def main(args):
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
station = metabundle.get_station(args.metadata, apply_sdm=True)
else:
station = stations.lwa1
antennas = station.antennas
# Length of the FFT
LFFT = args.fft_length
# Make sure that the file chunk size contains is an integer multiple
# of the FFT length so that no data gets dropped
maxFrames = int((30000 * 260) / float(LFFT)) * LFFT
# It seems like that would be a good idea, however... TBW data comes one
# capture at a time so doing something like this actually truncates data
# from the last set of stands for the first integration. So, we really
# should stick with
maxFrames = (30000 * 260)
idf = LWA1DataFile(args.filename)
if not isinstance(idf, TBWFile):
raise RuntimeError("File '%s' does not appear to be a valid TBW file" %
os.path.basename(filename))
nFrames = idf.get_info('nframe')
srate = idf.get_info('sample_rate')
dataBits = idf.get_info('data_bits')
# The number of ant/pols in the file is hard coded because I cannot figure out
# a way to get this number in a systematic fashion
antpols = len(antennas)
nChunks = int(math.ceil(1.0 * nFrames / maxFrames))
if dataBits == 12:
nSamples = 400
else:
nSamples = 1200
# Read in the first frame and get the date/time of the first sample
# of the frame. This is needed to get the list of stands.
beginDate = idf.get_info('start_time').datetime
# File summary
print("Filename: %s" % args.filename)
print("Date of First Frame: %s" % str(beginDate))
print("Ant/Pols: %i" % antpols)
print("Sample Length: %i-bit" % dataBits)
print("Frames: %i" % nFrames)
print("Chunks: %i" % nChunks)
print("===")
# Setup the window function to use
if args.bartlett:
window = numpy.bartlett
elif args.blackman:
window = numpy.blackman
elif args.hanning:
window = numpy.hanning
else:
window = fxc.null_window
# Master loop over all of the file chunks
nChunks = 1
masterSpectra = numpy.zeros((nChunks, antpols, LFFT))
masterWeight = numpy.zeros((nChunks, antpols, LFFT))
readT, t, data = idf.read(0.061)
# Calculate the spectra for this block of data and then weight the results by
# the total number of frames read. This is needed to keep the averages correct.
# NB: The weighting is the same for the x and y polarizations because of how
# the data are packed in TBW
freq, tempSpec = fxc.SpecMaster(data,
LFFT=LFFT,
window=window,
pfb=args.pfb,
verbose=args.verbose)
for stand in range(masterSpectra.shape[1]):
masterSpectra[0, stand, :] = tempSpec[stand, :]
masterWeight[0, stand, :] = int(readT * srate / LFFT)
# We don't really need the data array anymore, so delete it
del (data)
# Apply the cable loss corrections, if requested
if args.gain_correct:
for s in range(masterSpectra.shape[1]):
currGain = antennas[s].cable.gain(freq)
for c in range(masterSpectra.shape[0]):
masterSpectra[c, s, :] /= currGain
# Now that we have read through all of the chunks, perform the final averaging by
# dividing by all of the chunks
spec = masterSpectra.mean(axis=0)
# The plots: This is setup for the current configuration of 20 antpols
if args.gain_correct & args.stack:
# Stacked spectra - only if cable loss corrections are to be applied
colors = [
'blue', 'green', 'red', 'cyan', 'magenta', 'black', 'purple',
'salmon', 'olive', 'maroon', 'saddlebrown', 'yellowgreen', 'teal',
'steelblue', 'seagreen', 'slategray', 'mediumorchid', 'lime',
'dodgerblue', 'darkorange'
]
for f in range(int(numpy.ceil(antpols / 20.))):
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
for i in range(f * 20, f * 20 + 20):
currSpectra = numpy.squeeze(numpy.log10(spec[i, :]) * 10.0)
ax1.plot(freq / 1e6,
currSpectra,
label='%i,%i' %
(antennas[i].stand.id, antennas[i].pol),
color=colors[i % 20])
ax1.set_xlabel('Frequency [MHz]')
ax1.set_ylabel('P.S.D. [dB/RBW]')
ax1.set_xlim([20, 88])
#ax1.set_ylim([10,90])
leg = ax1.legend(loc=0, ncol=3)
for l in leg.get_lines():
l.set_linewidth(1.7) # the legend line width
else:
for f in range(int(numpy.ceil(antpols / 20))):
# Normal plotting
fig = plt.figure()
figsY = 4
figsX = 5
fig.subplots_adjust(left=0.06,
bottom=0.06,
right=0.94,
top=0.94,
wspace=0.20,
hspace=0.50)
for i in range(f * 20, f * 20 + 20):
ax = fig.add_subplot(figsX, figsY, (i % 20) + 1)
try:
currSpectra = numpy.squeeze(numpy.log10(spec[i, :]) * 10.0)
except IndexError:
break
ax.plot(freq / 1e6,
currSpectra,
label='Stand: %i, Pol: %i (Dig: %i)' %
(antennas[i].stand.id, antennas[i].pol,
antennas[i].digitizer))
# If there is more than one chunk, plot the difference between the global
# average and each chunk
if nChunks > 1:
for j in range(nChunks):
# Some files are padded by zeros at the end and, thus, carry no
# weight in the average spectra. Skip over those.
if masterWeight[j, i, :].sum() == 0:
continue
# Calculate the difference between the spectra and plot
subspectra = numpy.squeeze(
numpy.log10(masterSpectra[j, i, :]) * 10.0)
diff = subspectra - currSpectra
ax.plot(freq / 1e6, diff)
ax.set_title(
'Stand: %i (%i); Dig: %i [%i]' %
(antennas[i].stand.id, antennas[i].pol,
antennas[i].digitizer, antennas[i].combined_status))
ax.set_xlabel('Frequency [MHz]')
ax.set_ylabel('P.S.D. [dB/RBW]')
ax.set_xlim([10, 90])
ax.set_ylim([10, 80])
# Save spectra image if requested
if args.output is not None:
base, ext = os.path.splitext(args.output)
outFigure = "%s-%02i%s" % (base, f + 1, ext)
fig.savefig(outFigure)
plt.draw()
print("RBW: %.1f Hz" % (freq[1] - freq[0]))
plt.show()
def main(args):
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
station = metabundle.get_station(args.metadata, apply_sdm=True)
else:
station = stations.lwana
antennas = []
for a in station.antennas:
if a.digitizer != 0:
antennas.append(a)
# Length of the FFT
LFFT = args.fft_length
idf = LWA1DataFile(args.filename)
nFramesFile = idf.get_info('nframe')
srate = idf.get_info('sample_rate')
antpols = len(antennas)
# Offset in frames for beampols beam/tuning/pol. sets
args.skip = idf.offset(args.skip)
# Make sure that the file chunk size contains is an integer multiple
# of the FFT length so that no data gets dropped. This needs to
# take into account the number of antpols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(
(2 * 10 * 750) / antpols * 512 / float(LFFT)) * LFFT / 512 * antpols
# Number of frames to integrate over
nFrames = int(args.average * srate / 512 * antpols)
nFrames = int(
1.0 * nFrames / antpols * 512 / float(LFFT)) * LFFT / 512 * antpols
args.average = 1.0 * nFrames / antpols * 512 / srate
# Number of remaining chunks
nChunks = int(math.ceil(1.0 * (nFrames) / maxFrames))
# Read in the first frame and get the date/time of the first sample
# of the frame. This is needed to get the list of stands.
beginDate = ephem.Date(
unix_to_utcjd(idf.get_info('start_time')) - DJD_OFFSET)
central_freq = idf.get_info('freq1')
# File summary
print("Filename: %s" % args.filename)
print("Date of First Frame: %s" % str(beginDate))
print("Ant/Pols: %i" % antpols)
print("Sample Rate: %i Hz" % srate)
print("Tuning Frequency: %.3f Hz" % central_freq)
print("Frames: %i (%.3f s)" %
(nFramesFile, 1.0 * nFramesFile / antpols * 512 / srate))
print("---")
print("Offset: %.3f s (%i frames)" %
(args.skip, args.skip * srate * antpols / 512))
print("Integration: %.3f s (%i frames; %i frames per stand/pol)" %
(args.average, nFrames, nFrames / antpols))
print("Chunks: %i" % nChunks)
# Sanity check
if args.skip * srate * antpols / 512 > nFramesFile:
raise RuntimeError("Requested offset is greater than file length")
if nFrames > (nFramesFile - args.skip * srate * antpols / 512):
raise RuntimeError(
"Requested integration time+offset is greater than file length")
# Setup the window function to use
if args.bartlett:
window = numpy.bartlett
elif args.blackman:
window = numpy.blackman
elif args.hanning:
window = numpy.hanning
else:
window = fxc.null_window
# Master loop over all of the file chunks
masterWeight = numpy.zeros((nChunks, antpols, LFFT))
masterSpectra = numpy.zeros((nChunks, antpols, LFFT))
for i in range(nChunks):
print("Working on chunk #%i of %i" % (i + 1, nChunks))
try:
readT, t, data = idf.read(args.average / nChunks)
except Exception as e:
print("Error: %s" % str(e))
continue
# Calculate the spectra for this block of data and then weight the results by
# the total number of frames read. This is needed to keep the averages correct.
freq, tempSpec = fxc.SpecMaster(data,
LFFT=LFFT,
window=window,
pfb=args.pfb,
verbose=args.verbose,
sample_rate=srate)
for stand in range(tempSpec.shape[0]):
masterSpectra[i, stand, :] = tempSpec[stand, :]
masterWeight[i, stand, :] = int(readT * srate / LFFT)
# Apply the cable loss corrections, if requested
if False:
for s in range(masterSpectra.shape[1]):
currGain = antennas[s].cable.gain(freq)
for c in range(masterSpectra.shape[0]):
masterSpectra[c, s, :] /= currGain
# Now that we have read through all of the chunks, perform the final averaging by
# dividing by all of the chunks
spec = numpy.squeeze(
(masterWeight * masterSpectra).sum(axis=0) / masterWeight.sum(axis=0))
# Put the frequencies in the best units possible
freq += central_freq
freq, units = _best_freq_units(freq)
# Deal with the `keep` options
if args.keep == 'all':
antpolsDisp = int(numpy.ceil(antpols / 20))
js = [i for i in range(antpols)]
else:
antpolsDisp = int(numpy.ceil(len(args.keep) * 2 / 20))
if antpolsDisp < 1:
antpolsDisp = 1
js = []
for k in args.keep:
for i, ant in enumerate(antennas):
if ant.stand.id == k:
js.append(i)
nPlot = len(js)
if nPlot < 20:
if nPlot % 4 == 0 and nPlot != 4:
figsY = 4
else:
figsY = 2
figsX = int(numpy.ceil(1.0 * nPlot / figsY))
else:
figsY = 4
figsX = 5
figsN = figsX * figsY
for i in range(antpolsDisp):
# Normal plotting
fig = plt.figure()
for k in range(i * figsN, i * figsN + figsN):
try:
j = js[k]
currSpectra = numpy.squeeze(numpy.log10(spec[j, :]) * 10.0)
except IndexError:
break
ax = fig.add_subplot(figsX, figsY, (k % figsN) + 1)
ax.plot(
freq,
currSpectra,
label='Stand: %i, Pol: %i (Dig: %i)' %
(antennas[j].stand.id, antennas[j].pol, antennas[j].digitizer))
# If there is more than one chunk, plot the difference between the global
# average and each chunk
if nChunks > 1 and not args.disable_chunks:
for k in range(nChunks):
# Some files are padded by zeros at the end and, thus, carry no
# weight in the average spectra. Skip over those.
if masterWeight[k, j, :].sum() == 0:
continue
# Calculate the difference between the spectra and plot
subspectra = numpy.squeeze(
numpy.log10(masterSpectra[k, j, :]) * 10.0)
diff = subspectra - currSpectra
ax.plot(freq, diff)
ax.set_title('Stand: %i (%i); Dig: %i [%i]' %
(antennas[j].stand.id, antennas[j].pol,
antennas[j].digitizer, antennas[j].combined_status))
ax.set_xlabel('Frequency [%s]' % units)
ax.set_ylabel('P.S.D. [dB/RBW]')
ax.set_ylim([-10, 30])
# Save spectra image if requested
if args.output is not None:
base, ext = os.path.splitext(args.output)
outFigure = "%s-%02i%s" % (base, i + 1, ext)
fig.savefig(outFigure)
plt.draw()
print("RBW: %.4f %s" % ((freq[1] - freq[0]), units))
plt.show()
def test_station(self):
"""Test building a station from a tarball."""
station = metabundle.get_station(mdbFileADP)
def main(args):
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
try:
station = metabundle.get_station(args.metadata, apply_sdm=True)
except:
station = metabundleADP.get_station(args.metadata,
apply_sdm=True)
elif args.lwasv:
station = stations.lwasv
else:
station = stations.lwa1
antennas = []
for ant in station.antennas[0::2]:
if ant.combined_status == 33:
antennas.append(ant)
print("Displaying uv coverage for %i good stands" % len(antennas))
HA = 0.0
dec = station.lat * 180.0 / math.pi
uvw = uvutils.compute_uvw(antennas, HA=HA, dec=dec, freq=args.frequency)
uvw = numpy.squeeze(uvw[:, :, 0])
# Coursely grid the uv data to come up with a rough beam
grid = numpy.zeros((1 * 240, 1 * 240))
for i in range(uvw.shape[0]):
u = round((uvw[i, 0] + 120) * 1)
v = round((uvw[i, 1] + 120) * 1)
try:
grid[u, v] += 1
except IndexError:
pass
# Plot
# Part 1 - Setup
fig = plt.figure(figsize=(8, 8))
ax1 = plt.axes([0.30, 0.30, 0.60, 0.60])
ax2 = plt.axes([0.30, 0.05, 0.60, 0.15])
ax3 = plt.axes([0.05, 0.30, 0.15, 0.60])
ax4 = plt.axes([0.08, 0.08, 0.15, 0.15])
ax5 = plt.axes([0.32, 0.32, 0.15, 0.15])
# Part 2 - Beam response (in dB)
beam = numpy.fft.fft2(grid)
beam = numpy.fft.fftshift(beam)
beam = numpy.abs(beam)**2
beam = numpy.log10(beam) * 10.0
ax5.imshow(beam[40:200, 40:200],
interpolation="nearest",
vmin=numpy.median(beam),
vmax=beam.max())
ax5.xaxis.set_major_formatter(NullFormatter())
ax5.yaxis.set_major_formatter(NullFormatter())
# Part 3 - uv plane plot
c = ax1.scatter(uvw[:, 0], uvw[:, 1], c=uvw[:, 2], s=10.0, alpha=0.75)
d = ax1.scatter(-uvw[:, 0], -uvw[:, 1], c=-uvw[:, 2], s=10.0, alpha=0.75)
ax1.set_xlabel('u [$\\lambda$]')
ax1.set_ylabel('v [$\\lambda$]')
ax1.set_title(
'UV Coverage for HA=%+.3f$^h$, $\delta$=%+.3f$^\circ$ at %s' %
(HA, dec, station.name))
# Part 4 - uw plane plot
ax2.scatter(uvw[:, 0], uvw[:, 2], c=uvw[:, 2], s=10.0)
ax2.scatter(-uvw[:, 0], -uvw[:, 2], c=-uvw[:, 2], s=10.0)
ax2.xaxis.set_major_formatter(NullFormatter())
ax2.set_ylabel('w [$\\lambda$]')
# Part 5 - wv plane plot
ax3.scatter(uvw[:, 2], uvw[:, 1], c=uvw[:, 2], s=10.0)
ax3.scatter(-uvw[:, 2], -uvw[:, 1], c=-uvw[:, 2], s=10.0)
ax3.yaxis.set_major_formatter(NullFormatter())
ax3.set_xlabel('w [$\\lambda$]')
# Part 6 - Histogram of uvw distances in lambda
rad = numpy.zeros(uvw.shape[0])
for i in range(rad.shape[0]):
rad[i] = math.sqrt(uvw[i, 0]**2.0 + uvw[i, 1]**2.0 + uvw[i, 2]**2.0)
try:
ax4.hist(rad, 20)
except TypeError:
## I don't know why this happens
pass
ax4.set_xlabel('uvw Radius [$\lambda$]')
ax4.set_ylabel('Baselines')
# Plot adjustment
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
ax1.set_xlim([
numpy.floor(xlim[0] / 25.0) * 25.0,
numpy.ceil(xlim[1] / 25.0) * 25.0
])
ax1.set_ylim([
numpy.floor(ylim[0] / 25.0) * 25.0,
numpy.ceil(ylim[1] / 25.0) * 25.0
])
ax2.set_xlim(ax1.get_xlim())
ax2.yaxis.set_major_locator(MaxNLocator(nbins=4))
ax3.set_ylim(ax1.get_ylim())
ax3.xaxis.set_major_locator(MaxNLocator(nbins=4))
xlim = ax4.get_xlim()
ylim = ax4.get_ylim()
ax4.set_xlim([
numpy.floor(xlim[0] / 25.0) * 25.0,
numpy.ceil(xlim[1] / 25.0) * 25.0
])
ax4.set_ylim([
numpy.floor(ylim[0] / 5.e3) * 5.e3,
numpy.ceil(ylim[1] / 5.e3) * 5.e3
])
ax4.xaxis.set_major_locator(MaxNLocator(nbins=4))
ax4.yaxis.set_major_locator(MaxNLocator(nbins=4))
# Show n' save
plt.show()
if args.output is not None:
fig.savefig(args.output)
def main(args):
# Parse command line options
filename = args.filename
# Length of the FFT
LFFT = args.fft_length
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
station = metabundle.get_station(args.metadata, apply_sdm=True)
else:
station = stations.lwana
antennas = station.antennas
idf = LWA1DataFile(filename)
jd = astro.unix_to_utcjd(idf.get_info('start_time'))
date = str(ephem.Date(jd - astro.DJD_OFFSET))
nFpO = len(antennas)
sample_rate = idf.get_info('sample_rate')
nInts = idf.get_info('nframe') // nFpO
# Get valid stands for both polarizations
goodX = []
goodY = []
for i in range(len(antennas)):
ant = antennas[i]
if ant.combined_status != 33 and not args.all:
pass
else:
if ant.pol == 0:
goodX.append(ant)
else:
goodY.append(ant)
# Now combine both lists to come up with stands that
# are in both so we can form the cross-polarization
# products if we need to
good = []
for antX in goodX:
for antY in goodY:
if antX.stand.id == antY.stand.id:
good.append(antX.digitizer - 1)
good.append(antY.digitizer - 1)
# Report on the valid stands found. This is a little verbose,
# but nice to see.
print("Found %i good stands to use" % (len(good) // 2, ))
for i in good:
print("%3i, %i" % (antennas[i].stand.id, antennas[i].pol))
# Number of frames to read in at once and average
nFrames = int(args.avg_time * sample_rate / 512)
args.offset = idf.offset(args.offset)
nSets = idf.get_info('nframe') // nFpO // nFrames
nSets = nSets - int(args.offset * sample_rate / 512) // nFrames
central_freq = idf.get_info('freq1')
print("Data type: %s" % type(idf))
print("Samples per observations: %i per pol." % (nFpO / 2))
print("Sampling rate: %i Hz" % sample_rate)
print("Tuning frequency: %.3f Hz" % central_freq)
print("Captures in file: %i (%.1f s)" % (nInts, nInts * 512 / sample_rate))
print("==")
print("Station: %s" % station.name)
print("Date observed: %s" % date)
print("Julian day: %.5f" % jd)
print("Offset: %.3f s (%i frames)" %
(args.offset, args.offset * sample_rate / 512))
print("Integration Time: %.3f s" % (512 * nFrames / sample_rate))
print("Number of integrations in file: %i" % nSets)
# Make sure we don't try to do too many sets
if args.samples > nSets:
args.samples = nSets
# Loop over junks of 300 integrations to make sure that we don't overflow
# the FITS IDI memory buffer
s = 0
leftToDo = args.samples
basename = os.path.split(filename)[1]
basename, ext = os.path.splitext(basename)
while leftToDo > 0:
fitsFilename = "%s.FITS_%i" % (
basename,
(s + 1),
)
if leftToDo > 100:
chunk = 100
else:
chunk = leftToDo
process_chunk(idf,
station,
good,
fitsFilename,
int_time=args.avg_time,
LFFT=args.fft_length,
overlap=1,
pfb=args.pfb,
pols=args.products,
chunk_size=chunk)
s += 1
leftToDo = leftToDo - chunk
idf.close()
def main(args):
# Parse command line
toMark = numpy.array(args.stand)-1
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
try:
station = metabundle.get_station(args.metadata, apply_sdm=True)
except:
station = metabundleADP.get_station(args.metadata, apply_sdm=True)
elif args.lwasv:
station = stations.lwasv
else:
station = stations.lwa1
stands = station.stands
stands.sort()
# Load in the stand position data
data = numpy.zeros((len(stands)//2,3))
i = 0
for stand in stands[::2]:
data[i,0] = stand.x
data[i,1] = stand.y
data[i,2] = stand.z
i += 1
# Color-code the stands by their elevation
color = data[:,2]
# Plot the stands as colored circles
fig = plt.figure(figsize=(8,8))
ax1 = plt.axes([0.30, 0.30, 0.60, 0.60])
ax2 = plt.axes([0.30, 0.05, 0.60, 0.15])
ax3 = plt.axes([0.05, 0.30, 0.15, 0.60])
ax4 = plt.axes([0.05, 0.05, 0.15, 0.15])
c = ax1.scatter(data[:,0], data[:,1], c=color, s=40.0, alpha=0.50)
ax1.set_xlabel('$\Delta$X [E-W; m]')
ax1.set_xlim([-80, 80])
ax1.set_ylabel('$\Delta$Y [N-S; m]')
ax1.set_ylim([-80, 80])
ax1.set_title('%s Site: %.3f$^\circ$N, %.3f$^\circ$W' % (station.name, station.lat*180.0/numpy.pi, -station.long*180.0/numpy.pi))
ax2.scatter(data[:,0], data[:,2], c=color, s=40.0)
ax2.xaxis.set_major_formatter( NullFormatter() )
ax2.set_ylabel('$\Delta$Z [m]')
ax3.scatter(data[:,2], data[:,1], c=color, s=40.0)
ax3.yaxis.set_major_formatter( NullFormatter() )
ax3.set_xlabel('$\Delta$Z [m]')
# Explicitly mark those that need to be marked
if toMark.size != 0:
for i in range(toMark.size):
ax1.plot(data[toMark[i],0], data[toMark[i],1], marker='x', linestyle=' ', color='black')
ax2.plot(data[toMark[i],0], data[toMark[i],2], marker='x', linestyle=' ', color='black')
ax3.plot(data[toMark[i],2], data[toMark[i],1], marker='x', linestyle=' ', color='black')
if args.label:
ax1.annotate('%i' % (toMark[i]+1), xy=(data[toMark[i],0], data[toMark[i],1]), xytext=(data[toMark[i],0]+1, data[toMark[i],1]+1))
# Add and elevation colorbar to the right-hand side of the figure
plt.colorbar(c, cax=ax4, orientation='vertical', ticks=[-2, -1, 0, 1, 2])
# Set the axis limits
ax1.set_xlim([-60, 60])
ax1.set_ylim([-60, 60])
ax2.set_xlim( ax1.get_xlim() )
ax3.set_ylim( ax1.get_ylim() )
# Show n' save
plt.show()
if args.output is not None:
fig.savefig(args.output)
def main(args):
# Parse command line options
filename = args.filename
# Setup the LWA station information
if args.metadata is not None:
try:
station = stations.parse_ssmif(args.metadata)
except ValueError:
station = metabundle.get_station(args.metadata, apply_sdm=True)
else:
station = stations.lwa1
antennas = station.antennas
idf = LWA1DataFile(filename)
if not isinstance(idf, TBWFile):
raise RuntimeError("File '%s' does not appear to be a valid TBW file" %
os.path.basename(filename))
jd = idf.get_info('start_time').jd
date = idf.get_info('start_time').datetime
sample_rate = idf.get_info('sample_rate')
nInts = idf.get_info('nframe') // (30000 * len(antennas) // 2)
# Get valid stands for both polarizations
goodX = []
goodY = []
for i in range(len(antennas)):
ant = antennas[i]
if ant.combined_status != 33 and not args.all:
pass
else:
if ant.pol == 0:
goodX.append(ant)
else:
goodY.append(ant)
# Select which polarization to use
good = []
for antX in goodX:
for antY in goodY:
if antX.stand.id == antY.stand.id:
good.append(antX.digitizer - 1)
good.append(antY.digitizer - 1)
break
# Report on the valid stands found. This is a little verbose,
# but nice to see.
print("Found %i good stands to use" % (len(good) // 2, ))
for i in good:
print("%3i, %i" % (antennas[i].stand.id, antennas[i].pol))
# Number of frames to read in at once and average
nFrames = 30000
nSets = idf.get_info('nframe') // (30000 * len(antennas) // 2)
print("Data type: %s" % type(idf))
print("Captures in file: %i (%.3f s)" %
(nInts, nInts * 30000 * 400 / sample_rate))
print("==")
print("Station: %s" % station.name)
print("Date observed: %s" % date)
print("Julian day: %.5f" % jd)
print("Integration Time: %.3f s" % (400 * nFrames / sample_rate))
print("Number of integrations in file: %i" % nSets)
print("==")
basename = os.path.split(filename)[1]
basename, ext = os.path.splitext(basename)
if args.casa:
fitsFilename = "%s.ms_1" % (basename, )
else:
fitsFilename = "%s.FITS_1" % (basename, )
process_chunk(idf,
station,
good,
fitsFilename,
LFFT=args.fft_length,
overlap=1,
pfb=args.pfb,
pols=args.products)
idf.close()
