def read_data_from_ms(msfn, viscol="DATA", noisecol='SIGMA',
mode='tot',noise_est = False):
"""
Reads polarization or total intensity data in visibility and noise arrays.
Args:
msfn: Name of the MeasurementSet file from which to read the data
viscol: A string with the name of the MS column from which to read the
data [DATASET_STRING]
noisecol: A string with the name of the MS column from which to read
the noise or weights ['SIGMA']
mode: Flag to set whether the function should read in
polarization data ('pol') or total intensity data ('tot')
Returns:
vis
noise
"""
m = M.Messenger(2)
if mode == 'pol':
m.header2("Reading polarization data from the MeasurementSet...")
if mode == 'tot':
m.header2("Reading total intensity data from the MeasurementSet...")
viscol = viscol.lower()
noisecol = noisecol.lower()
if noise_est:
m.message("Performing simple noise estimate")
computenoise(msfn,viscol,m,minsamp=10)
ms.open(msfn)
meta = ms.metadata()
nspw = range(meta.nspw())
nchan = []
nvis = []
u = []
v = []
freq = []
if mode == 'pol':
Qvis = []
Uvis = []
Qsigma = []
Usigma = []
lambs2 = []
if mode == 'tot':
vis = []
sigma = []
# the Q,U OR the I part of the S Jones matrix (hence Spart)
# from the Stokes enumeration defined in the casa core libraries
# http://www.astron.nl/casacore/trunk/casacore/doc/html \
#/classcasa_1_1Stokes.html#e3cb0ef26262eb3fdfbef8273c455e0c
# this defines which polarization type the data columns correspond to
corr = ms.getdata(['axis_info'])['axis_info']['corr_axis']
corr_announce = "Correlation type detected to be "
ii = complex(0, 1)
if mode == 'pol':
if corr[0] == 'RR': # RR, RL, LR, LL
QSpart = np.array([0, 0.5, 0.5, 0])
USpart = np.array([0, -0.5 * ii, 0.5 * ii, 0])
corr_announce += "RR, RL, LR, LL"
elif corr[0] == 'I': # I, Q, U, V
QSpart = np.array([0, 1., 0, 0])
USpart = np.array([0, 0, 1., 0])
corr_announce += "I, Q, U, V"
elif corr[0] == 'XX': # XX, XY, YX, YY
QSpart = np.array([0.5, 0, 0, -0.5])
USpart = np.array([0, 0.5, 0.5, 0])
corr_announce += "XX, XY, YX, YY"
if mode == 'tot':
if corr[0] == 'RR': # RR, RL, LR, LL
Spart = np.array([0.5, 0, 0, 0.5])
corr_announce += "RR, RL, LR, LL"
elif corr[0] == 'I': # I, Q, U, V
Spart = np.array([1., 0, 0, 0])
corr_announce += "I, Q, U, V"
elif corr[0] == 'XX': # XX, XY, YX, YY
Spart = np.array([0.5, 0, 0, 0.5])
corr_announce += "XX, XY, YX, YY"
print corr_announce
for spw in nspw:
nchan.append(meta.nchan(spw))
ms.selectinit(datadescid=spw)
temp = ms.getdata([viscol,"axis_info"],ifraxis=False)
data_temp = temp[viscol]
info_temp = temp["axis_info"]
s_temp = ms.getdata(["sigma"], ifraxis=False)['sigma']
flags = 1. - ms.getdata(["flag"])['flag']
if not(np.sum(flags[0])==np.sum(flags[1])==np.sum(flags[2])==np.sum(flags[3])):
m.warn('Warning: Different flags for different correlations/channels.'+
'Hard flag is applied: If any correlation is flagged, this gets'+
'extended to all.')
maximum = np.ones(np.shape(flags[0]))
for i in range(4):
if flags[i].sum() < maximum.sum():
maximum = flags[i]
flag = maximum
else:
flag = flags[0]
#Start reading data.
if mode == 'tot':
vis_temp = flag * (Spart[0] * data_temp[0] + Spart[1] * data_temp[1] + Spart[2] *\
data_temp[2] + Spart[3] * data_temp[3])
sigma_temp = flag * (Spart[0] * s_temp[0] + Spart[1] * s_temp[1] + Spart[2] * s_temp[2] +\
Spart[3] * s_temp[3])
vis.append(vis_temp)
sigma.append(sigma_temp)
nvis.append(len(vis_temp[0]))
if mode == 'pol':
Qvis_temp = flag * (QSpart[0] * data_temp[0] + QSpart[1] * data_temp[1] + QSpart[2] *\
data_temp[2] + QSpart[3] * data_temp[3])
Qsigma_temp = flag * (QSpart[0] * s_temp[0] + QSpart[1] * s_temp[1] + QSpart[2] * s_temp[2] +\
QSpart[3] * s_temp[3])
Qvis.append(Qvis_temp)
Qsigma.append(Qsigma_temp)
Uvis_temp = flag * (USpart[0] * data_temp[0] + USpart[1] * data_temp[1] + USpart[2] *\
data_temp[2] + USpart[3] * data_temp[3])
Usigma_temp = flag * (USpart[0] * s_temp[0] + USpart[1] * s_temp[1] + USpart[2] * s_temp[2] +\
USpart[3] * s_temp[3])
Uvis.append(Uvis_temp)
Usigma.append(Usigma_temp)
nvis.append(len(Uvis_temp[0]))
#uvflat give all uv coordinates for the chosen spw in m
uflat = ms.getdata(['u'])['u']
vflat = ms.getdata(['v'])['v']
freqtemp = info_temp["freq_axis"]["chan_freq"]
freq.append(freqtemp)
lamb = C / freqtemp
if mode == 'pol':
lambs2.append(lamb**2 / PI)
#calculates uv coordinates per channel in #lambda
utemp = np.array([uflat/k for k in lamb])
vtemp = np.array([vflat/k for k in lamb])
#Reads the uv coordates into lists. Delete functions take care of flags.
u.append(utemp)
v.append(vtemp)
try:
summary = ms.summary()
except:
print "Warning: Could not create a summary"
summary = None
ms.close()
if mode =='tot':
return vis, sigma, u, v, freq, nchan, nspw, nvis, summary
if mode == 'pol':
return Qvis, Qsigma, Uvis, Usigma, freq, lamb, u, v, nchan, nspw, nvis
def read_pyratdata_from_ms(msfn, vis, noise, viscol="DATA", noisecol='SIGMA', mode='pol'):
"""
Reads polarization or total intensity data in visibility and noise arrays.
Args:
msfn: Name of the MeasurementSet file from which to read the data
visp: Pyrat data object
noisep: Pyrat data object
viscol: A string with the name of the MS column from which to read the
data [DATASET_STRING]
noisecol: A string with the name of the MS column from which to read
the noise or weights ['SIGMA']
mode: Flag to set whether the function should read in
polarization data ('pol') or total intensity data ('tot')
Returns:
vis
noise
"""
#Attention, in current setting, pyrat can't handle properly different flags on separate channels
# Messenger object for displaying messages
m = vis.m
if vis._initialized and noise._initialized:
m.warn("Requested data objects already exist. Using the " +
"previously parsed data.")
return
if mode == 'pol':
m.header2("Reading polarization data from the MeasurementSet...")
if mode == 'tot':
m.header2("Reading total intensity data from the MeasurementSet...")
viscol = viscol.lower()
noisecol = noisecol.lower()
ms.open(msfn)
meta = ms.metadata()
nspw = range(meta.nspw())
nchan = []
u = []
v = []
freq = []
if mode == 'pol':
lambs = []
# the Q,U OR the I part of the S Jones matrix (hence Spart)
# from the Stokes enumeration defined in the casa core libraries
# http://www.astron.nl/casacore/trunk/casacore/doc/html \
#/classcasa_1_1Stokes.html#e3cb0ef26262eb3fdfbef8273c455e0c
# this defines which polarization type the data columns correspond to
corr = ms.getdata(['axis_info'])['axis_info']['corr_axis']
corr_announce = "Correlation type detected to be "
ii = complex(0, 1)
if mode == 'pol':
if corr[0] == 'RR': # RR, RL, LR, LL
QSpart = np.array([0, 0.5, 0.5, 0])
USpart = np.array([0, -0.5 * ii, 0.5 * ii, 0])
corr_announce += "RR, RL, LR, LL"
elif corr[0] == 'I': # I, Q, U, V
QSpart = np.array([0, 1., 0, 0])
USpart = np.array([0, 0, 1., 0])
corr_announce += "I, Q, U, V"
elif corr[0] == 'XX': # XX, XY, YX, YY
QSpart = np.array([0.5, 0, 0, -0.5])
USpart = np.array([0, 0.5, 0.5, 0])
corr_announce += "XX, XY, YX, YY"
if mode == 'tot':
if corr[0] == 'RR': # RR, RL, LR, LL
Spart = np.array([0.5, 0, 0, 0.5])
corr_announce += "RR, RL, LR, LL"
elif corr[0] == 'I': # I, Q, U, V
Spart = np.array([1., 0, 0, 0])
corr_announce += "I, Q, U, V"
elif corr[0] == 'XX': # XX, XY, YX, YY
Spart = np.array([0.5, 0, 0, 0.5])
corr_announce += "XX, XY, YX, YY"
m.message(corr_announce)
for spw in nspw:
nchan.append(meta.nchan(spw))
ms.selectinit(datadescid=spw)
temp = ms.getdata([viscol,"axis_info"],ifraxis=False)
data_temp = temp[viscol]
info_temp = temp["axis_info"]
s_temp = ms.getdata(["sigma"], ifraxis=False)['sigma']
flags = 1. - ms.getdata(["flag"])['flag']
if not(np.sum(flags[0])==np.sum(flags[1])==np.sum(flags[2])==np.sum(flags[3])):
m.warn('Warning: Different flags for different correlations/channels.\
Hard flag is applied: If any correlation is flagged, this gets \
extended to all.')
maximum = np.ones(np.shape(flags[0]))
for i in range(4):
if flags[i].sum() < maximum.sum():
maximum = flags[i]
flag = maximum
if (np.sum(flag)==0):
m.warn('Spw ' + str(spw) + ' is completely flagged!\n')
continue
#Start reading data.
if mode == 'tot':
vis_temp = flag * (Spart[0] * data_temp[0] + Spart[1] * data_temp[1] + Spart[2] *\
data_temp[2] + Spart[3] * data_temp[3])
sigma_temp = flag * (Spart[0] * s_temp[0] + Spart[1] * s_temp[1] + Spart[2] * s_temp[2] +\
Spart[3] * s_temp[3])
vislist = np.array(vis_temp)
sigmalist = np.array(sigma_temp)
if mode == 'pol':
Qvis_temp = flag * (QSpart[0] * data_temp[0] + QSpart[1] * data_temp[1] + QSpart[2] *\
data_temp[2] + QSpart[3] * data_temp[3])
Qsigma_temp = flag * (QSpart[0] * s_temp[0] + QSpart[1] * s_temp[1] + QSpart[2] * s_temp[2] +\
QSpart[3] * s_temp[3])
Uvis_temp = flag * (USpart[0] * data_temp[0] + USpart[1] * data_temp[1] + USpart[2] *\
data_temp[2] + USpart[3] * data_temp[3])
Usigma_temp = flag * (USpart[0] * s_temp[0] + USpart[1] * s_temp[1] + USpart[2] * s_temp[2] +\
USpart[3] * s_temp[3])
Qvislist = np.array(Qvislist)
Qsigmalist = np.array(Qsigmalist)
Uvislist = np.array(Uvislist)
Usigmalist = np.array(Usigmalist)
# uvflat give all uv coordinates for the chosen spw in m
uflat = ms.getdata(['u'])['u']
vflat = ms.getdata(['v'])['v']
freqtemp = (info_temp["freq_axis"]["chan_freq"]).reshape(nchan[spw])
freq.append(freqtemp)
lamb = C / freqtemp
if mode == 'pol':
lambs.append(lamb)
#calculates uv coordinates per channel in #lambda
utemp = np.array([uflat/k for k in lamb])
vtemp = np.array([vflat/k for k in lamb])
#Reads the uv coordates into lists. Delete functions take care of flags
u.append(utemp)
v.append(vtemp)
if mode == 'tot':
vis.init_subgroup(spw, freqtemp, nrecs)
noise.init_subgroup(spw, freqtemp, nrecs)
for k in range(nchan[spw]):
vis.store_records(vislist[k], spw, k)
noise.store_records(sigmalist[k], spw, k)
vis.coords.put_coords(uchanlist[k],vchanlist[k],spw,k)
if mode == 'pol':
vis.init_subgroup(spw, lamb**2 / PI, nrecs)
noise.init_subgroup(spw, lamb**2 / PI, nrecs)
noise_array = np.real(np.sqrt(Qsigmalist * Qsigmalist.conjugate() +
Usigmalist * Usigmalist.conjugate()))
for h in range(nchan[spw]):
vis.store_records([Qvislist[h],Uvislist[h]], spw, h)
noise.store_records(noise_array[h], spw, h)
vis.coords.put_coords(uchanlist[h],vchanlist[h],spw,h)
ms.close()
