def setupABC( modelname, program, times, # list of float vars, # structure as above params, # ditto inputs, # ditto workdir, deleteWorkdir, pickling, abcIOname, baseSeq=[], particles=NPARTICLES, nbatch=NBATCH, beta=BETA, modelKernel=MODELKERNEL, distance=DISTANCE, timeout=model_bcmd.TIMEOUT): model = model_bcmd.model_bcmd(name=modelname, vars=vars, params=params, inputs=inputs, times=times, program=program, baseSeq=baseSeq, workdir=workdir, deleteWorkdir=deleteWorkdir, timeout=timeout) # it is not clear that we actually need a masked array, but... masked = numpy.transpose(numpy.ma.array([x['points'] for x in vars])) data = abcsbh.data.data(times, masked) io = abcsbh.input_output.input_output(abcIOname) io.create_output_folders([modelname], particles, pickling) algorithm = abcsbh.abcsmc.abcsmc(models=[model], nparticles=particles, modelprior=[1], data=data, beta=beta, nbatch=nbatch, modelKernel=modelKernel, debug=False, timing=False, distancefn=distance) return algorithm, io
def make_model(config): if config['dryrun']: return None model = model_bcmd.model_bcmd(name=config['name'], vars=config['vars'], params=config['params'], inputs=config['inputs'], times=config['times'], program=config['program'], fixed=config['param_unselect'], baseSeq=config['baseSeq'], workdir=config['model_io'], deleteWorkdir=False, debug=config['debug'], steady=config['steady']) return model
def make_model(config): if config['dryrun']: return None print 'Creating wrapper for model %s' % config['name'] model = model_bcmd.model_bcmd(name=config['name'], vars=config['vars'], params=config['params'], inputs=config['inputs'], times=config['times'], program=config['program'], baseSeq=config['baseSeq'], workdir=config['model_io'], timeout=config['timeout'], deleteWorkdir=False) return model
def make_model(config): if config['dryrun']: return None model = model_bcmd.model_bcmd( name=config['name'], vars=config['vars'], params=config['params'], inputs=config['inputs'], times=config['times'], program=config['program'], fixed=config['param_unselect'], baseSeq=config['baseSeq'], workdir=config['model_io'], deleteWorkdir=False, debug=config['debug'], steady=config['steady']) return model
def make_model(config): if config['dryrun']: return None print 'Creating wrapper for model %s' % config['name'] model = model_bcmd.model_bcmd( name=config['name'], vars=config['vars'], params=config['params'], inputs=config['inputs'], times=config['times'], program=config['program'], baseSeq=config['baseSeq'], workdir=config['model_io'], timeout=config['timeout'], deleteWorkdir=False ) return model
def setupABC ( modelname, program, times, # list of float vars, # structure as above params, # ditto inputs, # ditto workdir, deleteWorkdir, pickling, abcIOname, baseSeq = [], particles=NPARTICLES, nbatch=NBATCH, beta=BETA, modelKernel=MODELKERNEL, distance=DISTANCE, timeout=model_bcmd.TIMEOUT ): model = model_bcmd.model_bcmd( name=modelname, vars=vars, params=params, inputs=inputs, times=times, program=program, baseSeq=baseSeq, workdir=workdir, deleteWorkdir=deleteWorkdir, timeout=timeout ) # it is not clear that we actually need a masked array, but... masked = numpy.transpose(numpy.ma.array( [ x['points'] for x in vars ] )) data = abcsbh.data.data( times, masked ) io = abcsbh.input_output.input_output( abcIOname ) io.create_output_folders([modelname], particles, pickling ) algorithm = abcsbh.abcsmc.abcsmc( models = [model], nparticles = particles, modelprior = [1], data = data, beta = beta, nbatch = nbatch, modelKernel = modelKernel, debug = False, timing = False, distancefn = distance ) return algorithm, io