def summary_plot(out,ant_str='ant1-13',ptype='phase',pol='XX-YY'):
''' Makes a summary amplitude or phase plot for all baselines from ants in ant_str
in out dictionary.
'''
import matplotlib.pyplot as plt
ant_list = p.ant_str2list(ant_str)
nant = len(ant_list)
if ptype != 'amp' and ptype != 'phase':
print "Invalid plot type. Must be 'amp' or 'phase'."
return
poloff = 0
if pol != 'XX-YY':
poloff = 2
f, ax = plt.subplots(nant,nant)
f.subplots_adjust(hspace=0,wspace=0)
bl2ord = p.bl_list()
for axrow in ax:
for a in axrow:
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
for i in range(nant-1):
ai = ant_list[i]
for j in range(i+1,nant):
aj = ant_list[j]
if ptype == 'phase':
ax[i,j].imshow(np.angle(out['x'][bl2ord[ai,aj],0+poloff]))
ax[j,i].imshow(np.angle(out['x'][bl2ord[ai,aj],1+poloff]))
elif ptype == 'amp':
ax[i,j].imshow(np.abs(out['x'][bl2ord[ai,aj],0+poloff]))
ax[j,i].imshow(np.abs(out['x'][bl2ord[ai,aj],1+poloff]))
for i in range(nant):
ai = ant_list[i]
ax[i,i].text(0.5,0.5,str(ai+1),ha='center',va='center',transform=ax[i,i].transAxes,fontsize=14)
def flag_sk(out):
bl2ord = p.bl_list()
m = out['m']
sk = (m + 1.) / (m - 1.) * (m * out['p2'] / (out['p']**2) - 1)
u_lim = 1.25
l_lim = 0.75
sk_flag = np.logical_or(sk > u_lim, sk < l_lim)
nant, npol, nf, nt = m.shape
for i in range(nant - 1):
for j in range(i + 1, nant):
flags = np.logical_or(sk_flag[i], sk_flag[j])
out['x'][bl2ord[i, j], flags] = np.nan
for i in range(nant):
if 'meanp' in out:
out['meanp'][i, sk_flag[i]] = np.nan
out['a'][i, sk_flag[i]] = np.nan
return out
def flag_sk(out):
bl2ord = p.bl_list()
m = out['m']
sk = (m+1.)/(m-1.)*(m*out['p2']/(out['p']**2) - 1)
u_lim = 1.25
l_lim = 0.75
sk_flag = np.logical_or(sk > u_lim,sk < l_lim)
nant,npol,nf,nt = m.shape
for i in range(nant-1):
for j in range(i+1,nant):
flags = np.logical_or(sk_flag[i],sk_flag[j])
out['x'][bl2ord[i,j],flags] = np.nan
for i in range(nant):
if 'meanp' in out:
out['meanp'][i,sk_flag[i]] = np.nan
out['a'][i,sk_flag[i]] = np.nan
return out
def summary_plot(out, ant_str='ant1-13', ptype='phase', pol='XX-YY'):
''' Makes a summary amplitude or phase plot for all baselines from ants in ant_str
in out dictionary.
'''
import matplotlib.pyplot as plt
ant_list = p.ant_str2list(ant_str)
nant = len(ant_list)
if ptype != 'amp' and ptype != 'phase':
print "Invalid plot type. Must be 'amp' or 'phase'."
return
poloff = 0
if pol != 'XX-YY':
poloff = 2
f, ax = plt.subplots(nant, nant)
f.subplots_adjust(hspace=0, wspace=0)
bl2ord = p.bl_list()
for axrow in ax:
for a in axrow:
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
for i in range(nant - 1):
ai = ant_list[i]
for j in range(i + 1, nant):
aj = ant_list[j]
if ptype == 'phase':
ax[i, j].imshow(np.angle(out['x'][bl2ord[ai, aj], 0 + poloff]))
ax[j, i].imshow(np.angle(out['x'][bl2ord[ai, aj], 1 + poloff]))
elif ptype == 'amp':
ax[i, j].imshow(np.abs(out['x'][bl2ord[ai, aj], 0 + poloff]))
ax[j, i].imshow(np.abs(out['x'][bl2ord[ai, aj], 1 + poloff]))
for i in range(nant):
ai = ant_list[i]
ax[i, i].text(0.5,
0.5,
str(ai + 1),
ha='center',
va='center',
transform=ax[i, i].transAxes,
fontsize=14)
def getphasecor(data,
ant_str='ant1-14',
polist=[0, 1],
crange=[0, 4095],
pplot=False):
''' Routine adapted from Maria Loukitcheva's code, to find phase slopes in
correlated data on a geosynchronous satellite.
'''
def movingaverage(interval, window_size):
# Range of ones, scaled for unit area
window = np.ones(int(window_size)) / np.float(window_size)
return np.convolve(interval, window, 'same')
antlist = p.ant_str2list(ant_str)
print antlist + 1
ndon = False
if len(data[:, 0, 0, 0]) == 16:
ndon = True
if ndon:
chlist = antlist
else:
chlist = antlist[1:] - 1
bl2ord = p.bl_list()
if not ndon:
data = data[bl2ord[0, 1:]] # Consider only baselines with antenna 1
print data.shape
pcor = np.linspace(0, np.pi, 4096)
istep = np.arange(-50, 50) # Range of steps to search
chan = np.arange(4096)
phasecor = np.zeros((14, len(polist)))
start = crange[0]
end = crange[1]
#antennalist=[2]
if pplot:
import matplotlib.pylab as plt
f, ax = plt.subplots(len(antlist), len(polist))
for i, iant in enumerate(chlist):
for j, jpol in enumerate(polist):
phaseall = np.zeros(len(istep))
for k in range(len(istep)):
#if movaver:
# aver = movingaverage(data[bl2ord[0, antennalist[i]], j, :, 0],201)
# aver = cos(angle(aver) + istep[k] * pcor)
#else:
aver = np.cos(
2 * (np.angle(data[iant, jpol, :, 0]) + istep[k] * pcor))
phaseall[k] = np.polyfit(chan[start:end],
aver[start:end],
0,
full=True)[1]
#print phaseall
#print argmin(drange)
#print corind[argmin(drange)]
ioff = iant + 1
if ndon: ioff = iant
phasecor[ioff, j] = -istep[np.argmin(
phaseall)] # Correct delay is negative of the phase correction
phas0 = np.angle(data[iant, jpol, :, 0])
phas = phas0 + istep[np.argmin(phaseall)] * pcor
if pplot:
if ndon:
iax = i
else:
iax = i + 1
if len(polist) > 1:
ax[iax, j].plot(phas0 % (2 * np.pi), '.')
ax[iax, j].plot(phas % (2 * np.pi), '.')
ax[iax, j].plot(chan[start:end],
phas[start:end] % (2 * np.pi),
color='white')
ax[iax, j].set_ylim(-1, 7)
else:
ax[iax].plot(phas0 % (2 * np.pi), '.')
ax[iax].plot(phas % (2 * np.pi), '.')
ax[iax].plot(chan[start:end],
phas[start:end] % (2 * np.pi),
color='white')
ax[iax].set_ylim(-1, 7)
if len(polist) == 1:
# Reduce two-dimensional array with second dimension = 1 to a single dimension
return phasecor[:, 0]
return phasecor
def delay_centers(filename, satname, ants='ant1-13', band=23, doplot=False):
''' Reads specified 1-s capture file (specified as a filename string)
and analyzes data in either the 3.5-4 GHz band (band 6) or the
12-12.5 GHz band (band 23). First finds the peak amplitude of the
vector-summed measurements for delays, from -15 to 15 ns, on each
baseline, and then applies the optimum delay needed to correct
for the phase slope, optionally plotting the results.
The keyword nants can be used to limit the solution to a smaller
number of baselines.
If doplot is True, plots an overview plot of the corrected phases,
including the standard deviation of the fit as text in each box.
TODO: It then optionally reads the delay_centers.txt file from
the ACC and updates it.
'''
# First see if we can read delay_centers.txt from the ACC (return if failure)
userpass = '******'
try:
# Read delay center file from ACC
dlafile = urllib2.urlopen('ftp://' + userpass +
'acc.solar.pvt/parm/delay_centers.txt',
timeout=1)
lines = dlafile.readlines()
dcenx = []
dceny = []
for line in lines:
if line[0] != '#':
# Skip comment lines and takes next 2 numbers as delay
# centers [ns] in X & Y
dcenx.append(float(line.strip().split()[1]))
dceny.append(float(line.strip().split()[2]))
except:
print Time().iso, 'ACC connection for delay centers timed out.'
return [None] * 3
print 'Successfully read delay_centers.txt from ACC.'
# Get satellite frequencies and polarizations (this is to use channel centers for fitting,
# but is not yet completed)
sat, = gs.get_sat_info(names=[satname])
if band == 23:
freq = np.linspace(0, 4095, 4096) * 0.4 / 4096 + 12.15
elif band == 6:
freq = np.linspace(0, 4095, 4096) * 0.4 / 4096 + 3.65
else:
print 'Band MUST be either 23 (12-12.5 GHz) or 6 (3.5-4 GHz)'
return None, None, None
freqmhz = (freq * 10000. + 0.5).astype('int') / 10.
pol = sat['pollist']
if band == 23:
ridx, = np.where(pol == 'R')
else:
ridx, = np.where(pol == 'H')
rfrq = sat['freqlist'][ridx]
ridx = []
for f in rfrq:
try:
ridx.append(np.where(f == freqmhz)[0][0])
except:
pass
idxmin = ridx[0]
if band == 23:
lidx, = np.where(pol == 'L')
else:
lidx, = np.where(pol == 'V')
lfrq = sat['freqlist'][lidx]
lidx = []
for f in lfrq:
try:
lidx.append(np.where(f == freqmhz)[0][0])
except:
pass
idxmin = min(idxmin, lidx[0])
freq = freq[idxmin:]
ant_list = ant_str2list(ants)
nants = len(ant_list)
nbl = nants * (nants - 1) / 2
# Create M arrays for nants antennas, and c arrays for nbl baselines
Mx = np.zeros((nbl, nants))
My = np.zeros((nbl, nants))
cx = np.zeros(nbl, dtype='float')
cy = np.zeros(nbl, dtype='float')
# Read 1-s capture file of raw packets
out = p.rd_jspec(filename)
# Eliminate data on channels less than minimum, and perform sum over times
out['x'] = out['x'][:, :, idxmin:, :].sum(3)
out['a'] = out['a'][:, :, idxmin:, :].sum(3)
print 'Successfully read file', filename
# Get conversion from antenna pair (bl) to "data source" index
bl2ord = p.bl_list()
# Declare storage for delays in ns
tau_xx = np.zeros((nants, nants), dtype='float')
tau_yy = np.zeros((nants, nants), dtype='float')
tau_xy = np.zeros((nants, nants), dtype='float')
tau_yx = np.zeros((nants, nants), dtype='float')
tau_ns = np.zeros((nants, nants), dtype='float')
sigma = np.zeros((nants, nants), dtype='float')
iblx = 0
ibly = 0
if doplot:
f, ax = plt.subplots(nants, nants)
f.subplots_adjust(hspace=0, wspace=0)
for axrow in ax:
for a in axrow:
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
# Declare storage for auto-correlation (X vs. Y) delays in ns
tau_a = np.zeros(nants, dtype='float')
for i in range(nants):
ai = ant_list[i]
dat = out['a'][ai, 2]
tau_a[i] = auto_delay_search(dat, freq)
#plt.figure()
for i in range(0, nants - 1):
ai = ant_list[i]
for j in range(i + 1, nants):
aj = ant_list[j]
dat = out['x'][bl2ord[ai, aj], 0]
tau_0 = auto_delay_search(dat, freq)
tau_xx[i] = delay_search(dat, freq, tau_0)
dat = out['x'][bl2ord[ai, aj], 1]
tau_0 = auto_delay_search(dat, freq)
tau_yy[i] = delay_search(dat, freq, tau_0)
if doplot:
phi0 = tau_xx[i, j] * 2 * np.pi * freq
phix = np.angle(out['x'][bl2ord[ai, aj], 0]) - phi0
phix -= phix[2048]
ax[i, j].plot(freq, (phix + np.pi) % (2 * np.pi) - np.pi, '.')
phi0 = tau_yy[i, j] * 2 * np.pi * freq
phiy = np.angle(out['x'][bl2ord[ai, aj], 1]) - phi0
phiy -= phiy[2048]
ax[j, i].plot(freq, (phiy + np.pi) % (2 * np.pi) - np.pi, '.')
tau_ns[i, j] = tau_xx[i, j] # delay in nsec
tau_ns[j, i] = tau_yy[i, j] # delay in nsec
Mx[iblx, np.array((i, j))] = -1, 1
cx[iblx] = dcenx[aj] - dcenx[ai] - tau_ns[i, j]
iblx += 1
My[ibly, np.array((i, j))] = -1, 1
cy[ibly] = dceny[aj] - dceny[ai] - tau_ns[j, i]
ibly += 1
if doplot:
# Set axis scales and label the plots
for i in range(nants):
ai = ant_list[i]
ax[i, i].text(0.5,
0.5,
str(ai + 1),
ha='center',
va='center',
transform=ax[i, i].transAxes,
fontsize=14)
for j in range(nants):
ax[i, j].set_xlim(freq[0], freq[-1])
ax[i, j].set_ylim(-np.pi, np.pi)
print 'Successfully calculated delays from data.'
# Obtain solution, only for those baselines with sigma < 1.0
xdla, xr, _, _ = np.linalg.lstsq(Mx[:iblx], cx[:iblx]) # For X channel
ydla, yr, _, _ = np.linalg.lstsq(My[:ibly], cy[:ibly]) # For Y channel
print 'Successfully analyzed delays for mutual consistency.'
# Calculate new delays for ants based on Ant 1 as reference
newdlax = xdla - xdla[0] + dcenx[0]
newdlay = ydla - ydla[0] + dceny[0] - tau_a
f = open('/tmp/delay_centers_tmp.txt', 'w')
for line in lines:
if line[0] == '#':
print line,
f.write(line)
else:
ant = int(line[:6])
idx, = np.where((ant - 1) == ant_list)
if len(idx) == 0:
# No update for this antenna
fmt = '{:4d}*{:12.3f} {:12.3f} {:12.3f} {:12.3f}'
print fmt.format(ant, dcenx[ant - 1], dceny[ant - 1],
dcenx[ant - 1], dceny[ant - 1])
fmt = '{:4d} {:12.3f} {:12.3f}\n'
f.write(fmt.format(ant, dcenx[ant - 1], dceny[ant - 1]))
else:
idx = idx[0]
fmt = '{:4d} {:12.3f} {:12.3f} {:12.3f} {:12.3f}'
print fmt.format(ant, dcenx[ant - 1], dceny[ant - 1],
newdlax[idx], newdlay[idx])
fmt = '{:4d} {:12.3f} {:12.3f}\n'
f.write(fmt.format(ant, newdlax[idx], newdlay[idx]))
f.close()
fmt = '{:6.2f} ' * nants
for i in range(nants):
print fmt.format(*tau_ns[i])
return tau_ns, xr, yr
def readXdata(filename, filter=False, tp_only=False):
''' This routine reads the data from a single IDBfile.
Optiona Keywords:
filter boolean--if True, returns only non-zero frequencies
if False (default), returns uniform set of 500 frequencies
tp_only boolean--if True, returns only TP information
if False (default), returns everything (including
auto & cross correlations)
'''
# Open uv file for reading
uv = aipy.miriad.UV(filename)
good_idx = np.arange(len(uv['sfreq']))
if filter:
good_idx = []
# Read a bunch of records to get number of good frequencies, i.e. those with at least
# some non-zero data. Read 20 records for baseline 1-2, XX pol
uv.select('antennae', 0, 2, include=True)
uv.select('polarization', -5, -5, include=True)
for i in range(20):
preamble, data = uv.read()
idx, = data.nonzero()
if len(idx) > len(good_idx):
good_idx = copy.copy(idx)
uv.select('clear', 0, 0)
uv.rewind()
if 'source' in uv.vartable:
src = uv['source']
nf = len(good_idx)
freq = uv['sfreq'][good_idx]
npol = uv['npol']
nants = uv['nants']
nbl = nants * (nants - 1) / 2
bl2ord = p.bl_list(nants)
if not tp_only:
outa = np.zeros((nants, npol, nf, 600),
dtype=np.complex64) # Auto-correlations
outx = np.zeros((nbl, npol, nf, 600),
dtype=np.complex64) # Cross-correlations
outp = np.zeros((nants, 2, nf, 600), dtype=np.float)
outp2 = np.zeros((nants, 2, nf, 600), dtype=np.float)
outm = np.zeros((nants, 2, nf, 600), dtype=np.int)
uvwarray = np.zeros((nbl, 600, 3), dtype=np.float)
timearray = []
l = -1
tprev = 0
tsav = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = uv['antlist']
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
for preamble, data in uv.all():
uvw, t, (i0, j0) = preamble
i = antlist.index(i0 + 1)
j = antlist.index(j0 + 1)
if i > j:
# Reverse order of indices
j = antlist.index(i0 + 1)
i = antlist.index(j0 + 1)
# Assumes uv['pol'] is one of -5, -6, -7, -8
k = -5 - uv['pol']
if filter:
if len(data.nonzero()[0]) == nf:
if t != tprev:
# print preamble
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500, nants, 3)
ydata = uv['ysampler'].reshape(500, nants, 3)
outp[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 0], 0, 1)
outp[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 0], 0, 1)
outp2[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 1], 0,
1)
outp2[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 1], 0,
1)
outm[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 2], 0, 1)
outm[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 2], 0, 1)
if tp_only:
outa = None
outx = None
else:
if i0 == j0:
# This is an auto-correlation
outa[i0, k, :, l] = data[data.nonzero()]
else:
outx[bl2ord[i, j], k, :, l] = data[data.nonzero()]
if k == 3:
uvwarray[bl2ord[i, j], l] = uvw[data.nonzero()]
else:
if t != tprev:
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500, nants, 3)
ydata = uv['ysampler'].reshape(500, nants, 3)
outp[:, 0, :, l] = np.swapaxes(xdata[:, :, 0], 0, 1)
outp[:, 1, :, l] = np.swapaxes(ydata[:, :, 0], 0, 1)
outp2[:, 0, :, l] = np.swapaxes(xdata[:, :, 1], 0, 1)
outp2[:, 1, :, l] = np.swapaxes(ydata[:, :, 1], 0, 1)
outm[:, 0, :, l] = np.swapaxes(xdata[:, :, 2], 0, 1)
outm[:, 1, :, l] = np.swapaxes(ydata[:, :, 2], 0, 1)
if tp_only:
outa = None
outx = None
else:
if i0 == j0:
# This is an auto-correlation
outa[i0, k, :, l] = data
if k < 2 and np.sum(data != np.real(data)) > 0:
print preamble, uv['pol'], 'has imaginary data!'
else:
outx[bl2ord[i, j], k, :, l] = data
if k == 3: uvwarray[bl2ord[i, j], l] = uvw
# Truncate in case of early end of data
nt = len(timearray)
outp = outp[:, :, :, :nt]
outp2 = outp2[:, :, :, :nt]
outm = outm[:, :, :, :nt]
outa = outa[:, :, :, :nt]
outx = outx[:, :, :, :nt]
out = {
'a': outa,
'x': outx,
'uvw': uvwarray,
'fghz': freq,
'time': np.array(timearray),
'source': src,
'p': outp,
'p2': outp2,
'm': outm
}
return out
def readXdatmp(filename):
# This temporary routine reads the data from a single IDBfile where the
# polarization was written incorrectly. (-1,-2,0,0) instead of (-5,-6,-7,-8)
# the IDB array sorts basline pairs into correct order for an output
# array that just has 18 baselines (2 sets of 4 antennas correlated separately
# now just need to convert i,j into slot in 136-slot array: 15*16/2 corrs +16 auto
# this array corresponds to the first index (auto corr) for each antenna
# 16*(iant-1)-(iant-1)(iant-2)/2
ibl = np.array([
0, 16, 31, 45, 58, 70, 81, 91, 100, 108, 115, 121, 126, 130, 133, 135
])
# Open uv file for reading
uv = aipy.miriad.UV(filename)
good_idx = []
# Read a bunch of records to get number of good frequencies, i.e. those with at least
# some non-zero data. Read 20 records for baseline 1-2, XX pol
uv.select('antennae', 0, 2, include=True)
uv.select('polarization', -1, -1, include=True)
for i in range(20):
preamble, data = uv.read()
idx, = data.nonzero()
if len(idx) > len(good_idx):
good_idx = copy.copy(idx)
if 'source' in uv.vartable:
src = uv['source']
uv.select('clear', 0, 0)
uv.rewind()
nf = len(good_idx)
freq = uv['sfreq'][good_idx]
npol = uv['npol']
nants = uv['nants']
nbl = nants * (nants - 1) / 2
bl2ord = p.bl_list(nants)
outa = np.zeros((nants, npol, nf, 600),
dtype=np.complex64) # Auto-correlations
outx = np.zeros((nbl, npol, nf, 600),
dtype=np.complex64) # Cross-correlations
outp = np.zeros((nants, 2, nf, 600), dtype=np.float)
outp2 = np.zeros((nants, 2, nf, 600), dtype=np.float)
outm = np.zeros((nants, 2, nf, 600), dtype=np.int)
uvwarray = []
timearray = []
l = -1
tprev = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = uv['antlist']
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
kp = 0
for preamble, data in uv.all():
uvw, t, (i0, j0) = preamble
i = antlist.index(i0 + 1)
j = antlist.index(j0 + 1)
if i > j:
# Reverse order of indices
j = antlist.index(i0 + 1)
i = antlist.index(j0 + 1)
# Assumes uv['pol'] is -1, -2, 0, 0
if i == 0 and j == 0:
if uv['pol'] == -1:
k = 0
elif uv['pol'] == -2:
k = 1
elif uv['pol'] == 0:
if kp == 1:
k = 2
kp = 0
else:
k = 3
kp = 1
if k == 3:
uvwarray.append(uvw)
if len(data.nonzero()[0]) == nf:
if t != tprev:
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500, nants, 3)
ydata = uv['ysampler'].reshape(500, nants, 3)
outp[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 0], 0, 1)
outp[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 0], 0, 1)
outp2[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 1], 0, 1)
outp2[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 1], 0, 1)
outm[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 2], 0, 1)
outm[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 2], 0, 1)
if i0 == j0:
# This is an auto-correlation
outa[i0, k, :, l] = data[data.nonzero()]
else:
outx[bl2ord[i, j], k, :, l] = data[data.nonzero()]
out = {
'a': outa,
'x': outx,
'uvw': np.array(uvwarray),
'fghz': freq,
'time': np.array(timearray),
'source': src,
'p': outp,
'p2': outp2,
'm': outm
}
return out
def delay_centers(filename,satname,ants='ant1-13',band=23,doplot=False):
''' Reads specified 1-s capture file (specified as a filename string)
and analyzes data in either the 3.5-4 GHz band (band 6) or the
12-12.5 GHz band (band 23). First finds the peak amplitude of the
vector-summed measurements for delays, from -15 to 15 ns, on each
baseline, and then applies the optimum delay needed to correct
for the phase slope, optionally plotting the results.
The keyword nants can be used to limit the solution to a smaller
number of baselines.
If doplot is True, plots an overview plot of the corrected phases,
including the standard deviation of the fit as text in each box.
TODO: It then optionally reads the delay_centers.txt file from
the ACC and updates it.
'''
# First see if we can read delay_centers.txt from the ACC (return if failure)
userpass = '******'
try:
# Read delay center file from ACC
dlafile = urllib2.urlopen('ftp://'+userpass+'acc.solar.pvt/parm/delay_centers.txt',timeout=1)
lines = dlafile.readlines()
dcenx = []
dceny = []
for line in lines:
if line[0] != '#':
# Skip comment lines and takes next 2 numbers as delay
# centers [ns] in X & Y
dcenx.append(float(line.strip().split()[1]))
dceny.append(float(line.strip().split()[2]))
except:
print Time().iso,'ACC connection for delay centers timed out.'
return [None]*3
print 'Successfully read delay_centers.txt from ACC.'
# Get satellite frequencies and polarizations (this is to use channel centers for fitting,
# but is not yet completed)
sat, = gs.get_sat_info(names=[satname])
if band == 23:
freq = np.linspace(0,4095,4096)*0.4/4096 + 12.15
elif band ==6:
freq = np.linspace(0,4095,4096)*0.4/4096 + 3.65
else:
print 'Band MUST be either 23 (12-12.5 GHz) or 6 (3.5-4 GHz)'
return None, None, None
freqmhz = (freq*10000. + 0.5).astype('int')/10.
pol = sat['pollist']
if band == 23:
ridx, = np.where(pol == 'R')
else:
ridx, = np.where(pol == 'H')
rfrq = sat['freqlist'][ridx]
ridx = []
for f in rfrq:
try:
ridx.append(np.where(f == freqmhz)[0][0])
except:
pass
idxmin = ridx[0]
if band == 23:
lidx, = np.where(pol == 'L')
else:
lidx, = np.where(pol == 'V')
lfrq = sat['freqlist'][lidx]
lidx = []
for f in lfrq:
try:
lidx.append(np.where(f == freqmhz)[0][0])
except:
pass
idxmin = min(idxmin,lidx[0])
freq = freq[idxmin:]
ant_list = ant_str2list(ants)
nants = len(ant_list)
nbl = nants*(nants-1)/2
# Create M arrays for nants antennas, and c arrays for nbl baselines
Mx = np.zeros((nbl,nants))
My = np.zeros((nbl,nants))
cx = np.zeros(nbl,dtype='float')
cy = np.zeros(nbl,dtype='float')
# Read 1-s capture file of raw packets
out = p.rd_jspec(filename)
# Eliminate data on channels less than minimum, and perform sum over times
out['x'] = out['x'][:,:,idxmin:,:].sum(3)
out['a'] = out['a'][:,:,idxmin:,:].sum(3)
print 'Successfully read file',filename
# Get conversion from antenna pair (bl) to "data source" index
bl2ord = p.bl_list()
# Declare storage for delays in ns
tau_xx = np.zeros((nants,nants),dtype='float')
tau_yy = np.zeros((nants,nants),dtype='float')
tau_xy = np.zeros((nants,nants),dtype='float')
tau_yx = np.zeros((nants,nants),dtype='float')
tau_ns = np.zeros((nants,nants),dtype='float')
sigma = np.zeros((nants,nants),dtype='float')
iblx = 0
ibly = 0
if doplot:
f, ax = plt.subplots(nants,nants)
f.subplots_adjust(hspace=0,wspace=0)
for axrow in ax:
for a in axrow:
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
# Declare storage for auto-correlation (X vs. Y) delays in ns
tau_a = np.zeros(nants,dtype='float')
for i in range(nants):
ai = ant_list[i]
dat = out['a'][ai,2]
tau_a[i] = auto_delay_search(dat,freq)
#plt.figure()
for i in range(0,nants-1):
ai = ant_list[i]
for j in range(i+1,nants):
aj = ant_list[j]
dat = out['x'][bl2ord[ai,aj],0]
tau_0 = auto_delay_search(dat,freq)
tau_xx[i] = delay_search(dat,freq,tau_0)
dat = out['x'][bl2ord[ai,aj],1]
tau_0 = auto_delay_search(dat,freq)
tau_yy[i] = delay_search(dat,freq,tau_0)
if doplot:
phi0 = tau_xx[i,j]*2*np.pi*freq
phix = np.angle(out['x'][bl2ord[ai,aj],0]) - phi0
phix -= phix[2048]
ax[i,j].plot(freq, (phix + np.pi) % (2*np.pi) - np.pi , '.')
phi0 = tau_yy[i,j]*2*np.pi*freq
phiy = np.angle(out['x'][bl2ord[ai,aj],1]) - phi0
phiy -= phiy[2048]
ax[j,i].plot(freq, (phiy + np.pi) % (2*np.pi) - np.pi, '.')
tau_ns[i,j] = tau_xx[i,j] # delay in nsec
tau_ns[j,i] = tau_yy[i,j] # delay in nsec
Mx[iblx,np.array((i,j))] = -1,1
cx[iblx] = dcenx[aj] - dcenx[ai] - tau_ns[i,j]
iblx += 1
My[ibly,np.array((i,j))] = -1,1
cy[ibly] = dceny[aj] - dceny[ai] - tau_ns[j,i]
ibly += 1
if doplot:
# Set axis scales and label the plots
for i in range(nants):
ai = ant_list[i]
ax[i,i].text(0.5,0.5,str(ai+1),ha='center',va='center',transform=ax[i,i].transAxes,fontsize=14)
for j in range(nants):
ax[i,j].set_xlim(freq[0],freq[-1])
ax[i,j].set_ylim(-np.pi,np.pi)
print 'Successfully calculated delays from data.'
# Obtain solution, only for those baselines with sigma < 1.0
xdla,xr,_,_ = np.linalg.lstsq(Mx[:iblx],cx[:iblx]) # For X channel
ydla,yr,_,_ = np.linalg.lstsq(My[:ibly],cy[:ibly]) # For Y channel
print 'Successfully analyzed delays for mutual consistency.'
# Calculate new delays for ants based on Ant 1 as reference
newdlax = xdla - xdla[0] + dcenx[0]
newdlay = ydla - ydla[0] + dceny[0] - tau_a
f = open('/tmp/delay_centers_tmp.txt','w')
for line in lines:
if line[0] == '#':
print line,
f.write(line)
else:
ant = int(line[:6])
idx, = np.where((ant-1) == ant_list)
if len(idx) == 0:
# No update for this antenna
fmt = '{:4d}*{:12.3f} {:12.3f} {:12.3f} {:12.3f}'
print fmt.format(ant,dcenx[ant-1],dceny[ant-1],dcenx[ant-1],dceny[ant-1])
fmt = '{:4d} {:12.3f} {:12.3f}\n'
f.write(fmt.format(ant,dcenx[ant-1],dceny[ant-1]))
else:
idx = idx[0]
fmt = '{:4d} {:12.3f} {:12.3f} {:12.3f} {:12.3f}'
print fmt.format(ant,dcenx[ant-1],dceny[ant-1],newdlax[idx],newdlay[idx])
fmt = '{:4d} {:12.3f} {:12.3f}\n'
f.write(fmt.format(ant,newdlax[idx],newdlay[idx]))
f.close()
fmt = '{:6.2f} '*nants
for i in range(nants): print fmt.format(*tau_ns[i])
return tau_ns, xr, yr
import numpy as np
import matplotlib.pylab as plt
from pcapture2 import bl_list
bl2ord = bl_list()
def rot_sim(indict):
''' Simulates effect of non-ideal feed behavior on input data for a single baseline.
If 'unrot' is true, it takes the data as output data and applies the inverse
of the corresponding Mueller matrix to it.
Input is a dictionary (indict) with the following keys (and their defaults if
omitted)
'data' : 4 x ntimes array of data, corresponding to a set of XX, XY, YX, YY,
in that order, for each time. No default (error return if omitted)
'chi1' : Parallactic (or rotation) angle of first antenna (default 0) (scalar or ntimes array)
'chi2' : Parallactic (or rotation) angle of second antenna (default 0) (scalar or ntimes array)
'a1' : Relative amplitude of Y wrt X for first antenna (default 1)
'a2' : Relative amplitude of Y wrt X for second antenna (default 1)
'd1' : Relative cross-talk between X and Y for first antenna (default 0)
'd2' : Relative cross-talk between X and Y for second antenna (default 0)
'unrot': Whether to rotate or unrotate the input data (default False)
'verbose': Print some diagnostic messages
'doplot': Create some plots of the before and after amplitudes and phases
Result is a plot of input and output. Returns the rotated or unrotated data in
the same form as the input data.
'''
# Input is a dictionary, contained needed keys. Any missing
# keys are filled in with defaults:
data = indict.get('data') # No default
if data is None:
def idb_readXdata(filename, filter=False):
'''This routine reads the data from a single IDBfile. Optional
Keywords: filter boolean--if True, returns only non-zero
frequencies if False (default), returns all frequencies. This
differs from Dale's version in that it includes all
correlations, drops the tp_only option, and the outputs that
are not in the UDB files.
'''
# Open uv file for reading
uv = aipy.miriad.UV(filename)
nf_orig = len(uv['sfreq'])
good_idx = np.arange(nf_orig)
if filter:
good_idx = []
# Read a bunch of records to get number of good frequencies,
# i.e. those with at least some non-zero data. Read 20
# records for baseline 1-2, XX pol
uv.select('antennae',0,2,include=True)
uv.select('polarization',-5,-5,include=True)
for i in range(20):
preamble, data = uv.read()
idx, = data.nonzero()
if len(idx) > len(good_idx):
good_idx = copy.copy(idx)
uv.select('clear',0,0)
uv.rewind()
#endif
#set up outputs
nf = len(good_idx)
print 'NF: ', nf
freq = uv['sfreq'][good_idx]
npol = uv['npol']
polarr = np.array([-5, -6, -7, -8])
nants = uv['nants']
nbl = nants*(nants-1)/2
nblc = nbl+nants
# all-correlations, add a mask for the output vis array
outx0 = np.zeros((nf, nblc, npol, 600),dtype=np.complex64)
omask = np.zeros((nf, nblc, npol, 600),dtype=np.int32)
outx = ma.masked_array(outx0, mask = omask)
i0array = np.zeros((nblc, 600), dtype = np.int32)
j0array = np.zeros((nblc, 600), dtype = np.int32)
outpx = np.zeros((nf*nants, 600), dtype=np.float)
outpy = np.zeros((nf*nants, 600), dtype=np.float)
uvwarray = np.zeros((3, nblc, 600), dtype=np.float)
delayarray = np.zeros((nants, 600), dtype=np.float)
#lists for time arrays
utarray = []
timearray = []
lstarray = []
l = -1
tprev = 0
tsav = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = strip_non_printable(uv['antlist'])
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
#endelse
#keep autocorrelations in the array
bl2ord = p.bl_list()
for ij in range(len(antlist)):
bl2ord[ij, ij] = nbl+ij
#endfor
for preamble, data in uv.all():
uvw, t, (i0,j0) = preamble
i = antlist.index(i0+1)
j = antlist.index(j0+1)
if i > j:
# Reverse order of indices
j = antlist.index(i0+1)
i = antlist.index(j0+1)
#endif
# Assumes uv['pol'] is one of -5, -6, -7, -8
k = -5 - uv['pol']
if filter:
if len(data.nonzero()[0]) == nf:
if t != tprev:
# New time
l += 1
if l == 600:
break
#endif
tprev = t
timearray.append(t)
utarray.append(uv['ut'])
try:
lstarray.append(uv['lst'])
except:
pass
#endexcept
xdata0 = uv['xsampler'].reshape(nf_orig,nants,3)
xdata = xdata0[good_idx, :, 0]
outpx[:,l] = xdata.reshape(nf*nants)
ydata0 = uv['ysampler'].reshape(nf_orig,nants,3)
ydata = ydata0[good_idx, :, 0]
outpy[:,l] = ydata.reshape(nf*nants)
delayarray[:,l] = uv['delay']
#endif
outx[:, bl2ord[i,j],k,l] = data[data.nonzero()]
if k == 3:
uvwarray[:, bl2ord[i,j],l] = uvw
i0array[bl2ord[i,j],l] = i0
j0array[bl2ord[i,j],l] = j0
#endif
#endif, for len(data.nonzero()) == nf
else:
if t != tprev:
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
utarray.append(uv['ut'])
try:
lstarray.append(uv['lst'])
except:
pass
#endexcept
xdata = uv['xsampler'].reshape(nf,nants,3)
outpx[:,l] = xdata[:, :, 0].reshape(nf*nants)
ydata = uv['ysampler'].reshape(nf,nants,3)
outpy[:,l] = ydata[:, :, 0].reshape(nf*nants)
delayarray[:,l] = uv['delay']
#endif
outx[:,bl2ord[i,j],k,l] = data
if k == 3:
uvwarray[:,bl2ord[i,j],l] = uvw
i0array[bl2ord[i,j],l] = i0
j0array[bl2ord[i,j],l] = j0
#endif
#endelse (not filter)
#endfor
# Truncate in case of early end of data
nt = len(timearray)
outpx = outpx[:,:nt]
outpy = outpy[:,:nt]
outx = outx[:,:,:,:nt]
uvwarray = uvwarray[:, :, :nt]
delayarray = delayarray[:, :nt]
if len(lstarray) != 0:
pass
else:
tarray = Time(timearray,format='jd')
for t in tarray:
lstarray.append(el.eovsa_lst(t))
#endfor
#endelse
#i0 and j0 should always be the same
i0array = i0array[:,0]
j0array = j0array[:,0]
#timearray, lstarray and utarray are lists
out = {'x':outx,'uvw':uvwarray,'time':np.array(timearray),'px':outpx,'py':outpy,'i0':i0array,'j0':j0array,'lst':np.array(lstarray),'pol':polarr,'delay':delayarray,'ut':np.array(utarray),'file0':filename}
return out
def readXdata(filename):
'''This routine reads the data from a single UDB file. '''
# Open uv file for reading
uv = aipy.miriad.UV(filename)
# Read all to get a time array
utcount = 0
ut = 0.0
for preamble, data in uv.all():
# Look for time change
if preamble[1] != ut:
ut = preamble[1]
utcount = utcount+1
#endif
#endfor
uv.rewind()
#set up outputs
src = uv['source']
sfreq = uv['sfreq']
nf = len(sfreq)
sdf = uv['sdf']
npol = uv['npol']
polarr = np.array([-5, -6, -7, -8])
nants = uv['nants']
nbl = nants*(nants-1)/2
nblc = nants+nbl
# all-correlations, add a mask for the output vis array
outx0 = np.zeros((nf, nblc, npol, utcount),dtype=np.complex64)
omask = np.zeros((nf, nblc, npol, utcount),dtype=np.int32)
outx = ma.masked_array(outx0, mask = omask)
i0array = np.zeros((nblc, utcount), dtype = np.int32)
j0array = np.zeros((nblc, utcount), dtype = np.int32)
outpx = np.zeros((nants*nf, utcount), dtype=np.float)
outpy = np.zeros((nants*nf, utcount), dtype=np.float)
uvwarray = np.zeros((3, nblc, utcount), dtype=np.float)
delayarray = np.zeros((nants, utcount), dtype=np.float)
utarray = []
timearray = []
lstarray = []
l = -1
tprev = 0
tsav = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = strip_non_printable(uv['antlist'])
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
#endelse
#keep autocorrelations in the array
bl2ord = p.bl_list()
for ij in range(len(antlist)):
bl2ord[ij, ij] = nbl+ij
#endfor
xxx = 0
for preamble, data in uv.all():
uvw, t, (i0,j0) = preamble
i = antlist.index(i0+1)
j = antlist.index(j0+1)
if i > j: #this should not happen post-2016
# Reverse order of indices
j = antlist.index(i0+1)
i = antlist.index(j0+1)
# Assumes uv['pol'] is one of -5, -6, -7, -8
# Endif
k = -5 - uv['pol']
if t != tprev:
# New time
l += 1
if l == utcount:
break
#endif
tprev = t
try:
lstarray.append(uv['lst'])
except:
pass
timearray.append(t)
utarray.append(uv['ut'])
xdata = uv['xtsys']
ydata = uv['ytsys']
outpx[:, l] = xdata
outpy[:, l] = ydata
delayarray[:, l] = uv['delay']
#endif
outx[:,bl2ord[i,j],k,l] = data
#apply mask
outx[:,bl2ord[i,j],k,l].mask = data.mask
if k == 3:
uvwarray[:, bl2ord[i,j],l] = uvw
i0array[bl2ord[i,j],l] = i0
j0array[bl2ord[i,j],l] = j0
#endif
#endfor
if len(lstarray) != 0:
pass
else:
tarray = Time(timearray,format='jd')
for t in tarray:
lstarray.append(el.eovsa_lst(t))
#endfor
#endelse
#i0 and j0 should always be the same
i0array = i0array[:,0]
j0array = j0array[:,0]
#timearray, lstarray and utarray are lists
out = {'x':outx,'uvw':uvwarray,'time':np.array(timearray),'px':outpx,'py':outpy,'i0':i0array,'j0':j0array,'lst':np.array(lstarray),'pol':polarr,'delay':delayarray,'ut':np.array(utarray),'file0':filename}
return out
def readXdata(filename, filter=False, tp_only=False):
''' This routine reads the data from a single IDBfile.
Optiona Keywords:
filter boolean--if True, returns only non-zero frequencies
if False (default), returns uniform set of 500 frequencies
tp_only boolean--if True, returns only TP information
if False (default), returns everything (including
auto & cross correlations)
'''
# Open uv file for reading
uv = aipy.miriad.UV(filename)
good_idx = np.arange(len(uv['sfreq']))
if filter:
good_idx = []
# Read a bunch of records to get number of good frequencies, i.e. those with at least
# some non-zero data. Read 20 records for baseline 1-2, XX pol
uv.select('antennae',0,2,include=True)
uv.select('polarization',-5,-5,include=True)
for i in range(20):
preamble, data = uv.read()
idx, = data.nonzero()
if len(idx) > len(good_idx):
good_idx = copy.copy(idx)
uv.select('clear',0,0)
uv.rewind()
if 'source' in uv.vartable:
src = uv['source']
nf = len(good_idx)
freq = uv['sfreq'][good_idx]
npol = uv['npol']
nants = uv['nants']
nbl = nants*(nants-1)/2
bl2ord = p.bl_list(nants)
if not tp_only:
outa = np.zeros((nants,npol,nf,600),dtype=np.complex64) # Auto-correlations
outx = np.zeros((nbl,npol,nf,600),dtype=np.complex64) # Cross-correlations
outp = np.zeros((nants,2,nf,600),dtype=np.float)
outp2 = np.zeros((nants,2,nf,600),dtype=np.float)
outm = np.zeros((nants,2,nf,600),dtype=np.int)
uvwarray = np.zeros((nbl,600,3),dtype=np.float)
timearray = []
l = -1
tprev = 0
tsav = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = uv['antlist']
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
for preamble, data in uv.all():
uvw, t, (i0,j0) = preamble
i = antlist.index(i0+1)
j = antlist.index(j0+1)
if i > j:
# Reverse order of indices
j = antlist.index(i0+1)
i = antlist.index(j0+1)
# Assumes uv['pol'] is one of -5, -6, -7, -8
k = -5 - uv['pol']
if filter:
if len(data.nonzero()[0]) == nf:
if t != tprev:
# print preamble
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500,nants,3)
ydata = uv['ysampler'].reshape(500,nants,3)
outp[:,0,:,l] = np.swapaxes(xdata[good_idx,:,0],0,1)
outp[:,1,:,l] = np.swapaxes(ydata[good_idx,:,0],0,1)
outp2[:,0,:,l] = np.swapaxes(xdata[good_idx,:,1],0,1)
outp2[:,1,:,l] = np.swapaxes(ydata[good_idx,:,1],0,1)
outm[:,0,:,l] = np.swapaxes(xdata[good_idx,:,2],0,1)
outm[:,1,:,l] = np.swapaxes(ydata[good_idx,:,2],0,1)
if tp_only:
outa = None
outx = None
else:
if i0 == j0:
# This is an auto-correlation
outa[i0,k,:,l] = data[data.nonzero()]
else:
outx[bl2ord[i,j],k,:,l] = data[data.nonzero()]
if k == 3: uvwarray[bl2ord[i,j],l] = uvw[data.nonzero()]
else:
if t != tprev:
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500,nants,3)
ydata = uv['ysampler'].reshape(500,nants,3)
outp[:,0,:,l] = np.swapaxes(xdata[:,:,0],0,1)
outp[:,1,:,l] = np.swapaxes(ydata[:,:,0],0,1)
outp2[:,0,:,l] = np.swapaxes(xdata[:,:,1],0,1)
outp2[:,1,:,l] = np.swapaxes(ydata[:,:,1],0,1)
outm[:,0,:,l] = np.swapaxes(xdata[:,:,2],0,1)
outm[:,1,:,l] = np.swapaxes(ydata[:,:,2],0,1)
if tp_only:
outa = None
outx = None
else:
if i0 == j0:
# This is an auto-correlation
outa[i0,k,:,l] = data
if k < 2 and np.sum(data != np.real(data)) > 0:
print preamble,uv['pol'], 'has imaginary data!'
else:
outx[bl2ord[i,j],k,:,l] = data
if k == 3: uvwarray[bl2ord[i,j],l] = uvw
# Truncate in case of early end of data
nt = len(timearray)
outp = outp[:,:,:,:nt]
outp2 = outp2[:,:,:,:nt]
outm = outm[:,:,:,:nt]
outa = outa[:,:,:,:nt]
outx = outx[:,:,:,:nt]
out = {'a':outa, 'x':outx, 'uvw':uvwarray, 'fghz':freq, 'time':np.array(timearray),'source':src,'p':outp,'p2':outp2,'m':outm}
return out
def readXdatmp(filename):
# This temporary routine reads the data from a single IDBfile where the
# polarization was written incorrectly. (-1,-2,0,0) instead of (-5,-6,-7,-8)
# the IDB array sorts basline pairs into correct order for an output
# array that just has 18 baselines (2 sets of 4 antennas correlated separately
# now just need to convert i,j into slot in 136-slot array: 15*16/2 corrs +16 auto
# this array corresponds to the first index (auto corr) for each antenna
# 16*(iant-1)-(iant-1)(iant-2)/2
ibl = np.array( [ 0,16,31,45,58,70,81,91,100,108,115,121,126,130,133,135 ])
# Open uv file for reading
uv = aipy.miriad.UV(filename)
good_idx = []
# Read a bunch of records to get number of good frequencies, i.e. those with at least
# some non-zero data. Read 20 records for baseline 1-2, XX pol
uv.select('antennae',0,2,include=True)
uv.select('polarization',-1,-1,include=True)
for i in range(20):
preamble, data = uv.read()
idx, = data.nonzero()
if len(idx) > len(good_idx):
good_idx = copy.copy(idx)
if 'source' in uv.vartable:
src = uv['source']
uv.select('clear',0,0)
uv.rewind()
nf = len(good_idx)
freq = uv['sfreq'][good_idx]
npol = uv['npol']
nants = uv['nants']
nbl = nants*(nants-1)/2
bl2ord = p.bl_list(nants)
outa = np.zeros((nants,npol,nf,600),dtype=np.complex64) # Auto-correlations
outx = np.zeros((nbl,npol,nf,600),dtype=np.complex64) # Cross-correlations
outp = np.zeros((nants,2,nf,600),dtype=np.float)
outp2 = np.zeros((nants,2,nf,600),dtype=np.float)
outm = np.zeros((nants,2,nf,600),dtype=np.int)
uvwarray = []
timearray = []
l = -1
tprev = 0
# Use antennalist if available
if 'antlist' in uv.vartable:
ants = uv['antlist']
antlist = map(int, ants.split())
else:
antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
kp = 0
for preamble, data in uv.all():
uvw, t, (i0,j0) = preamble
i = antlist.index(i0+1)
j = antlist.index(j0+1)
if i > j:
# Reverse order of indices
j = antlist.index(i0+1)
i = antlist.index(j0+1)
# Assumes uv['pol'] is -1, -2, 0, 0
if i == 0 and j == 0:
if uv['pol'] == -1:
k = 0
elif uv['pol'] == -2:
k = 1
elif uv['pol'] == 0:
if kp == 1:
k = 2
kp = 0
else:
k = 3
kp = 1
if k == 3:
uvwarray.append(uvw)
if len(data.nonzero()[0]) == nf:
if t != tprev:
# New time
l += 1
if l == 600:
break
tprev = t
timearray.append(t)
xdata = uv['xsampler'].reshape(500,nants,3)
ydata = uv['ysampler'].reshape(500,nants,3)
outp[:,0,:,l] = np.swapaxes(xdata[good_idx,:,0],0,1)
outp[:,1,:,l] = np.swapaxes(ydata[good_idx,:,0],0,1)
outp2[:,0,:,l] = np.swapaxes(xdata[good_idx,:,1],0,1)
outp2[:,1,:,l] = np.swapaxes(ydata[good_idx,:,1],0,1)
outm[:,0,:,l] = np.swapaxes(xdata[good_idx,:,2],0,1)
outm[:,1,:,l] = np.swapaxes(ydata[good_idx,:,2],0,1)
if i0 == j0:
# This is an auto-correlation
outa[i0,k,:,l] = data[data.nonzero()]
else:
outx[bl2ord[i,j],k,:,l] = data[data.nonzero()]
out = {'a':outa, 'x':outx, 'uvw':np.array(uvwarray), 'fghz':freq, 'time':np.array(timearray),'source':src,'p':outp,'p2':outp2,'m':outm}
return out
