def __init__(self, number, snapshot_dir = None, output_loc = None, snapshot_loc = None):
# Output File
number = '%03d' % number
self.f1 = "outfile" + number + ".hdf5"
if output_loc is not None:
self.f1 = output_loc + "/" + self.f1
# Snapshot Directory
if snapshot_dir is None:
snapshot_dir = "snap" + number
if snapshot_loc is not None:
snapshot_dir = snapshot_loc + "/" + snapshot_dir
self.snapshot_dir = snapshot_dir # Updates Here
ps.set_snapshot_dir(snapshot_dir) # Updates Elsewhere
# Bodies
self.bodies = ps.parse_file(self.f1)
# Simulation Info
self.read_info_file()
# Time
self.times = quantities.AdaptingVectorQuantity()
# Initialize States
self.init_states()
self.num_snapshots = len(self.times)
self.save_star_separation()
# Sorted Bodies + Keys (Change this to particle sets instead)
self.unbound = np.zeros(len(self.times), dtype = np.ndarray) # These are arrays of Particle sets
self.central = np.zeros(len(self.times), dtype = np.ndarray)
self.passing = np.zeros(len(self.times), dtype = np.ndarray)
self.both = np.zeros(len(self.times), dtype = np.ndarray)
self.tmp_mask = np.zeros(len(self.times)) # for before the mask is applied
self.mask = np.zeros(len(self.times)) # 1 indicates 'sorting' has occured at particular timestep
# This mask needs to be incorporated into functions using sorted sets
# For instance, the self.times array will need to be restricted to masked values
################# DO NOT MODIFY THE MASK DIRECTLY!!!!!! (switch to private?) ################
# Restore Sorts
print " *** Restoring *** "
self.restore()
# Misc
self.tmp_movie_dir = "tmp_movies_for_factory"
def __init__(self,
number,
snapshot_base=None,
output_base=None,
location=None,
count=6,
iter_num=None,
grand=[]):
# Output File
if count == 3:
number = '%03d' % int(number) # Old Format
else:
number = '%06d' % int(
number
) # New Format (will fail if count = 4 or 5, which is bad)
# Check if part of a suite
if iter_num is not None:
number += "_%02d" % int(
iter_num) ##### This formatting will be added soon #####
if output_base is None:
self.f1 = "outfile" + number + ".hdf5"
else:
self.f1 = "" + output_base + number + ".hdf5"
# Snapshot Directory
if snapshot_base is None:
snapshot_dir = "sim" + number
else:
snapshot_dir = "" + snapshot_base + number
if location is not None:
snapshot_dir = location + "/" + snapshot_dir ## assume this is the directory for everything ##
self.snapshot_dir = snapshot_dir # Updates Here
ps.set_snapshot_dir(snapshot_dir) # Updates Elsewhere
# Check if Encompassing Suite
if (len(grand) > 0):
# If the output
print "Grand Output File", self.f1
if (os.path.exists(self.snapshot_dir) and os.path.exists(self.f1)):
pass
else:
ps.mkdir(self.snapshot_dir) # initialize base directory
# (1) Merge Output Files
self.merge_output_files(
number, snapshot_base, output_base, location, count,
grand) ####### not implemented yet #######
# (2) Merge Info Files (.txt and .p) ^^^
self.snapshot_dir = snapshot_dir # Updates Here
ps.set_snapshot_dir(
snapshot_dir
) # Updates Elsewhere <<<<---- NOTE: IT WAS RESET!!!!!!
# Bodies
self.bodies = ps.parse_file(self.f1)
# Simulation Info
self.read_info_file()
# Time
self.times = quantities.AdaptingVectorQuantity()
# Initialize States
self.init_states()
self.num_snapshots = len(self.times)
self.save_star_separation()
# Sorted Bodies + Keys (Change this to particle sets instead)
self.unbound = np.zeros(len(
self.times), dtype=np.ndarray) # These are arrays of Particle sets
self.central = np.zeros(len(self.times), dtype=np.ndarray)
self.passing = np.zeros(len(self.times), dtype=np.ndarray)
self.both = np.zeros(len(self.times), dtype=np.ndarray)
self.tmp_mask = np.zeros(len(
self.times)) # for before the mask is applied
self.mask = np.zeros(
len(self.times
)) # 1 indicates 'sorting' has occured at particular timestep
# This mask needs to be incorporated into functions using sorted sets
# For instance, the self.times array will need to be restricted to masked values
################# DO NOT MODIFY THE MASK DIRECTLY!!!!!! (switch to private?) ################
# Restore Sorts
print " *** Restoring *** "
self.restore()
# Misc
self.tmp_movie_dir = "tmp_movies_for_factory"
def __init__(self, number, snapshot_base = None, output_base = None, location = None, count = 6,
iter_num = None, grand = []):
# Output File
if count == 3:
number = '%03d' % int(number) # Old Format
else:
number = '%06d' % int(number) # New Format (will fail if count = 4 or 5, which is bad)
# Check if part of a suite
if iter_num is not None:
number += "_%02d" % int(iter_num) ##### This formatting will be added soon #####
if output_base is None:
self.f1 = "outfile" + number + ".hdf5"
else:
self.f1 = "" + output_base + number + ".hdf5"
# Snapshot Directory
if snapshot_base is None:
snapshot_dir = "sim" + number
else:
snapshot_dir = "" + snapshot_base + number
if location is not None:
snapshot_dir = location + "/" + snapshot_dir ## assume this is the directory for everything ##
self.snapshot_dir = snapshot_dir # Updates Here
ps.set_snapshot_dir(snapshot_dir) # Updates Elsewhere
# Check if Encompassing Suite
if (len(grand) > 0):
# If the output
print "Grand Output File", self.f1
if (os.path.exists(self.snapshot_dir) and os.path.exists(self.f1)):
pass
else:
ps.mkdir(self.snapshot_dir) # initialize base directory
# (1) Merge Output Files
self.merge_output_files(number, snapshot_base, output_base, location,
count, grand) ####### not implemented yet #######
# (2) Merge Info Files (.txt and .p) ^^^
self.snapshot_dir = snapshot_dir # Updates Here
ps.set_snapshot_dir(snapshot_dir) # Updates Elsewhere <<<<---- NOTE: IT WAS RESET!!!!!!
# Bodies
self.bodies = ps.parse_file(self.f1)
# Simulation Info
self.read_info_file()
# Time
self.times = quantities.AdaptingVectorQuantity()
# Initialize States
self.init_states()
self.num_snapshots = len(self.times)
self.save_star_separation()
# Sorted Bodies + Keys (Change this to particle sets instead)
self.unbound = np.zeros(len(self.times), dtype = np.ndarray) # These are arrays of Particle sets
self.central = np.zeros(len(self.times), dtype = np.ndarray)
self.passing = np.zeros(len(self.times), dtype = np.ndarray)
self.both = np.zeros(len(self.times), dtype = np.ndarray)
self.tmp_mask = np.zeros(len(self.times)) # for before the mask is applied
self.mask = np.zeros(len(self.times)) # 1 indicates 'sorting' has occured at particular timestep
# This mask needs to be incorporated into functions using sorted sets
# For instance, the self.times array will need to be restricted to masked values
################# DO NOT MODIFY THE MASK DIRECTLY!!!!!! (switch to private?) ################
# Restore Sorts
print " *** Restoring *** "
self.restore()
# Misc
self.tmp_movie_dir = "tmp_movies_for_factory"
