def _init_solutions(self, label, load_from, interpolate, save_to, exportables):
"""
Internal helper implementation for init_solutions(): this initializes a pair of solution databases.
Args:
label (str):
the Jones matrix label
load_from (str):
filename of solution DB to load from. Can be empty or None.
interpolate (bool):
True if solutions are allowed to be interpolated.
save_to (str):
filename of solution DB to save to. Can be empty or None.
exportables (dict):
Dictionary of {key: (empty_value, axis_list)} describing the solutions that will be saved.
As returned by exportable_solutions() of the gain machine.
"""
# init solutions from database
if load_from:
print>>log(0, "blue"), "{} solutions will be initialized from {}".format(label, load_from)
if "//" in load_from:
filename, prefix = load_from.rsplit("//", 1)
else:
filename, prefix = load_from, label
self._init_sols[label] = param_db.load(filename), prefix, interpolate
# create database to save to
if save_to:
# define parameters in DB
for sol_label, (empty_value, axes) in exportables.iteritems():
self.define_param(save_to, "{}:{}".format(label, sol_label), empty_value, axes)
print>> log(0), "{} solutions will be saved to {}".format(label, save_to)
def extract_from_db(dbfile, name="G:gain"):
"""Extract from dbfile"""
LOGGER.debug("Loading gains database {}".format(dbfile))
try:
dbdata = db.load(dbfile)
gains = dbdata[name]
gainscube = gains.get_cube()
data = gainscube.data[-1] # remove n_dir
except Exception as e:
# print(e)
data = np.load(dbfile)
data = data[-1] #,0] # assuming ntchunks is 1, remove n_dir
return data
def __init__(self, gmfactory, ifrgain_opts, compute=True):
"""
Initializes the IFR-based gains machinery.
Args:
gmfactory: a GainMachine Factory is used to manage the solution databases
ifrgain_opts: dict of options
compute: if False, gains are not computed even if options ask them to
"""
from cubical.main import expand_templated_name
self.gmfactory = gmfactory
load_from = expand_templated_name(ifrgain_opts['load-from'])
save_to = expand_templated_name(ifrgain_opts['save-to'])
self._ifrgains_per_chan = ifrgain_opts['per-chan']
self._ifrgain = None
self._nfreq = gmfactory.grid["freq"]
nfreq, nant, ncorr = [
len(gmfactory.grid[axis]) for axis in ("freq", "ant", "corr")
]
if load_from:
filename = load_from
print(ModColor.Str(
"applying baseline-based corrections (BBCs) from {}".format(
filename),
col="green"),
file=log(0))
if "//" in filename:
filename, prefix = filename.rsplit("//", 1)
else:
filename, prefix = filename, "BBC"
parm = param_db.load(filename).get(prefix)
if parm is None:
print(ModColor.Str(" no solutions for '{}' in {}".format(
prefix, filename)),
file=log(0))
else:
self._ifrgain = parm.reinterpolate(
freq=gmfactory.grid["freq"]).filled()
if tuple(self._ifrgain.shape) != (nfreq, nant, nant, ncorr,
ncorr):
print(ModColor.Str(
" invalid BBC shape {}, will ignore".format(
self._ifrgain.shape)),
file=log(0))
self._ifrgain = None
else:
print(" loaded per-channel BBCs of shape {}".format(
filename, self._ifrgain.shape),
file=log(0))
if not self._ifrgains_per_chan:
print(" using one mean value across band",
file=log(0))
self._ifrgain[np.newaxis,
...] = self._ifrgain.mean(axis=0)
# reset off-diagonal values, if needed
if ifrgain_opts["apply-2x2"]:
print(ModColor.Str(
" using full 2x2 BBCs. You'd better know what you're doing!",
col="green"),
file=log(0))
else:
self._ifrgain[..., (0, 1), (1, 0)] = 1
print(" using parallel-hand BBCs only", file=log(0))
if save_to and compute:
self._compute_2x2 = ifrgain_opts["compute-2x2"]
# setup axes for IFR-based gains
axes = ["freq", "ant1", "ant2", "corr1", "corr2"]
# define the ifrgain parameter
self._save_filename = save_to
parm = gmfactory.define_param(self._save_filename,
"BBC",
1 + 0j,
axes,
interpolation_axes=["freq"])
self._ifrgains_grid = {
axis: parm.grid[i]
for i, axis in enumerate(axes)
}
# initialize accumulators for M.D^H and D.D^H terms
self._mdh_sum = np.ma.zeros(parm.shape,
gmfactory.ctype,
fill_value=0)
self._ddh_sum = np.ma.zeros(parm.shape,
gmfactory.ctype,
fill_value=0)
#
print(
"will compute & save suggested baseline-based corrections (BBCs) to {}"
.format(self._save_filename),
file=log(0))
print(
" (these can optionally be applied in a subsequent CubiCal run)",
file=log(0))
else:
self._ifrgains_grid = None
def reload(self):
"""Reloads saved IFR gain solutions from database"""
return param_db.load(self._save_filename)["BBC"].get_cube()