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
