files = glob(args.glob)
elif args.files:
files = args.files
else:
sys.exit("Must specify either --glob or --files")
#Because we are impatient and want to compute statistics before all the jobs are finished,
# there may be some directories that do not have a flatchains.hdf5 file
flatchainList = []
for file in files:
try:
# If we've specified HDF5, use h5read
# If we've specified csv, use csvread
root, ext = os.path.splitext(file)
if ext == ".hdf5":
flatchainList.append(utils.h5read(file, args.burn, args.thin))
elif ext == ".csv":
flatchainList.append(utils.csvread(file, args.burn, args.thin))
except OSError as e:
print("{} does not exist, skipping. Or error {}".format(file, e))
print("Using a total of {} flatchains".format(len(flatchainList)))
if args.cat:
assert len(flatchainList) > 1, "If concatenating samples, must provide more than one flatchain"
utils.cat_list(args.output, flatchainList)
# Assume that if we are using either args.chain, or args.triangle, we are only suppling one
# chain, since these commands don't make sense otherwise.
if args.chain:
assert len(flatchainList) == 1, "If plotting Markov Chain, only specify one flatchain"
from Starfish import utils
# Determine all of the orders we will be fitting
spectra = Starfish.data["files"]
orders = Starfish.data["orders"]
for spectrum_id in range(len(spectra)):
for order in orders:
npoly = Starfish.config["cheb_degree"]
if order == orders[-1]:
# Use cheb degree - 1 for the last order
npoly -= 1
fname_phi = Starfish.specfmt.format(spectrum_id, order) + "phi.json"
phi = PhiParam.load(fname_phi)
fname_mc = args.rundir + "/" + Starfish.specfmt.format(
spectrum_id, order) + "/mc.hdf5"
flatchain = utils.h5read(fname_mc)
pars = flatchain[-1, :]
phi.cheb = pars[:npoly]
phi.sigAmp = float(pars[npoly])
phi.logAmp = float(pars[npoly + 1])
phi.l = float(pars[npoly + 2])
phi.save()
# from http://www.astropython.org/snippet/2009/10/chdir-context-manager
@contextlib.contextmanager
def chdir(dirname=None):
curdir = os.getcwd()
try:
if dirname is not None:
os.chdir(dirname)
yield
finally:
os.chdir(curdir)
paths = glob("s*o*/mc.hdf5")
if args.triangle:
flatchain = utils.h5read("mc.hdf5", args.burn)
utils.plot(flatchain, base="")
cmd = [
"chain.py", "--files", "mc.hdf5", "-t", "--burn",
"{}".format(args.burn)
]
for path in paths:
dirname, fname = path.split("/")
# CD to this directory using context managers
with chdir(dirname):
subprocess.call(cmd)
if args.chain:
# from http://www.astropython.org/snippet/2009/10/chdir-context-manager
@contextlib.contextmanager
def chdir(dirname=None):
curdir = os.getcwd()
try:
if dirname is not None:
os.chdir(dirname)
yield
finally:
os.chdir(curdir)
paths = glob("s*o*/mc.hdf5")
if args.triangle:
flatchain = utils.h5read("mc.hdf5", args.burn)
utils.plot(flatchain, base="")
cmd = ["chain.py", "--files", "mc.hdf5", "-t", "--burn", "{}".format(args.burn)]
for path in paths:
dirname, fname = path.split("/")
# CD to this directory using context managers
with chdir(dirname):
subprocess.call(cmd)
if args.chain:
flatchain = utils.h5read("mc.hdf5", args.burn)
from Starfish import utils
# Determine all of the orders we will be fitting
spectra = Starfish.data["files"]
orders = Starfish.data["orders"]
for spectrum_id in range(len(spectra)):
for order in orders:
npoly = Starfish.config["cheb_degree"]
if order == orders[-1]:
# Use cheb degree - 1 for the last order
npoly -= 1
fname_phi = Starfish.specfmt.format(spectrum_id, order) + "phi.json"
phi = PhiParam.load(fname_phi)
fname_mc = args.rundir + "/" + Starfish.specfmt.format(spectrum_id, order) + "/mc.hdf5"
flatchain = utils.h5read(fname_mc)
pars = flatchain[-1,:]
phi.cheb = pars[:npoly]
phi.sigAmp = float(pars[npoly])
phi.logAmp = float(pars[npoly + 1])
phi.l = float(pars[npoly + 2])
phi.save()
files = glob(args.glob)
elif args.files:
files = args.files
else:
sys.exit("Must specify either --glob or --files")
#Because we are impatient and want to compute statistics before all the jobs are finished,
# there may be some directories that do not have a flatchains.hdf5 file
flatchainList = []
for file in files:
try:
# If we've specified HDF5, use h5read
# If we've specified csv, use csvread
root, ext = os.path.splitext(file)
if ext == ".hdf5":
flatchainList.append(utils.h5read(file, args.burn, args.thin))
elif ext == ".csv":
flatchainList.append(utils.csvread(file, args.burn, args.thin))
except OSError as e:
print("{} does not exist, skipping. Or error {}".format(file, e))
print("Using a total of {} flatchains".format(len(flatchainList)))
if args.cat:
assert len(
flatchainList
) > 1, "If concatenating samples, must provide more than one flatchain"
utils.cat_list(args.output, flatchainList)
# Assume that if we are using either args.chain, or args.triangle, we are only suppling one
# chain, since these commands don't make sense otherwise.