def driver(request):
from pyRSD.rsd import GalaxySpectrum
# add the PYRSD_DATA env var
os.environ['PYRSD_DATA'] = data_dir
# inititalize the model
config = {}
config['z'] = 0.55
config['cosmo_filename'] = 'runPB.ini'
config['kmin'] = 1e-3
config['kmax'] = 0.6
config['interpolate'] = True
m = GalaxySpectrum(**config)
# load the model
with cache_manager(m, "runPB_galaxy.npy") as model:
pass
# initialize the driver
path = os.path.join(data_dir, 'examples', 'params.dat')
driver = FittingDriver(path, init_model=False)
driver.model = model
# set fiducial
driver.set_fiducial()
return driver
def driver(request):
from pyRSD.rsd import QuasarSpectrum
# add the PYRSD_DATA env var
os.environ['PYRSD_DATA'] = data_dir
# inititalize the model
config = {}
config['z'] = 0.55
config['cosmo_filename'] = 'runPB.ini'
config['kmin'] = 1e-3
config['kmax'] = 0.6
m = QuasarSpectrum(**config)
# initialize the driver
path = os.path.join(data_dir, 'examples', 'params_qso.dat')
driver = FittingDriver(path, init_model=False)
driver.model = m
# set fiducial
driver.set_fiducial()
return driver
def initialize_algorithm(self):
"""
Initialize the driver that runs the desired algorithm.
"""
# ``analysis`` mode
if self.mode == 'analyze':
from pyRSD.rsdfit import analysis
kws = {k: getattr(self, k) for k in self._config}
driver = analysis.AnalysisDriver(**kws)
# ``mcmc`` or ``nlopt`` mode
elif self.mode in ['mcmc', 'nlopt']:
params_file = os.path.join(self.folder, params_filename)
# initialize from an existing directory, with existing parameter file
if os.path.isdir(
self.folder
) and self.params == params_file and os.path.exists(params_file):
driver = FittingDriver.from_directory(self.folder,
model_file=self.model)
# initalize a new object from scratch
else:
init_model = self.model is None
driver = FittingDriver(self.params, init_model=init_model)
# initialize and save a model, if we need to
if not init_model:
driver.model = self.model
else:
if self.comm.rank == 0 and not self.no_save_model:
model_dir = driver.params.get('model_dir', self.folder)
driver.theory.model.to_npy(
os.path.join(model_dir, model_filename))
# only one rank needs to write out
if self.comm.rank == 0:
driver.to_file(os.path.join(self.folder, params_filename))
# have everyone wait
self.comm.barrier()
# store the command line arguments
driver.params.add('solver_type',
value=self.mode) # either nlopt or mcmc
driver.params.add('iterations', value=self.iterations)
if self.mode == 'mcmc':
driver.params.add('walkers', value=self.walkers)
# set max iterations for LBFGS
if self.mode == 'nlopt':
options = driver.params['lbfgs_options'].value
options['max_iter'] = self.iterations
# check if we need to find previous result
init_from = driver.params.get('init_from', None)
if init_from == 'result':
start_from = driver.params.get('start_from', None)
if start_from is None:
start_from = find_init_result(self.folder)
elif os.path.isdir(start_from):
start_from = find_init_result(start_from)
if not os.path.exists(start_from):
raise rsd_io.ConfigurationError(
"`start_from` parameter `%s` is not a valid path" %
start_from)
driver.params.add('start_from', value=start_from)
# ``restart`` mode
elif self.mode == 'restart':
# load the driver from param file, optionally reading model from file
driver = FittingDriver.from_directory(self.folder,
model_file=self.model)
# set driver values from command line
if self.burnin is not None:
driver.params.add('burnin', value=self.burnin)
self.algorithm = driver
