def clump_tracker(fprefix, param=None, directory=None, nsmooth=32, verbose=True):
"""
Finds and tracks clumps over a simulation with multiple time steps and
calculates various physical properties of the clumps.
Runs all the steps necessary to find/track clumps, these are:
get_fnames
pFind_clumps
pClump_properties
pLink2
multilink
build_clumps
If the iord property is not found, the linking will only work if the number
of particles remains constant through the simulation
**ARGUMENTS**
fprefix : str
Prefix of the simulation outputs
param : str (recommended)
Filename of a .param file for the simulation
directory : str (optional)
Directory to search through. Default is current working directory
nsmooth : int (optional)
Number of nearest neighbors used for particle smoothing in the
simulation. This is used in the definition of a density threshold
for clump finding.
verbose : bool (optional)
Verbosity flag. Default is True
**RETURNS**
clump_list : list
A list containing dictionaries for all clumps foujohn obryan fiddlend in the simulation
See clump_properties for a list of the properties calculated for clumps
"""
# Get a list of all snapshot files
fnames = get_fnames(fprefix, directory)
nfiles = len(fnames)
# Run the clump (halo) finder
if verbose: print "\n\nRunning clump finder on {} files\n\n".format(nfiles)
clumpnum_list = pFind_clumps(fnames, nsmooth, param, verbose=verbose)
nclumps = np.zeros(nfiles, dtype=int)
for i, clumpnums in enumerate(clumpnum_list):
nclumps[i] = clumpnums.max()
if nclumps.max() <= 0:
if verbose: print 'No clumps found'
return []
# Calculate the physical properties of the clumps
if verbose: print "\n\nCalculating the physical of properties of clumps\n\n"
properties = pClump_properties(fnames, clumpnum_list)
# Link clumps on consecutive time-steps
if verbose: print "\n\nLinking Clumps\n\n"
link_list = pLink2(properties)
# Link on multiple time-steps
multilink_list = multilink(link_list)
# Build the clumps
clump_list = build_clumps(multilink_list, properties, fnames, param)
return clump_list
def pSpiralpower_t(simdirs, fprefix, rbins=100, thetabins=100, binspacing='log', \
rlim=None, paramnames=None, center=True):
"""
Parallel implementation of spiralpower_t for looping over simulation
diretories. Assumes simulation outputs are standard ChaNGa format.
Only simdirs and paramnames
can change from simulation to simulation. See spiralpower_t for other
parameters
Parameters
----------
simdirs : list or arraylike
list of simulation directories (strings)
fprefix : str or list
prefix or prefixes of simulation snapshots (see pychanga.get_fnames)
**kwargs
See spiralpower_t for other parameters
Returns
-------
output : dict
Output for every simulation (see spiralpower_t)
"""
nSim = len(simdirs)
if not hasattr(paramnames, '__iter__'):
paramnames = nSim*[paramnames]
if not hasattr(fprefix, '__iter__'):
fprefix = nSim * [fprefix]
# Generate the list of arguments for each simulation
arglist = []
for i in range(nSim):
flist = get_fnames(fprefix[i], simdirs[i])
paramname = paramnames[i]
arglist.append([flist, rbins, thetabins, binspacing, rlim, paramname, \
center])
pool = Pool(cpu_count())
try:
results = pool.map(_spiralpower_t, arglist, chunksize=1)
finally:
pool.close()
pool.join()
power = []
t = []
r = []
for result in results:
power.append(result[0])
t.append(result[1])
r.append(result[2])
output = {'power':power, 't': t, 'r': r}
return output
def pSpiralpower_t(simdirs, fprefix, rbins=100, thetabins=100, binspacing='log', \
rlim=None, paramnames=None, center=True):
"""
Parallel implementation of spiralpower_t for looping over simulation
diretories. Assumes simulation outputs are standard ChaNGa format.
Only simdirs and paramnames
can change from simulation to simulation. See spiralpower_t for other
parameters
Parameters
----------
simdirs : list or arraylike
list of simulation directories (strings)
fprefix : str or list
prefix or prefixes of simulation snapshots (see pychanga.get_fnames)
**kwargs
See spiralpower_t for other parameters
Returns
-------
output : dict
Output for every simulation (see spiralpower_t)
"""
nSim = len(simdirs)
if not hasattr(paramnames, '__iter__'):
paramnames = nSim * [paramnames]
if not hasattr(fprefix, '__iter__'):
fprefix = nSim * [fprefix]
# Generate the list of arguments for each simulation
arglist = []
for i in range(nSim):
flist = get_fnames(fprefix[i], simdirs[i])
paramname = paramnames[i]
arglist.append([flist, rbins, thetabins, binspacing, rlim, paramname, \
center])
pool = Pool(cpu_count())
try:
results = pool.map(_spiralpower_t, arglist, chunksize=1)
finally:
pool.close()
pool.join()
power = []
t = []
r = []
for result in results:
power.append(result[0])
t.append(result[1])
r.append(result[2])
output = {'power': power, 't': t, 'r': r}
return output
def clump_tracker(fprefix,
param=None,
directory=None,
nsmooth=32,
verbose=True):
"""
Finds and tracks clumps over a simulation with multiple time steps and
calculates various physical properties of the clumps.
Runs all the steps necessary to find/track clumps, these are:
get_fnames
pFind_clumps
pClump_properties
pLink2
multilink
build_clumps
If the iord property is not found, the linking will only work if the number
of particles remains constant through the simulation
**ARGUMENTS**
fprefix : str
Prefix of the simulation outputs
param : str (recommended)
Filename of a .param file for the simulation
directory : str (optional)
Directory to search through. Default is current working directory
nsmooth : int (optional)
Number of nearest neighbors used for particle smoothing in the
simulation. This is used in the definition of a density threshold
for clump finding.
verbose : bool (optional)
Verbosity flag. Default is True
**RETURNS**
clump_list : list
A list containing dictionaries for all clumps foujohn obryan fiddlend in the simulation
See clump_properties for a list of the properties calculated for clumps
"""
# Get a list of all snapshot files
fnames = get_fnames(fprefix, directory)
nfiles = len(fnames)
# Run the clump (halo) finder
if verbose: print "\n\nRunning clump finder on {} files\n\n".format(nfiles)
clumpnum_list = pFind_clumps(fnames, nsmooth, param, verbose=verbose)
nclumps = np.zeros(nfiles, dtype=int)
for i, clumpnums in enumerate(clumpnum_list):
nclumps[i] = clumpnums.max()
if nclumps.max() <= 0:
if verbose: print 'No clumps found'
return []
# Calculate the physical properties of the clumps
if verbose:
print "\n\nCalculating the physical of properties of clumps\n\n"
properties = pClump_properties(fnames, clumpnum_list)
# Link clumps on consecutive time-steps
if verbose: print "\n\nLinking Clumps\n\n"
link_list = pLink2(properties)
# Link on multiple time-steps
multilink_list = multilink(link_list)
# Build the clumps
clump_list = build_clumps(multilink_list, properties, fnames, param)
return clump_list