def main():
parser = argparse.ArgumentParser(formatter_class=argparse.\
RawDescriptionHelpFormatter, description=textwrap.dedent("""
Relative redundant calibration of visibilities
Takes a given HERA visibility dataset in uvh5 file format and performs
relative redundant calibration (up to the overall amplitude, overall
phase, and phase gradient degenerate parameters) for each frequency channel
and each time integration in the dataset.
Returns a pickled pandas dataframe of the Scipy optimization results for
the relative redundant calibration for each set of frequency channel and
time integration.
"""))
parser.add_argument('jd_time',
help='Fractional JD time of dataset to \
calibrate',
metavar='JD',
type=str)
parser.add_argument('-o', '--out', required=False, default=None, \
metavar='O', type=str, help='Output csv and df name')
parser.add_argument('-p', '--pol', required=True, metavar='P', type=str, \
help='Polarization {"ee", "en", "nn", "ne"}')
parser.add_argument('-c', '--chans', required=False, default=None, metavar='C', \
type=str, help='Frequency channels to calibrate \
{0, 1023}' )
parser.add_argument('-t', '--tints', required=False, default=None, metavar='T', \
type=str, help='Time integrations to calibrate \
{0, 59}' )
parser.add_argument('-f', '--flag_type', required=False, default='first', \
metavar='F', type=str, help='Flag type e.g. "first", \
"omni", "abs"' )
parser.add_argument('-d', '--dist', required=True, metavar='D', \
type=str, help='Fitting distribution for calibration \
{"cauchy", "gaussian"}' )
parser.add_argument('-m', '--method', required=False, default='cartesian', \
metavar='M', type=str, help='Method to use - {"cartesian", \
"polar", "RP"}, where RP stands for reduced parameters' )
parser.add_argument('-l', '--logamp', required=False, action='store_true', \
help='Use logamp method to force positive gain amplitudes')
parser.add_argument('-g', '--tilt_reg', required=False, action='store_true', \
help='Add regularization term to constrain tilt shifts to 0')
parser.add_argument('-a', '--gphase_reg', required=False, action='store_true', \
help='Add regularization term to constrain the gain phase mean')
parser.add_argument('-i', '--initp_jd', required=False, default=None, metavar='I', \
type=int, help='JD of to find datasets to reuse initial parameters')
parser.add_argument('-v', '--noise', required=False, action='store_true', \
help='Use noise from autos in nlogL calculations')
parser.add_argument('-u', '--out_dir', required=False, default=None, metavar='U', \
type=str, help='Out directory to store dataframe')
parser.add_argument('-n', '--new_df', required=False, action='store_true', \
help='Write data to a new dataframe')
args = parser.parse_args()
startTime = datetime.datetime.now()
out_fn = args.out
default_fn = 'rel_df.{}.{}.{}'.format(args.jd_time, args.pol, args.dist)
if out_fn is None:
out_fn = default_fn
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
out_fn = os.path.join(args.out_dir, out_fn)
if out_fn is not None:
default_fn = os.path.join(args.out_dir, default_fn)
out_csv = fn_format(out_fn, 'csv')
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = os.path.exists(out_csv)
pkl_exists = os.path.exists(out_pkl)
if csv_exists or pkl_exists:
if args.new_df:
out_csv = new_fn(out_csv, None, startTime)
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = False
pkl_exists = False
zen_fn = find_zen_file(args.jd_time)
bad_ants = get_bad_ants(zen_fn)
flag_type = args.flag_type
if flag_type is not None:
flag_fn = find_flag_file(args.jd_time, flag_type)
else:
flag_fn = None
freq_chans = mod_str_arg(args.chans)
time_ints = mod_str_arg(args.tints)
hd = HERAData(zen_fn)
pchans = args.chans
if pchans is None:
pchans = '0~{}'.format(hd.Nfreqs - 1)
ptints = args.tints
if ptints is None:
ptints = '0~{}'.format(hd.Ntimes - 1)
print('Running relative redundant calibration on visibility dataset {} for '\
'polarization {}, frequency channel(s) {} and time integration(s) {} '\
'with {} assumed noise distribution\n'.\
format(os.path.basename(zen_fn), args.pol, pchans, ptints, args.dist))
if freq_chans is None:
freq_chans = numpy.arange(hd.Nfreqs)
if time_ints is None:
time_ints = numpy.arange(hd.Ntimes)
indices = ['freq', 'time_int']
no_tints = len(time_ints)
iter_dims = list(numpy.ndindex((len(freq_chans), no_tints)))
skip_cal = False
# skipping freqs and tints that are already in the dataframe
if csv_exists or pkl_exists:
cmap_f = dict(map(reversed, enumerate(freq_chans)))
cmap_t = dict(map(reversed, enumerate(time_ints)))
if csv_exists:
df = pd.read_csv(out_csv, usecols=indices)
idx_arr = df.values
elif pkl_exists:
df_pkl = pd.read_pickle(out_pkl)
idx_arr = df_pkl.index.values
done = [(cmap_f[f], cmap_t[t]) for (f, t) in idx_arr if (f in freq_chans \
and t in time_ints)]
iter_dims = [idim for idim in iter_dims if idim not in done]
if not any(iter_dims):
print('Solutions to all specified frequency channels and time '\
'integrations already exist in {}\n'.format(out_pkl))
skip_cal = True
if not skip_cal:
grp = group_data(zen_fn, args.pol, freq_chans, time_ints, \
bad_ants, flag_path=flag_fn, noise=args.noise)
if not args.noise:
_, RedG, cData = grp
noisec = None
else:
_, RedG, cData, cNData = grp
flags = cData.mask
cData = cData.data
# to get fields for the csv header
ants = numpy.unique(RedG[:, 1:])
no_ants = ants.size
no_unq_bls = numpy.unique(RedG[:, 0]).size
cRedG = relabelAnts(RedG)
psize = (no_ants + no_unq_bls) * 2
if args.tilt_reg:
ant_pos_arr = flt_ant_pos(hd.antpos, ants)
else:
ant_pos_arr = None
# discarding 'jac', 'hess_inv', 'nfev', 'njev'
slct_keys = ['success', 'status', 'message', 'fun', 'nit', 'x']
header = slct_keys[:-1] + list(numpy.arange(psize)) + indices
# remove flagged channels from iter_dims
if True in flags:
flg_chans = numpy.where(flags.all(axis=(1, 2)))[0] # indices
print('Flagged channels for visibility dataset {} are: {}\n'.\
format(os.path.basename(zen_fn), freq_chans[flg_chans]))
iter_dims = [
idim for idim in iter_dims if idim[0] not in flg_chans
]
if not iter_dims: # check if slices to solve are empty
print('All specified channels are flagged. Exiting.')
sys.exit()
if args.initp_jd is not None:
jd_time2 = match_lst(args.jd_time, args.initp_jd)
if len(str(jd_time2)) < 13:
jd_time2 = str(
jd_time2
) + '0' # add a trailing 0 that is omitted in float
rel_df_path1 = find_rel_df(jd_time2, args.pol, args.dist)
if isinstance(jd_time2, str):
jd_time2 = float(jd_time2)
last_df = pd.read_pickle('jd_lst_map_idr2.pkl')
last1 = last_df[last_df['JD_time'] == float(
args.jd_time)]['LASTs'].values[0]
last2 = last_df[last_df['JD_time'] == jd_time2]['LASTs'].values[0]
_, offset = find_nearest(last2, last1[0])
rel_df1 = pd.read_pickle(rel_df_path1)
rel_df1 = rel_df1[
rel_df1.index.get_level_values('time_int') >= offset]
next_row = numpy.where(last_df['JD_time'] == jd_time2)[0][0] + 1
rel_df_path2 = find_rel_df(last_df.iloc[next_row]['JD_time'], args.pol, \
args.dist)
rel_df2 = pd.read_pickle(rel_df_path2)
rel_df2 = rel_df2[
rel_df2.index.get_level_values('time_int') < offset]
rel_df_c = pd.concat([rel_df1, rel_df2])
# filter by specified channels and time integrations
time_ints_offset = (time_ints + offset) % hd.Ntimes
freq_flt = numpy.in1d(rel_df_c.index.get_level_values('freq'),
freq_chans)
tint_flt = numpy.in1d(rel_df_c.index.get_level_values('time_int'),
time_ints_offset)
rel_df_c = rel_df_c[freq_flt & tint_flt]
time_ints2 = numpy.tile(
rel_df_c.index.get_level_values('time_int').unique().values,
freq_chans.size)
iter_dims = [
idim + (tint, ) for idim, tint in zip(iter_dims, time_ints2)
]
phase_reg_initp = True
else:
phase_reg_initp = False
def cal(credg, distribution, coords, no_unq_bls, no_ants, logamp, \
tilt_reg, gphase_reg, ant_pos_arr, obsvis, noise, initp):
"""Relative redundant calibration with doRelCal: unconstrained
minimizer using cartesian coordinates - this is the fastest solver
:param credg: Grouped baselines, condensed so that antennas are
consecutively labelled. See relabelAnts
:type credg: ndarray
:param distribution: Distribution to fit likelihood {'gaussian', 'cauchy'}
:type distribution: str
:param coords: Coordinate system in which gain and visibility parameters
have been set up
:type coords: str {"cartesian", "polar"}
:param no_unq_bls: Number of unique baselines (equivalently the number of
redundant visibilities)
:type no_unq_bls: int
:param no_ants: Number of antennas for given observation
:type no_ants: int
:param logamp: The logarithm of the amplitude initial parameters is taken,
such that only positive solutions can be returned. Only if coords=="polar".
:type logamp: bool
:param tilt_reg: Add regularization term to constrain tilt shifts to 0
:type tilt_reg: bool
:param gphase_reg: Add regularization term to constrain the gain phase mean
:type gphase_reg: bool
:param ant_pos_arr: Array of filtered antenna position coordinates for the antennas
in ants. See flt_ant_pos.
:type ant_pos_arr: ndarray
:param obsvis: Observed sky visibilities for a given frequency and given time,
reformatted to have format consistent with redg
:type obsvis: ndarray
:param noise: Noise array to feed into log-likelihood calculations
:type noise: ndarray
:param initp: Initial parameter guesses for true visibilities and gains
:type initp: ndarray, None
:return: Optimization result for the solved antenna gains and true sky
visibilities
:rtype: Scipy optimization result object
"""
res_rel, initp_new = doRelCal(credg, obsvis, no_unq_bls, no_ants, \
coords=coords, distribution=distribution, noise=noise, \
norm_gains=True, logamp=logamp, tilt_reg=tilt_reg, \
gphase_reg=gphase_reg, ant_pos_arr=ant_pos_arr, initp=initp, \
return_initp=True, phase_reg_initp=phase_reg_initp)
res_rel = {key: res_rel[key] for key in slct_keys}
# use solution for next solve in iteration
if res_rel['success']:
initp = initp_new
return res_rel, initp
def cal_RP(credg, distribution, no_unq_bls, no_ants, logamp, \
tilt_reg, gphase_reg, ant_pos_arr, obsvis, noise, initp):
"""Relative redundant calibration with doRelCalRP: constrained
minimizer (by reducing the number of parameters) using polar
coordinates
:param credg: Grouped baselines, condensed so that antennas are
consecutively labelled. See relabelAnts
:type credg: ndarray
:param distribution: Distribution to fit likelihood {'gaussian', 'cauchy'}
:type distribution: str
:param no_unq_bls: Number of unique baselines (equivalently the number of
redundant visibilities)
:type no_unq_bls: int
:param no_ants: Number of antennas for given observation
:type no_ants: int
:param logamp: The logarithm of the amplitude initial parameters is taken,
such that only positive solutions can be returned. Only if coords=="polar".
:type logamp: bool
:param tilt_reg: Add regularization term to constrain tilt shifts to 0
:type tilt_reg: bool
:param gphase_reg: Add regularization term to constrain the gain phase mean
:type gphase_reg: bool
:param ant_pos_arr: Array of filtered antenna position coordinates for the antennas
in ants. See flt_ant_pos.
:type ant_pos_arr: ndarray
:param obsvis: Observed sky visibilities for a given frequency and given time,
reformatted to have format consistent with redg
:type obsvis: ndarray
:param noise: Noise array to feed into log-likelihood calculations
:type noise: ndarray
:param initp: Initial parameter guesses for true visibilities and gains
:type initp: ndarray, None
:return: Optimization result for the solved antenna gains and true sky
visibilities
:rtype: Scipy optimization result object
"""
res_rel, initp_ = doRelCalRP(credg, obsvis, no_unq_bls, no_ants, \
distribution=distribution, noise=noise, constr_phase=True, \
amp_constr='prod', bounded=True, logamp=logamp, tilt_reg=tilt_reg, \
gphase_reg=gphase_reg, ant_pos_arr=gphase_reg, initp=initp)
res_rel = {key: res_rel[key] for key in slct_keys}
# use solution for next solve in iteration
if res_rel['success']:
initp = initp_
return res_rel, initp
if args.method.upper() == 'RP':
RelCal = functools.partial(cal_RP, cRedG, args.dist, no_unq_bls, no_ants, \
args.logamp, args.tilt_reg, args.gphase_reg, \
ant_pos_arr)
coords = 'polar'
else:
RelCal = functools.partial(cal, cRedG, args.dist, args.method, no_unq_bls, \
no_ants, args.logamp, args.tilt_reg, \
args.gphase_reg, ant_pos_arr)
coords = args.method
stdout = io.StringIO()
with redirect_stdout(stdout): # suppress output
with open(out_csv, 'a') as f: # write / append to csv file
writer = DictWriter(f, fieldnames=header)
if not csv_exists:
writer.writeheader()
initp = None
for i, iter_dim in enumerate(iter_dims):
if args.initp_jd is not None:
initp = rel_df_c.loc[(freq_chans[iter_dim[0]], iter_dim[2])]\
[len(slct_keys[:-1]):-2].values.astype(float)
if args.noise:
noisec = cNData[iter_dim[:2]]
res_rel, initp = RelCal(cData[iter_dim[:2]], noisec, initp)
# expanding out the solution
for j, param in enumerate(res_rel['x']):
res_rel[j] = param
# reset initp after each frequency slice
if not (i + 1) % no_tints and args.initp_jd is None:
initp = None
del res_rel['x']
res_rel.update({indices[0]:freq_chans[iter_dim[0]], \
indices[1]:time_ints[iter_dim[1]]})
writer.writerow(res_rel)
print('Relative calibration results saved to csv file {}'.format(
out_csv))
df = pd.read_csv(out_csv)
if csv_exists:
freqs = df['freq'].unique()
tints = df['time_int'].unique()
if cData.shape[0] != freqs.size or cData.shape[1] != tints.size:
_, _, cData = group_data(zen_fn, args.pol, freqs, tints, \
bad_ants, flag_path=flag_fn)
cData = cData.data
df.set_index(indices, inplace=True)
# we now append the residuals as additional columns
df = append_residuals_rel(df, cData, cRedG, coords, out_fn=None)
if pkl_exists and not csv_exists:
df = pd.concat([df, df_pkl])
df.sort_values(by=indices, inplace=True)
df.to_pickle(out_pkl)
print('Relative calibration results dataframe pickled to {}'.format(
out_pkl))
# creating metadata file
out_md = default_fn.rsplit('.', 1)[0] + '.md.pkl'
if not os.path.exists(out_md):
md = {'no_ants':no_ants, 'no_unq_bls':no_unq_bls, 'redg':RedG, \
'antpos':hd.antpos, 'last':hd.lsts, 'Nfreqs':hd.Nfreqs, \
'Ntimes':hd.Ntimes}
with open(out_md, 'wb') as f:
pickle.dump(md, f, protocol=pickle.HIGHEST_PROTOCOL)
print(
'Relative calibration metadata pickled to {}\n'.format(out_md))
print('Script run time: {}'.format(datetime.datetime.now() - startTime))
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.\
RawDescriptionHelpFormatter, description=textwrap.dedent("""
Check the relative redundant calibration of visibilities
The relative calibration script rel_cal.py reuses the previous solution to
initialize the next solver. While this greatly speeds up the code, there is
the worry that the solver gets stuck in a local minimum. This script takes a
random sample of frequency and time integration slices from a dataframe of
results from relative calibration, and re-runs relative calibration on them
with vanilla initial parameter guesses of 1+1j for the gains and 0+0j for
the visibilities, and verifies if these results match with the original
results by checking both their negative log-likelhoods and their gain
amplitudes. We expect these to match, but we do not expect the gain and
visibility solution phases to be equal, since there are still some additional
degeneracies (overall phase & tilt shifts) that have not been accounted for.
Returns a pickled pandas dataframe of the Scipy optimization results for
the relative redundant calibration for each set of randomly chosen frequency
channel and time integration slices chosen from the rel_df results dataframe
"""))
parser.add_argument('rel_df',
help='Relative calibration results dataframe \
in pickle file format',
metavar='df',
type=str)
parser.add_argument('-o', '--out', required=False, default=None, \
metavar='O', type=str, help='Output csv and df name')
parser.add_argument('-c', '--no_checks', required=False, default=50, \
metavar='C', type=int, help='Number of checks')
parser.add_argument('-t', '--tol', required=False, default=0.01, \
metavar='T', type=float, help='Tolerance for the \
negative log-likelihood of relative calibration results \
to match' )
parser.add_argument('-w', '--overwrite', required=False, action='store_true', \
help='Overwrite existing check csv and dataframe')
parser.add_argument('-r', '--rel_dir', required=False, default=None, metavar='R', \
type=str, help='Directory in which rel_dfs are stored')
parser.add_argument('-u', '--out_dir', required=False, default=None, metavar='U', \
type=str, help='Out directory to store dataframe')
parser.add_argument('-k', '--keep_csv', required=False, action='store_true', \
help='Keep csv file')
args = parser.parse_args()
startTime = datetime.datetime.now()
sout = args.rel_df.split('.')
jd_time = str('{}.{}'.format(sout[1], sout[2]))
pol = sout[3]
dist = sout[4]
no_checks = args.no_checks
out_fn = args.out
if out_fn is None:
out_fn = 'check_rel_df.{}.{}.{}'.format(jd_time, pol, dist)
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
out_fn = os.path.join(args.out_dir, out_fn)
out_csv = fn_format(out_fn, 'csv')
csv_exists = os.path.exists(out_csv)
if csv_exists:
if args.overwrite:
os.remove(out_csv)
out_df = out_csv.rsplit('.', 1)[0] + '.pkl'
df_exists = os.path.exists(out_df)
if df_exists:
if args.overwrite:
os.remove(out_df)
df_exists = False
match_keys = ['loglkl_match', 'gamp_match']
if not df_exists:
zen_fn = find_zen_file(jd_time)
bad_ants = get_bad_ants(zen_fn)
print('Checking the relative redundant calibration results for {}\n'.\
format(args.rel_df))
if args.rel_dir is not None:
rel_dir_path = os.path.join(args.rel_dir, args.rel_df)
else:
rel_dir_path = args.rel_df
rel_df = pd.read_pickle(rel_dir_path)
no_checks = min(no_checks, len(rel_df.index))
rnd_idxs = numpy.random.choice(rel_df.index.values, no_checks, \
replace=False)
rnd_chans = numpy.unique([rnd_idx[0] for rnd_idx in rnd_idxs])
fmap = dict(map(reversed, enumerate(rnd_chans)))
hd, RedG, cData = group_data(zen_fn, pol, rnd_chans, None, bad_ants)
cData = cData.data
freq_chans = numpy.arange(hd.Nfreqs)
time_ints = numpy.arange(hd.Ntimes)
# to get fields for the csv header
no_ants = numpy.unique(RedG[:, 1:]).size
no_unq_bls = numpy.unique(RedG[:, 0]).size
cRedG = relabelAnts(RedG)
psize = (no_ants + no_unq_bls) * 2
indices = ['freq', 'time_int']
slct_keys = ['success', 'status', 'message', 'fun', 'nit', 'x']
header = slct_keys[:-1] + match_keys + list(
numpy.arange(psize)) + indices
stdout = io.StringIO()
with redirect_stdout(stdout): # suppress output
with open(out_csv, 'a') as f: # write / append to csv file
writer = DictWriter(f, fieldnames=header)
writer.writeheader()
for iter_dim in rnd_idxs:
res_rel = doRelCal(cRedG, cData[fmap[iter_dim[0]], iter_dim[1]], \
no_unq_bls, no_ants, coords='cartesian', \
distribution=dist, norm_gains=True)
res_rel = {key: res_rel[key] for key in slct_keys}
# checking results
res_rel[match_keys[0]] = numpy.abs(norm_residuals(rel_df.\
loc[iter_dim]['fun'], res_rel['fun'])) < args.tol
res_gamp = numpy.abs(split_rel_results(rel_df.loc[iter_dim][5:-2].\
values.astype(float), no_unq_bls)[1])
check_gamp = numpy.abs(split_rel_results(res_rel['x'], \
no_unq_bls)[1])
res_rel[match_keys[1]] = (numpy.abs(norm_residuals(res_gamp, \
check_gamp)) < args.tol).all()
# expanding out the solution
for i, param in enumerate(res_rel['x']):
res_rel[i] = param
del res_rel['x']
res_rel.update({indices[0]:iter_dim[0], \
indices[1]:iter_dim[1]})
writer.writerow(res_rel)
df = pd.read_csv(out_csv)
df.set_index(indices, inplace=True)
df.sort_values(by=indices, inplace=True)
df.to_pickle(out_df)
if not args.keep_csv:
os.remove(out_csv)
else:
print('Checked relative calibration results saved to csv file {}'.\
format(out_csv))
print('Checked relative calibration results dataframe pickled to {}\n'.\
format(out_df))
else:
df = pd.read_pickle(out_df)
print('Checked relative calibration results already exists in {} - '\
'specify --overwrite as an argument to perform check again.\n'.\
format(out_df))
no_checks = len(df.index)
matches = df[match_keys].values
all_match = matches.all()
if all_match:
pmatch = 'All'
else:
pmatch = '{}% of'.format(
round(100 * numpy.sum(matches[:, 1]) / matches.shape[0], 2))
print('{} iterations from the {} randomly selected frequency and time '\
'slices match the original results at a tolerance of {}%.\n'.\
format(pmatch, no_checks, args.tol*100))
if not all_match:
print('Mismatched iterations are {}\n'.format(
df[~df['gamp_match']].index.values))
print('Script run time: {}'.format(datetime.datetime.now() - startTime))
def XDgroup_data(JD_time, JDs, pol, chans=None, tints=None, bad_ants=True, \
use_flags='first', noise=False, use_cal=None, verbose=False):
"""Returns redundant baseline grouping and reformatted dataset, with
external flags applied, if specified
:param JD_time: Julian time of 1st dataset, which sets times for others
:type JD_time: str
:param JDs: Julian days of data
:type JDs: list, ndarray
:param pol: Polarization of data
:type pol: str
:param chans: Frequency channel(s) {0, 1023} (None to choose all)
:type chans: array-like, int, or None
:param tints: Time integrations {0, 59} (None to choose all)
:type tints: array-like, int, or None
:param bad_ants: Flag known bad antennas, optional
:type bad_ants: bool
:param use_flags: Use flags to mask data
:type use_flags: str
:param noise: Also calculate noise from autocorrelations
:type noise: bool
:param use_cal: calfits file extension to use to calibrate data
:type use_cal: str, None
:param verbose: Print data gathering steps for each dataset
:type verbose: bool
:return hd: HERAData class
:rtype hd: HERAData class
:return redg: Grouped baselines, as returned by groupBls
:rtype redg: ndarray
:return cdata: Grouped visibilities with flags in numpy MaskedArray format,
with format consistent with redg and dimensions (freq chans,
time integrations, baselines)
:rtype cdata: MaskedArray
"""
if isinstance(chans, int):
chans = np.asarray([chans])
if isinstance(tints, int):
tints = np.asarray([tints])
zen_fn = find_zen_file(JD_time)
flags_fn = find_flag_file(JD_time, use_flags)
hd = HERAData(zen_fn)
if tints is None:
tints = np.arange(hd.Ntimes)
if bad_ants:
bad_ants = union_bad_ants(JDs)
else:
bad_ants = None
if use_cal is None:
cal_path = None
else:
cal_path = find_flag_file(JD_time, use_cal)
if not verbose:
grp_data = suppressOutput(group_data)
else:
grp_data = group_data
grp = grp_data(zen_fn,
pol,
chans=chans,
tints=tints,
bad_ants=bad_ants,
flag_path=flags_fn,
noise=noise,
cal_path=cal_path)
_, redg, cMData = grp[:3]
cMData = cMData[np.newaxis, :]
if noise:
cNoise = grp[3]
cNoise = cNoise[np.newaxis, :]
JD_day = int(float(JD_time))
if JD_day in JDs:
JDs = list(JDs)
JDs.remove(JD_day)
for jd_i in JDs:
JD_time_ia = match_lst(JD_time, jd_i)
# aligning datasets in LAST
last_df = pd.read_pickle(
os.path.join(os.path.dirname(__file__), 'jd_lst_map_idr2.pkl'))
last1 = last_df[last_df['JD_time'] == float(
JD_time)]['LASTs'].values[0]
last2 = last_df[last_df['JD_time'] == float(
JD_time_ia)]['LASTs'].values[0]
_, offset = find_nearest(last2, last1[0])
tints_i = (tints + offset) % 60
scnd_dataset = all(tints + offset > hd.Ntimes - 1)
single_dataset = all(tints + offset < hd.Ntimes - 1) or scnd_dataset
if not single_dataset:
tints_ia, tints_ib = np.split(tints_i, np.where(tints_i == 0)[0])
else:
tints_ia = tints_i
if scnd_dataset:
next_row = numpy.where(
last_df['JD_time'] == float(JD_time_ia))[0][0] + 1
JD_time_ib = last_df.iloc[next_row]['JD_time']
JD_time_ia = JD_time_ib
JD_time_ia = check_jdt(JD_time_ia)
zen_fn_ia = find_zen_file(JD_time_ia)
flags_fn_ia = find_flag_file(JD_time_ia, use_flags)
if use_cal is not None:
cal_path_ia = find_flag_file(JD_time_ia, use_cal)
else:
cal_path_ia = None
grp_a = grp_data(zen_fn_ia, pol, chans=chans, tints=tints_ia, \
bad_ants=bad_ants, flag_path=flags_fn_ia, noise=noise, \
cal_path=cal_path_ia)
cMData_ia = grp_a[2]
if not single_dataset:
next_row = numpy.where(
last_df['JD_time'] == float(JD_time_ia))[0][0] + 1
JD_time_ib = last_df.iloc[next_row]['JD_time']
JD_time_ib = check_jdt(JD_time_ib)
zen_fn_ib = find_zen_file(JD_time_ib)
flags_fn_ib = find_flag_file(JD_time_ib, use_flags)
if use_cal is not None:
cal_path_ib = find_flag_file(JD_time_ib, use_cal)
else:
cal_path_ib = None
grp_b = grp_data(zen_fn_ib, pol, chans=chans, tints=tints_ib, \
bad_ants=bad_ants, flag_path=flags_fn_ib, \
noise=noise, cal_path=cal_path_ib)
cMData_ib = grp_b[2]
cMData_i = numpy.ma.concatenate((cMData_ia, cMData_ib), axis=1)
else:
cMData_i = cMData_ia
cMData_i = cMData_i[np.newaxis, :]
cMData = numpy.ma.concatenate((cMData, cMData_i), axis=0)
if noise:
cNoise_ia = grp_a[3]
if not single_dataset:
cNoise_ib = grp_b[3]
cNoise_i = np.concatenate((cNoise_ia, cNoise_ib), axis=1)
else:
cNoise_i = cNoise_ia
cNoise_i = cNoise_i[np.newaxis, :]
cNoise = np.concatenate((cNoise, cNoise_i), axis=0)
if noise:
return hd, redg, cMData, cNoise
else:
return hd, redg, cMData
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.\
RawDescriptionHelpFormatter, description=textwrap.dedent("""
Absolute optimal calibration of relatively calibrated visibility solutions
Takes the relatively calibrated visibility solutions of HERA data, and
constrains their degenerate parameters, such that average amplitude of
antenna gains is set to 1, the average phase of antenna gains is set to 0,
the overall phase if set to 0 and the phase gradients are 0.
Returns a pickled pandas dataframe of the Scipy optimization results for
the absolute optimal calibration.
"""))
parser.add_argument('jd_time', help='Fractional JD time of dataset to \
analyze', metavar='JD', type=str)
parser.add_argument('-o', '--out', required=False, default=None, \
metavar='O', type=str, help='Output csv and df name')
parser.add_argument('-p', '--pol', required=True, metavar='P', type=str, \
help='Polarization {"ee", "en", "nn", "ne"}')
parser.add_argument('-c', '--chans', required=False, default=None, metavar='C', \
type=str, help='Frequency channels to fit {0, 1023}')
parser.add_argument('-t', '--tints', required=False, default=None, metavar='T', \
type=str, help='Time integrations to fit {0, 59}')
parser.add_argument('-d', '--dist', required=True, metavar='D', \
type=str, help='Fitting distribution for calibration \
{"cauchy", "gaussian"}')
parser.add_argument('-a', '--ref_ant_idx', required=False, default=16, metavar='A', \
type=int, help='Reference antenna index to set the overall \
phase')
parser.add_argument('-l', '--logamp', required=False, action='store_true', \
help='Use logamp method to force positive gain amplitudes')
parser.add_argument('-r', '--rel_dir', required=False, default=None, metavar='R', \
type=str, help='Directory in which relative calibration \
results dataframes are located')
parser.add_argument('-u', '--out_dir', required=False, default=None, metavar='U', \
type=str, help='Out directory to store dataframe')
parser.add_argument('-n', '--new_df', required=False, action='store_true', \
help='Write data to a new csv file')
parser.add_argument('-k', '--compression', required=False, default=None, metavar='K', \
type=str, help='Compression to use when pickling results dataframe')
args = parser.parse_args()
startTime = datetime.datetime.now()
out_fn = args.out
if out_fn is None:
out_fn = 'opt_df.{}.{}.{}'.format(args.jd_time, args.pol, args.dist)
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
out_fn = os.path.join(args.out_dir, out_fn)
out_csv = fn_format(out_fn, 'csv')
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = os.path.exists(out_csv)
pkl_exists = os.path.exists(out_pkl)
if csv_exists or pkl_exists:
if args.new_df:
out_csv = new_fn(out_csv, None, startTime)
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = False
pkl_exists = False
freq_chans = mod_str_arg(args.chans)
time_ints = mod_str_arg(args.tints)
zen_fn = find_zen_file(args.jd_time)
bad_ants = get_bad_ants(zen_fn)
flag_fn = find_flag_file(args.jd_time, 'first') # returns None if not found
rel_df_path = find_rel_df(args.jd_time, args.pol, args.dist, args.rel_dir)
rel_df = pd.read_pickle(rel_df_path)
# retrieving visibility metadata
md_fn = 'rel_df.{}.{}.md.pkl'.format(args.jd_time, args.pol)
if args.rel_dir is not None:
md_fn = os.path.join(args.rel_dir, md_fn)
with open(md_fn, 'rb') as f:
md = pickle.load(f)
antpos = md['antpos']
no_unq_bls = md['no_unq_bls']
redg = md['redg']
pchans = args.chans
if pchans is None:
pchans = '0~{}'.format(md['Nfreqs']-1)
ptints = args.tints
if ptints is None:
ptints = '0~{}'.format(md['Ntimes']-1)
print('Running absolute optimal calibration for visibility dataset {} '\
'for frequency channel(s) {} and time integration(s) {} '\
'with {} assumed noise distribution\n'.\
format(os.path.basename(zen_fn), pchans, ptints, args.dist))
if freq_chans is None:
freq_chans = numpy.arange(md['Nfreqs'])
if time_ints is None:
time_ints = numpy.arange(md['Ntimes'])
# filter by specified channels and time integrations
freq_flt = numpy.in1d(rel_df.index.get_level_values('freq'), freq_chans)
tint_flt = numpy.in1d(rel_df.index.get_level_values('time_int'), time_ints)
rel_df = rel_df[freq_flt & tint_flt]
# only getting frequencies and time integrations that exist in the df
freq_chans = rel_df.index.get_level_values('freq').unique().values
time_ints = rel_df.index.get_level_values('time_int').unique().values
indices = ['freq', 'time_int']
no_tints = len(time_ints)
iter_dims = list(numpy.ndindex((len(freq_chans), no_tints)))
if not iter_dims:
raise ValueError('No frequency channels or time integrations to '\
'iterate over - check that the specified --chans and --tints exist '\
'in the relative calibration results dataframes')
skip_cal = False
# skipping freqs and tints that are already in dataframe
if csv_exists or pkl_exists:
cmap_f = dict(map(reversed, enumerate(freq_chans)))
cmap_t = dict(map(reversed, enumerate(time_ints)))
if csv_exists:
df = pd.read_csv(out_csv, usecols=indices)
idx_arr = df.values
elif pkl_exists:
df_pkl = pd.read_pickle(out_pkl)
idx_arr = df_pkl.reset_index()[indices].values
done = [(cmap_f[f], cmap_t[t]) for (f, t) in idx_arr if (f in freq_chans \
and t in time_ints)]
iter_dims = [idim for idim in iter_dims if idim not in done]
if not any(iter_dims):
print('Solutions to all specified frequency channels and time '\
'integrations already exist in {}\n'.format(out_pkl))
skip_cal = True
if not skip_cal:
hd, RedG, cData = group_data(zen_fn, args.pol, freq_chans, time_ints, \
bad_ants, flag_path=flag_fn)
flags = cData.mask
cData = cData.data
ants = numpy.unique(RedG[:, 1:])
no_ants = ants.size
no_unq_bls = numpy.unique(RedG[:, 0]).size
cRedG = relabelAnts(RedG)
# removing 'jac', 'hess_inv', 'nfev', 'njev'
slct_keys = ['success', 'status', 'message', 'fun', 'nit', 'x']
no_deg_params = 4 # overall amplitude, overall phase, x & y phase gradients
psize = no_ants*2 + no_deg_params + no_unq_bls*2
header = slct_keys[:-1] + list(numpy.arange(psize)) + indices
ant_pos_arr = flt_ant_pos(hd.antpos, ants)
ant_sep = red_ant_sep(RedG, hd.antpos)
def get_w_alpha(res_rel_vis, new_deg_params):
"""Apply degenerate parameters found from optimal absolute
calibration to visibility solutions from relative redundant
calibration
:param res_rel_vis: Visibility solutions
:type res_rel_vis: ndarray
:param new_deg_params: Degenerate parameters
optimal calibration
:type new_deg_params: ndarray
:return: Degenerately transformed visibility solutions
:rtype: ndarray
"""
return degVis(ant_sep, res_rel_vis, *new_deg_params[[0, 2, 3]])
stdout = io.StringIO()
with redirect_stdout(stdout): # suppress output
with open(out_csv, 'a') as f: # write / append to csv file
writer = DictWriter(f, fieldnames=header)
if not csv_exists:
writer.writeheader()
initp = None
for i, iter_dim in enumerate(iter_dims):
# get absolute optimal calibrated solutions
rel_idim = (freq_chans[iter_dim[0]], time_ints[iter_dim[1]])
res_rel_vis, _ = split_rel_results(rel_df.loc[rel_idim]\
[len(slct_keys)-1:-2].values.astype(float), no_unq_bls)
res_opt = doOptCal(cRedG, cData[iter_dim], no_ants, ant_pos_arr, \
ant_sep, res_rel_vis, distribution=args.dist, \
ref_ant_idx=args.ref_ant_idx, logamp=args.logamp, \
initp=initp)
res_opt = {key:res_opt[key] for key in slct_keys}
# get the new visibility solutions
w_alpha = get_w_alpha(res_rel_vis, res_opt['x'][-no_deg_params:])
w_alpha_comps = decomposeCArray(w_alpha)
all_params = numpy.append(res_opt['x'], w_alpha_comps)
# expanding out the solution
for j, param in enumerate(all_params):
res_opt[j] = param
# to use solution for next solve in iteration
if res_opt['success']:
initp = res_opt['x']
# reset initp after each frequency slice
if not (i+1)%no_tints:
initp = None
del res_opt['x']
res_opt.update({indices[0]:rel_idim[0], \
indices[1]:rel_idim[1]})
writer.writerow(res_opt)
print('Absolute optimal calibration results saved to csv file {}'\
.format(out_csv))
df = pd.read_csv(out_csv)
df.set_index(indices, inplace=True)
df = append_residuals_opt(df, cData, cRedG, out_fn=None)
if pkl_exists and not csv_exists:
df = pd.concat([df, df_pkl])
df.sort_values(by=indices, inplace=True)
if args.compression is not None:
out_pkl += '.{}'.format(args.compression)
print('{} compression used in pickling the dataframe'.format(args.compression))
df.to_pickle(out_pkl, compression=args.compression)
print('Absolute optimal calibration results dataframe pickled to {}'\
.format(out_pkl))
print('Script run time: {}'.format(datetime.datetime.now() - startTime))
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.\
RawDescriptionHelpFormatter, description=textwrap.dedent("""
Across days relative redundant calibration of visibilities
Takes HERA visibility datasets across several JDs in uvh5 file format,
aligns them in LAST and then performs relative redundant calibration
(up to the overall amplitude, overall phase, and phase gradient degenerate
parameters) for each frequency channel and each time integration in the dataset.
Returns a pickled pandas dataframe of the Scipy optimization results for
the relative redundant calibration for each set of frequency channel and
time integration.
"""))
parser.add_argument('jd_time',
help='Fractional JD time of dataset to \
align other dataframes to',
metavar='JD',
type=str)
parser.add_argument('-j', '--jds', required=True, metavar='J', \
type=str, help='JDs to calibrate')
parser.add_argument('-p', '--pol', required=True, metavar='P', type=str, \
help='Polarization {"ee", "en", "nn", "ne"}')
parser.add_argument('-c', '--chans', required=False, default=None, metavar='C', \
type=str, help='Frequency channels to calibrate \
{0, 1023}' )
parser.add_argument('-t', '--tints', required=False, default=None, metavar='T', \
type=str, help='Time integrations to calibrate \
{0, 59}' )
parser.add_argument('-f', '--flag_type', required=False, default='first', \
metavar='F', type=str, help='Flag type e.g. "first", \
"omni", "abs"' )
parser.add_argument('-d', '--dist', required=True, metavar='D', \
type=str, help='Fitting distribution for calibration \
{"cauchy", "gaussian"}' )
parser.add_argument('-v', '--noise', required=False, action='store_true', \
help='Use noise from autos in nlogL calculations')
parser.add_argument('-cf', '--chan_flag_pct', required=False, default=None, \
metavar='CFP', type=float, help='Flag channel if more than \
X% of day/time slices for a given channel are flagged' )
parser.add_argument('-o', '--out', required=False, default=None, \
metavar='O', type=str, help='Output csv and df name')
parser.add_argument('-u', '--out_dir', required=False, default=None, metavar='U', \
type=str, help='Out directory to store dataframe')
parser.add_argument('-n', '--new_df', required=False, action='store_true', \
help='Write data to a new dataframe')
parser.add_argument('-k', '--compression', required=False, default=None, metavar='K', \
type=str, help='Compression to use when pickling results dataframe')
args = parser.parse_args()
startTime = datetime.datetime.now()
zen_fn = find_zen_file(args.jd_time)
hd = HERAData(zen_fn)
out_fn = args.out
default_fn = 'xd_rel_df.{}.{}.{}'.format('{:.4f}'.format(hd.lsts[0]), \
args.pol, args.dist)
if out_fn is None:
out_fn = default_fn
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
out_fn = os.path.join(args.out_dir, out_fn)
if out_fn is not None:
default_fn = os.path.join(args.out_dir, default_fn)
out_csv = fn_format(out_fn, 'csv')
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = os.path.exists(out_csv)
pkl_exists = os.path.exists(out_pkl)
if csv_exists or pkl_exists:
if args.new_df:
out_csv = new_fn(out_csv, None, startTime)
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = False
pkl_exists = False
JDs = args.jds
if JDs == 'idr2_jds':
JDs = numpy.asarray(idr2_jds)
elif JDs == 'idr2_jdsx':
JDs = numpy.asarray(idr2_jdsx)
else:
if '_' in JDs:
JDs = numpy.asarray(JDs.split('_'), dtype=int)
else:
JDs = mod_str_arg(JDs)
JDs = numpy.intersect1d(JDs, idr2_jds)
freq_chans = mod_str_arg(args.chans)
time_ints = mod_str_arg(args.tints)
pchans = args.chans
if pchans is None:
pchans = '0~{}'.format(hd.Nfreqs - 1)
ptints = args.tints
if ptints is None:
ptints = '0~{}'.format(hd.Ntimes - 1)
print('Running relative redundant calibration across JDs {} between LASTS '\
'{:.4f} and {:.4f} for polarization {}, frequency channel(s) {} '\
'and time integration(s) {}, with {} assumed noise distribution.\n'.\
format(' '.join(map(str, JDs)), hd.lsts[0], hd.lsts[-1], args.pol, \
pchans, ptints, args.dist))
if freq_chans is None:
freq_chans = numpy.arange(hd.Nfreqs)
if time_ints is None:
time_ints = numpy.arange(hd.Ntimes)
indices = ['freq', 'time_int']
no_tints = len(time_ints)
iter_dims = list(numpy.ndindex((len(freq_chans), no_tints)))
skip_cal = False
# skipping freqs and tints that are already in the dataframe
if csv_exists or pkl_exists:
cmap_f = dict(map(reversed, enumerate(freq_chans)))
cmap_t = dict(map(reversed, enumerate(time_ints)))
if csv_exists:
df = pd.read_csv(out_csv, usecols=indices)
idx_arr = df.values
elif pkl_exists:
df_pkl = pd.read_pickle(out_pkl)
idx_arr = df_pkl.index.values
done = [(cmap_f[f], cmap_t[t]) for (f, t) in idx_arr if (f in freq_chans \
and t in time_ints)]
iter_dims = [idim for idim in iter_dims if idim not in done]
if not any(iter_dims):
print('Solutions to all specified frequency channels and time '\
'integrations already exist in {}\n'.format(out_pkl))
skip_cal = True
if not skip_cal:
stdout = io.StringIO()
with redirect_stdout(stdout): # suppress output
grp = XDgroup_data(args.jd_time, JDs, args.pol, chans=freq_chans, \
tints=time_ints, use_flags=args.flag_type, \
noise=args.noise)
if not args.noise:
_, RedG, cData = grp
noisec = None
else:
_, RedG, cData, cNData = grp
flags = cData.mask
cData = cData.data
# to get fields for the csv header
ants = numpy.unique(RedG[:, 1:])
no_ants = ants.size
no_unq_bls = numpy.unique(RedG[:, 0]).size
cRedG = relabelAnts(RedG)
psize = (no_ants * JDs.size + no_unq_bls) * 2
# discarding 'jac', 'hess_inv', 'nfev', 'njev'
slct_keys = ['success', 'status', 'message', 'fun', 'nit', 'x']
header = slct_keys[:-1] + list(numpy.arange(psize)) + indices
# remove flagged channels from iter_dims
if isinstance(flags, numpy.bool_):
# If all flags are the same
flags = [flags]
if True in flags:
if args.chan_flag_pct is None:
flg_chans = numpy.unique(
numpy.where(flags.all(axis=(0, 2, 3)))[0])
print('Flagged channels across all days are: {}\n'.\
format(freq_chans[flg_chans]))
else:
flg_pct = args.chan_flag_pct / 100
flg_chans = numpy.unique(numpy.where(flags.all(axis=3).mean(axis=(0, 2)) \
> flg_pct)[0])
print('Flagged channels across all days and those that are '\
'more than {}% flagged for their given day/time slice are: {}\n'.\
format(args.chan_flag_pct, freq_chans[flg_chans] ))
iter_dims = [
idim for idim in iter_dims if idim[0] not in flg_chans
]
if not iter_dims: # check if slices to solve are empty
print('All specified channels are flagged. Exiting.')
sys.exit()
def cal(credg, distribution, no_unq_bls, no_ants, obsvis, noise,
initp):
"""Relative redundant calibration across days with doRelCalD:
default implementation with unconstrained minimizer using cartesian
coordinates
"""
res_rel, initp_new = doRelCalD(credg, obsvis, no_unq_bls, no_ants, \
distribution=distribution, noise=noise, initp=initp, \
return_initp=True, xd=True)
res_rel = {key: res_rel[key] for key in slct_keys}
# use solution for next solve in iteration
if res_rel['success']:
initp = initp_new
return res_rel, initp
RelCal = functools.partial(cal, cRedG, args.dist, no_unq_bls, no_ants)
with redirect_stdout(stdout): # suppress output
with open(out_csv, 'a') as f: # write / append to csv file
writer = DictWriter(f, fieldnames=header)
if not csv_exists:
writer.writeheader()
initp = None
for i, iter_dim in enumerate(iter_dims):
if args.noise:
noisec = cNData[:, iter_dim[0], iter_dim[1], :]
res_rel, initp = RelCal(cData[:, iter_dim[0], iter_dim[1], :], \
noisec, initp)
# expanding out the solution
for j, param in enumerate(res_rel['x']):
res_rel[j] = param
# reset initp after each frequency slice
if not (i + 1) % no_tints:
initp = None
del res_rel['x']
res_rel.update({indices[0]:freq_chans[iter_dim[0]], \
indices[1]:time_ints[iter_dim[1]]})
writer.writerow(res_rel)
print('Relative calibration results saved to csv file {}'.format(
out_csv))
df = pd.read_csv(out_csv)
if csv_exists:
freqs = df['freq'].unique()
tints = df['time_int'].unique()
if cData.shape[0] != freqs.size or cData.shape[1] != tints.size:
_, _, cData = XDgroup_data(args.jd_time, JDs, args.pol, chans=freqs,
tints=tints, use_flags=args.flag_type, \
noise=None)
cData = cData.data
df.set_index(indices, inplace=True)
# we now append the residuals as additional columns
df = append_residuals_rel(df, cData, cRedG, 'cartesian', out_fn=None)
if pkl_exists and not csv_exists:
df = pd.concat([df, df_pkl])
df.sort_values(by=indices, inplace=True)
if args.compression is not None:
out_pkl += '.{}'.format(args.compression)
print('{} compression used in pickling the dataframe'.format(
args.compression))
df.to_pickle(out_pkl, compression=args.compression)
print('Relative calibration results dataframe pickled to {}'.format(
out_pkl))
# creating metadata file
out_md = default_fn.rsplit('.', 1)[0] + '.md.pkl'
if not os.path.exists(out_md):
md = {'no_ants':no_ants, 'no_unq_bls':no_unq_bls, 'redg':RedG, \
'antpos':hd.antpos, 'last':hd.lsts, 'Nfreqs':hd.Nfreqs, \
'Ntimes':hd.Ntimes, 'JDs':JDs}
with open(out_md, 'wb') as f:
pickle.dump(md, f, protocol=pickle.HIGHEST_PROTOCOL)
print(
'Relative calibration metadata pickled to {}\n'.format(out_md))
print('Script run time: {}'.format(datetime.datetime.now() - startTime))
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.\
RawDescriptionHelpFormatter, description=textwrap.dedent("""
Degenerate fitting of relatively calibrated visibility solutions
Takes the relatively calibrated visibility solutions of HERA data, and fits
degenerate parameters (overall amplitude, overall phase, and phase gradient)
to two adjacent datasets in either LAST, frequency, or JD.
Returns a pickled pandas dataframe of the Scipy optimization results for
the degenerate fitting.
"""))
parser.add_argument('jd_time', help='Fractional JD time of dataset to \
analyze', metavar='JD', type=str)
parser.add_argument('-o', '--out', required=False, default=None, \
metavar='O', type=str, help='Output csv and df name')
parser.add_argument('-p', '--pol', required=True, metavar='P', type=str, \
help='Polarization {"ee", "en", "nn", "ne"}')
parser.add_argument('-x', '--deg_dim', required=True, metavar='X', type=str, \
help='Which dimension to compare relatively calibrated \
visibility solutions {"tint", "freq", "jd"}')
parser.add_argument('-m', '--coords', required=False, default='cartesian', \
metavar='M', type=str, help='Coordinates used for rel cal \
results - {"cartesian", "polar"}')
parser.add_argument('-c', '--chans', required=False, default=None, metavar='C', \
type=str, help='Frequency channels to fit {0, 1023}')
parser.add_argument('-t', '--tints', required=False, default=None, metavar='T', \
type=str, help='Time integrations to fit {0, 59}')
parser.add_argument('-d', '--dist', required=True, default='cauchy', metavar='D', \
type=str, help='Fitting distribution for calibration \
{"cauchy", "gaussian"}')
parser.add_argument('-j', '--tgt_jd', required=False, default=None, metavar='J', \
type=int, help='JD day for fitting across JDs - only if \
deg_dim = "jd". Default to pick consecutive JD day')
parser.add_argument('-r', '--rel_dir', required=False, default=None, metavar='R', \
type=str, help='Directory in which relative calibration \
results dataframes are located')
parser.add_argument('-u', '--out_dir', required=False, default=None, metavar='U', \
type=str, help='Out directory to store dataframe')
parser.add_argument('-n', '--new_df', required=False, action='store_true', \
help='Write data to a new csv file')
parser.add_argument('-k', '--compression', required=False, default=None, metavar='K', \
type=str, help='Compression to use when pickling results dataframe')
args = parser.parse_args()
startTime = datetime.datetime.now()
pjd = ''
if args.deg_dim == 'jd':
tgt_jd = args.tgt_jd
if tgt_jd is None:
# choose consecutive JD as default
tgt_jd = int(float(args.jd_time)) + 1
pjd = '.' + str(tgt_jd)
out_fn = args.out
if out_fn is None:
out_fn = 'deg_df.{}.{}.{}{}.{}'.format(args.jd_time, args.pol, \
args.deg_dim, pjd, args.dist)
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
out_fn = os.path.join(args.out_dir, out_fn)
out_csv = fn_format(out_fn, 'csv')
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = os.path.exists(out_csv)
pkl_exists = os.path.exists(out_pkl)
if csv_exists or pkl_exists:
if args.new_df:
out_csv = new_fn(out_csv, None, startTime)
out_pkl = out_csv.rsplit('.', 1)[0] + '.pkl'
csv_exists = False
pkl_exists = False
freq_chans = mod_str_arg(args.chans)
time_ints = mod_str_arg(args.tints)
rel_df_path = find_rel_df(args.jd_time, args.pol, args.dist, args.rel_dir)
rel_df = pd.read_pickle(rel_df_path)
# retrieving visibility metadata
md_fn = 'rel_df.{}.{}.md.pkl'.format(args.jd_time, args.pol)
if args.rel_dir is not None:
md_fn = os.path.join(args.rel_dir, md_fn)
with open(md_fn, 'rb') as f:
md = pickle.load(f)
ant_pos = md['antpos']
no_unq_bls = md['no_unq_bls']
RedG = md['redg']
ant_sep = red_ant_sep(RedG, ant_pos)
pchans = args.chans
if pchans is None:
pchans = '0~{}'.format(md['Nfreqs']-1)
ptints = args.tints
if ptints is None:
ptints = '0~{}'.format(md['Ntimes']-1)
pdict = {'freq':'frequency channels', 'tint':'time integrations', \
'jd':'Julian days'}
print('Running degenerate translation on adjacent {} for visibility dataset {} '\
'for frequency channel(s) {} and time integration(s) {} with {} '\
'assumed noise distribution\n'.format(pdict[args.deg_dim], \
os.path.basename(find_zen_file(args.jd_time)), pchans, ptints, args.dist))
if freq_chans is None:
freq_chans = numpy.arange(md['Nfreqs'])
if time_ints is None:
time_ints = numpy.arange(md['Ntimes'])
# filter by specified channels and time integrations
freq_flt = numpy.in1d(rel_df.index.get_level_values('freq'), freq_chans)
tint_flt = numpy.in1d(rel_df.index.get_level_values('time_int'), time_ints)
rel_df = rel_df[freq_flt & tint_flt]
# only getting frequencies and time integrations that exist in the df
freq_chans = rel_df.index.get_level_values('freq').unique().values
time_ints = rel_df.index.get_level_values('time_int').unique().values
if args.deg_dim == 'freq':
indices = ['freq1', 'freq2', 'time_int']
# getting adjacent frequency channel pairs
iter_dims = [idim for idim in zip(freq_chans, freq_chans[1:]) if \
idim[1] - idim[0] == 1]
iter_dims = [idim+(time_int,) for idim in iter_dims for time_int in \
time_ints] # adding time integrations
a, b, c, d = 0, 1, 2, 2 # for iteration indexing
if args.deg_dim == 'tint':
indices = ['time_int1', 'time_int2', 'freq']
# getting adjacent LAST (time integration) pairs
iter_dims = [idim for idim in zip(time_ints, time_ints[1:]) if \
idim[1] - idim[0] == 1]
iter_dims = [idim+(freq_chan,) for idim in iter_dims for freq_chan in \
freq_chans] # adding frequency channels
a, b, c, d = 2, 2, 0, 1 # for iteration indexing
if args.deg_dim == 'jd':
indices = ['time_int1', 'time_int2', 'freq']
# find dataset from specified JD that contains visibilities at the same LAST
jd_time2 = match_lst(args.jd_time, tgt_jd)
if len(str(jd_time2)) < 13:
jd_time2 = str(jd_time2) + '0' # add a trailing 0 that is omitted in float
rel_df_path2 = find_rel_df(jd_time2, args.pol, args.dist, args.rel_dir)
if isinstance(jd_time2, str):
jd_time2 = float(jd_time2)
# aligning datasets in LAST
last_df = pd.read_pickle('jd_lst_map_idr2.pkl')
last1 = last_df[last_df['JD_time'] == float(args.jd_time)]['LASTs'].values[0]
last2 = last_df[last_df['JD_time'] == jd_time2]['LASTs'].values[0]
_, offset = find_nearest(last2, last1[0])
rel_df2 = pd.read_pickle(rel_df_path2)
rel_df2 = rel_df2[rel_df2.index.get_level_values('time_int') >= offset]
next_row = numpy.where(last_df['JD_time'] == jd_time2)[0][0] + 1
rel_df_path3 = find_rel_df(last_df.iloc[next_row]['JD_time'], args.pol, \
args.dist, args.rel_dir)
rel_df3 = pd.read_pickle(rel_df_path3)
rel_df3 = rel_df3[rel_df3.index.get_level_values('time_int') < offset]
# combined results dataframes that is now alinged in LAST by row number
# with rel_df:
rel_df_c = pd.concat([rel_df2, rel_df3])
# pairing time_ints from rel_df and rel_df_c that match in LAST
# time_ints2 = rel_df_c.index.get_level_values('time_int').unique().values
time_ints2 = numpy.arange(offset, offset + md['Ntimes']) % md['Ntimes']
iter_dims = [idim for idim in zip(time_ints, time_ints2[time_ints])]
iter_dims = [idim+(freq_chan,) for idim in iter_dims for freq_chan in \
freq_chans]
iter_dims = sorted(iter_dims, key=lambda row: row[2]) # iterate across
# LAST first - should speed up fitting
a, b, c, d = 2, 2, 0, 1 # for iteration indexing
else:
rel_df_c = rel_df
if not iter_dims:
raise ValueError('No frequency channels or time integrations to '\
'iterate over - check that the specified --chans '\
'and --tints exist in the relative calibration '\
'results dataframes')
skip_cal = False
# skipping freqs and tints that are already in dataframe
if csv_exists or pkl_exists:
if csv_exists:
df = pd.read_csv(out_csv, usecols=indices)
idx_arr = df.values
elif pkl_exists:
df_pkl = pd.read_pickle(out_pkl)
idx_arr = df_pkl.reset_index()[indices].values
iter_dims = [idim for idim in iter_dims if not \
numpy.equal(idx_arr, numpy.asarray(idim)).all(1).any()]
if not any(iter_dims):
print('Solutions to all specified frequency channels and time '\
'integrations already exist in {}\n'.format(out_pkl))
skip_cal = True
if not skip_cal:
# removing 'jac', 'hess_inv', 'nfev', 'njev'
slct_keys = ['success', 'status', 'message', 'fun', 'nit', 'x']
no_deg_params = 3 # overall amplitude, x phase gradient, y phase gradient
header = slct_keys[:-1] + list(numpy.arange(no_deg_params)) + indices
stdout = io.StringIO()
with redirect_stdout(stdout): # suppress output
with open(out_csv, 'a') as f: # write / append to csv file
writer = DictWriter(f, fieldnames=header)
if not csv_exists:
writer.writeheader()
initp = None
for iter_dim in iter_dims:
# get relatively calibrated solutions
resx1 = rel_df.loc[iter_dim[a], iter_dim[c]][len(slct_keys)-1:-2]\
.values.astype(float)
resx2 = rel_df_c.loc[iter_dim[b], iter_dim[d]][len(slct_keys)-1:-2]\
.values.astype(float)
rel_vis1, _ = split_rel_results(resx1, no_unq_bls, coords=args.coords)
rel_vis2, _ = split_rel_results(resx2, no_unq_bls, coords=args.coords)
res_deg = doDegVisVis(ant_sep, rel_vis1, rel_vis2, \
distribution=args.dist, initp=initp)
res_deg = {key:res_deg[key] for key in slct_keys}
# expanding out the solution
for i, param in enumerate(res_deg['x']):
res_deg[i] = param
# to use solution for next solve in iteration
# if res_deg['success']:
# initp = res_deg['x']
del res_deg['x']
res_deg.update({indices[i]:iter_dim[i] for i in \
range(no_deg_params)})
writer.writerow(res_deg)
print('Degenerate fitting results saved to csv file {}'.format(out_csv))
df = pd.read_csv(out_csv)
df_indices = indices.copy()
mv_col = df_indices.pop(1)
df.set_index(df_indices, inplace=True)
cols = list(df.columns.values)
cols.remove(mv_col)
df = df[[mv_col]+cols]
# we now append the residuals as additional columns
df = append_residuals_deg(df, rel_df, rel_df_c, md, out_fn=None)
if pkl_exists and not csv_exists:
df = pd.concat([df, df_pkl])
df.sort_values(by=indices, inplace=True)
if args.compression is not None:
out_pkl += '.{}'.format(args.compression)
print('{} compression used in pickling the dataframe'.format(args.compression))
df.to_pickle(out_pkl, compression=args.compression)
print('Degenerate fitting results dataframe pickled to {}'.format(out_pkl))
print('Script run time: {}'.format(datetime.datetime.now() - startTime))