace(VirtualPhaseSpaceGenerator):
     """Implementation of a phase-space generator that lines up integration variables
-    with s- and t-channels specifying a paticular provided topology.
+    with s- and t-channels specifying a particular provided topology.
     """
     
+    # The lowest value that the center of mass energy can take.
+    # Below 1 GeV non-perturbative effects dominate and factorization does not apply
+    absolute_Ecm_min = 1.
+    
     def __init__(self, *args, **opts):
         
         if 'model' not in opts:
@@ -166,23 +260,862 @@
                 "option 'topology' when instantiating the class %s."%self.__class__.__name__)
         self.topology = opts.pop('topology')
         
-        super(MultiChannelPhasespace, self).__init__(*args, **opts)
+        super(SingleChannelPhasespace, self).__init__(*args, **opts)
+        
+        if 'path' not in opts:
+            # In order to make resutls deterministic, force using the first available path as opposed 
+            # as a random one.
+            path = self.get_random_path(select_first=True)
+        else:
+            path = opts.pop('path')
+        self.path = path
+        
+        ##topology_string = self.get_topology_string(self.topology,path_to_print=self.path)
+        ##misc.sprint(topology_string)
+        
+        """
+        N_t = len(self.topology[1]) - 1 # number of t-channels
+        N_s = len(self.topology[0]) # number of s-channels
+        
+        min_index = 0
+        max_index = N_s
+        for i in range(min_index,max_index):
+            self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'distr_s_%d' % (i+1-min_index)
+        min_index = max_index
+        max_index += N_t -1 
+        for i in range(min_index,max_index):
+            self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'unif_s_%d' % (i+1-min_index)
+        min_index = max_index
+        max_index += N_t
+        for i in range(min_index,max_index):
+            self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 't_%d' % (i+1-min_index)
+        min_index = max_index
+        max_index += N_s
+        for i in range(min_index,max_index):
+            self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'cos_theta_%d' % (i+1-min_index)
+        min_index = max_index
+        max_index += N_t + N_s
+        for i in range(min_index,max_index):
+            self.dimensions[self.dim_name_to_position['x_%d' % (i+1)]].name = 'phi_%d' % (i+1-min_index)
+        """
         
         # One can do additional business here upon instantiating this PS generator, like renaming
         # the random variables to names describing the "propagators generated" with them. 
-
-    def get_PS_point(self, random_variables, **opts):
-        """Generate a complete PS point, including Bjorken x's,
-        dictating a specific choice of incoming particle's momenta.
-        """
-
-        #
-        # TODO IMPLEMENTATION
-        # 
-        # For now just return a random PS point from flat generation
-        return FlatInvertiblePhasespace(self.initial_masses,self.masses,self.beam_Es,
-                                        beam_types  = self.beam_types).get_PS_point(random_variables)
-        # raise NotImplementedError
+         
+    def get_topology_string(self,topology_to_print,path_to_print=None):
+        """Example of a nice way to printout what these topologies are:"""
+        if  topology_to_print[0] == None:
+            topology_string = '\n no s-channels \nand t-channels: %s'%\
+                (', '.join('%s > %d(%d)'%(
+                    ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
+                    vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[1]))
+        else:
+            topology_string = '\ns-channels:     %s\nand t-channels: %s'%\
+                (', '.join('%s > %d(%d)'%(
+                    ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
+                    vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[0]), 
+                  ', '.join('%s > %d(%d)'%(
+                    ' '.join('%d(%d)'%(leg['number'],leg['id']) for leg in vertex['legs'][:-1]),
+                    vertex['legs'][-1]['number'],vertex['legs'][-1]['id']) for vertex in topology_to_print[1]))
+        if path_to_print != None:
+            topology_string += '\nselected path:  %s'%path_to_print
+        return topology_string
+    
+    def uniform_distr(self,r,min,max):
+        """distributes r uniformly within (min, max), with jacobian dvariable"""
+        dvariable = (max-min)
+        variable = min + dvariable*r
+        return variable, dvariable
+    
+    def inv_uniform_distr(self,variable,min,max):
+        """inverse of uniform_distr, obtain r and inverse jacobian dvariable from variable"""
+        assert(min<=variable<= max)
+        dvariable = (max-min)
+        r = (variable-min)/dvariable
+        return r, dvariable
+    
+    def massless_distr(self,r,min,max,nu=1.1,m2 = 0):
+        """distributes r within (min, max), with jacobian dvariable \propto variable^(nu)
+        for cross-section \propto 1/(s-m)^2
+        m2 is a small parameter < 0, that fixes numerical problems when min=0,
+        instead of setting min=small number, this method still allows to map to min=0"""
+        if min == 0 and m2==0:
+            m2 = -self.absolute_Ecm_min**2
+        if nu != 1:
+            variable = ((max-m2)**(1.-nu)*r+(min-m2)**(1.-nu)*(1.-r))**((1.-nu)**(-1.))+m2
+            dvariable = ((variable-m2)**nu)*((max-m2)**(1.-nu)-(min-m2)**(1.-nu))/(1.-nu)
+        else:
+            variable = math.exp(r*math.log(max-m2)+(1.-r)*math.log(min-m2))+m2
+            dvariable = (math.log(max-m2)-math.log(min-m2))*(variable-m2)
+        assert(dvariable>=0)
+        return variable, dvariable
+    
+    def inv_massless_distr(self,variable,min,max,nu=1.1,m2=0):
+        """inverse of massless_distr, obtain r and inverse jacobian dvariable from variable"""
+        assert(min<=variable<=max)
+        if min == 0 and m2==0:
+            m2 = -self.absolute_Ecm_min**2
+        if nu != 1:
+            r = ((variable-m2)**(1.-nu)-(min-m2)**(1.-nu))/((max-m2)**(1.-nu)-(min-m2)**(1.-nu))
+            dvariable = ((variable-m2)**nu)*((max-m2)**(1.-nu)-(min-m2)**(1.-nu))/(1.-nu)
+        else:
+            r = (math.log(variable-m2)-math.log(min-m2))/(math.log(max-m2)-math.log(min-m2))
+            dvariable = (math.log(max-m2)-math.log(min-m2))*(variable-m2)
+        assert(dvariable>=0)
+        return r, dvariable
+    
+    def massive_distr(self,r,mass,width,min,max):
+        """distributes r within (min, max), with jacobian dvariable \propto 1/Breit-Winger
+        for cross-section \propto Breit-Wigner"""
+        mass = mass.real
+        width = width.real
+        y_1 = math.atan((min-mass**2)/(mass*width))
+        y_2 = math.atan((max-mass**2)/(mass*width))               
+        variable = mass**2 + mass*width*math.tan(y_1+(y_2-y_1)*r)
+        dvariable = (y_2-y_1)*((variable-mass**2)**2 + (mass*width)**2)/(mass*width)
+        # equivalently: dvariable = mass*width*(y_2-y_1)*math.cos(y_1+(y_2-y_1)*r)**(-2)
+        assert(dvariable>=0)
+        return variable, dvariable
+    
+    def inv_massive_distr(self,variable,mass,width,min,max):
+        """inverse of massive_distr, obtain r and inverse jacobian dvariable from variable"""
+        assert(min<=variable<=max)
+        mass = mass.real
+        width = width.real
+        y_1 = math.atan((min-mass**2)/(mass*width))
+        y_2 = math.atan((max-mass**2)/(mass*width))
+        r = (math.atan((variable-mass**2)/(mass*width))-y_1)/(y_2-y_1)
+        dvariable = (y_2-y_1)*((variable-mass**2)**2 + (mass*width)**2)/(mass*width)
+        assert(dvariable>=0)
+        return r, dvariable
+    
+    def inv_t(self,p_2,p1_2,p2_2,p3_2,p4_2,cos_theta):
+        """Mandelstam invariant t=(p1-p3)^2 formula C21 in https://arxiv.org/pdf/hep-ph/0008033.pdf
+        p=p1+p2 is at rest;
+        p1, p2 are opposite along z-axis
+        p3, p4 are opposite along the direction defined by theta
+        theta is the angle in the center of mass frame between p1 & p3"""
+        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
+        t = p3_2+p1_2 - nom/(2*p_2)
+        if t>0:
+            t = 0
+        assert(t<=0)
+        return t
+    
+    def cos_theta_from_inv_t(self,p_2,p1_2,p2_2,p3_2,p4_2,t):
+        """https://arxiv.org/pdf/hep-ph/0008033.pdf forula C21
+        invert t=(p1-p3)^2 to cos_theta = ..."""
+        nom = (t-p3_2-p1_2)*2*p_2 + (p_2+p3_2-p4_2)*(p_2+p1_2-p2_2)
+        denom = math.sqrt(self.Lambda(p_2,p3_2,p4_2))*math.sqrt(self.Lambda(p_2,p1_2,p2_2))
+        cos_theta = nom/denom
+        assert(-1<=cos_theta<=1)
+        return cos_theta
+    
+    def angles_to_rotate_along_z(self,p):
+        """gives the angles phi and theta,
+        so that \vec{p} can be parametrized as |\vec{p}|*(cos phi*sin theta, sin phi*sin theta, cos theta)"""
+        theta = math.acos(p[3]/p.rho())
+        if p[1]> 0:
+            phi = math.atan(p[2]/p[1])
+        elif p[1]< 0:
+            phi = math.atan(p[2]/p[1]) + math.pi
+        else:
+            phi = 0
+        return phi,theta
+    
+    def rotate_along_z_inv(self,p,phi,theta):
+        """rotates a 4 vector p in space
+        the z-axis (0,0,1) rotates to (cos phi*sin theta, sin phi*sin theta, cos theta)"""
+        l = LorentzVector([p[0],0,0,0])
+        l[1] = math.cos(theta)*math.cos(phi)*p[1] -math.sin(phi)*p[2] +math.sin(theta)*math.cos(phi)*p[3]
+        l[2] = math.cos(theta)*math.sin(phi)*p[1] + math.cos(phi)*p[2] + math.sin(theta)*math.sin(phi)*p[3]
+        l[3] = -math.sin(theta)*p[1] +math.cos(theta)*p[3]
+        return l
+    
+    def Lambda(self,x,y,z):
+        return x**2+y**2+z**2-2*x*y-2*x*z-2*y*z
+    
+    def get_cm_momenta(self,s,p3_2,p4_2,cos_theta,phi):
+        """generates p3, p4 in the center of mass frame, with E_cm^2 = s
+        the direction is defined by the angles cos_theta and phi"""
+        assert(-1 <= cos_theta <= 1.)
+        assert(s>0)
+        q = 1./(2*math.sqrt(s))
+        sin_theta = math.sqrt(1-cos_theta**2)
+        p3 = LorentzVector([1.,sin_theta*math.cos(phi),sin_theta*math.sin(phi), cos_theta])
+        p4 = LorentzVector([1.,-sin_theta*math.cos(phi),-sin_theta*math.sin(phi), -cos_theta])
+        p3[0] *= (s+p3_2-p4_2)*q
+        p4[0] *= (s+p4_2-p3_2)*q
+        assert(self.Lambda(s,p3_2,p4_2)>0)
+        rho = math.sqrt(self.Lambda(s,p3_2,p4_2))
+        for i in xrange(1,4):
+            p3[i] *= rho*q
+            p4[i] *= rho*q
+        wgt_PS = rho/(8*s)
+        return p3,p4,wgt_PS
+    
+    def get_two_body_PS_wgt(self,s,p3_2,p4_2):
+        """returns the two body PS_wgt that is also generated in generate_cm_momenta"""
+        assert(s>0)
+        assert(self.Lambda(s,p3_2,p4_2)>0)
+        rho = math.sqrt(self.Lambda(s,p3_2,p4_2))
+        wgt_PS = rho/(8*s)
+        return wgt_PS
+    
+    def get_random_path(self, select_first=False):
+        """Generates a random path in which the (distributed and uniform) invariants are sampled.
+        For now, only paths are generated that go from two known outer legs to the inner leg.
+        Breit-Wigner competition (in distributed invariants) is not taken into account yet.
+        When setting the flag 'select_first' to True, this is made deterministic and the first possible
+        path is selected instead."""
+        
+        max_leg_nr = self.n_initial+ self.n_final
+        min_leg_nr = self.topology[1][-1].get('legs')[-1].get('number')
+        numbers = range(min_leg_nr,0)+range(3,max_leg_nr+1)+[1]
+        
+        """distr_inv stores vertices for distributed (=s-channel) invariants that
+        can be used to generate a next invariant (available)
+        were already used to generate an invariant (finished)
+        third category: vertices that haven't been used already but are not available yet to generate invariants
+        distr_inv['finished'] will eventually be ordered from outer (final) legs to inner legs"""
+        
+        distr_inv = {'available': [],'finished': []}
+        # kinematics stores if a leg is available (True/False)
+        kinematics = dict((nr,{'is_available': False}) for nr in numbers)
+        
+        # final legs are available
+        for nr in kinematics:
+            if nr > 2:
+                kinematics[nr]['is_available'] = True
+        
+        # when no t-channels, first s-channel invariant is fixed
+        if len(self.topology[1]) == 1:
+            last_vertex = self.topology[0][-1]
+            leg1_nr = last_vertex.get('legs')[0].get('number')
+            leg2_nr = last_vertex.get('legs')[1].get('number')
+            kinematics[leg1_nr]['is_available'] = False
+            kinematics[leg2_nr]['is_available'] = False
+            
+        # find out which s-channel vertices are available
+        for i, vertex in enumerate(self.topology[0]):
+            leg1_nr = vertex.get('legs')[0].get('number')
+            leg2_nr = vertex.get('legs')[1].get('number') 
+            if kinematics[leg1_nr]['is_available'] and kinematics[leg2_nr]['is_available']:
+                distr_inv['available'].append(i)
+        
+        # generate a path in which distributed (=s-channel) invariants are generated
+        # in general the ordering is relevant since it changes the boundaries
+        # TODO: Breit-Wigner competition, for now, (final) outer to inner vertices
+        if len(self.topology[0]) > 0:
+            nr_available_vertices = len(distr_inv['available'])
+            while (nr_available_vertices > 0):
+                if not select_first:
+                    i = random.choice(distr_inv['available'])                    
+                else:
+                    i = distr_inv['available'][0]                    
+                vertex = self.topology[0][i]
+                leg1_nr = vertex.get('legs')[0].get('number')
+                leg2_nr = vertex.get('legs')[1].get('number')
+                leg3_nr = vertex.get('legs')[-1].get('number')
+                kinematics[leg1_nr]['is_available'] = False
+                kinematics[leg2_nr]['is_available'] = False
+                kinematics[leg3_nr]['is_available'] = True
+                # distr_inv['finished'] is ordered from outer (final) legs to inner legs
+                distr_inv['finished'].append(i)
+                distr_inv['available'].remove(i)   
+                    
+                # check if another vertex is available now
+                # if no t-channels (i.e. first s-channel invariant fixed), skip the fixed (=last) vertex
+                if len(self.topology[1])==1:
+                    s_channel_range= len(self.topology[0][:-1])
+                else:
+                    s_channel_range = len(self.topology[0])
+                for m in xrange(i,s_channel_range):
+                    if m in distr_inv['finished']:
+                        continue
+                    vertex = self.topology[0][m]
+                    leg1_nr = vertex.get('legs')[0].get('number')
+                    leg2_nr = vertex.get('legs')[1].get('number')
+                    if leg3_nr == leg1_nr or leg3_nr == leg2_nr:
+                        if kinematics[leg2_nr+leg1_nr-leg3_nr]['is_available']:
+                            distr_inv['available'].append(m)
+                            break
+                nr_available_vertices= len(distr_inv['available'])
+        
+        t_channel_path = []
+        
+        """find a path to generate the uniformly sampled invariants
+        idea: save pairs of vertices that correspond to a uniform invariant
+        randomly pick a vertex i<last, save in t_channel_path [(0,i),(i+1,last)]
+        for pair in t_channel_path pick random vertex j<last, do the same thing (-> i.e. (0,j) (j+1,i))
+        save in t_channel_path except either j+1 = i, or 0=j
+        to find possible pairs of vertices, one needs to go from outermost to innermost t-channels."""
+        
+        if len(self.topology[1]) > 2:
+            start = 0
+            end = len(self.topology[1])-1
+            if not select_first:
+                i = random.choice(range(start,end))
+            else:
+                i = range(start,end)[0]
+            if i != 0:
+                t_channel_path.append((0,i))
+            if i+1 != len(self.topology[1])-1:
+                t_channel_path.append((i+1,len(self.topology[1])-1))
+            for tuple in t_channel_path:
+                start = tuple[0]
+                end = tuple[1]
+                if not select_first:
+                    i = random.choice(range(start,end))
+                else:
+                    i = range(start,end)[0]
+                if i != start:
+                    t_channel_path.append((start,i))
+                if i+1 != end: 
+                    t_channel_path.append((i+1,end))
+        
+        """paths are ordered lists, their order will determine in what order the invariants will be generated
+        elements of s_channel_path are the indices of the s-channel vertices
+        e.g. s_channel_path = [0,4,1,2,...]
+        elements of t_channel_path are tuples of indices of the t-channel vertices
+        e.g. t_channel_path = [(0,4),(5,6),(0,2),(3,4),...])"""
+        s_channel_path = distr_inv['finished']
+        t_channel_path.reverse()
+        
+        return [s_channel_path,t_channel_path]
+        
+
+    def get_PS_point(self, input_variables, path=None, **opts):
+        """ Generates a complete PS point, including Bjorken x's, dictating a specific choice
+        of incoming particle's momenta,"""
+        
+        # PROBLEMS:
+        #    0) line 644, tolerance=...
+        #    1) line 1067, s-channel momentum. pt_cut, boost to c
+        #    2) line 1044,1092,1120, momentum conservation, t-channel, s-channel, tot
+        #    3) line 795, 961, no_phase_space
+        #    4) line 632, randomly pick one path every time, or stick with one
+   
+        if path is None:
+            path = self.path
+            #path = self.get_random_path()
+                
+        # kinematics is the object with all important info it it
+        max_leg_nr = self.n_initial+ self.n_final
+        min_leg_nr = self.topology[1][-1].get('legs')[-1].get('number')
+        numbers = range(min_leg_nr,0)+range(3,max_leg_nr+1)+[1]
+        kinematics = dict((nr,{'inv_mass': None, 'momentum': None, 'is_available': False, 'inv_mass_limits': None}) for nr in numbers)
+
+        wgt = 1. #store the weight, coming from the importance sampling and reparameterization (reconstruct mode)
+        tolerance = 1e-3
+        
+        if isinstance(input_variables, LorentzVectorList):
+            reconstruct = True
+            PS_point = input_variables
+            output_variables = self.get_dimensions()
+            kinematics[min_leg_nr]['momentum'] = PS_point[1]
+            kinematics[1]['momentum'] = PS_point[0]
+            for nr in xrange(3,max_leg_nr+1):
+                kinematics[nr]['momentum'] = PS_point[nr-1]
+        else:
+            reconstruct = False
+            random_variables = input_variables
+            if random_variables is None:
+                random_variables = self.dimensions.random_sample()  
+            output_momenta = [LorentzVector()]*max_leg_nr
+        variable_index = 0 #index to pick random variable, there might be a nicer way to do it
+        
+        # fill final and initial mass info into kinematics
+        kinematics[min_leg_nr]['inv_mass'] = self.initial_masses[1]**2
+        kinematics[1]['inv_mass'] = self.initial_masses[0]**2
+        for nr in xrange(3,max_leg_nr+1):
+            kinematics[nr]['inv_mass'] = self.masses[nr-3]**2
+            kinematics[nr]['is_available'] = True #flag is necessary to calculate limits
+        
+        # TODO: Breit-Wigner competition: tau is always generated first for now: can lead to problems in BW comp.
+        # get the bjorken x's in case of pp-collison
+        if self.beam_types[0]==self.beam_types[1]==1:
+            # definition: ycm = 1/2*log(xb_1/xb_2)
+            # definition: tau = xb_1*xb_2
+            if reconstruct:
+                E_cm = math.sqrt((kinematics[1]['momentum']+kinematics[min_leg_nr]['momentum']).square())
+                tau = E_cm**2/self.collider_energy**2
+                ycm = None # need Bjorken x's as inputs for that
+            else:
+                x_ycm = random_variables[self.dim_name_to_position['ycms']]
+                x_tau = random_variables[self.dim_name_to_position['tau']]
+            tot_final_state_masses = sum(self.masses)
+            tau_min = (max(tot_final_state_masses, self.absolute_Ecm_min)/self.collider_energy)**2
+            tau_max = 1.
+            if len(self.topology[1]) == 1: #if there are no t-channels, tau is distributed
+                last_vertex = self.topology[0][-1]
+                id = last_vertex.get('legs')[-1].get('id')
+                particle = self.model.get_particle(id)
+                mass_param = particle.get('mass')
+                if mass_param.lower() == 'zero':
+                    if reconstruct:
+                        x_tau, wgt_jac = self.inv_massless_distr(tau, tau_min, tau_max)
+                    else:
+                        tau, wgt_jac = self.massless_distr(x_tau,tau_min,tau_max)
+                else:
+                    mass = self.model.get('parameter_dict')[mass_param]/self.collider_energy
+                    width = self.model.get('parameter_dict')[particle.get('width')]/self.collider_energy
+                    if reconstruct:
+                        x_tau, wgt_jac = self.inv_massive_distr(tau, mass, width, tau_min, tau_max)
+                    else:
+                        tau, wgt_jac = self.massive_distr(x_tau,mass,width, tau_min, tau_max)
+            else: #if there are t-channels, tau is uniform
+                if reconstruct:
+                    x_tau, wgt_jac = self.inv_uniform_distr(tau,tau_min,tau_max)
+                else:
+                    tau, wgt_jac = self.uniform_distr(x_tau,tau_min,tau_max)
+            wgt *= wgt_jac
+            # ycm always sampled uniformly
+            ycm_min = 0.5 * math.log(tau)
+            ycm_max = -ycm_min
+            if reconstruct:
+                x_ycm, wgt_jac = None, (ycm_max-ycm_min)
+                output_variables[self.dim_name_to_position['tau']] = x_tau
+                output_variables[self.dim_name_to_position['ycms']] = x_ycm
+            else:
+                ycm, wgt_jac = self.uniform_distr(x_ycm,ycm_min,ycm_max)
+                xb_1 = math.sqrt(tau)*math.exp(ycm)
+                xb_2 = math.sqrt(tau)*math.exp(-ycm)
+                E_cm = math.sqrt(tau)*self.collider_energy
+            wgt *= wgt_jac
+        elif self.beam_types[0]==self.beam_types[1]==0: # set default in case of ll-collision
+            xb_1 = 1.
+            xb_2 = 1.
+            E_cm = self.collider_energy
+        else:
+            raise InvalidCmd("This basic PS generator does not yet support collider mode (%d,%d)."%self.beam_types)  
+
+        # Also generate the ISR collinear factorization convolutoin variables xi<i> if
+        # necessary. In order for the + distributions of the PDF counterterms and integrated
+        # collinear ISR counterterms to hit the PDF only (and not the matrix elements or
+        # observables functions), a change of variable is necessary: xb_1' = xb_1 * xi1
+        if self.correlated_beam_convolution:
+            # Both xi1 and xi2 must be set equal then
+            xi1 = random_variables[self.dim_name_to_position['xi']]
+            xi2 = random_variables[self.dim_name_to_position['xi']]
+        else:
+            if self.is_beam_factorization_active[0]:
+                xi1 = random_variables[self.dim_name_to_position['xi1']]
+            else:
+                xi1 = None
+            if self.is_beam_factorization_active[1]:
+                xi2 = random_variables[self.dim_name_to_position['xi2']]
+            else:
+                xi2 = None
+
+        if not reconstruct:
+            """generate initial momenta in CENTER OF MASS frame, along z-axis
+            -> ALL output_momenta are going to be in this frame
+            BUT: cuts are dependent on lab frame, therefore return Bjorken x's too"""
+            leg1_nr = 1
+            leg2_nr = min_leg_nr
+            kinematics[leg1_nr]['momentum'] = E_cm/2.*LorentzVector([1.,0,0, 1.])
+            kinematics[leg2_nr]['momentum'] = E_cm/2.*LorentzVector([1.,0,0, -1.])
+        
+        # if there are no t-channels do the first s-channel, its invariant mass is fixed
+        if len(self.topology[1])==1:
+            # set the momentum of the very first s-channel propagator (p_initial1+p_initial2)
+            last_vertex = self.topology[0][-1]
+            leg1_nr = 1
+            leg2_nr = min_leg_nr
+            leg3_nr = last_vertex.get('legs')[-1].get('number')
+            kinematics[leg3_nr]['inv_mass'] = E_cm**2
+            kinematics[leg3_nr]['momentum'] = kinematics[leg1_nr]['momentum'] + kinematics[leg2_nr]['momentum']
+          
+        # set the invariant masses for the s-channels, all distributed
+        # remember: if there is only one s-channel, there is no invariant to generate
+        if len(self.topology[0]) > 0:
+            for i in path[0]:
+                vertex = self.topology[0][i]
+                leg1_nr = vertex.get('legs')[0].get('number')
+                leg2_nr = vertex.get('legs')[1].get('number')
+                leg3_nr = vertex.get('legs')[-1].get('number')
+                kinematics[leg1_nr]['is_available'] = False #flag is necessary to calculate limits
+                kinematics[leg2_nr]['is_available'] = False
+                sum_masses = sum([math.sqrt(kinematics[nr]['inv_mass']) for nr in kinematics.keys() if kinematics[nr]['is_available']])
+                s_max = (E_cm - sum_masses)**2
+                m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
+                m2 = math.sqrt(kinematics[leg2_nr]['inv_mass'])
+                s_min = (m1 + m2)**2
+                kinematics[leg3_nr]['inv_mass_limits'] = (s_min,s_max)
+                #use importance sampling
+                id = vertex.get('legs')[-1].get('id')
+                particle = self.model.get_particle(id)
+                mass_param = particle.get('mass')
+                variable_index += 1
+                if reconstruct:
+                    assert(kinematics[leg3_nr]['momentum']==None)
+                    kinematics[leg3_nr]['momentum'] = kinematics[leg1_nr]['momentum']+kinematics[leg2_nr]['momentum']
+                    s = kinematics[leg3_nr]['momentum'].square()
+                    if 0. < s_min-s < tolerance**2*self.absolute_Ecm_min**2:
+                        s = s_min
+                    if 0. < s-s_max < tolerance**2*s_max:
+                        s = s_max
+                    kinematics[leg3_nr]['inv_mass'] = s
+                    if mass_param.lower() == 'zero':
+                        x_s, wgt_jac = self.inv_massless_distr(s,s_min,s_max)
+                    else:
+                        mass = self.model.get('parameter_dict')[mass_param]
+                        width = self.model.get('parameter_dict')[particle.get('width')]
+                        if width == 0:
+                            x_s, wgt_jac = self.inv_massless_distr(s, s_min, s_max, m2=mass**2)
+                        else:
+                            x_s, wgt_jac = self.inv_massive_distr(s,mass,width, s_min, s_max)
+                    output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_s
+                else:
+                    assert(kinematics[leg3_nr]['inv_mass'] == None)
+                    random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                    if mass_param.lower() == 'zero':
+                            s, wgt_jac = self.massless_distr(random_variable,s_min,s_max)
+                    else:
+                        mass = self.model.get('parameter_dict')[mass_param]
+                        width = self.model.get('parameter_dict')[particle.get('width')]
+                        if width == 0:
+                            s, wgt_jac = self.massless_distr(random_variable, s_min, s_max, m2=mass**2)
+                        else:
+                            s, wgt_jac = self.massive_distr(random_variable,mass,width, s_min, s_max)
+                    # think about a better solution to handle if particle is produced at rest
+                    no_phase_space = 0.*max([s,m1**2,m2**2])**2 >= self.Lambda(s,m1**2,m2**2) >= -tolerance**2*s**2
+                    if no_phase_space:
+                        misc.sprint('This kinematic configuration leaves no phase space ', self.Lambda(s,m1**2,m2**2))
+                        return None,0.,(xb_1, xi1) , (xb_2, xi2)
+                    kinematics[leg3_nr]['inv_mass'] = s
+                wgt *= wgt_jac
+                kinematics[leg3_nr]['is_available'] = True
+        
+        # if there is more than one t-channel, there are uniform invariants to be generated
+        uniform_inv = []
+        if len(self.topology[1]) > 2:
+            # go from innermost to outermost vertex pair in t-channel path
+            for item in path[1]:
+                # extract the path-info into uniform_inv dictionary
+                uniform_inv.append({'vertices': (item[0],item[1]), 'inv_mass': None, 'is_available': False, 'inv_mass_limits': None, 'momentum': None})
+            for k,item in enumerate(uniform_inv):
+                start = item['vertices'][0]
+                finish = item['vertices'][1]
+                if (start+1) == finish:
+                    vertex1 = self.topology[1][start]
+                    vertex2 = self.topology[1][finish]
+                    leg1_nr = vertex1.get('legs')[1].get('number')
+                    leg2_nr = vertex2.get('legs')[1].get('number')
+                    m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
+                    m2 =  math.sqrt(kinematics[leg2_nr]['inv_mass'])
+                    kinematics[leg1_nr]['is_available'] = False #flag is necessary to calculate limits
+                    kinematics[leg2_nr]['is_available'] = False
+                    if reconstruct:
+                        p1 = kinematics[leg1_nr]['momentum']
+                        p2 = kinematics[leg2_nr]['momentum']
+                else:
+                    for l in reversed(xrange(0,k)):
+                        if uniform_inv[l]['vertices'][0] == start:
+                            m1 = math.sqrt(uniform_inv[l]['inv_mass'])
+                            if reconstruct:
+                                p1 = uniform_inv[l]['momentum']
+                            uniform_inv[l]['is_available'] = False
+                            break
+                    else:
+                        vertex1 = self.topology[1][start]
+                        leg1_nr = vertex1.get('legs')[1].get('number')
+                        m1 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
+                        if reconstruct:
+                                p1 = kinematics[leg1_nr]['momentum']
+                        kinematics[leg1_nr]['is_available'] = False
+                    for l in reversed(xrange(0,k)):
+                        if uniform_inv[l]['vertices'][1] == finish:
+                            m2 = math.sqrt(uniform_inv[l]['inv_mass'])
+                            if reconstruct:
+                               p2 = uniform_inv[l]['momentum']
+                            uniform_inv[l]['is_available'] = False
+                            break
+                    else:
+                        vertex2 = self.topology[1][finish]
+                        leg2_nr = vertex1.get('legs')[1].get('number')
+                        m2 = math.sqrt(kinematics[leg1_nr]['inv_mass'])
+                        if reconstruct:
+                            p2 = kinematics[leg2_nr]['momentum']
+                        kinematics[leg2_nr]['is_available'] = False
+                sum_masses = sum([math.sqrt(kinematics[nr]['inv_mass']) for nr in kinematics.keys() if kinematics[nr]['is_available']])
+                sum_masses += sum([math.sqrt(inv['inv_mass']) for inv in uniform_inv if inv['is_available']])
+                s_max = (E_cm - sum_masses)**2
+                s_min = (m1 + m2)**2
+                item['inv_mass_limits']= (s_min,s_max)
+                variable_index += 1
+                if reconstruct:
+                    item['momentum']=p1+p2
+                    s = item['momentum'].square()
+                    if 0. < s_min-s < tolerance**2*self.absolute_Ecm_min**2:
+                        s = s_min
+                    if 0. < s-s_max < tolerance**2*s_max:
+                        s = s_max
+                    item['inv_mass'] = s
+                    x_s, wgt_jac = self.inv_uniform_distr(s,s_min,s_max)
+                    output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_s
+                else:
+                    random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                    s, wgt_jac = self.uniform_distr(random_variable,s_min,s_max)
+                    item['inv_mass'] = s
+                wgt *= wgt_jac
+                item['is_available'] = True
+        
+        # all invariant masses are generated, so for consistency set all available-flags to False
+        # REMARK: there are some that say True but are not available i.e.
+        #     1) if no t-channels and more than one s-channel -> last vertex final leg 1,2 say True
+        #     2) if there's t-channels:
+        #            one single: final leg 1,2 say True;
+        #            more than one: either final leg 1 or 2 say True
+        for item in uniform_inv:
+            item['is_available'] = False
+        for nr in kinematics.keys():
+            kinematics[nr]['is_available'] = False
+        
+        # sample the t-variables (uniform if massive, 1/x^nu if massless)
+        # go from outermost to innermost t-channel
+        # structure: leg1 + leg2 -> leg3 + leg4
+        # if there are t-channels
+        if len(self.topology[1]) > 1:
+            uniform_inv.append({'vertices': (0,len(self.topology[1])-1), 'inv_mass': E_cm**2, 'is_available': False, 'momentum': None})
+            uniform_inv.reverse()
+            for k,item in enumerate(uniform_inv):
+                start = item['vertices'][0]
+                finish = item['vertices'][1]
+                leg1_nr = self.topology[1][start].get('legs')[0].get('number')
+                leg2_nr = self.topology[1][finish].get('legs')[-1].get('number')
+                p1 = kinematics[leg1_nr]['momentum'].copy()
+                p2 = kinematics[leg2_nr]['momentum'].copy()
+                if leg1_nr < 0:
+                    # t-channel propagator momenta always point upwards
+                    # this code procedure requires p1 to point downwards
+                    p1 = -p1.copy()
+                # p is the sum of incoming momenta
+                p = p1+p2
+                if (start + 1) ==finish:
+                    leg3_nr = self.topology[1][start].get('legs')[1].get('number')
+                    leg4_nr = self.topology[1][finish].get('legs')[1].get('number')
+                    if reconstruct:
+                        p3 = kinematics[leg3_nr]['momentum']
+                        p4 = kinematics[leg4_nr]['momentum']
+                    p3_2 = kinematics[leg3_nr]['inv_mass']
+                    p4_2 = kinematics[leg4_nr]['inv_mass']
+                    prop_leg = self.topology[1][start].get('legs')[-1]
+                else:
+                    for inv in uniform_inv[k+1:]:
+                        if inv['vertices'][0] == start:
+                            leg3_nr = None
+                            if reconstruct:
+                                p3 = inv['momentum']
+                            p3_2 = inv['inv_mass']
+                            prop_leg = self.topology[1][inv['vertices'][1]].get('legs')[-1]
+                            break
+                        elif inv['vertices'][0] == start + 1:
+                            leg3_nr = self.topology[1][start].get('legs')[1].get('number')
+                            if reconstruct:
+                                p3 = kinematics[leg3_nr]['momentum']
+                            p3_2 = kinematics[leg3_nr]['inv_mass']
+                            prop_leg = self.topology[1][inv['vertices'][0]].get('legs')[0]
+                            break
+                    for inv in uniform_inv[k+1:]:
+                        if inv['vertices'][1] == finish:
+                            leg4_nr = None
+                            if reconstruct:
+                                p4 = inv['momentum']
+                            p4_2 = inv['inv_mass']
+                            #not needed because already determined above
+                            #prop_leg = self.topology[1][inv['vertices'][0]].get('legs')[0]
+                            break
+                        elif inv['vertices'][1] == finish - 1:
+                            leg4_nr = self.topology[1][finish].get('legs')[1].get('number')
+                            if reconstruct:
+                                p4 = kinematics[leg4_nr]['momentum']
+                            p4_2 = kinematics[leg4_nr]['inv_mass']
+                            #not needed because already determined above
+                            #prop_leg = self.topology[1][inv['vertices'][1]].get('legs')[-1]
+                            break
+                prop_id = prop_leg.get('id')
+                s = p.square()
+                assert(s >= 0)
+                if not reconstruct:
+                    # think about a better solution to handle if particle is produced at rest
+                    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
+                    if no_phase_space:
+                        misc.sprint('This kinematic configuration leaves no phase space ', self.Lambda(s,p3_2,p4_2))
+                        return None,0.,(xb_1, xi1) , (xb_2, xi2)
+                t_max = self.inv_t(s, p1.square(), p2.square(), p3_2, p4_2, 1.)
+                if abs(t_max) < tolerance**2*self.absolute_Ecm_min**2:
+                    t_max = 0. 
+                t_min = self.inv_t(s, p1.square(), p2.square(), p3_2, p4_2, -1.)
+                assert(t_min<=t_max<=0.)
+                particle = self.model.get_particle(prop_id)
+                mass_param = particle.get('mass')
+                prop_nr = prop_leg.get('number')
+                assert(prop_nr<0)
+                variable_index += 1
+                if reconstruct:
+                    kinematics[prop_nr]['momentum'] = p3-p1
+                    t = kinematics[prop_nr]['momentum'].square()
+                    if abs(t) < tolerance**2*self.absolute_Ecm_min**2:
+                        t = t_max
+                    if 0. < t_min-t < tolerance**2*t_min:
+                        t = t_min
+                    kinematics[prop_nr]['inv_mass'] = t
+                    if mass_param.lower() == 'zero':
+                        # t is always negative, distribute abs(t)
+                        x_abs_t,wgt_jac = self.inv_massless_distr(abs(t), abs(t_max), abs(t_min))
+                    else:
+                        x_abs_t, wgt_jac = self.inv_uniform_distr(abs(t), abs(t_max), abs(t_min))
+                    output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_abs_t
+                else:
+                    random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                    if mass_param.lower() == 'zero':
+                        # t is always negative, distribute abs(t)
+                        abs_t,wgt_jac = self.massless_distr(random_variable, abs(t_max), abs(t_min))
+                        t = -abs_t
+                    else:
+                        abs_t, wgt_jac = self.uniform_distr(random_variable, abs(t_max), abs(t_min))
+                        t = -abs_t
+                wgt *= wgt_jac
+                cos_theta = self.cos_theta_from_inv_t(s, p1.square(), p2.square(), p3_2, p4_2, t)
+                variable_index += 1
+                if reconstruct:
+                    x_phi, wgt_jac = None, 2*math.pi
+                    output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_phi
+                else:
+                    random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                    phi, wgt_jac = self.uniform_distr(random_variable, 0, 2*math.pi)
+                wgt *= wgt_jac
+                if reconstruct:
+                    wgt_PS = self.get_two_body_PS_wgt(s, p3_2, p4_2)
+                else:
+                    p3, p4, wgt_PS = self.get_cm_momenta(s,p3_2,p4_2,cos_theta,phi)
+                #t-channel only factor:
+                wgt_PS *= 2.*s/(math.sqrt(self.Lambda(s,p3_2,p4_2))*math.sqrt(self.Lambda(s,p1.square(),p2.square())))
+                wgt *= wgt_PS
+                
+                if not reconstruct:
+                    p1_com = p1.copy()
+                    p1_com.boost(-p.boostVector())
+                    
+                    # test if boost is correct
+                    """
+                    p2_com = p2.copy()
+                    p2_com.boost(-p.boostVector())
+                    assert(all(abs(x) < tolerance for x in (p1_com+p2_com)[1:]))
+                    """
+                    
+                    phi, theta = self.angles_to_rotate_along_z(p1_com)
+                    
+                    # this is to test if rotation works as expected
+                    """
+                    phi1,theta1 = (math.pi/2,math.pi/6)
+                    ptest = LorentzVector([10,math.cos(phi1)*math.sin(theta1),math.sin(phi1)*math.sin(theta1),math.cos(theta1)])
+                    ptest_z = LorentzVector([10,0,0,1])
+                    misc.sprint('\n',ptest)
+                    phi2,theta2 = self.angles_to_rotate_along_z(ptest)
+                    misc.sprint(phi1-phi2,theta1-theta2) #should be 0,0
+                    misc.sprint('\n',self.rotate_along_z_inv(ptest_z,phi2, theta2)-ptest) #should be [0,0,0,0]
+                    """
+                    
+                    p3 = self.rotate_along_z_inv(p3,phi, theta)
+                    p4 = self.rotate_along_z_inv(p4,phi, theta)
+                    assert(abs((p3-p1_com).square()-t) < tolerance**2*s) 
+                    
+                    p3.boost(p.boostVector())
+                    p4.boost(p.boostVector())
+                    
+                    if leg3_nr != None:
+                        assert(kinematics[leg3_nr]['momentum'] == None)
+                        kinematics[leg3_nr]['momentum'] = p3
+                    if leg4_nr != None:
+                        assert(kinematics[leg4_nr]['momentum'] == None)
+                        kinematics[leg4_nr]['momentum'] = p4
+                        
+                    assert(kinematics[prop_nr]['momentum'] == None)
+                    # t-propagator-momentum always points upwards
+                    kinematics[prop_nr]['momentum'] = p3-p1
+                    assert(abs(kinematics[prop_nr]['momentum'].square()-t) < tolerance**2*s)
+                    assert(all(abs(x) < tolerance*math.sqrt(s) for x in (p-p3-p4)))
+        
+        # s-channel momentum generation
+        # start at the very end of topology[0] and go upwards, ordering doesn't matter
+        # structure leg0 -> leg1 + leg2
+        for vertex in reversed(self.topology[0]):
+            leg0_nr = vertex.get('legs')[-1].get('number')
+            leg1_nr = vertex.get('legs')[0].get('number')
+            leg2_nr = vertex.get('legs')[1].get('number')
+            s = kinematics[leg0_nr]['inv_mass']
+            p = kinematics[leg0_nr]['momentum']
+            p1_2 = kinematics[leg1_nr]['inv_mass']
+            p2_2 = kinematics[leg2_nr]['inv_mass']
+            variable_index += 1
+            if reconstruct:
+                x_cos_theta, wgt_jac = None, 2
+                output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_cos_theta
+            else:
+                # this is for the case where a massless propagator is produced almost on shell, this leads
+                # to a boost to velocity c which is numerically unstable
+                # it's not an actual problem since it can only happen when massless splits into 2 massless,
+                # e.g. e- > e- a, photon or gluon radiation. This would eventually be treated by pt_cuts anyways
+                if s < tolerance**2*self.absolute_Ecm_min**2:
+                    #misc.sprint(s)
+                    return None,0.,(xb_1, xi1) , (xb_2, xi2)
+                random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                cos_theta, wgt_jac = self.uniform_distr(random_variable, -1, 1)
+            wgt *= wgt_jac
+            variable_index += 1
+            if reconstruct:
+                x_phi, wgt_jac = None, 2*math.pi
+                output_variables[self.dim_name_to_position['x_%d' % variable_index]] = x_phi
+            else:
+                random_variable = random_variables[self.dim_name_to_position['x_%d' % variable_index]]
+                phi, wgt_jac = self.uniform_distr(random_variable, 0, 2*math.pi)
+            wgt *= wgt_jac
+            if reconstruct:
+                wgt_PS = self.get_two_body_PS_wgt(s, p1_2, p2_2)
+            else:
+                p1,p2,wgt_PS = self.get_cm_momenta(s, p1_2, p2_2, cos_theta, phi)
+            wgt *= wgt_PS
+            if not reconstruct:
+                p1.boost(p.boostVector())
+                p2.boost(p.boostVector())
+                assert(kinematics[leg1_nr]['momentum'] == None and kinematics[leg2_nr]['momentum'] == None)
+                kinematics[leg1_nr]['momentum'] = p1
+                kinematics[leg2_nr]['momentum'] = p2
+                
+                if not (all(abs(x) < tolerance*math.sqrt(s) for x in (p-p1-p2))):
+                    logger.critical('Possible loss of precision with tolerance %.2e: %s'%(tolerance*math.sqrt(s), str(p-p1-p2)))
+                #assert(all(abs(x) < tolerance*math.sqrt(s) for x in (p-p1-p2)))
+        
+        if not reconstruct:
+            # sanity check if all random variables were actually used
+            if self.beam_types == (1,1):
+                assert(variable_index +2 == len(random_variables))
+            if self.beam_types == (0,0):
+                assert(variable_index== len(random_variables))
+        
+        if not reconstruct:
+            # save final momenta into output_momenta -> PS_point
+            for nr in kinematics:
+                if nr > 0:
+                    output_momenta[nr-1] = kinematics[nr]['momentum']
+                if nr == min_leg_nr:
+                    output_momenta[1] = kinematics[nr]['momentum']
+            PS_point = LorentzVectorList(output_momenta)
+            
+            # test if total momentum is conserved
+            p_start = sum(PS_point[0:2])
+            p_finish = sum(PS_point[2:])
+            assert(all(abs(x) < tolerance*self.collider_energy for x in (p_start-p_finish)))
+        
+        assert(wgt>0)
+        
+        if reconstruct:
+            return output_variables, wgt
+        else:
+            return LorentzVectorList(PS_point), wgt, (xb_1, xi1) , (xb_2, xi2)
 
 class FlatInvertiblePhasespace(VirtualPhaseSpaceGenerator):
     """Implementation following S. Platzer, arxiv:1308.2922"""
@@ -310,7 +1243,7 @@
                 output_momenta[1] = LorentzVector([E2/2.0 , 0., 0., -Z/2.0])
         return
 
-    def get_PS_point(self, random_variables):
+    def get_PS_point(self, random_variables, **opts):
         """Generate a complete PS point, including Bjorken x's,
         dictating a specific choice of incoming particle's momenta.
         """

=== modified file 'madgraph/integrator/vegas3_integrator.py'
--- madgraph/integrator/vegas3_integrator.py	2018-09-21 18:30:26 +0000
+++ madgraph/integrator/vegas3_integrator.py	2019-01-17 08:58:59 +0000
@@ -13,12 +13,14 @@
 #
 ################################################################################
 
+import traceback
 import sys
 import time
 import os
 import logging
 import random
 from multiprocessing import Process
+from aetypes import Enum
 
 if __name__ == '__main__':
     sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.path.pardir, os.path.pardir))
@@ -216,7 +218,13 @@
         
         all_results = []
         for integrand in self.integrands:
-            res = integrand(fct_inputs, np.array([], dtype=int), integrator_jacobian=jacobian)
+            try:
+                res = integrand(fct_inputs, np.array([], dtype=int), integrator_jacobian=jacobian)
+            except AssertionError, err:
+                traceback.print_tb(sys.exc_info()[-1])
+                logger.warning('Assertion error encountered.')
+                res = 0.
+
             res *= integrand.get_dimensions().volume()
             all_results.append(res)
             
@@ -395,7 +403,13 @@
                                                   (self.tot_func_evals,self.cluster.nb_core))
             logger.debug('Vegas3 returned final results : %s'%summed_result)
             logger.debug("===============================================================")
-
+        
+        #out_data = open('0result.dat','a')
+        #out_data.write('%.16e, '%(summed_result.mean)+'%.16e, '%(summed_result.sdev)+'%i\n'%(self.integrands[0].counter))
+        #out_data.close()
+        ##misc.sprint('\n%.16e, '%(summed_result.mean)+'%.16e, '%(summed_result.sdev)+'%i\n'%(self.integrands[0].counter))
+        self.integrands[0].counter = 0
+        
         return summed_result.mean, summed_result.sdev
 
     def show_grid(self, n_grid=40, shrink=False, axes=None):
@@ -413,6 +427,431 @@
             self.vegas3_integrator.map.show_grid(n_grid, shrink, axes=axes)
 
 
+class Channel():
+    """ class that specifies a channel and its parameters when using the multi-channel integration technique."""
+    def __init__(self,i,integrand,adaptive_wgt,selector_wgt):
+        self.nr = i
+        self.full_integrand = integrand
+        self.adaptive_wgt = adaptive_wgt
+        self.selector_wgt = selector_wgt
+        self.dimension = len(integrand.continuous_dimensions)
+        # TODO: Hypercubes of vegas not used here (see add_point)
+        self.integrator = vegas.Integrator(self.dimension*[[0.,1.]])
+        self.reset()
+        return
+
+    def integrand(self,random_variable,adaptive_wgts):
+        """ Current integrand of channel. Adaptive_wgts change over course of integration. """
+        res = self.full_integrand(random_variable, np.array([], dtype=int),
+                                  adaptive_wgts=adaptive_wgts,
+                                  channel_nr=self.nr)
+        return res
+    
+    def update(self):
+        """ Compute estimates of integral, integral^2, sdev, rel variance. """
+        self.results_squared = [res**2 for res in self.results]
+        assert(self.n == len(self.results))
+        self.n = len(self.results)
+        self.result = np.mean(self.results)
+        self.result_squared = np.mean(self.results_squared)
+        self.sdev = np.sqrt((self.result_squared-self.result**2)/self.n)
+        self.rel_variance = (self.result_squared-self.result**2)/self.result**2
+        return
+    
+    def reset(self):
+        """ (Re)sets all data gathered while integrating to zero/empty. """
+        self.results = []          # each evaluation of integrand*wgt
+        self.random_variables = [] # = y in vegas manual
+        self.grid_variables = []   # = x in vegas manual
+        self.results_squared = []  # each evaluation of (integrand*wgt)^2
+        self.wgts = []             # = wgt in vegas manual
+        self.result = 0.           # current estimate of integrand (calculated in update)
+        self.result_squared = 0.   # current estimate of integrand^2 (calculated in update)
+        self.sdev = 0.             # current estimate of sdev (calculated in update)
+        self.n = 0                 # number of total evaluation (==len(results))
+        self.rel_variance = 0.     # current estimate of variance/result^2 (calculated in update)
+        self.n_passed_cuts = 0     # number of evaluations that passed cuts
+        return
+        
+    def add_point(self,adaptive_wgts):
+        """ Adds an integration point sampled according to vegas3 grid and evaluates the integrand. """
+        y = np.random.uniform(0., 1., (1, self.dimension))
+        x = np.empty(y.shape, float)
+        wgt = np.empty(y.shape[0], float)
+        self.integrator.map.map(y,x,wgt)
+        y = y[0]
+        x= x[0]
+        wgt = wgt[0]
+        #misc.sprint('\n%s \n%s \n%e'%(x,y,wgt))
+        self.random_variables += [y]
+        self.grid_variables += [x]
+        self.wgts += [wgt]
+        try:
+            res = self.integrand(x,adaptive_wgts)*wgt
+        except AssertionError, err:
+            traceback.print_tb(sys.exc_info()[-1])
+            misc.sprint('Assertion error encountered.')
+            res = 0.
+        self.results += [res]
+        if res != 0.:
+            self.n_passed_cuts += 1
+        self.n += 1
+        return
+        
+    def adjust_to_new_grid(self):
+        # currently not used!
+        """Integrand values can be reused during vegas3 grid updates.
+        For a given x, there's an old y & old wgt from the old grid that is known
+        and a new y & new wgt from the new grid that is unknown, yet.
+        The xs are invariant by construction.
+        Here the new y & new wgt are found and the integrand updated as integrand*=new_wgt/old_wgt."""
+        grid = self.integrator.map.grid
+        # collect new ys from old xs
+        for j,x in enumerate(self.grid_variables):
+            y_new = np.empty_like(x)
+            for d in xrange(len(grid[:,0])):
+                for i in xrange(len(grid[0,:])-1):
+                    if grid[d,i]<=x[d]<grid[d,i+1]:
+                        delta = (x[d]-grid[d,i])/(grid[d,i+1]-grid[d,i])
+                        y_new[d] = (delta + i)*1./self.integrator.map.ninc#[d]
+            # overwrite old ys with new ones
+            #misc.sprint('\n',self.random_variables[j],'\n',y_new)
+            self.random_variables[j]=y_new
+        # check with built in function if I inverted correctly:
+        check_x = np.empty_like(self.random_variables)
+        new_wgt = np.empty(check_x.shape[0],np.float_)
+        self.integrator.map.map(np.array(self.random_variables),check_x,new_wgt)
+        #misc.sprint(check_x[0],self.grid_variables[0],self.integrator.map(np.array(self.random_variables[0])))
+        assert(np.all(x==x_alt for x,x_alt in zip(self.grid_variables,check_x)))
+        # adjust all saved results with the new wgts, so that they are reusable
+        for i in xrange(len(self.results)):
+            self.results[i] *= new_wgt[i]/self.wgts[i]
+            #misc.sprint(new_wgt[i]/self.wgts[i])
+            self.wgts[i] = new_wgt[i]
+        return
+    
+    def header_string(self):
+        msg = '{:<7}  {:>14}  {:>12}  {:>7}  {:<22}'.format('channel','#passed /  tot',
+                                                              'adaptive wgt','rel var','estimate')
+        return msg
+    
+    def summary_string(self):
+        """ Returns a string with the current state of the integration. Formatted according to header_string. """
+        msg = '{:<7}  {:>14}  {:>12}  {:>7}  {:>28}'.format(self.nr,'%4d / %4d'%(self.n_passed_cuts,self.n),
+                                                               '%.10f'%self.adaptive_wgt,
+                                                               '%3.2f'%self.rel_variance,
+                                                               '%.6e +- %.6e'%(self.result,self.sdev))
+        return msg
+
+    def get_n_points(self,abs_acc):
+        """ estimates the number of Monte Carlo points needed to arrive at an accuracy abs_acc of THIS CHANNEL. """
+        if self.sdev < abs_acc:
+            n_points = 0
+        else:
+            #var = self.sdev**2*self.n
+            #n_improve = var/abs_target_accuracy**2 - self.n
+            n_points = int((self.sdev**2/abs_acc**2-1.)*self.n)
+        return n_points
+    
+class MultiChannelVegas3Integrator(integrators.VirtualIntegrator):
+    """ Mutli-Channel integrator: Class that handles channels specified as instances of class Channel."""
+    
+    def __init__(self, integrands, adaptive_wgts=None, selector_wgts=None, **opts):
+        """ specifies:
+                adaptive_wgts: channel weights (usually denoted by \alpha), sum up to one
+                selector_wgts: channel more often sampled if its selector wgt is large, sum up to one """
+
+        super(MultiChannelVegas3Integrator,self).__init__(integrands, **opts)
+        
+        # TODO: #flattening_strategy = {
+        #       channel_id: {'flattening_technique': 'diagram_fuction to call'/'jacobians',
+        #       'use_alphas': True/False}}
+        
+        #np.random.seed(0)
+        
+        self.start_time = time.time()
+        self.time = time.time()
+        
+        self.neval = opts.pop('n_points') #number of evaluations of first iteration, doubled after every itn
+        self.rel_ch_acc = 0.1 #relative target accuracy of each channel
+        self.v_acc = 0.01 #relative change of vegas grid
+        self.a_acc = 0.01 #relative change of channel weights (alpha)
+        self.min_nitn = 3
+        self.max_nitn = opts.pop('n_iterations')
+        assert(self.min_nitn<=self.max_nitn)
+        self.rel_acc = 0.01 #relative target accuracy of final integral
+        
+        # channel properties
+        if len(integrands)>1:
+            misc.sprint('This multi channel integrator works with only one process/integrand for now.')
+            raise
+        self.n_channels = len(self.integrands[0].topologies_to_processes)
+        if adaptive_wgts is None: #uniform
+            adaptive_wgts = [1./self.n_channels]*self.n_channels
+        if selector_wgts is None: #uniform
+            selector_wgts = [1./self.n_channels]*self.n_channels
+        self.adaptive_wgts = adaptive_wgts
+        self.selector_wgts = selector_wgts
+        
+        
+        # initialize channels
+        integrand = self.integrands[0]
+        self.channels = []
+        for i in xrange(self.n_channels):
+            self.channels += [Channel(i,integrand,self.adaptive_wgts[i],self.selector_wgts[i])]
+        
+        # variables for the full integrand   
+        self.result = 0.
+        self.sdev = 0.
+        self.n = 0
+        self.n_passed_cuts = 0
+        self.rel_variance = 0.
+        return
+    
+    def reset(self):
+        """ Deletes all integration data, except for grid and channel weights (alpha) """
+        for channel in self.channels:
+            channel.reset()
+        return
+    
+    def update(self):
+        """ Combine the estimates from all the channels to give an estimate of the full integral. """
+        for channel in self.channels:
+            channel.update()
+        self.result = sum([channel.result*channel.adaptive_wgt for channel in self.channels])
+        self.n = sum([channel.n for channel in self.channels])
+        self.n_passed_cuts = sum([channel.n_passed_cuts for channel in self.channels])
+        W = sum([channel.result_squared*channel.adaptive_wgt for channel in self.channels])
+        self.sdev = np.sqrt((W-self.result**2)/self.n)
+        self.rel_variance = (W-self.result**2)/self.result**2 
+        return
+    
+    def adapt_vegas(self,alpha):
+        """ Adapts vegas grid of each channel with saved function evaluations
+        and returns a measure for the change of the grid. """
+        ch_wgt_change = []
+        for channel in self.channels:
+            old_wgts = np.array(channel.integrator.map.inc)
+            channel.integrator.map.add_training_data(np.array(channel.random_variables),np.array(channel.results))
+            channel.integrator.map.adapt(alpha=alpha)
+            new_wgts = np.array(channel.integrator.map.inc)
+            wgt_change = []
+            wgt_change = [abs(new-old)/old for new,old in zip(new_wgts,old_wgts)]
+            wgt_change = np.mean(wgt_change,axis=1) # sum within dimension (already normalized wgts)
+            wgt_change = np.mean(wgt_change) #mean along each PS dimension
+            ch_wgt_change += [wgt_change]
+        return np.array(ch_wgt_change)
+    
+    
+    def get_channels_with_points(self,n_points=None,rel_target_accuracy=None,training=-1):
+        if n_points == None and rel_target_accuracy != None:
+            self.update()
+            if training == True:
+                abs_target_accuracies = [rel_target_accuracy*channel.result for channel in self.channels]
+                n_points = [channel.get_n_points(abs_target_accuracies[channel.nr]) for channel in self.channels]
+                #if n_points > self.max_refine_neval:
+                #    n_points = self.max_refine_neval
+            elif training == False: 
+                abs_target_accuracy = rel_target_accuracy*self.result
+                n_points = self.get_n_points(abs_target_accuracy)
+                n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
+                abs_target_accuracy_alt = rel_target_accuracy*self.result/np.sqrt(self.n_channels)
+                n_points_alt = [channel.get_n_points(abs_target_accuracy_alt) for channel in self.channels]
+                if np.mean(n_points)>np.mean(n_points_alt):
+                    n_points = n_points_alt
+            else:
+                logger.info('Set training either True or False.')
+                raise
+        elif n_points != None and rel_target_accuracy == None:
+            n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
+        else:
+            self.selector_wgts = self.adaptive_wgts
+            for channel in self.channels:
+                channel.selector_wgt = channel.adaptive_wgt
+            logger.info('Either choose n_points or rel_target_accuracy.')
+            raise
+        channels_with_points = {channel.nr: n_points[channel.nr] for channel in self.channels}
+        return channels_with_points
+    
+        
+    def get_n_points(self,abs_target_accuracy):
+        """ estimates the number of Monte Carlo points needed to arrive at an accuracy abs_target_accuracy of FULL INTEGRAL. """
+        if self.sdev < abs_target_accuracy:
+            n_points = 0
+        else:
+            #var = self.sdev**2*self.n
+            #n_improve = var/abs_target_accuracy**2 - self.n
+            n_points = int((self.sdev**2/abs_target_accuracy**2-1.)*self.n)
+        return n_points
+    
+    
+    def adapt_wgts(self):
+        """ One adaptation of adaptive_wgts. Per iteration there are n_vegas_adapt adaptations of the vegas grid.
+            There are at least adapt_min_n_points points evaluations. If rel_target_accuracy is not reached,
+            increase max n_max_improve_itns of n_improve_steps."""
+        #now update the adaptive_wgts and normalize
+        old_wgts = self.adaptive_wgts
+        #misc.sprint(old_adaptive_wgts)
+        all_adaptive_wgts = []
+        for channel in self.channels:
+            channel.adaptive_wgt *= np.sqrt(channel.result_squared)#**alpha
+            all_adaptive_wgts += [channel.adaptive_wgt]
+        adaptive_norm = sum(all_adaptive_wgts)
+        for channel in self.channels:
+            channel.adaptive_wgt *= 1./adaptive_norm
+            all_adaptive_wgts[channel.nr] = channel.adaptive_wgt
+        self.adaptive_wgts = all_adaptive_wgts
+        new_wgts = self.adaptive_wgts
+        wgt_change = [abs(new-old)/old for new,old in zip(new_wgts,old_wgts)]
+        wgt_change = np.mean(wgt_change) #(already normalized wgts)
+        return (wgt_change)
+    
+    def summary_string(self):
+        """ Returns a string with the current state of the integration of all channels and their sum. """
+        msg = '{:<7}  {:>14}  {:>12}  {:>7}  {:>28}'.format('full','%4d / %4d'%(self.n_passed_cuts,self.n),'-',
+                                                             '%3.2f'%self.rel_variance,
+                                                             '%.6e +- %.6e'%(self.result,self.sdev))
+        return msg
+    
+    def print_summary(self):
+        for channel in self.channels:  
+            channel.update()    
+            logger.info(channel.summary_string())
+        logger.info('-'*70)
+        self.update()
+        logger.info(self.summary_string())
+        return
+        
+    def get_channel_points(self,n_points):
+        """ distributes a total of n_points on channels according to selector_wgts.
+        returns a dict: {channel_nr: n_pts} """
+        n_points = [int(n_points*channel.selector_wgt) for channel in self.channels]
+        pts_per_channels = {channel.nr: n_points[channel.nr] for channel in self.channels}
+        return pts_per_channels
+    
+    def get_refine_channel_points(self,rel_acc):
+        """ estimates number of points needed to reach rel_acc. returns a dict: {channel_nr: n_pts} """
+        abs_acc = rel_acc*self.result/np.sqrt(self.n_channels)
+        abs_ch_acc = [abs_acc/channel.adaptive_wgt for channel in self.channels]
+        n_points = [channel.get_n_points(abs_ch_acc[channel.nr]) for channel in self.channels]
+        refine_pts_per_channels = {channel.nr: n_points[channel.nr] for channel in self.channels}
+        return refine_pts_per_channels
+    
+    def survey(self, neval, max_nitn, min_nitn=5, a_acc=0.01, v_acc=0.01):
+        """ survey stage of integration: girds and channel weights (alpha) are trained.
+        while both the vegas grid and the channel weights have not stabilized (reach a_acc,v_acc),
+        do an iteration with double the number of points neval.
+        When adding points in parallel, print a full estimate after every "update_time" seconds.
+        run a minimum and a maximum of iterations. """
+        msg = '='*27 + ' training stage '+'='*27
+        logger.info(msg,'$MG:color:GREEN')
+        logger.info(self.channels[0].header_string())
+        
+        update_time = 10
+        tot_it = 0
+        a_it = 0
+        v_it = 0
+        while not (a_acc == -1 and v_acc == -1):
+            self.reset()
+            channel_points = self.get_channel_points(neval*2**(tot_it)) #double every time
+            t0 = time.time()
+            while sum(channel_points.values()) > 0:
+                for nr in channel_points.keys():
+                    if channel_points[nr] == 0:
+                        channel_points.pop(nr)
+                    else:
+                        self.channels[nr].add_point(self.adaptive_wgts)
+                        channel_points[nr] -= 1
+                if (time.time()-t0) > update_time:
+                    total_time = time.time()-self.start_time
+                    minutes = int(total_time/60)
+                    time_string = '%d min %.2f sec'%(minutes,total_time-minutes*60)
+                    self.update()
+                    logger.info(self.summary_string()+'    '+'\033[94m'+time_string+'\033[0m')
+                    t0 = time.time()
+            self.print_summary()
+            # if max is reached stop
+            if tot_it == max_nitn:
+                v_acc = -1
+                a_acc = -1
+            elif tot_it > 0 and tot_it >= min_nitn:
+                if v_acc == -1 and a_acc != -1:
+                    if a_change < a_acc:
+                        a_acc = -1
+                elif v_acc != -1 and a_acc == -1:
+                    if mean_v_change < v_acc:
+                        v_acc = -1
+                else:
+                    if mean_v_change < v_acc and a_change < a_acc:
+                        v_acc = -1
+                        a_acc = -1
+            if v_acc != -1:
+                v_change = self.adapt_vegas(alpha=.5)
+                v_it += 1
+                mean_v_change = np.mean(v_change)
+                logger.info('#%d vegas grid changed by %.2f%%'%(v_it,mean_v_change*1e2),'$MG:color:GREEN')
+                #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)])),'$MG:color:GREEN')
+            if a_acc != -1:
+                a_change = self.adapt_wgts()
+                a_it += 1
+                logger.info('#%d: adaptive weights changed by %.2f%%'%(a_it,a_change*1e2),'$MG:color:GREEN') 
+            tot_it += 1
+        return
+    
+    def refine(self,rel_acc):
+        """ refine stage of integration.
+        estimates number of points needed to reach rel_acc.
+        after every "update_time" seconds, prints out current estimate of full integrand
+        and restimates number of points needed to reach rel_acc. """
+        
+        msg = '='*26 + ' refinement stage '+'='*26
+        logger.info(msg,'$MG:color:GREEN')
+        logger.info(self.channels[0].header_string())
+        
+        update_time = 10
+        refine_channel_points = self.get_refine_channel_points(rel_acc=rel_acc)
+        for nr,pts in refine_channel_points.items():
+            if pts != 0:
+                logger.info('ESTIMATE: Refining channel %d with %d additional points.'%(nr,pts))
+        t0 = time.time()
+        while sum(refine_channel_points.values()) > 0:
+            for nr in refine_channel_points.keys():
+                if refine_channel_points[nr] == 0:
+                    refine_channel_points.pop(nr)
+                else:
+                    self.channels[nr].add_point(self.adaptive_wgts)
+                    refine_channel_points[nr] -= 1
+            if (time.time()-t0) > update_time:
+                total_time = time.time()-self.start_time
+                minutes = int(total_time/60)
+                time_string = '%d min %.2f sec'%(int(total_time/60),total_time-minutes*60)
+                self.update()
+                logger.info(self.summary_string()+'    '+'\033[94m'+time_string+'\033[0m')
+                t0 = time.time()
+                refine_channel_points = self.get_refine_channel_points(rel_acc=self.rel_acc)
+                for nr,pts in refine_channel_points.items():
+                    if pts != 0:
+                        logger.info('ESTIMATE: Refining channel %d with %d additional points.'%(nr,pts))
+        self.print_summary()
+        return
+    
+    def integrate(self):
+        """ combines survey and refine stage. and returns final result and Monte Carlo error. """
+        
+        self.survey(neval=self.neval,
+                   max_nitn=self.max_nitn, min_nitn=self.min_nitn,
+                   a_acc=self.a_acc, v_acc=self.v_acc)#,rel_ch_acc=self.rel_ch_acc)
+        
+        if self.result == 0:
+            logger.info('Do training first.','$MG:color:BLUE')
+            stop
+                
+        self.refine(rel_acc=self.rel_acc)
+        
+        misc.sprint('\n%.16e, '%(self.result)+'%.16e, '%(self.sdev)+'%i\n'%(self.n_passed_cuts))
+        return self.result, self.sdev
+        
+        
 # Wrapper around the original Vegas.Integrator which adds the jacobian of the point as
 # the last input of the 1-dimensional array passed to the integrand function in the 
 # __call__ function of the integrator.
@@ -576,7 +1015,7 @@
                                 sum_wf2[s, t] * neval - sum_wf[s] * sum_wf[t]
                                 ) / (neval - 1.)
                         if var[s, s] <= 0:
-                            var[s, s] = mean[s] ** 2 * 1e-15 + TINY
+                            var[s, s] = mean[s] ** 2 * 1e-15
                     sigf2 = abs(sum_wf2[0, 0] * neval - sum_wf[0] * sum_wf[0])
                     if self.beta > 0 and self.adapt:
                         if not self.minimize_mem:

=== modified file 'madgraph/interface/ME7_interface.py'
--- madgraph/interface/ME7_interface.py	2018-11-27 18:23:17 +0000
+++ madgraph/interface/ME7_interface.py	2019-01-17 08:58:59 +0000
@@ -12,6 +12,7 @@
 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
 #
 ################################################################################
+from gvar.ode import Integrator
 """A user friendly command line interface to steer ME7 integration.
    Uses the cmd package for command interpretation and tab completion.
 """
@@ -630,6 +631,7 @@
                           'refresh_filters'     : 'auto',
                           'compile'             : 'auto',
                           'seed'                : None,
+                          'PS_generator'        : None,
                           # Here we store a list of lambda function to apply as filters
                           # to the integrand we must consider
                           'integrands'          : [lambda integrand: True],
@@ -669,11 +671,20 @@
             
             elif key in ['--veto_integrands', '--veto_itg']:
                 launch_options['integrands'].extend(self.get_integrand_filters(value, 'reject'))
+            
+            elif key == '--PS_generator':
+                launch_options['PS_generator'] = value
+            
             elif key=='--run_name':
                 launch_options['run_name'] = value
             else:
                 raise InvalidCmd("Option '%s' for the launch command not recognized."%key)
 
+        # Automatically adjust the default value of the PS_generator depending on the selected integrator
+        if launch_options['PS_generator'] is None:
+            if launch_options['integrator'] == 'MC_VEGAS3':
+                launch_options['PS_generator'] = 'MCPS'
+
         return new_args, launch_options
 
     def parse_display_integrands(self, args):
@@ -791,10 +802,15 @@
     # parameter controllable by user commands, eventually.
     integrator_verbosity = 1 if logger.level > logging.DEBUG else 2
     _integrators = {
+       'MC_VEGAS3' : (vegas3_integrator.MultiChannelVegas3Integrator, 
+                  { 'n_iterations'            : 10,
+                    'n_points'                : 1000                  } ),
+        
        'NAIVE' : (integrators.SimpleMonteCarloIntegrator, 
                   { 'n_iterations'            : 10,
                     'n_points_per_iterations' : 100,
                     'accuracy_target'         : None,
+                    'save_points_to_file'     : None, #'./TEST.dat',
                     'verbosity'               : integrator_verbosity  } ),
     
        'VEGAS3' : (vegas3_integrator.Vegas3Integrator,
@@ -803,7 +819,7 @@
                      'n_iterations_refine'     : 10,
                      'n_points_refine'         : 2000,
                      'verbosity'               : integrator_verbosity  } ),
-    
+        
        'VEGAS' : (pyCubaIntegrator.pyCubaIntegrator, 
                   { 'algorithm' : 'Vegas', 
                     'verbosity' : integrator_verbosity,
@@ -925,11 +941,16 @@
                 max_order = max_integrand_order
         return max_integrand_order
 
-    def synchronize(self, **opts):
-        """ Re-compile all necessary resources and sync integrands with the cards and model"""
+    def synchronize(self, integrands_to_synchronize=None, **opts):
+        """ Re-compile all necessary resources and sync integrands with the cards and model.
+        The option integrands_to_synchronize offers the possibility of specifying which integrands
+        must be specified. If None, then all will be synchronised."""
         
         logger.info("Synchronizing MadEvent7 internal status with cards and matrix elements source codes...")
         
+        if integrands_to_synchronize is None:
+            integrands_to_synchronize = self.all_integrands
+        
         self.run_card = banner_mod.RunCardME7(pjoin(self.me_dir,'Cards','run_card.dat'))
         # Check if run_card values are supported.
         self.run_card.check_validity()
@@ -938,8 +959,12 @@
             param_card = pjoin(self.me_dir,'Cards','param_card.dat'), 
             scale=self.run_card['scale'], 
             complex_mass_scheme=self.complex_mass_scheme)
-
-        for integrand in self.all_integrands:
+        
+        self.options['PS_generator'] = opts['PS_generator']
+        
+        #misc.sprint(self.options)
+        
+        for integrand in integrands_to_synchronize:
             integrand.synchronize(self.model, self.run_card, self.options)
         
         # Try and import some options from those provided to this function
@@ -997,11 +1022,16 @@
         This is super naive and only for illustrative purposes for now."""
         
         args = self.split_arg(line)
+
         new_args, launch_options = self.parse_launch(args)
 
+        integrands_to_consider = ME7_integrands.ME7IntegrandList([
+            itg for itg in self.all_integrands
+            if all(filter(itg) for filter in launch_options['integrands']) ])
+
         # In principle we want to start by recompiling the process output
         # to make sure that everything is up to date.
-        self.synchronize(**launch_options)
+        self.synchronize(integrands_to_synchronize=integrands_to_consider, **launch_options)
 
         # Setup parallelization
         self.configure_run_mode(self.options['run_mode'])
@@ -1011,14 +1041,12 @@
         integrator_options = self._integrators[integrator_name][1]
 
         integrator_options['verbosity'] = launch_options['verbosity']
+
         integrator_options['cluster'] = self.cluster
 
         if integrator_name=='VEGAS3':
             integrator_options['parallelization'] = self.cluster
 
-        integrands_to_consider = ME7_integrands.ME7IntegrandList([
-            itg for itg in self.all_integrands
-            if all(filter(itg) for filter in launch_options['integrands']) ])
         self.integrator = self._integrators[integrator_name][0](
             integrands_to_consider, **integrator_options)
         

=== modified file 'madgraph/interface/madgraph_interface.py'
--- madgraph/interface/madgraph_interface.py	2019-01-14 20:52:22 +0000
+++ madgraph/interface/madgraph_interface.py	2019-01-17 08:58:59 +0000
@@ -4586,8 +4586,9 @@
             warn = 'WARNING: You try to draw decay chain diagrams without first running output.\n'
             warn += '\t  The decay processes will be drawn separately'
             logger.warning(warn)
-
+        
         (options, args) = _draw_parser.parse_args(args)
+        
         options = draw_lib.DrawOption(options)
         start = time.time()
 
@@ -4600,7 +4601,7 @@
         for amp in amplitudes:
             filename = pjoin(args[0], 'diagrams_' + \
                                     amp.get('process').shell_string() + ".eps")
-
+            
             if selection=='all' and type != 'loop':
                 diags=amp.get('diagrams')
             elif selection=='born':
@@ -4611,7 +4612,7 @@
                 if len(diags) > 5000:
                     logger.warning('Displaying only the first 5000 diagrams')
                     diags = base_objects.DiagramList(diags[:5000])
-
+            
             plot = draw.MultiEpsDiagramDrawer(diags,
                                           filename,
                                           model=self._curr_model,

=== modified file 'madgraph/iolibs/drawing_eps.py'
--- madgraph/iolibs/drawing_eps.py	2016-09-03 13:26:00 +0000
+++ madgraph/iolibs/drawing_eps.py	2019-01-17 08:58:59 +0000
@@ -674,7 +674,7 @@
         #compute the number of pages
         self.npage = 1
         self.diagram_type = diagram_type
-
+        
         diagramlist = [d for d in diagramlist if not (isinstance(d, loop_objects.LoopUVCTDiagram) or \
                 (isinstance(d, loop_objects.LoopDiagram) and d.get('type') < 0))]
         diagramlist = base_objects.DiagramList(diagramlist)
@@ -686,7 +686,7 @@
             add = (len(diagramlist) - limit -1) // \
                      (self.second_scale['nb_col'] * self.second_scale['nb_line'])
             self.npage += self.lower_scale + add
-            
+   
         if diagramlist:
             # diagramlist Argument should be a DiagramList object
             assert(isinstance(diagramlist, base_objects.DiagramList))

=== modified file 'tests/parallel_tests/compare_ME7_with_ME6.py'
--- tests/parallel_tests/compare_ME7_with_ME6.py	2018-07-18 10:53:48 +0000
+++ tests/parallel_tests/compare_ME7_with_ME6.py	2019-01-17 08:58:59 +0000
@@ -84,16 +84,24 @@
        
     def compare_cross_section(self, my_proc_list = [], orders = {}, model = 'sm',
                         filename = "", print_result = False, append_output=False,
-                        tolerance = 1e-02, ME6_options={}, ME7_options={}):
+                        tolerance = 1e-01, ME6_options={}, ME7_options={}):
         """ """
      
         ME6_runner = madevent_comparator.ME6Runner(**ME6_options)
         ME6_runner.setup(MG5DIR)
         ME6_runner.store_proc_card = True
 
-        ME7_runner = madevent_comparator.ME7Runner(**ME7_options)
-        ME7_runner.setup(MG5DIR)
-        ME7_runner.store_proc_card = True
+        ME7_options['PS_generator'] = 'MCPS'
+        
+        ME7_runner_MCPS = madevent_comparator.ME7Runner(**ME7_options)
+        ME7_runner_MCPS.setup(MG5DIR)
+        ME7_runner_MCPS.store_proc_card = True
+        
+        ME7_options['PS_generator'] = 'FLATPS'
+        
+        ME7_runner_FLATPS = madevent_comparator.ME7Runner(**ME7_options)
+        ME7_runner_FLATPS.setup(MG5DIR)
+        ME7_runner_FLATPS.store_proc_card = True
         
         self.nb_test +=1      
         if os.path.exists(pjoin(MG5DIR,'models','paralel_test_model_%s' % model)):
@@ -103,11 +111,11 @@
         
         # Create and setup a comparator
         my_comp = madevent_comparator.MadEventComparator(allow_no_present=True)
-        my_comp.set_me_runners(ME6_runner,ME7_runner)
+        my_comp.set_me_runners(ME6_runner,ME7_runner_MCPS,ME7_runner_FLATPS)
 
         # Run the actual comparison
         my_comp.run_comparison(my_proc_list,
-                               ['paralel_test_model_%s' % model, model], orders)
+                               ['paralel_test_model_%s' % model, model, model], orders)
 
         # Print the output
         if filename:
@@ -115,7 +123,7 @@
                 mystream = open(filename, 'a')
             else:
                 mystream = open(filename, 'w')
-            my_comp.output_result(filename=mystream)
+            my_comp.output_result(filename=mystream,tolerance=tolerance)
             mystream.close()
         
         if print_result:
@@ -172,18 +180,28 @@
     # An example of a comparison against values recomputed live with ME6
     def test_ME7_paralel_cross_sm(self):
         """Test a short list of sm processes"""
+        
+        #print(opts)
+        
+        #if 'PS_generator' in opts:
+        #    misc.sprint('hello')
+        #self.PS_gen = opts['PS_generator']
+        
         # Create a list of processes to check automatically                                                                                                                             
-        proc_lists = [['p p > t t~'], ['u d~ > W+ j'], ['u d~ > W+ j j']]
+        #proc_lists = [['p p > t t~'], ['u d~ > W+ j'], ['u d~ > W+ j j']]
+        proc_lists= [(['u c > h > u c e+ e- mu+ mu- $$ c u / a s d s~ d~'],0,99)]
+        #proc_lists = [['u d > u d  mu+ mu-']]
+        #proc_lists = [['u d~ > W+ j j']]
         #proc_lists = [['p p > t t~']]
         #proc_lists = [['u d~ > W+ j', 'u d~ > W+ j j']]
         # Store list of non-zero processes and results in file                                                                                                     
         pickle_file = os.path.join(_pickle_path, "short_ME7_parraleltest_cross_sm.pkl")
         if os.path.isfile('short_ME7_cs_sm.log'):
             os.remove('short_ME7_cs_sm.log')
-        for my_proc_list in proc_lists:
+        for my_proc_list, QCDorder, QEDorder in proc_lists:
             print 'Now running process(es) %s ...'%str(my_proc_list)
             self.compare_cross_section(my_proc_list,
-                             orders = {'QED':99, 'QCD':99},
+                             orders = {'QED':QEDorder, 'QCD':QCDorder},
                              filename = "short_ME7_cs_sm.log",
                              append_output = True,
                              ME6_options={'accuracy':0.01},

=== modified file 'tests/parallel_tests/madevent_comparator.py'
--- tests/parallel_tests/madevent_comparator.py	2017-12-01 19:13:40 +0000
+++ tests/parallel_tests/madevent_comparator.py	2019-01-17 08:58:59 +0000
@@ -81,6 +81,7 @@
             self.results.append(runner.run(proc_list, model[i], orders))
             cpu_time2 = time.time()
             logging.info(" Done in %0.3f s" % (cpu_time2 - cpu_time1))
+            misc.sprint(" Done in %0.3f s" % (cpu_time2 - cpu_time1))
 #            logging.info(" (%i/%i with zero ME)" % \
 #                    (len([res for res in self.results[-1] if res[0][0] == 0.0]),
 #                     len(proc_list)))
@@ -510,7 +511,7 @@
 
         for i, proc in enumerate(proc_list):
             v5_string += 'add process ' + proc + ' ' + couplings + \
-                         '@%i' % i + '\n'
+                         '@%i' % i + ' --diagram_filter' + '\n'
         v5_string += "output %s -f\n" % \
                      os.path.join(self.mg5_path, self.temp_dir_name)
         v5_string += "launch -i --multicore\n"
@@ -635,6 +636,9 @@
     def __init__(self, *args, **opts):
 
         self.n_points = 1000
+        if 'PS_generator' in opts:
+            self.PS_generator = opts.pop('PS_generator')
+            self.name = 'ME7 %s'  %self.PS_generator
         if 'n_points' in opts:
             self.n_points = opts.pop('n_points')
         self.integrator = 'VEGAS3'
@@ -660,7 +664,7 @@
     def format_ME7_proc_card(self, proc_list, model, orders):
         """Create a proc_card.dat string following v5 conventions."""
 
-        perturbation_orders = '--LO'
+        perturbation_orders = '--LO --diagram_filter'
         v5_string = "import model %s\n" % os.path.join(self.model_dir, model)
         v5_string += "set automatic_html_opening False\n"
         couplings =  me_comparator.MERunner.get_coupling_definitions(orders)
@@ -670,7 +674,7 @@
         v5_string += "output %s -f\n" % \
                      os.path.join(self.mg5_path, self.temp_dir_name)
         v5_string += "launch %s \n"%(os.path.join(self.mg5_path, self.temp_dir_name))
-        v5_string += "launch --integrator=%s --n_points=%d\n"%(self.integrator, self.n_points)
+        v5_string += "launch --integrator=%s --n_points=%d --PS_generator=%s\n"%(self.integrator, self.n_points,self.PS_generator)
         v5_string += "exit\n"
         v5_string += "exit\n"
         return v5_string

=== modified file 'tests/time_db'
--- tests/time_db	2019-01-14 20:52:22 +0000
+++ tests/time_db	2019-01-17 08:58:59 +0000
@@ -6,29 +6,28 @@
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.LegTest testMethod=test_values_for_prop>]> 0.0001220703125
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayModel testMethod=test_find_full_sm_decay_groups_advance>]> 2.03565192223
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_DecayAmplitude testMethod=test_get_amplitude_givenfinal>]> 0.241197824478
-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_drawing_eps.TestDrawingOption testMethod=test_option_multi_type>]> 0.0388491153717
 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.MappingsTest testMethod=test_SoftVsFinalMapping_invertible>]> 0.0732071399689
 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_import_v4.ProcCardV4ReaderTest testMethod=test_line_creation>]> 0.0172848701477
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_color_algebra.ColorFactorTest testMethod=test_f_product>]> 0.00337600708008
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasMatrixElementTest testMethod=test_generate_helas_diagrams_uux_gepem>]> 0.0183539390564
 <__main__.TestSuiteModified tests=[<tests.unit_tests.interface.test_edit_card.TestEditCardCmd testMethod=test_autocompletion>]> 0.0173709392548
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_subtraction.SubsetTest testMethod=test_union>]> 0.000160932540894
-<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_color_algebra.ColorObjectTest testMethod=test_T_simplify>]> 0.000298023223877
 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd.TestCmdShell2 testMethod=test_standalone_cpp_output>]> 2.42965912819
 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_save_model.IOSaveModel testMethod=test_error_interaction_save>]> 7.20024108887e-05
-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_usermod.Test_ADDON_UFO testMethod=test_identify_particle>]> 0.0015971660614
+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_phase_space_generators.PhaseSpaceGeneratorsTest testMethod=test_phase_space_volume>]> 293.296576977
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_color_algebra.ColorFactorTest testMethod=test_sextet_products>]> 0.017058134079
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.test_aloha_creation testMethod=test_aloha_FFV_MG4>]> 0.106870889664
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_process_checks.TestMatrixElementChecker testMethod=test_get_momenta>]> 0.0900881290436
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_write_param.TestParamWritting testMethod=test_order_param>]> 0.0765249729156
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestVariable testMethod=testmultvarint>]> 7.29560852051e-05
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAddVariable testMethod=testmultadd_legacy>]> 0.000188112258911
-<__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_drawing.TestLoopDrawer testMethod=test_NLO_draw_all_reconstructed_dd_eemm>]> 0.120349884033
+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_lhe_parser.TESTLHEParser testMethod=test_read_write_lhe>]> 0.00316500663757
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.MultiProcessTest testMethod=test_setget_process_correct>]> 0.000885009765625
-<__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd.TestCmdShell2 testMethod=test_ungroup_decay>]> 1.91394805908
+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.TestIdentifyMETag testMethod=test_identify_me_tag_qq_qqg>]> 0.144216060638
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_diquark_models.TestColorSextetModel testMethod=test_check_u_u_six_g>]> 0.506669998169
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.ModelTest testMethod=test_check_majoranas>]> 0.00102400779724
-<__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestFKSCommon testMethod=test_legs_to_color_link_string>]> 0.00512909889221
+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasMatrixElementTest testMethod=test_fermionfactor_epem_sepsemepem>]> 0.074609041214
+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_coupling_orders_uux_ddxng>]> 0.389806032181
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_subtraction.SingularStructureOperatorTest testMethod=test_from_string>]> 0.000759124755859
 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_helas_objects.testFKSHelasObjects testMethod=test_fks_helas_multi_process_ppwj>]> 9.92277693748
 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_drawing_eps.TestDrawingS_EPS testMethod=test_schedular>]> 0.184823036194
@@ -38,7 +37,7 @@
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.InteractionTest testMethod=test_generating_dict_to_1>]> 0.00120282173157
 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_generate_helas_diagrams_gg_gogo>]> 0.014230966568
 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_MG5aMC_distribution.TestMG5aMCDistribution testMethod=test_OfflineHEPToolsInstaller>]> 0.00254702568054
-<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_walkers.WalkersTest testMethod=test_LorentzNLOWalker_approach_limit>]> 1.98571395874
+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_drawing.TestVertexPoint testMethod=test_building>]> 0.000118970870972
 <__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_phase_space_generators.FlatCollinearWalkerTest testMethod=test_invertible>]> 0.0202708244324
 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_helas_objects.testFKSHelasObjects testMethod=test_set_color_links>]> 1.08148097992
 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd_amcatnlo.MECmdShell testMethod=test_raise_invalid_path_hwpp>]> 69.1746711731
@@ -49,30 +48,30 @@
 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5EW.ML5EWTest testMethod=test_long_sm_vs_stored_HCR_gg_hh_QCD>]> 0.00105404853821
 <__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_diagram_generation.LoopDiagramGenerationTest testMethod=test_diagram_generation_gg_ng>]> 1.61963582039
 <__main__.TestSuiteModified tests=[<tests.parallel_tests.compare_with_old_mg5_version.OLDMG5Comparator testMethod=test_short_sm>]> 6.53617286682
-<__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd_amcatnlo.MECmdShell testMethod=test_check_singletop_fastjet>]> 152.502398014
+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_walkers.WalkersTest testMethod=test_FinalLorentzDisjointWalker_invertible>]> 0.0626809597015
+<__main__.TestSuiteModified tests=[<tests.unit_tests.integrator.test_mappings.WalkersTest testMethod=test_FinalLorentzNLOWalker_invertible>]> 0.122242212296
+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.IOExportFKSTest testMethod=test_write_leshouche_file>]> 0.00247812271118
 <__main__.TestSuiteModified tests=[<tests.unit_tests.interface.test_cmd.TestValidCmd testMethod=test_InvalidCmd>]> 0.300333976746
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_helas_objects.HelasMultiProcessTest testMethod=test_decay_chain_different_order3>]> 0.0537929534912
 <__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_aloha.TestAlohaWriter testMethod=test_change_number_format_python>]> 0.000648021697998
-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_generate_ufo_helas_diagrams_gg_gogo>]> 0.0135381221771
 <__main__.TestSuiteModified tests=[<tests.acceptance_tests.test_cmd.TestCmdShell2 testMethod=test_invalid_operations_for_generate>]> 0.143531084061
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@@ -215,11 +215,10 @@
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+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DiagramGenerationTest testMethod=test_diagram_generation_uux_gg>]> 0.00705409049988
+<__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_diagram_generation.DecayChainAmplitudeTest testMethod=test_setget_process_exceptions>]> 0.000777006149292
 <__main__.TestSuiteModified tests=[<tests.unit_tests.fks.test_fks_common.TestLinkRBConfSM testMethod=test_link_uuuug_guug>]> 0.0394339561462
 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_aloha.TestMultVariable testMethod=testsummulvar>]> 0.000197887420654
-<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_decay.Test_AbstractModel testMethod=test_help_generate_ab_amplitude>]> 0.233986139297
+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCardRule testMethod=test_read_write_param_card>]> 0.00176000595093
 <__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_v4.FullHelasOutputTest testMethod=test_configs_4f_decay>]> 0.0363750457764
-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_fks.TestFKSOutput testMethod=test_w_nlo_gen_gosam>]> 0.396389007568
+<__main__.TestSuiteModified tests=[<tests.unit_tests.loop.test_loop_diagram_generation.LoopEWDiagramGenerationTest testMethod=test_diagram_generation_uux_epem_EW>]> 7.10565686226
 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_ML5.ML5Test testMethod=test_long_sm_vs_stored_ML5_gs_emvexc>]> 13.0895609856
-<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_misc.IOMiscTest testMethod=test_parse_info_str_correct>]> 0.000116109848022
+<__main__.TestSuiteModified tests=[<tests.unit_tests.various.test_check_param_card.TestParamCardRule testMethod=test_check_param>]> 0.0894939899445
 <__main__.TestSuiteModified tests=[<tests.parallel_tests.test_madweight.TestMadWeight testMethod=test_short_mw_wa>]> 11.9575219154
 <__main__.TestSuiteModified tests=[<tests.unit_tests.core.test_base_objects.LegTest testMethod=test_setget_leg_exceptions>]> 0.000114917755127
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+<__main__.TestSuiteModified tests=[<tests.unit_tests.iolibs.test_export_python.IOExportPythonTest testMethod=test_export_matrix_element_python_madevent_group>]> 0.0395710468292
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\ No newline at end of file

=== modified file 'tests/unit_tests/integrator/test_phase_space_generators.py'
--- tests/unit_tests/integrator/test_phase_space_generators.py	2019-01-14 20:52:22 +0000
+++ tests/unit_tests/integrator/test_phase_space_generators.py	2019-01-17 08:58:59 +0000
@@ -12,24 +12,124 @@
 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
 #
 ##########################################################################################
-"""Unit test library for phase-space generation."""
+"""Unit test library for the various phase-space generation/handling features."""
 
 import madgraph.integrator.phase_space_generators as PS
+import madgraph.core.subtraction as subtraction
 import madgraph.core.base_objects as base_objects
+import madgraph.integrator.vectors as vectors
 import madgraph.various.misc as misc
 import models.import_ufo as import_ufo
 from madgraph import MG5DIR
 
+import copy
+import math
 import random
 import os
 
 import tests.unit_tests as unittest
+import tests.input_files.simple_qcd as simple_qcd
 
 pjoin = os.path.join
 
 #=========================================================================================
+# Shorthands for initial and final state
+#=========================================================================================
+
+INITIAL = base_objects.Leg.INITIAL
+FINAL   = base_objects.Leg.FINAL
+
+assert subtraction.SubtractionLeg.INITIAL == INITIAL
+assert subtraction.SubtractionLeg.FINAL   == FINAL
+
+#=========================================================================================
+# Test Vectors
+#=========================================================================================
+
+class VectorsTest(unittest.TestCase):
+    """Test class for BaseVector, Vector and LorentzVector."""
+
+    def setUp(self):
+        pass
+
+    def test_Vector_basic(self):
+        """Test the basic operations for vectors."""
+
+        # Component-wise addition and subtraction
+        v1 = PS.Vector([1., 0., 2., 3.])
+        v2 = PS.Vector(4*[0.5, ])
+        self.assertEqual(v1 + v2, PS.Vector([1.5, 0.5, 2.5, 3.5]))
+        v3 = PS.Vector([1., 2., 0.5, -1.])
+        self.assertEqual(v1 - v3, PS.Vector([0., -2., 1.5, 4.]))
+        v3 += PS.Vector([1., 0., 0., 0.])
+        self.assertEqual(v3, PS.Vector([2., 2., 0.5, -1.]))
+        v3 -= PS.Vector([0.5, 1.5, 0., 0.])
+        self.assertEqual(v3, PS.Vector([1.5, 0.5, 0.5, -1.]))
+
+        # Multiplication and division by scalars
+        self.assertEqual(v2 * 2., PS.Vector(4*[1., ]))
+        self.assertEqual(v1 / 4., PS.Vector([0.25, 0., 0.5, 0.75]))
+        v3 *= 3
+        self.assertEqual(v3, PS.Vector([4.5, 1.5, 1.5, -3.]))
+        v3 /= 1.5
+        self.assertEqual(v3, PS.Vector([3., 1., 1., -2.]))
+        self.assertEqual(3 * v1, v1 * 3)
+
+        # Negative
+        self.assertEqual(-v3, PS.Vector([-3., -1., -1., 2.]))
+
+    def test_Vector_Euclid(self):
+        """Test scalar products and related functions for the class Vector."""
+
+        # Square and norm
+        v1 = PS.Vector([0., 1., -2.])
+        self.assertEqual(v1.square(), 5.)
+        v2 = PS.Vector([3., 4., 0.])
+        self.assertEqual(abs(v2), 5.)
+        v2n = 0.2 * v2
+        v2.normalize()
+        self.assertEqual(v2, v2n)
+        self.assertEqual(PS.Vector.dot(v1, v2), 0.8)
+        v3 = PS.Vector([random.random() for _ in range(3)])
+        w = PS.Vector([random.random() for _ in range(3)])
+        v3p = v3.project_onto(w)
+        v3t = v3.component_orthogonal_to(w)
+        self.assertEqual(v3, v3p + v3t)
+        self.assertAlmostEqual(PS.Vector.dot(v3t, w), 0.)
+        self.assertAlmostEqual(abs(v3p+w), abs(v3p)+abs(w))
+
+    def test_Vector_Minkowski(self):
+        """Test the relativistic norm."""
+
+        # Square and norm
+        v1 = PS.LorentzVector([1, 0, 0, +1])
+        v2 = PS.LorentzVector([1, 0, 0, -1])
+        self.assertAlmostEqual(v1.square(), 0, 10)
+        self.assertAlmostEqual(v2.square(), 0, 10)
+        self.assertAlmostEqual(v1.dot(v2), 2, 10)
+
+        # Test the rotoboost
+        #   Definition
+        v1 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
+        v2 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
+        m = random.random()
+        v1.set_square(m**2)
+        v2.set_square(m**2)
+        for x, y in zip(v2.rotoboost(v2, v1), v1):
+            self.assertAlmostEqual(x,y,10)
+        #   Inverse
+        v3 = PS.LorentzVector([0, ] + [random.random() for _ in range(3)])
+        v3.set_square(m**2)
+        v4 = PS.LorentzVector([random.random() for _ in range(4)])
+        v4_old = v4.get_copy()
+        v4.rotoboost(v1, v3)
+        v4.rotoboost(v3, v1)
+        for x, y in zip(v4, v4_old):
+            self.assertAlmostEqual(x,y,10)
+
+#===============================================================================
 # Test the phase-space generators
-#=========================================================================================
+#===============================================================================
 
 class PhaseSpaceGeneratorsTest(unittest.TestCase):
     """ Test various phase-space generators."""
@@ -48,6 +148,670 @@
                 complex_mass_scheme=False)
         self.model = model_with_params_set
 
+        # A specific sets of s- and t-channels for this test:
+
+        ####################################################################
+        # a) A simple unique massless photon s-channel from u u~ > d d~ / z w+ QCD=0
+        ####################################################################
+        
+        self.massless_photon_schannel_specifier = (
+            # s-channels first:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 15,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': -1,
+                            'number': 4,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 1,
+                            'number': 3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 22,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        })
+                    ])
+                }),
+            ]),
+            # t-channels then:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 34,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 2,
+                            'number': 1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 22,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': -2,
+                            'number': -2,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        })
+                    ])
+                }),
+            ])
+        )                          
+        
+        ####################################################################
+        # a) A simple unique massive Z-boson s-channel from u u~ > d d~ / a w+ QCD = 0
+        ####################################################################
+        
+        self.massive_zboson_schannel_specifier = (
+            # s-channels first:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 22,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': -1,
+                            'number': 4,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 1,
+                            'number': 3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+            ]),
+            # t-channels then:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 40,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 2,
+                            'number': 1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': -2,
+                            'number': -2,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+            ]),
+        )
+        
+        ####################################################################
+        # b) A simple unique massive Z-boson t-channel from generate u d > u d /a w+ w- QCD=0 --LO
+        ####################################################################
+        
+        self.massive_zboson_tchannel_specifier = (
+            # s-channels first:
+            base_objects.VertexList([]),
+            # t-channels then:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 63,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': -2,
+                            'number': 1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 2,
+                            'number': 3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 22,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 1,
+                            'number': 4,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': -1,
+                            'number': -2,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+            ]),
+        )
+        
+        ####################################################################
+        # b) A simple unique massless photon t-channel from u d > u d / z
+        ####################################################################
+        
+        self.massless_photon_tchannel_specifier = (
+            # s-channels first:
+            base_objects.VertexList([]),
+            # t-channels then:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 63,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': -2,
+                            'number': 1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 2,
+                            'number': 3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 22,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 22,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 22,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 1,
+                            'number': 4,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': -1,
+                            'number': -2,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+            ]),
+        )
+        
+        
+        
+        ###############################################################################
+        # c) A complicated fully decayed VBF topology: 
+        #    from: generate u c > h > u c e+ e- mu+ mu- $$ c u / a s d s~ d~ QCD=0 --LO
+        ###############################################################################
+        self.vbf_topology_s_and_t_channel_specifier = (
+            # s-channels first:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 41,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 13,
+                            'number': 8,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': -13,
+                            'number': 7,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 40,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 11,
+                            'number': 6,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': -11,
+                            'number': 5,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -2,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 13,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -2,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 25,
+                            'number': -3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                })
+            ]),
+            # t-channels then:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 63,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': -2,
+                            'number': 1,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 2,
+                            'number': 3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -4,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 13,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -4,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None  
+                        }),
+                        base_objects.Leg({
+                            'id': 25,
+                            'number': -3,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -5,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 64,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -5,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None  
+                        }),
+                        base_objects.Leg({
+                            'id': 4,
+                            'number': 4,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': -4,
+                            'number': -6,
+                            'state': False,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+            ]),
+        )
+
+
+        ###############################################################################
+        # d) A complicated fully decayed VBF topology: 
+        #    from: generate e- e+ > h > e+ e- mu+ mu- ta+ ta- $$ e+ e- \ a QCD=0 --diagram_filter --LO
+        ###############################################################################
+        # where diagram filter removes the first three diagrams
+        # import model sm-dario
+        self.vbf_topology_s_and_t_channel_specifier2 = (
+            # s-channels first:
+            base_objects.VertexList([
+                base_objects.Vertex({
+                    'id': 42,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 15,
+                            'number': 8,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None  
+                        }),
+                        base_objects.Leg({
+                            'id': -15,
+                            'number': 7,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 41,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 13,
+                            'number': 6,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': -13,
+                            'number': 5,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -2,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                    ])
+                }),
+                base_objects.Vertex({
+                    'id': 13,
+                    'legs': base_objects.LegList([
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -1,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None   
+                        }),
+                        base_objects.Leg({
+                            'id': 23,
+                            'number': -2,
+                            'state': True,
+                            'from_group': True,
+                            'loop_line': False,
+                            'onshell': None
+                        }),
+                        base_objects.Leg({
+                            'id': 25,
+                            'number': -3,
+                            'state': True,
+                            'from_group': True,