# Set whether we have multiple timesteps for each set
if timesteps is 'last':
multi_timesteps = False
else:
multi_timesteps = True
fiducials, designs, timesteps_labels = \
files_sorter(PREFIX, timesteps=timesteps,
append_prefix=True)
if MULTICORE:
use_mpi = False
if use_mpi:
pool = MPIPool(loadbalance=False)
if not pool.is_master():
# Wait for instructions from the master process.
pool.wait()
sys.exit(0)
else:
from multiprocessing import Pool
# Default to 10 for now. Will change if this works.
pool = Pool(processes=10)
else:
pool = None
if fiducial_num == "fid_comp": # Run all the comparisons of fiducials
print "Fiducials to compare %s" % (fiducials[face].keys())
else:
cube_output = output_folder
# Grab all of the fits files
fits_files = glob.glob(folder+"*.fits")
# Trying noise levels scaled by their brightness distribs
if add_noise:
rms_noise = 'scaled'
elif is_obs:
rms_noise = None
else:
rms_noise = 0.001
np.random.seed(248954785)
pool = MPIPool(loadbalance=False)
if not pool.is_master():
# Wait for instructions from the master process.
pool.wait()
sys.exit(0)
pool.map(single_input, zip(fits_files,
repeat(add_noise),
repeat(rms_noise),
repeat(output_folder),
repeat(cube_output)))
pool.close()
# Set whether we have multiple timesteps for each set
if timesteps is 'last':
multi_timesteps = False
else:
multi_timesteps = True
fiducials, designs, timesteps_labels = \
files_sorter(PREFIX, timesteps=timesteps,
append_prefix=True)
if MULTICORE:
use_mpi = False
if use_mpi:
pool = MPIPool(loadbalance=False)
if not pool.is_master():
# Wait for instructions from the master process.
pool.wait()
sys.exit(0)
else:
from multiprocessing import Pool
# Default to 10 for now. Will change if this works.
pool = Pool(processes=10)
else:
pool = None
if fiducial_num == "fid_comp": # Run all the comparisons of fiducials
print "Fiducials to compare %s" % (fiducials[face].keys())