# -*- mode: python; coding: utf-8 -*-
# Copyright 2012-2015 Peter Williams <peter@newton.cx> and collaborators.
# Licensed under the MIT License.
"""This module implements an algorithm to compute light curves for point
sources in interferometric visibility data. CASA doesn’t have a task to do this.
The algorithm is accessible from the command line as ``casatask dftphotom``,
but it can also be invoked from within Python. For help on usage from the
command line, run ``casatask dftphotom --help``. The command’s help text will
also describe some of the parameters below in more detail than is currently
found here.
"""
from __future__ import absolute_import, division, print_function
__all__ = 'Config dftphotom dftphotom_cli'.split()
import six, sys, os.path, numpy as np
from six.moves import range
from ... import binary_type, text_type
# Note: zany spacing so that Sphinx can parse the file correctly.
from . ..astutil import *
from . ..cli import check_usage, die, warn
from . ..kwargv import ParseKeywords, Custom
from . import util
from .util import sanitize_unicode as b
dftphotom_doc = \
"""
casatask dftphotom vis=MS [keywords...]
Extract photometry from the visibilities in a measurement set. See the full
documentation for some important caveats.
vis=
Path of the MeasurementSet dataset to read. Required.
out=
Path to which data will be written. If unspecified, data are written
to standard output.
rephase=RA,DEC
Phase the data to extract fluxes in the specified direction. If
unspecified, the data are not rephased, i.e. the flux at the
phase center is extracted. RA and DEC should be in sexigesimal
with fields separated by colons.
array=, baseline=, field=, observation=, polarization=, scan=,
scanintent=, spw=, taql=, time=, uvdist=
MeasurementSet selectors used to filter the input data.
Default polarization is 'RR,LL'. All polarizations are averaged
together, so mixing parallel- and cross-hand pols is almost
never what you want to do.
NOTE: only whole spectral windows can be selected. If you attempt to select
channels within a window, a warning will be printed and that component of
the selection will be ignored.
datacol=
Name of the column to use for visibility data. Defaults to 'data'.
You might want it to be 'corrected_data'.
datascale=
Multiply fluxes by this number before reporting them. Defaults to
1e6, so that the output is in terms of microjanskys if the data are
correctly flux-scaled. The textual output has two decimal places so
adjusting this value can give better results if your characteristic
fluxes are significantly different than this.
believeweights=[t|f]
Defaults to false, which means that we assume that the 'weight'
column in the dataset is NOT scaled such that the variance in the
visibility samples is equal to 1/weight. In this case
uncertainties are assessed from the scatter of all the visibilities
in each timeslot. If true, we trust that variance=1/weight and
propagate this in the standard way.
format=[humane(default)|pandas]
The format of the output. The default is "humane" which is described
below. The "pandas" format is slightly less human-friendly but can
be read directly into a Pandas DataFrame with pandas.read_table().
IMPORTANT: the fundamental assumption of this task is that the only
signal in the visibilities is from a point source at the phasing
center. We also assume that all sampled polarizations get equal
contributions from the source (though you can resample the Stokes
parameters on the fly, so this is not quite the same thing as
requiring the source be unpolarized).
In the "humane" format, the output columns are:
MJD(TT) dt[min] re reErr im imErr mag magErr npts
with sorted MJDs, one record for each timestamp present in the dataset,
with all fluxes in ujy. dt is simply (MJD - MJD[0]) * 1440. The units
of re, im, mag, and their uncertainties are microjansky by default,
but see the datascale keyword, and there's no way to know if the
data have actually been flux-calibrated or not.
"""
[docs]class Config(ParseKeywords):
vis = Custom(str, required=True)
"""The path to the visibility MeasurementSet to process. No default; you
must specify a value before calling :func:`dftphotom`."""
datacol = 'data'
"""A string specifying which visibility data column to process: ``data``,
``corrected_data``, or ``model_data``. Default ``'data'``.
"""
believeweights = False
"""Whether to trust that the data-weight information in the MS accurately
describe the noise in their corresponding visibilities. Default False.
"""
@Custom(str, uiname='out')
def outstream(val):
if val is None:
return sys.stdout
try:
return open(val, 'w')
except Exception as e:
die('cannot open path "%s" for writing', val)
datascale = 1e6
"""The amount by which to scale the computed values before emitting them as
text. The default is 1e6, which means that the output will be in
microJanskys if the underlying data are calibrated to Jansky units.
"""
@Custom(str, default='humane')
def format(val):
if val is None or val == 'humane':
return HumaneOutputFormat()
if val == 'pandas':
return PandasOutputFormat()
die('unrecognized output format %r', val)
@Custom([str, str], default=None)
def rephase(val):
if val is None:
return None
try:
ra = parsehours(val[0])
dec = parsedeglat(val[1])
except Exception as e:
die('cannot parse "rephase" values as RA/dec: %s', e)
return ra, dec
# MeasurementSet filters
array = str
baseline = str
field = str
observation = str
polarization = 'RR,LL'
scan = str
scanintent = str
spw = str
taql = str
time = str
uvdist = str
loglevel = 'warn'
[docs]def dftphotom(cfg):
"""Run the discrete-Fourier-transform photometry algorithm.
See the module-level documentation and the output of ``casatask dftphotom
--help`` for help. All of the algorithm configuration is specified in the
*cfg* argument, which is an instance of :class:`Config`.
"""
tb = util.tools.table()
ms = util.tools.ms()
me = util.tools.measures()
# Read stuff in. Even if the weight values don't have their
# absolute scale set correctly, we can still use them to set the
# relative weighting of the data points.
#
# datacol is (ncorr, nchan, nchunk)
# flag is (ncorr, nchan, nchunk)
# weight is (ncorr, nchunk)
# uvw is (3, nchunk)
# time is (nchunk)
# axis_info.corr_axis is (ncorr)
# axis_info.freq_axis.chan_freq is (nchan, 1) [for now?]
#
# Note that we apply msselect() again when reading the data because
# selectinit() is broken, but the invocation here is good because it
# affects the results from ms.range() and friends.
if ':' in (cfg.spw or ''):
warn('it looks like you are attempting to select channels within one or more spws')
warn('this is NOT IMPLEMENTED; I will average over the whole spw instead')
ms.open(b(cfg.vis))
totrows = ms.nrow()
ms_sels = dict((n, cfg.get(n)) for n in util.msselect_keys
if cfg.get(n) is not None)
ms.msselect(b(ms_sels))
rangeinfo = ms.range(b'data_desc_id field_id'.split())
ddids = rangeinfo['data_desc_id']
fields = rangeinfo['field_id']
colnames = [cfg.datacol] + 'flag weight time axis_info'.split()
rephase = (cfg.rephase is not None)
if fields.size != 1:
# I feel comfortable making this a fatal error, even if we're
# not rephasing.
die('selected data should contain precisely one field; got %d', fields.size)
if rephase:
fieldid = fields[0]
tb.open(b(os.path.join(cfg.vis, 'FIELD')))
phdirinfo = tb.getcell(b'PHASE_DIR', fieldid)
tb.close()
if phdirinfo.shape[1] != 1:
die('trying to rephase but target field (#%d) has a '
'time-variable phase center, which I can\'t handle', fieldid)
ra0, dec0 = phdirinfo[:,0] # in radians.
# based on intflib/pwflux.py, which was copied from
# hex/hex-lib-calcgainerr:
dra = cfg.rephase[0] - ra0
dec = cfg.rephase[1]
l = np.sin(dra) * np.cos(dec)
m = np.sin(dec) * np.cos(dec0) - np.cos(dra) * np.cos(dec) * np.sin(dec0)
n = np.sin(dec) * np.sin(dec0) + np.cos(dra) * np.cos(dec) * np.cos(dec0)
n -= 1 # makes the work below easier
lmn = np.asarray([l, m, n])
colnames.append('uvw')
# Also need this although 99% of the time `ddid` and `spwid` are the same
tb.open(b(os.path.join(cfg.vis, 'DATA_DESCRIPTION')))
ddspws = np.asarray(tb.getcol(b'SPECTRAL_WINDOW_ID'))
tb.close()
tbins = {}
colnames = b(colnames)
for ddindex, ddid in enumerate(ddids):
# Starting in CASA 4.6, selectinit(ddid) stopped actually filtering
# your data to match the specified DDID! What garbage. Work around
# with our own filtering.
ms_sels['taql'] = 'DATA_DESC_ID == %d' % ddid
ms.msselect(b(ms_sels))
ms.selectinit(ddid)
if cfg.polarization is not None:
ms.selectpolarization(b(cfg.polarization.split(',')))
ms.iterinit(maxrows=4096)
ms.iterorigin()
while True:
cols = ms.getdata(items=colnames)
if rephase:
# With appropriate spw/DDID selection, `freqs` has shape
# (nchan, 1). Convert to m^-1 so we can multiply against UVW
# directly.
freqs = cols['axis_info']['freq_axis']['chan_freq']
assert freqs.shape[1] == 1, 'internal inconsistency, chan_freq??'
freqs = freqs[:,0] * util.INVERSE_C_MS
for i in range(cols['time'].size): # all records
time = cols['time'][i]
# get out of UTC as fast as we can! For some reason
# giving 'unit=s' below doesn't do what one might hope it would.
# CASA can convert to a variety of timescales; TAI is probably
# the safest conversion in terms of being helpful while remaining
# close to the fundamental data, but TT is possible and should
# be perfectly precise for standard applications.
mq = me.epoch(b'utc', b({'value': time / 86400., 'unit': 'd'}))
mjdtt = me.measure(b(mq), b'tt')['m0']['value']
tdata = tbins.get(mjdtt, None)
if tdata is None:
tdata = tbins[mjdtt] = [0., 0., 0., 0., 0]
if rephase:
uvw = cols['uvw'][:,i]
ph = np.exp((0-2j) * np.pi * np.dot(lmn, uvw) * freqs)
for j in range(cols['flag'].shape[0]): # all polns
# We just average together all polarizations right now!
# (Not actively, but passively by just iterating over them.)
data = cols[cfg.datacol][j,:,i]
flags = cols['flag'][j,:,i]
# XXXXX casacore is currently (ca. 2012) broken and
# returns the raw weights from the dataset rather than
# applying the polarization selection. Fortunately all of
# our weights are the same, and you can never fetch more
# pol types than the dataset has, so this bit works
# despite the bug.
w = np.where(~flags)[0]
if not w.size:
continue # all flagged
if rephase:
data *= ph
d = data[w].mean()
# account for flagged parts. 90% sure this is the
# right thing to do:
wt = cols['weight'][j,i] * w.size / data.size
wd = wt * d
# note a little bit of a hack here to encode real^2 and
# imag^2 separately:
wd2 = wt * (d.real**2 + (1j) * d.imag**2)
tdata[0] += wd
tdata[1] += wd2
tdata[2] += wt
tdata[3] += wt**2
tdata[4] += 1
if not ms.iternext():
break
ms.reset() # reset selection filter so we can get next DDID
ms.close()
# Could gain some efficiency by using a better data structure than a dict().
smjd = sorted(six.iterkeys(tbins))
cfg.format.header(cfg)
for mjd in smjd:
wd, wd2, wt, wt2, n = tbins[mjd]
if n < 3: # not enough data for meaningful statistics
continue
dtmin = 1440 * (mjd - smjd[0])
r_sc = wd.real / wt * cfg.datascale
i_sc = wd.imag / wt * cfg.datascale
r2_sc = wd2.real / wt * cfg.datascale**2
i2_sc = wd2.imag / wt * cfg.datascale**2
if cfg.believeweights:
ru_sc = wt**-0.5 * cfg.datascale
iu_sc = wt**-0.5 * cfg.datascale
else:
rv_sc = r2_sc - r_sc**2 # variance among real/imag msmts
iv_sc = i2_sc - i_sc**2
ru_sc = np.sqrt(rv_sc * wt2) / wt # uncert in mean real/img values
iu_sc = np.sqrt(iv_sc * wt2) / wt
mag = np.sqrt(r_sc**2 + i_sc**2)
umag = np.sqrt(r_sc**2 * ru_sc**2 + i_sc**2 * iu_sc**2) / mag
cfg.format.row(cfg, mjd, dtmin, r_sc, ru_sc, i_sc, iu_sc, mag, umag, n)
[docs]def dftphotom_cli(argv):
"""Command-line access to the :func:`dftphotom` algorithm.
This function implements the behavior of the command-line ``casatask
dftphotom`` tool, wrapped up into a single callable function. The argument
*argv* is a list of command-line arguments, in Unix style where the zeroth
item is the name of the command.
"""
check_usage(dftphotom_doc, argv, usageifnoargs=True)
cfg = Config().parse(argv[1:])
util.logger(cfg.loglevel)
dftphotom(cfg)