def process(options, args): if options.seed: random.seed(int(options.seed_value)) if options.singlethreaded: rank = 0 size = 1 else: rank = MPI.COMM_WORLD.rank # The process ID (integer 0-3 for 4-process run) size = MPI.COMM_WORLD.size # The number of processes in the job. # get the hostname if options.singlethreaded: ip = 'localhost' else: ip = socket.gethostbyname(socket.gethostname()) logging.debug("Process %d ip address is : %s" % (rank, ip)) f2 = open(options.id_filename, 'r') info = json.load(f2) f2.close() logging.debug("Loading Lookup") f1 = h5py.File(options.lookup_filename, 'r') lookup = {} for beam in info['beams']: logging.debug("Loading beam %s" % (beam['name'])) lookup[beam['name']] = f1[beam['name']][...] f1.close() barrier(options.singlethreaded) logging.debug("Loading Initial Bfield") f1 = h5py.File(options.bfield_filename, 'r') real_bfield = f1['id_Bfield'][...] f1.close() logging.debug(real_bfield) barrier(options.singlethreaded) logging.debug("Loading magnets") mags = magnets.Magnets() mags.load(options.magnets_filename) logging.debug('mpi runenr calling fg.generate_reference_magnets()') ref_mags = fg.generate_reference_magnets(mags) logging.debug('mpi runenr calling MagLists()') ref_maglist = magnets.MagLists(ref_mags) logging.debug('after ref_maglist') ref_total_id_field = fg.generate_id_field(info, ref_maglist, ref_mags, lookup) pherr, ref_trajectories = mt.calculate_phase_error(info, ref_total_id_field) barrier(options.singlethreaded) #epoch_path = os.path.join(args[0], 'epoch') #next_epoch_path = os.path.join(args[0], 'nextepoch') # start by creating the directory to put the initial population in population = [] estar = options.e # Load the initial genome initialgenome = ID_BCell() initialgenome.load(options.genome_filename) referencegenome = ID_BCell() referencegenome.load(options.genome_filename) # make the initial population for i in range(options.setup): # create a fresh maglist newgenome = ID_Shim_BCell() newgenome.create(info, lookup, mags, initialgenome.genome, ref_trajectories, options.number_of_changes, real_bfield) population.append(newgenome) # gather the population trans = [] for i in range(size): trans.append(population) allpop = alltoall(options.singlethreaded, trans) barrier(options.singlethreaded) newpop = [] for pop in allpop: newpop += pop # Need to deal with replicas and old genomes popdict = {} for genome in newpop: fitness_key = "%1.8E" % (genome.fitness) if fitness_key in popdict.keys(): if popdict[fitness_key].age < genome.age: popdict[fitness_key] = genome else: popdict[fitness_key] = genome newpop = [] for genome in popdict.values(): if genome.age < options.max_age: newpop.append(genome) newpop.sort(key=lambda x: x.fitness) newpop = newpop[options.setup * rank:options.setup * (rank + 1)] for genome in newpop: logging.debug("genome fitness: %1.8E Age : %2i Mutations : %4i" % (genome.fitness, genome.age, genome.mutations)) #Checkpoint best solution if rank == 0: logging.debug("Best fitness so far is %f" % (newpop[0].fitness)) newpop[0].save(args[0]) # now run the processing for i in range(options.iterations): barrier(options.singlethreaded) logging.debug("Starting itteration %i" % (i)) nextpop = [] for genome in newpop: # now we have to create the offspring # TODO this is for the moment logging.debug("Generating children for %s" % (genome.uid)) number_of_children = options.setup number_of_mutations = mutations(options.c, estar, genome.fitness, options.scale) children = genome.generate_children(number_of_children, number_of_mutations, info, lookup, mags, ref_trajectories, real_bfield=real_bfield) # now save the children into the new file for child in children: nextpop.append(child) # and save the original nextpop.append(genome) # gather the population trans = [] for i in range(size): trans.append(nextpop) allpop = alltoall(options.singlethreaded, trans) newpop = [] for pop in allpop: newpop += pop popdict = {} for genome in newpop: fitness_key = "%1.8E" % (genome.fitness) if fitness_key in popdict.keys(): if popdict[fitness_key].age < genome.age: popdict[fitness_key] = genome else: popdict[fitness_key] = genome newpop = [] for genome in popdict.values(): if genome.age < options.max_age: newpop.append(genome) newpop.sort(key=lambda x: x.fitness) estar = newpop[0].fitness * 0.99 logging.debug("new estar is %f" % (estar)) newpop = newpop[options.setup * rank:options.setup * (rank + 1)] #Checkpoint best solution if rank == 0: initialgenome.genome.mutate_from_list(newpop[0].genome) initialgenome.fitness = newpop[0].fitness initialgenome.uid = "A" + newpop[0].uid initialgenome.save(args[0]) saveh5(args[0], initialgenome, referencegenome, info, mags, real_bfield, lookup) # After the save reload the original data initialgenome.load(options.genome_filename) newpop[0].save(args[0]) for genome in newpop: logging.debug( "genome fitness: %1.8E Age : %2i Mutations : %4i" % (genome.fitness, genome.age, genome.mutations)) barrier(options.singlethreaded) barrier(options.singlethreaded) # gather the population trans = [] for i in range(size): trans.append(nextpop) allpop = alltoall(options.singlethreaded, trans) newpop = [] for pop in allpop: newpop += pop newpop.sort(key=lambda x: x.fitness) newpop = newpop[options.setup * rank:options.setup * (rank + 1)] #Checkpoint best solution if rank == 0: initialgenome.genome.mutate_from_list(newpop[0].genome) initialgenome.age_bcell() initialgenome.save(args[0]) newpop[0].save(args[0])
outfile = os.path.join(args[0], os.path.split(filename)[1] + '.h5') fg.output_fields(outfile, options.id_filename, options.lookup_filename, options.magnets_filename, genome.genome) sys.exit(0) if options.setup > 0: print("Running setup") for i in range(options.setup): # create a fresh maglist maglist = magnets.MagLists(mags) maglist.shuffle_all() genome = ID_BCell(options.id_filename, options.lookup_filename, options.magnets_filename) genome.create(maglist) genome.save(args[0]) for filename in args[1::]: print("Processing file %s" % (filename)) # load the genome genome = ID_BCell(options.id_filename, options.lookup_filename, options.magnets_filename) genome.load(filename) # now we have to create the offspring # TODO this is for the moment number_of_children = options.processing number_of_mutations = mutations(options.c, options.e, genome.fitness, options.scale) children = genome.generate_children(number_of_children,
if genome.age < options.max_age: newpop.append(genome) newpop.sort(key=lambda x: x.fitness) estar = newpop[0].fitness * 0.99 logging.debug("new estar is %f" % (estar) ) newpop = newpop[options.setup*rank:options.setup*(rank+1)] #Checkpoint best solution if rank == 0: initialgenome.genome.mutate_from_list(newpop[0].genome) initialgenome.fitness = newpop[0].fitness initialgenome.uid = "A"+newpop[0].uid initialgenome.save(args[0]) saveh5(args[0], initialgenome, referencegenome, info, mags, real_bfield) # After the save reload the original data initialgenome.load(options.genome_filename) newpop[0].save(args[0]) for genome in newpop: logging.debug("genome fitness: %1.8E Age : %2i Mutations : %4i" % (genome.fitness, genome.age, genome.mutations)) MPI.COMM_WORLD.Barrier() MPI.COMM_WORLD.Barrier() # gather the population trans = [] for i in range(size):
genome.load(filename) outfile = os.path.join(args[0], os.path.split(filename)[1]+'.h5') fg.output_fields(outfile, options.id_filename, options.lookup_filename, options.magnets_filename, genome.genome); sys.exit(0) if options.setup > 0: print("Running setup") for i in range(options.setup): # create a fresh maglist maglist = magnets.MagLists(mags) maglist.shuffle_all() genome = ID_BCell(options.id_filename, options.lookup_filename, options.magnets_filename) genome.create(maglist) genome.save(args[0]) for filename in args[1::]: print("Processing file %s" % (filename)) # load the genome genome = ID_BCell(options.id_filename, options.lookup_filename, options.magnets_filename) genome.load(filename) # now we have to create the offspring # TODO this is for the moment number_of_children = options.processing number_of_mutations = mutations(options.c, options.e, genome.fitness, options.scale) children = genome.generate_children(number_of_children, number_of_mutations) # now save the children into the new file