def test_CorrelatedDataMS_MultiIF(self):
"""Test the utils.CorrelatedDataMS class on a file with multiple IFs."""
# Get some data
data = self._init_data()
# Filename and time
testTime, testFile = time.time(), os.path.join(self.testPath,
'ms-test-MultiIF.ms')
# Start the file
fits = Ms(testFile, ref_time=testTime, overwrite=True)
fits.set_stokes(['xx'])
fits.set_frequency(data['freq'])
fits.set_frequency(data['freq'] + 30e6)
fits.set_geometry(data['site'], data['antennas'])
fits.add_data_set(
astro.utcjd_to_taimjd(astro.unix_to_utcjd(testTime)), 6.0,
data['bl'],
numpy.concatenate([data['vis'], 10 * data['vis']], axis=1))
fits.write()
fits.close()
# Open the measurement set
ms = utils.CorrelatedDataMS(testFile)
self.assertEqual(ms.freq.size, 2 * data['freq'].size)
ds = ms.get_data_set(1, include_auto=True)
numpy.testing.assert_allclose(ds.XX.data[:, :data['freq'].size],
data['vis'])
numpy.testing.assert_allclose(ds.XX.data[:, data['freq'].size:],
10 * data['vis'])
ms.close()
def test_CorrelatedDataMS(self):
"""Test the utils.CorrelatedDataMS class."""
testTime, testFile = time.time(), os.path.join(self.testPath,
'ms-test-W.ms')
# Get some data
data = self._init_data()
# Start the table
tbl = Ms(testFile, ref_time=testTime)
tbl.set_stokes(['xx'])
tbl.set_frequency(data['freq'])
tbl.set_geometry(data['site'], data['antennas'])
tbl.add_data_set(astro.utcjd_to_taimjd(astro.unix_to_utcjd(testTime)),
6.0, data['bl'], data['vis'])
tbl.write()
# Open the measurement set
ms = utils.CorrelatedDataMS(testFile)
# Basic functions (just to see that they run)
junk = ms.get_antennaarray()
junk = ms.get_observer()
junk = ms.get_data_set(1)
# Error checking
self.assertRaises(IndexError, ms.get_data_set, 2)
tbl.close()
def _get_antennaarray(station, stands, utime, freqs):
"""
Given a LWA station object, a list of stands, an observation time, and
a list of frequencies in Hz, build an aipy AntennaArray object.
"""
return vis.build_sim_array(station,
stands,
freqs / 1e9,
jd=astro.unix_to_utcjd(utime))
def main(args):
idf = LWA1DataFile(args[0])
nFramesFile = idf.getInfo('nFrames')
srate = idf.getInfo('sampleRate')
beam = idf.getInfo('beam')
beampols = idf.getInfo('beampols')
# Date
beginDate = ephem.Date(unix_to_utcjd(idf.getInfo('tStart')) - DJD_OFFSET)
# File summary
print "Filename: %s" % args[0]
print "Date of First Frame: %s" % str(beginDate)
print "Beam: %i" % beam
print "Tune/Pols: %ii" % beampols
print "Sample Rate: %i Hz" % srate
print "Frames: %i (%.3f s)" % (nFramesFile, 1.0 * nFramesFile / beampols * 4096 / srate)
print "---"
def test_CorrelatedDataMS_compressed(self):
"""Test the utils.CorrelatedDataMS class on a compressed file."""
# Get some data
data = self._init_data()
# Filename and time
testTime, testFile = time.time(), os.path.join(self.testPath,
'ms-test-MultiIF.ms')
# Start the file
fits = Ms(testFile, ref_time=testTime, overwrite=True)
fits.set_stokes(['xx'])
fits.set_frequency(data['freq'])
fits.set_frequency(data['freq'] + 30e6)
fits.set_geometry(data['site'], data['antennas'])
fits.add_data_set(
astro.utcjd_to_taimjd(astro.unix_to_utcjd(testTime)), 6.0,
data['bl'],
numpy.concatenate([data['vis'], 10 * data['vis']], axis=1))
fits.write()
fits.close()
# Compress
compressedFile = os.path.splitext(testFile)[0] + '.tar.gz'
cmd = [
'tar', 'czf', compressedFile, '-C', self.testPath,
os.path.basename(testFile)
]
subprocess.check_call(cmd)
# Open the measurement set
ms = utils.CorrelatedDataMS(compressedFile)
self.assertEqual(ms.freq.size, 2 * data['freq'].size)
ds = ms.get_data_set(1, include_auto=True)
numpy.testing.assert_allclose(ds.XX.data[:, :data['freq'].size],
data['vis'])
numpy.testing.assert_allclose(ds.XX.data[:, data['freq'].size:],
10 * data['vis'])
ms.close()
def main(args):
reference = args.ref_source
filenames = args.filename
#
# Gather the station meta-data from its various sources
#
dataDict = numpy.load(filenames[0])
ssmifContents = dataDict['ssmifContents']
if ssmifContents.shape == ():
site = lwa1
else:
fh, tempSSMIF = tempfile.mkstemp(suffix='.txt', prefix='ssmif-')
fh = open(tempSSMIF, 'w')
for line in ssmifContents:
fh.write('%s\n' % line)
fh.close()
site = parse_ssmif(tempSSMIF)
os.unlink(tempSSMIF)
print(site.name)
observer = site.get_observer()
antennas = site.antennas
nAnts = len(antennas)
#
# Find the reference source
#
srcs = [ephem.Sun(),]
for line in _srcs:
srcs.append( ephem.readdb(line) )
refSrc = None
for i in xrange(len(srcs)):
if srcs[i].name == reference:
refSrc = srcs[i]
if refSrc is None:
print("Cannot find reference source '%s' in source list, aborting." % reference)
sys.exit(1)
#
# Parse the input files
#
data = []
time = []
freq = []
oldRef = None
oldMD5 = None
maxTime = -1
for filename in filenames:
dataDict = numpy.load(filename)
ref_ant = dataDict['ref'].item()
refX = dataDict['refX'].item()
refY = dataDict['refY'].item()
tInt = dataDict['tInt'].item()
times = dataDict['times']
phase = dataDict['simpleVis']
central_freq = dataDict['central_freq'].item()
ssmifContents = dataDict['ssmifContents']
beginDate = datetime.utcfromtimestamp(times[0])
observer.date = beginDate.strftime("%Y/%m/%d %H:%M:%S")
# Make sure we aren't mixing reference antennas
if oldRef is None:
oldRef = ref_ant
if ref_ant != oldRef:
raise RuntimeError("Dataset has different reference antennas than previous (%i != %i)" % (ref_ant, oldRef))
# Make sure we aren't mixing SSMIFs
ssmifMD5 = md5sum(ssmifContents)
if oldMD5 is None:
oldMD5 = ssmifMD5
if ssmifMD5 != oldMD5:
raise RuntimeError("Dataset has different SSMIF than previous (%s != %s)" % (ssmifMD5, oldMD5))
print("Central Frequency: %.3f Hz" % central_freq)
print("Start date/time: %s" % beginDate.strftime("%Y/%m/%d %H:%M:%S"))
print("Integration Time: %.3f s" % tInt)
print("Number of time samples: %i (%.3f s)" % (phase.shape[0], phase.shape[0]*tInt))
allRates = {}
for src in srcs:
src.compute(observer)
if src.alt > 0:
fRate = getFringeRate(antennas[0], antennas[refX], observer, src, central_freq)
allRates[src.name] = fRate
# Calculate the fringe rates of all sources - for display purposes only
print("Starting Fringe Rates:")
for name in allRates.keys():
fRate = allRates[name]
print(" %-4s: %+6.3f mHz" % (name, fRate*1e3))
freq.append( central_freq )
time.append( numpy.array([unix_to_utcjd(t) for t in times]) )
data.append( phase )
## Save the length of the `time` entry so that we can trim them
## all down to the same size later
if time[-1].size > maxTime:
maxTime = time[-1].size
# Pad with NaNs to the same length
for i in xrange(len(filenames)):
nTimes = time[i].size
if nTimes < maxTime:
## Pad 'time'
newTime = numpy.zeros(maxTime, dtype=time[i].dtype)
newTime += numpy.nan
newTime[0:nTimes] = time[i][:]
time[i] = newTime
## Pad 'data'
newData = numpy.zeros((maxTime, data[i].shape[1]), dtype=data[i].dtype)
newData += numpy.nan
newData[0:nTimes,:] = data[i][:,:]
data[i] = newData
# Convert to 2-D and 3-D numpy arrays
freq = numpy.array(freq)
time = numpy.array(time)
data = numpy.array(data)
#
# Sort the data by frequency
#
order = numpy.argsort(freq)
freq = numpy.take(freq, order)
time = numpy.take(time, order, axis=0)
data = numpy.take(data, order, axis=0)
#
# Find the fringe stopping averaging times
#
ls = {}
for fStart in xrange(20, 90, 5):
fStop = fStart + 5
l = numpy.where( (freq >= fStart*1e6) & (freq < fStop*1e6) )[0]
if len(l) > 0:
ls[fStart] = l
ms = {}
for fStart in ls.keys():
m = 1e6
for l in ls[fStart]:
good = numpy.where( numpy.isfinite(time[l,:]) == 1 )[0]
if len(good) < m:
m = len(good)
ms[fStart] = m
print("Minimum fringe stopping times:")
for fStart in sorted(ls.keys()):
fStop = fStart + 5
m = ms[fStart]
print(" >=%i Mhz and <%i MHz: %.3f s" % (fStart, fStop, m*tInt,))
#
# Report on progress and data coverage
#
nFreq = len(freq)
print("Reference stand #%i (X: %i, Y: %i)" % (ref_ant, refX, refY))
print("-> X: %s" % str(antennas[refX]))
print("-> Y: %s" % str(antennas[refY]))
print("Using a set of %i frequencies" % nFreq)
print("->", freq/1e6)
#
# Compute source positions/fringe stop and remove the source
#
print("Fringe stopping on '%s':" % refSrc.name)
pbar = ProgressBar(max=freq.size*520)
for i in xrange(freq.size):
fq = freq[i]
for j in xrange(data.shape[2]):
# Compute the times in seconds relative to the beginning
times = time[i,:] - time[i,0]
times *= 24.0
times *= 3600.0
# Compute the fringe rates across all time
fRate = [None,]*data.shape[1]
for k in xrange(data.shape[1]):
jd = time[i,k]
try:
currDate = datetime.utcfromtimestamp(utcjd_to_unix(jd))
except ValueError:
pass
observer.date = currDate.strftime("%Y/%m/%d %H:%M:%S")
refSrc.compute(observer)
if j % 2 == 0:
fRate[k] = getFringeRate(antennas[j], antennas[refX], observer, refSrc, fq)
else:
fRate[k] = getFringeRate(antennas[j], antennas[refY], observer, refSrc, fq)
# Create the basis rate and the residual rates
baseRate = fRate[0]
residRate = numpy.array(fRate) - baseRate
# Fringe stop to more the source of interest to the DC component
data[i,:,j] *= numpy.exp(-2j*numpy.pi* baseRate*(times - times[0]))
data[i,:,j] *= numpy.exp(-2j*numpy.pi*residRate*(times - times[0]))
# Calculate the geometric delay term across all time
gDelay = [None,]*data.shape[1]
for k in xrange(data.shape[1]):
jd = time[i,k]
try:
currDate = datetime.utcfromtimestamp(utcjd_to_unix(jd))
except ValueError:
pass
observer.date = currDate.strftime("%Y/%m/%d %H:%M:%S")
refSrc.compute(observer)
az = refSrc.az
el = refSrc.alt
if j % 2 == 0:
gDelay[k] = getGeoDelay(antennas[j], antennas[refX], az, el, Degrees=False)
else:
gDelay[k] = getGeoDelay(antennas[j], antennas[refY], az, el, Degrees=False)
# Create the basis delay and the residual delays
baseDelay = gDelay[0]
residDelay = numpy.array(gDelay) - baseDelay
# Remove the array geometry
data[i,:,j] *= numpy.exp(-2j*numpy.pi*fq* baseDelay)
data[i,:,j] *= numpy.exp(-2j*numpy.pi*fq*residDelay)
pbar.inc()
sys.stdout.write("%s\r" % pbar.show())
sys.stdout.flush()
sys.stdout.write('\n')
# Average down to remove other sources/the correlated sky
print("Input (pre-averaging) data shapes:")
print(" time:", time.shape)
print(" data:", data.shape)
time = time[:,0]
data2 = numpy.zeros((data.shape[0], data.shape[2]), dtype=data.dtype)
for j in xrange(data2.shape[1]):
for fStart in ls.keys():
l = ls[fStart]
m = ms[fStart]
data2[l,j] = data[l,:m,j].mean(axis=1)
data = data2
print("Output (post-averaging) data shapes:")
print(" time:", time.shape)
print(" data:", data.shape)
#
# Save
#
outname = args.output
outname, ext = os.path.splitext(outname)
outname = "%s-ref%03i%s" % (outname, ref_ant, ext)
numpy.savez(outname, ref_ant=ref_ant, refX=refX, refY=refY, freq=freq, time=time, data=data, ssmifContents=ssmifContents)
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 = 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 * 20) / 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 * 20)
idf = LWA1DataFile(args.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 = ephem.Date(
unix_to_utcjd(idf.get_info('start_time')) - DJD_OFFSET)
# 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):
# Select the multirate module to use
if args.jit:
from jit import multirate
else:
import multirate
# Build up the station
site = stations.lwa1
## Updated 2018/3/8 with solutions from the 2018 Feb 28 eLWA
## run. See createConfigFile.py for details.
site.lat = 34.068956328 * numpy.pi / 180
site.long = -107.628103026 * numpy.pi / 180
site.elev = 2132.96837346
observer = site.get_observer()
# Parse the correlator configuration
config, refSrc, filenames, metanames, foffsets, readers, antennas = read_correlator_configuration(
args.filename)
try:
args.fft_length = config['channels']
args.dump_time = config['inttime']
print(
"NOTE: Set FFT length to %i and dump time to %.3f s per user defined configuration"
% (args.fft_length, args.dump_time))
except (TypeError, KeyError):
pass
if args.duration == 0.0:
args.duration = refSrc.duration
args.duration = min([args.duration, refSrc.duration])
# Length of the FFT
LFFT = args.fft_length
# Get the raw configuration
fh = open(args.filename, 'r')
rawConfig = fh.readlines()
fh.close()
# Antenna report
print("Antennas:")
for ant in antennas:
print(" Antenna %i: Stand %i, Pol. %i (%.3f us offset)" %
(ant.id, ant.stand.id, ant.pol, ant.cable.clock_offset * 1e6))
# Open and align the files
fh = []
nFramesFile = []
srate = []
beams = []
tunepols = []
beampols = []
tStart = []
cFreqs = []
bitDepths = []
buffers = []
grossOffsets = []
for i, (filename, metaname,
foffset) in enumerate(zip(filenames, metanames, foffsets)):
fh.append(open(filename, "rb"))
go = numpy.int32(antennas[2 * i].cable.clock_offset)
antennas[2 * i + 0].cable.clock_offset -= go
antennas[2 * i + 1].cable.clock_offset -= go
grossOffsets.append(go)
if go != 0:
print("Correcting time tags for gross offset of %i s" %
grossOffsets[i])
print(" Antenna clock offsets are now at %.3f us, %.3f us" %
(antennas[2 * i + 0].cable.clock_offset * 1e6,
antennas[2 * i + 1].cable.clock_offset * 1e6))
if readers[i] is vdif:
header = vdif.read_guppi_header(fh[i])
readers[i].FRAME_SIZE = readers[i].get_frame_size(fh[i])
nFramesFile.append(os.path.getsize(filename) // readers[i].FRAME_SIZE)
if readers[i] is vdif:
junkFrame = readers[i].read_frame(fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] *
2.0)
readers[i].DATA_LENGTH = junkFrame.payload.data.size
beam, pol = junkFrame.id
elif readers[i] is drx:
junkFrame = readers[i].read_frame(fh[i])
while junkFrame.header.decimation == 0:
junkFrame = readers[i].read_frame(fh[i])
readers[i].DATA_LENGTH = junkFrame.payload.data.size
beam, tune, pol = junkFrame.id
fh[i].seek(-readers[i].FRAME_SIZE, 1)
beams.append(beam)
srate.append(junkFrame.sample_rate)
if readers[i] is vdif:
tunepols.append(readers[i].get_thread_count(fh[i]))
beampols.append(tunepols[i])
elif readers[i] is drx:
beampols.append(max(readers[i].get_frames_per_obs(fh[i])))
skip = args.skip + foffset
if skip != 0:
print("Skipping forward %.3f s" % skip)
print("-> %.6f (%s)" % (junkFrame.time, junkFrame.time.datetime))
offset = int(skip * srate[i] / readers[i].DATA_LENGTH)
fh[i].seek(beampols[i] * readers[i].FRAME_SIZE * offset, 1)
if readers[i] is vdif:
junkFrame = readers[i].read_frame(
fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] * 2.0)
else:
junkFrame = readers[i].read_frame(fh[i])
fh[i].seek(-readers[i].FRAME_SIZE, 1)
print("-> %.6f (%s)" % (junkFrame.time, junkFrame.time.datetime))
tStart.append(junkFrame.time + grossOffsets[i])
# Get the frequencies
cFreq1 = 0.0
cFreq2 = 0.0
for j in xrange(64):
if readers[i] is vdif:
junkFrame = readers[i].read_frame(
fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] * 2.0)
s, p = junkFrame.id
if p == 0:
cFreq1 = junkFrame.central_freq
else:
pass
elif readers[i] is drx:
junkFrame = readers[i].read_frame(fh[i])
b, t, p = junkFrame.id
if p == 0:
if t == 1:
cFreq1 = junkFrame.central_freq
else:
cFreq2 = junkFrame.central_freq
else:
pass
fh[i].seek(-64 * readers[i].FRAME_SIZE, 1)
cFreqs.append([cFreq1, cFreq2])
try:
bitDepths.append(junkFrame.header.bits_per_sample)
except AttributeError:
bitDepths.append(8)
# Setup the frame buffers
if readers[i] is vdif:
buffers.append(VDIFFrameBuffer(threads=[0, 1]))
elif readers[i] is drx:
buffers.append(
DRXFrameBuffer(beams=[
beam,
],
tunes=[1, 2],
pols=[0, 1],
nsegments=16))
for i in xrange(len(filenames)):
# Align the files as close as possible by the time tags
if readers[i] is vdif:
timetags = []
for k in xrange(16):
junkFrame = readers[i].read_frame(fh[i])
timetags.append(junkFrame.header.frame_in_second)
fh[i].seek(-16 * readers[i].FRAME_SIZE, 1)
j = 0
while (timetags[j + 0] != timetags[j + 1]):
j += 1
fh[i].seek(readers[i].FRAME_SIZE, 1)
nFramesFile[i] -= j
elif readers[i] is drx:
pass
# Align the files as close as possible by the time tags
if readers[i] is vdif:
junkFrame = readers[i].read_frame(fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] *
2.0)
else:
junkFrame = readers[i].read_frame(fh[i])
fh[i].seek(-readers[i].FRAME_SIZE, 1)
j = 0
while junkFrame.time + grossOffsets[i] < max(tStart):
if readers[i] is vdif:
for k in xrange(beampols[i]):
try:
junkFrame = readers[i].read_frame(
fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] * 2.0)
except errors.SyncError:
print("Error - VDIF @ %i" % (i, ))
fh[i].seek(readers[i].FRAME_SIZE, 1)
continue
else:
for k in xrange(beampols[i]):
junkFrame = readers[i].read_frame(fh[i])
j += beampols[i]
jTime = j * readers[i].DATA_LENGTH / srate[i] / beampols[i]
print("Shifted beam %i data by %i frames (%.4f s)" %
(beams[i], j, jTime))
# Set integration time
tRead = 1.0
nFrames = int(round(tRead * srate[-1] / readers[-1].DATA_LENGTH))
tRead = nFrames * readers[-1].DATA_LENGTH / srate[-1]
nFramesV = tRead * srate[0] / readers[0].DATA_LENGTH
nFramesD = nFrames
while nFramesV != int(nFramesV):
nFrames += 1
tRead = nFrames * readers[-1].DATA_LENGTH / srate[-1]
nFramesV = tRead * srate[0] / readers[0].DATA_LENGTH
nFramesD = nFrames
nFramesV = int(nFramesV)
# Read in some data
tFileV = nFramesFile[0] / beampols[0] * readers[0].DATA_LENGTH / srate[0]
tFileD = nFramesFile[-1] / beampols[-1] * readers[-1].DATA_LENGTH / srate[
-1]
tFile = min([tFileV, tFileD])
if args.duration > 0.0:
duration = args.duration
duration = tRead * int(round(duration / tRead))
tFile = duration
# Date
beginMJDs = []
beginDates = []
for i in xrange(len(filenames)):
if readers[i] is vdif:
junkFrame = readers[i].read_frame(fh[i],
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] *
2.0)
else:
junkFrame = readers[i].read_frame(fh[i])
fh[i].seek(-readers[i].FRAME_SIZE, 1)
beginMJDs.append(junkFrame.time.mjd)
beginDates.append(junkFrame.time.datetime)
# Set the output base filename
if args.tag is None:
outbase = os.path.basename(filenames[0])
outbase = os.path.splitext(outbase)[0][:8]
else:
outbase = args.tag
# Report
for i in xrange(len(filenames)):
print("Filename: %s" % os.path.basename(filenames[i]))
print(" Type/Reader: %s" % readers[i].__name__)
print(" Date of First Frame: %s" % beginDates[i])
print(" Sample Rate: %i Hz" % srate[i])
print(" Tuning 1: %.3f Hz" % cFreqs[i][0])
print(" Tuning 2: %.3f Hz" % cFreqs[i][1])
print(" Bit Depth: %i" % bitDepths[i])
print(" ===")
print(" Phase Center:")
print(" Name: %s" % refSrc.name)
print(" RA: %s" % refSrc._ra)
print(" Dec: %s" % refSrc._dec)
print(" ===")
print(" Data Read Time: %.3f s" % tRead)
print(" Data Reads in File: %i" % int(tFile / tRead))
print(" ")
nVDIFInputs = sum([1 for reader in readers if reader is vdif])
nDRXInputs = sum([1 for reader in readers if reader is drx])
print("Processing %i VDIF and %i DRX input streams" %
(nVDIFInputs, nDRXInputs))
print(" ")
nFramesV = int(round(tRead * srate[0] / readers[0].DATA_LENGTH))
framesPerSecondV = int(srate[0] / readers[0].DATA_LENGTH)
nFramesB = nFrames
framesPerSecondB = srate[-1] / readers[-1].DATA_LENGTH
if nVDIFInputs:
print("VDIF Frames/s: %.6f" % framesPerSecondV)
print("VDIF Frames/Integration: %i" % nFramesV)
if nDRXInputs:
print("DRX Frames/s: %.6f" % framesPerSecondB)
print("DRX Frames/Integration: %i" % nFramesB)
if nVDIFInputs * nDRXInputs:
print("Sample Count Ratio: %.6f" %
(1.0 * (nFramesV * readers[0].DATA_LENGTH) /
(nFramesB * 4096), ))
print("Sample Rate Ratio: %.6f" % (srate[0] / srate[-1], ))
print(" ")
vdifLFFT = LFFT * (2 if nVDIFInputs else 1
) # Fix to deal with LWA-only correlations
drxLFFT = vdifLFFT * srate[-1] / srate[0]
while drxLFFT != int(drxLFFT):
vdifLFFT += 1
drxLFFT = vdifLFFT * srate[-1] / srate[0]
vdifLFFT = vdifLFFT // (2 if nVDIFInputs else 1
) # Fix to deal with LWA-only correlations
drxLFFT = int(drxLFFT)
if nVDIFInputs:
print("VDIF Transform Size: %i" % vdifLFFT)
if nDRXInputs:
print("DRX Transform Size: %i" % drxLFFT)
print(" ")
vdifPivot = 1
if abs(cFreqs[0][0] - cFreqs[-1][1]) < abs(cFreqs[0][0] - cFreqs[-1][0]):
vdifPivot = 2
if nVDIFInputs == 0 and args.which != 0:
vdifPivot = args.which
if nVDIFInputs * nDRXInputs:
print("VDIF appears to correspond to tuning #%i in DRX" % vdifPivot)
elif nDRXInputs:
print("Correlating DRX tuning #%i" % vdifPivot)
print(" ")
nChunks = int(tFile / tRead)
tSub = args.subint_time
tSub = tRead / int(round(tRead / tSub))
tDump = args.dump_time
tDump = tSub * int(round(tDump / tSub))
nDump = int(tDump / tSub)
tDump = nDump * tSub
nInt = int((nChunks * tRead) / tDump)
print("Sub-integration time is: %.3f s" % tSub)
print("Integration (dump) time is: %.3f s" % tDump)
print(" ")
if args.gpu is not None:
try:
from jit import xcupy
xcupy.select_gpu(args.gpu)
xcupy.set_memory_usage_limit(1.5 * 1024**3)
multirate.xengine = xcupy.xengine
multirate.xengine_full = xcupy.xengine_full
print(
"Loaded GPU X-engine support on GPU #%i with %.2f GB of device memory"
% (args.gpu, xcupy.get_memory_usage_limit() / 1024.0**3))
except ImportError as e:
pass
subIntTimes = []
subIntCount = 0
fileCount = 0
wallStart = time.time()
done = False
oldStartRel = [0 for i in xrange(nVDIFInputs + nDRXInputs)]
username = getpass.getuser()
for i in xrange(nChunks):
wallTime = time.time()
tStart = []
tStartB = []
vdifRef = [0 for j in xrange(nVDIFInputs * 2)]
drxRef = [0 for j in xrange(nDRXInputs * 2)]
# Read in the data
with InterProcessLock('/dev/shm/sc-reader-%s' % username) as lock:
try:
dataV *= 0.0
dataD *= 0.0
except NameError:
dataV = numpy.zeros(
(len(vdifRef), readers[0].DATA_LENGTH * nFramesV),
dtype=numpy.float32)
dataD = numpy.zeros(
(len(drxRef), readers[-1].DATA_LENGTH * nFramesD),
dtype=numpy.complex64)
for j, f in enumerate(fh):
if readers[j] is vdif:
## VDIF
k = 0
while k < beampols[j] * nFramesV:
try:
cFrame = readers[j].read_frame(
f,
central_freq=header['OBSFREQ'],
sample_rate=header['OBSBW'] * 2.0)
buffers[j].append(cFrame)
except errors.SyncError:
print("Error - VDIF @ %i, %i" % (i, j))
f.seek(readers[j].FRAME_SIZE, 1)
continue
except errors.EOFError:
done = True
break
frames = buffers[j].get()
if frames is None:
continue
for cFrame in frames:
std, pol = cFrame.id
sid = 2 * j + pol
if k == 0:
tStart.append(cFrame.time)
tStart[-1] = tStart[-1] + grossOffsets[j]
tStartB.append(get_better_time(cFrame))
tStartB[-1][
0] = tStart[-1][0] + grossOffsets[j]
for p in (0, 1):
psid = 2 * j + p
vdifRef[
psid] = cFrame.header.seconds_from_epoch * framesPerSecondV + cFrame.header.frame_in_second
count = cFrame.header.seconds_from_epoch * framesPerSecondV + cFrame.header.frame_in_second
count -= vdifRef[sid]
dataV[sid,
count * readers[j].DATA_LENGTH:(count + 1) *
readers[j].DATA_LENGTH] = cFrame.payload.data
k += 1
elif readers[j] is drx:
## DRX
k = 0
while k < beampols[j] * nFramesD:
try:
cFrame = readers[j].read_frame(f)
buffers[j].append(cFrame)
except errors.SyncError:
print("Error - DRX @ %i, %i" % (i, j))
continue
except errors.EOFError:
done = True
break
frames = buffers[j].get()
if frames is None:
continue
for cFrame in frames:
beam, tune, pol = cFrame.id
if tune != vdifPivot:
continue
bid = 2 * (j - nVDIFInputs) + pol
if k == 0:
tStart.append(cFrame.time)
tStart[-1] = tStart[-1] + grossOffsets[j]
tStartB.append(get_better_time(cFrame))
tStartB[-1][
0] = tStart[-1][0] + grossOffsets[j]
for p in (0, 1):
pbid = 2 * (j - nVDIFInputs) + p
drxRef[pbid] = cFrame.payload.timetag
count = cFrame.payload.timetag
count -= drxRef[bid]
count //= (4096 * int(196e6 / srate[-1]))
### Fix from some LWA-SV files that seem to cause the current LSL
### ring buffer problems
if count < 0:
continue
try:
dataD[bid, count *
readers[j].DATA_LENGTH:(count + 1) *
readers[j].
DATA_LENGTH] = cFrame.payload.data
k += beampols[j] // 2
except ValueError:
k = beampols[j] * nFramesD
break
print('RR - Read finished in %.3f s for %.3fs of data' %
(time.time() - wallTime, tRead))
# Figure out which DRX tuning corresponds to the VDIF data
if nDRXInputs > 0:
dataD /= 7.0
# Time tag alignment (sample based)
## Initial time tags for each stream and the relative start time for each stream
if args.verbose:
### TT = time tag
print('TT - Start', tStartB)
tStartMin = min([sec for sec, frac in tStartB])
tStartRel = [(sec - tStartMin) + frac for sec, frac in tStartB]
## Sample offsets between the streams
offsets = []
for j in xrange(nVDIFInputs + nDRXInputs):
offsets.append(
int(round(nsround(max(tStartRel) - tStartRel[j]) * srate[j])))
if args.verbose:
print('TT - Offsets', offsets)
## Roll the data to apply the sample offsets and then trim the ends to get rid
## of the rolled part
for j, offset in enumerate(offsets):
if j < nVDIFInputs:
if offset != 0:
idx0 = 2 * j + 0
idx1 = 2 * j + 1
tStart[j] += offset / (srate[j])
tStartB[j][1] += offset / (srate[j])
dataV[idx0, :] = numpy.roll(dataV[idx0, :], -offset)
dataV[idx1, :] = numpy.roll(dataV[idx1, :], -offset)
else:
if offset != 0:
idx0 = 2 * (j - nVDIFInputs) + 0
idx1 = 2 * (j - nVDIFInputs) + 1
tStart[j] += offset / (srate[j])
tStartB[j][1] += offset / (srate[j])
dataD[idx0, :] = numpy.roll(dataD[idx0, :], -offset)
dataD[idx1, :] = numpy.roll(dataD[idx1, :], -offset)
vdifOffsets = offsets[:nVDIFInputs]
drxOffsets = offsets[nVDIFInputs:]
## Apply the corrections to the original time tags and report on the sub-sample
## residuals
if args.verbose:
print('TT - Adjusted', tStartB)
tStartMinSec = min([sec for sec, frac in tStartB])
tStartMinFrac = min([frac for sec, frac in tStartB])
tStartRel = [(sec - tStartMinSec) + (frac - tStartMinFrac)
for sec, frac in tStartB]
if args.verbose:
print('TT - Residual',
["%.1f ns" % (r * 1e9, ) for r in tStartRel])
for k in xrange(len(tStartRel)):
antennas[2 * k +
0].cable.clock_offset -= tStartRel[k] - oldStartRel[k]
antennas[2 * k +
1].cable.clock_offset -= tStartRel[k] - oldStartRel[k]
oldStartRel = tStartRel
# Setup everything we need to loop through the sub-integrations
nSub = int(tRead / tSub)
nSampV = int(srate[0] * tSub)
nSampD = int(srate[-1] * tSub)
#tV = i*tRead + numpy.arange(dataV.shape[1]-max(vdifOffsets), dtype=numpy.float64)/srate[ 0]
if nDRXInputs > 0:
tD = i * tRead + numpy.arange(dataD.shape[1] - max(drxOffsets),
dtype=numpy.float64) / srate[-1]
# Loop over sub-integrations
for j in xrange(nSub):
## Select the data to work with
tSubInt = tStart[0] + (
j + 1) * nSampV / srate[0] - nSampV // 2 / srate[0]
#tVSub = tV[j*nSampV:(j+1)*nSampV]
if nDRXInputs > 0:
tDSub = tD[j * nSampD:(j + 1) * nSampD]
dataVSub = dataV[:, j * nSampV:(j + 1) * nSampV]
#if dataVSub.shape[1] != tVSub.size:
# dataVSub = dataVSub[:,:tVSub.size]
#if tVSub.size == 0:
# continue
dataDSub = dataD[:, j * nSampD:(j + 1) * nSampD]
if nDRXInputs > 0:
if dataDSub.shape[1] != tDSub.size:
dataDSub = dataDSub[:, :tDSub.size]
if tDSub.size == 0:
continue
## Update the observation
observer.date = astro.unix_to_utcjd(tSubInt) - astro.DJD_OFFSET
refSrc.compute(observer)
## Correct for the LWA dipole power pattern
if nDRXInputs > 0:
dipoleX, dipoleY = jones.get_lwa_antenna_gain(
observer, refSrc, freq=cFreqs[-1][vdifPivot - 1])
dataDSub[0::2, :] /= numpy.sqrt(dipoleX)
dataDSub[1::2, :] /= numpy.sqrt(dipoleY)
## Get the Jones matrices and apply
## NOTE: This moves the LWA into the frame of the VLA
if nVDIFInputs * nDRXInputs > 0:
lwaToSky = jones.get_matrix_lwa(observer, refSrc)
skyToVLA = jones.get_matrix_vla(observer, refSrc, inverse=True)
dataDSub = jones.apply_matrix(
dataDSub,
numpy.matrix(skyToVLA) * numpy.matrix(lwaToSky))
## Correlate
delayPadding = multirate.get_optimal_delay_padding(
antennas[:2 * nVDIFInputs],
antennas[2 * nVDIFInputs:],
LFFT=drxLFFT,
sample_rate=srate[-1],
central_freq=cFreqs[-1][vdifPivot - 1],
pol='*',
phase_center=refSrc)
if nVDIFInputs > 0:
freqV, feoV, veoV, deoV = multirate.fengine(
dataVSub,
antennas[:2 * nVDIFInputs],
LFFT=vdifLFFT,
sample_rate=srate[0],
central_freq=cFreqs[0][0] - srate[0] / 4,
pol='*',
phase_center=refSrc,
delayPadding=delayPadding)
if nDRXInputs > 0:
freqD, feoD, veoD, deoD = multirate.fengine(
dataDSub,
antennas[2 * nVDIFInputs:],
LFFT=drxLFFT,
sample_rate=srate[-1],
central_freq=cFreqs[-1][vdifPivot - 1],
pol='*',
phase_center=refSrc,
delayPadding=delayPadding)
## Rotate the phase in time to deal with frequency offset between the VLA and LWA
if nDRXInputs * nVDIFInputs > 0:
subChanFreqOffset = (cFreqs[0][0] - cFreqs[-1][vdifPivot - 1]
) % (freqD[1] - freqD[0])
if i == 0 and j == 0:
## FC = frequency correction
tv, tu = bestFreqUnits(subChanFreqOffset)
print(
"FC - Applying fringe rotation rate of %.3f %s to the DRX data"
% (tv, tu))
freqD += subChanFreqOffset
for w in xrange(feoD.shape[2]):
feoD[:, :,
w] *= numpy.exp(-2j * numpy.pi * subChanFreqOffset *
tDSub[w * drxLFFT])
## Sort out what goes where (channels and antennas) if we don't already know
try:
if nVDIFInputs > 0:
freqV = freqV[goodV]
feoV = numpy.roll(feoV, -goodV[0],
axis=1)[:, :len(goodV), :]
if nDRXInputs > 0:
freqD = freqD[goodD]
feoD = numpy.roll(feoD, -goodD[0],
axis=1)[:, :len(goodD), :]
except NameError:
### Frequency overlap
fMin, fMax = -1e12, 1e12
if nVDIFInputs > 0:
fMin, fMax = max([fMin,
freqV.min()]), min([fMax,
freqV.max()])
if nDRXInputs > 0:
fMin, fMax = max([fMin,
freqD.min()]), min([fMax,
freqD.max()])
### Channels and antennas (X vs. Y)
if nVDIFInputs > 0:
goodV = numpy.where((freqV >= fMin) & (freqV <= fMax))[0]
aXV = [
k for (k, a) in enumerate(antennas[:2 * nVDIFInputs])
if a.pol == 0
]
aYV = [
k for (k, a) in enumerate(antennas[:2 * nVDIFInputs])
if a.pol == 1
]
if nDRXInputs > 0:
goodD = numpy.where((freqD >= fMin) & (freqD <= fMax))[0]
aXD = [
k for (k, a) in enumerate(antennas[2 * nVDIFInputs:])
if a.pol == 0
]
aYD = [
k for (k, a) in enumerate(antennas[2 * nVDIFInputs:])
if a.pol == 1
]
### Validate the channel alignent and fix it if needed
if nVDIFInputs * nDRXInputs != 0:
pd = freqV[goodV[0]] - freqD[goodD[0]]
# Need to shift?
if abs(pd) >= 1.01 * abs(subChanFreqOffset):
## Need to shift
if pd < 0.0:
goodV = goodV[1:]
else:
goodD = goodD[1:]
# Need to trim?
if len(goodV) > len(goodD):
## Yes, goodV is too long
goodV = goodV[:len(goodD)]
elif len(goodD) > len(goodV):
## Yes, goodD is too long
goodD = goodD[:len(goodV)]
else:
## No, nothing needs to be done
pass
# Validate
fd = freqV[goodV] - freqD[goodD]
try:
assert (fd.min() >= -1.01 * subChanFreqOffset)
assert (fd.max() <= 1.01 * subChanFreqOffset)
## FS = frequency selection
tv, tu = bestFreqUnits(freqV[1] - freqV[0])
print("FS - Found %i, %.3f %s overalapping channels" %
(len(goodV), tv, tu))
tv, tu = bestFreqUnits(freqV[goodV[-1]] -
freqV[goodV[0]])
print("FS - Bandwidth is %.3f %s" % (tv, tu))
print("FS - Channels span %.3f MHz to %.3f MHz" %
(freqV[goodV[0]] / 1e6, freqV[goodV[-1]] / 1e6))
except AssertionError:
raise RuntimeError(
"Cannot find a common frequency set between the input data: offsets range between %.3f Hz and %.3f Hz, expected %.3f Hz"
% (fd.min(), fd.max(), subChanFreqOffset))
### Apply
if nVDIFInputs > 0:
freqV = freqV[goodV]
feoV = numpy.roll(feoV, -goodV[0],
axis=1)[:, :len(goodV), :]
if nDRXInputs > 0:
freqD = freqD[goodD]
feoD = numpy.roll(feoD, -goodD[0],
axis=1)[:, :len(goodD), :]
try:
nchan = freqV.size
fdt = feoV.dtype
vdt = veoV.dtype
except NameError:
nchan = freqD.size
fdt = feoD.dtype
vdt = veoD.dtype
## Setup the intermediate F-engine products and trim the data
### Figure out the minimum number of windows
nWin = 1e12
if nVDIFInputs > 0:
nWin = min([nWin, feoV.shape[2]])
nWin = min(
[nWin,
numpy.argmax(numpy.cumsum(veoV.sum(axis=0))) + 1])
if nDRXInputs > 0:
nWin = min([nWin, feoD.shape[2]])
nWin = min(
[nWin,
numpy.argmax(numpy.cumsum(veoD.sum(axis=0))) + 1])
### Initialize the intermediate arrays
try:
assert (feoX.shape[2] == nWin)
except (NameError, AssertionError):
feoX = numpy.zeros((nVDIFInputs + nDRXInputs, nchan, nWin),
dtype=fdt)
feoY = numpy.zeros((nVDIFInputs + nDRXInputs, nchan, nWin),
dtype=fdt)
veoX = numpy.zeros((nVDIFInputs + nDRXInputs, nWin), dtype=vdt)
veoY = numpy.zeros((nVDIFInputs + nDRXInputs, nWin), dtype=vdt)
### Trim
if nVDIFInputs > 0:
feoV = feoV[:, :, :nWin]
veoV = veoV[:, :nWin]
if nDRXInputs > 0:
feoD = feoD[:, :, :nWin]
veoD = veoD[:, :nWin]
## Sort it all out by polarization
for k in xrange(nVDIFInputs):
feoX[k, :, :] = feoV[aXV[k], :, :]
feoY[k, :, :] = feoV[aYV[k], :, :]
veoX[k, :] = veoV[aXV[k], :]
veoY[k, :] = veoV[aYV[k], :]
for k in xrange(nDRXInputs):
feoX[k + nVDIFInputs, :, :] = feoD[aXD[k], :, :]
feoY[k + nVDIFInputs, :, :] = feoD[aYD[k], :, :]
veoX[k + nVDIFInputs, :] = veoD[aXD[k], :]
veoY[k + nVDIFInputs, :] = veoD[aYD[k], :]
## Cross multiply
try:
sfreqXX = freqV
sfreqYY = freqV
except NameError:
sfreqXX = freqD
sfreqYY = freqD
svisXX, svisXY, svisYX, svisYY = multirate.xengine_full(
feoX, veoX, feoY, veoY)
## Accumulate
if subIntCount == 0:
subIntTimes = [
tSubInt,
]
freqXX = sfreqXX
freqYY = sfreqYY
visXX = svisXX / nDump
visXY = svisXY / nDump
visYX = svisYX / nDump
visYY = svisYY / nDump
else:
subIntTimes.append(tSubInt)
visXX += svisXX / nDump
visXY += svisXY / nDump
visYX += svisYX / nDump
visYY += svisYY / nDump
subIntCount += 1
## Save
if subIntCount == nDump:
subIntCount = 0
fileCount += 1
### CD = correlator dump
outfile = "%s-vis2-%05i.npz" % (outbase, fileCount)
numpy.savez(outfile,
config=rawConfig,
srate=srate[0] / 2.0,
freq1=freqXX,
vis1XX=visXX,
vis1XY=visXY,
vis1YX=visYX,
vis1YY=visYY,
tStart=numpy.mean(
numpy.array(subIntTimes, dtype=numpy.float64)),
tInt=tDump)
print(
"CD - writing integration %i to disk, timestamp is %.3f s"
% (fileCount,
numpy.mean(numpy.array(subIntTimes,
dtype=numpy.float64))))
if fileCount == 1:
print("CD - each integration is %.1f MB on disk" %
(os.path.getsize(outfile) / 1024.0**2, ))
if (fileCount - 1) % 25 == 0:
print(
"CD - average processing time per integration is %.3f s"
% ((time.time() - wallStart) / fileCount, ))
etc = (nInt - fileCount) * (time.time() -
wallStart) / fileCount
eth = int(etc / 60.0) // 60
etm = int(etc / 60.0) % 60
ets = etc % 60
print(
"CD - estimated time to completion is %i:%02i:%04.1f" %
(eth, etm, ets))
if done:
break
# Cleanup
etc = time.time() - wallStart
eth = int(etc / 60.0) // 60
etm = int(etc / 60.0) % 60
ets = etc % 60
print("Processing finished after %i:%02i:%04.1f" % (eth, etm, ets))
print("Average time per integration was %.3f s" % (etc / fileCount, ))
for f in fh:
f.close()
def main(args):
# Setup the site information
station = stations.lwasv
ants = station.antennas
nAnt = len([a for a in ants if a.pol == 0])
phase_freq_range = [0, 0]
for filename in args.filename:
# Open the file and get ready to go
idf = CORFile(filename)
nBL = idf.get_info('nbaseline')
nchan = idf.get_info('nchan')
tInt = idf.get_info('tint')
nFpO = nBL * nchan / 72
nInts = idf.get_info('nframe') / nFpO
jd = astro.unix_to_utcjd(idf.get_info('start_time'))
date = idf.get_info('start_time').datetime
central_freq = idf.get_info('freq1')
central_freq = central_freq[len(central_freq)/2]
print("Data type: %s" % type(idf))
print("Samples per observations: %i" % (nFpO,))
print("Integration Time: %.3f s" % tInt)
print("Tuning frequency: %.3f Hz" % central_freq)
print("Captures in file: %i (%.1f s)" % (nInts, nInts*tInt))
print("==")
print("Station: %s" % station.name)
print("Date observed: %s" % date)
print("Julian day: %.5f" % jd)
print(" ")
# Offset into the file
offset = idf.offset(args.skip)
if offset != 0.0:
print("Skipped %.3f s into the file" % offset)
# Open the file and go
nFiles = int(args.duration / tInt)
if nFiles == 0:
nFiles = numpy.inf
fileCount = 0
while fileCount < nFiles:
try:
tInt, tStart, data = idf.read(tInt)
except Exception as e:
print("ERROR: %s" % str(e))
break
freqs = idf.get_info('freq1')
beginJD = astro.unix_to_utcjd( tStart )
beginTime = datetime.utcfromtimestamp( tStart )
if freqs[0] != phase_freq_range[0] or freqs[-1] != phase_freq_range[1]:
print("Updating phasing for %.3f to %.3f MHz" % (freqs[0]/1e6, freqs[-1]/1e6))
phase_freq_range[0] = freqs[ 0]
phase_freq_range[1] = freqs[-1]
k = 0
phase = numpy.zeros((nBL, nchan, 2, 2), dtype=numpy.complex64)
gaix = [a.cable.gain(freqs) for a in ants if a.pol == 0]
gaiy = [a.cable.gain(freqs) for a in ants if a.pol == 1]
dlyx = [a.cable.delay(freqs) - a.stand.z / speedOfLight for a in ants if a.pol == 0]
dlyy = [a.cable.delay(freqs) - a.stand.z / speedOfLight for a in ants if a.pol == 1]
for i in xrange(nAnt):
for j in xrange(i, nAnt):
phase[k,:,0,0] = numpy.exp(2j*numpy.pi*freqs*(dlyx[i] - dlyx[j])) \
/ numpy.sqrt(gaix[i]*gaix[j])
phase[k,:,0,1] = numpy.exp(2j*numpy.pi*freqs*(dlyx[i] - dlyy[j])) \
/ numpy.sqrt(gaix[i]*gaiy[j])
phase[k,:,1,0] = numpy.exp(2j*numpy.pi*freqs*(dlyy[i] - dlyx[j])) \
/ numpy.sqrt(gaiy[i]*gaix[j])
phase[k,:,1,1] = numpy.exp(2j*numpy.pi*freqs*(dlyy[i] - dlyy[j])) \
/ numpy.sqrt(gaiy[i]*gaiy[j])
k += 1
for i in xrange(data.shape[-1]):
data[...,i] *= phase
# Convert to a dataDict
try:
blList # pylint: disable=used-before-assignment
except NameError:
blList = uvutils.get_baselines(ants[0::2], include_auto=True)
if args.output is None:
outname = os.path.basename(filename)
outname = os.path.splitext(outname)[0]
outname = "%s_%i_%s.ms" % (outname, int(beginJD-astro.MJD_OFFSET), beginTime.strftime("%H_%M_%S"))
else:
base, ext = os.path.splitext(args.output)
if ext == '':
ext = '.ms'
outname = "%s_%i_%s%s" % (base, int(beginJD-astro.MJD_OFFSET), beginTime.strftime("%H_%M_%S"), ext)
fits = measurementset.Ms(outname, ref_time=tStart)
fits.set_stokes(['xx', 'xy', 'yx', 'yy'])
fits.set_frequency(freqs)
fits.set_geometry(station, ants[0::2])
obsTime = astro.unix_to_taimjd(tStart)
fits.add_data_set(obsTime, tInt, blList, data[:,:,0,0,0], pol='xx')
fits.add_data_set(obsTime, tInt, blList, data[:,:,0,1,0], pol='xy')
fits.add_data_set(obsTime, tInt, blList, data[:,:,1,0,0], pol='yx')
fits.add_data_set(obsTime, tInt, blList, data[:,:,1,1,0], pol='yy')
fits.write()
fits.close()
fileCount += 1
idf.close()
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 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 options
config = parseOptions(args)
# Length of the FFT
LFFT = config['LFFT']
# Open the file and find good data (not spectrometer data)
filename = config['args'][0]
fh = open(filename, "rb")
nFramesFile = os.path.getsize(filename) / drx.FrameSize
try:
for i in xrange(5):
junkFrame = drspec.readFrame(fh)
raise RuntimeError(
"ERROR: '%s' appears to be a DR spectrometer file, not a raw DRX file"
% filename)
except errors.syncError:
fh.seek(0)
while True:
try:
junkFrame = drx.readFrame(fh)
try:
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
break
except ZeroDivisionError:
pass
except errors.syncError:
fh.seek(-drx.FrameSize + 1, 1)
fh.seek(-drx.FrameSize, 1)
beam, tune, pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
# Offset in frames for beampols beam/tuning/pol. sets
inoffset = config['offset']
offset = int(config['offset'] * srate / 4096 * beampols)
offset = int(1.0 * offset / beampols) * beampols
fh.seek(offset * drx.FrameSize, 1)
# Iterate on the offsets until we reach the right point in the file. This
# is needed to deal with files that start with only one tuning and/or a
# different sample rate.
while True:
## Figure out where in the file we are and what the current tuning/sample
## rate is
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t1 = junkFrame.getTime()
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
fh.seek(-drx.FrameSize, 1)
## See how far off the current frame is from the target
tDiff = t1 - (t0 + config['offset'])
## Half that to come up with a new seek parameter
tCorr = -tDiff / 2.0
cOffset = int(tCorr * srate / 4096 * beampols)
cOffset = int(1.0 * cOffset / beampols) * beampols
offset += cOffset
## If the offset is zero, we are done. Otherwise, apply the offset
## and check the location in the file again/
if cOffset is 0:
break
fh.seek(cOffset * drx.FrameSize, 1)
# Update the offset actually used
config['offset'] = t1 - t0
offset = int(round(config['offset'] * srate / 4096 * beampols))
offset = int(1.0 * offset / beampols) * beampols
# 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 beampols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(1.0 * config['maxFrames'] / beampols * 4096 /
float(LFFT)) * LFFT / 4096 * beampols
# Number of frames to integrate over
print "Line 673: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols
nFramesAvg = int(config['average'] * srate / 4096 * beampols)
if (nFramesAvg == 0):
nFramesAvg = 1 * beampols
else:
nFramesAvg = int(1.0 * nFramesAvg / beampols * 4096 /
float(LFFT)) * LFFT / 4096 * beampols
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
print "Line 678: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
if config['metadata'] is not None:
config['duration'] = 0
if config['duration'] == 0:
config['duration'] = 1.0 * nFramesFile / beampols * 4096 / srate
else:
config['duration'] = int(
round(config['duration'] * srate * beampols / 4096) / beampols *
4096 / srate)
nChunks = int(round(config['duration'] / config['average']))
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg * nChunks
print "Line 693: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg, " nChunks ", nChunks
# Date & Central Frequency
t1 = junkFrame.getTime()
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b, t, p = junkFrame.parseID()
if p == 0 and t == 1:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags >> 32) &
(2**32 - 1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags >> 32) &
(2**32 - 1)) / 2**32
else:
pass
fh.seek(-4 * drx.FrameSize, 1)
config['freq1'] = centralFreq1
config['freq2'] = centralFreq2
# File summary
print "Filename: %s" % filename
print "Date of First Frame: %s" % str(beginDate)
print "Beams: %i" % beams
print "Tune/Pols: %i %i %i %i" % tunepols
print "Sample Rate: %i Hz" % srate
print "Tuning Frequency: %.3f Hz (1); %.3f Hz (2)" % (centralFreq1,
centralFreq2)
print "Frames: %i (%.3f s)" % (nFramesFile,
1.0 * nFramesFile / beampols * 4096 / srate)
print "---"
print "Offset: %.3f s (%i frames)" % (config['offset'], offset)
print "Integration: %.6f s (%i frames; %i frames per beam/tune/pol)" % (
config['average'], nFramesAvg, nFramesAvg / beampols)
print "Duration: %.3f s (%i frames; %i frames per beam/tune/pol)" % (
config['average'] * nChunks, nFrames, nFrames / beampols)
print "Chunks: %i" % nChunks
print " "
# Estimate clip level (if needed)
if config['estimate']:
clip1, clip2 = estimateClipLevel(fh, beampols)
else:
clip1 = config['clip']
clip2 = config['clip']
# Make the pseudo-antennas for Stokes calculation
antennas = []
for i in xrange(4):
if i / 2 == 0:
newAnt = stations.Antenna(1)
else:
newAnt = stations.Antenna(2)
if i % 2 == 0:
newAnt.pol = 0
else:
newAnt.pol = 1
antennas.append(newAnt)
# Setup the output file
outname = os.path.split(filename)[1]
outname = os.path.splitext(outname)[0]
if (config['return'] == 'FFT'):
outname = '%s-%d-waterfall-complex.hdf5' % (outname, inoffset)
else:
outname = '%s-waterfall.hdf5' % outname
if os.path.exists(outname):
#yn = raw_input("WARNING: '%s' exists, overwrite? [Y/n] " % outname)
#if yn not in ('n', 'N'):
# os.unlink(outname)
#else:
raise RuntimeError("Output file '%s' already exists" % outname)
f = hdfData.createNewFile(outname)
# Look at the metadata and come up with a list of observations. If
# there are no metadata, create a single "observation" that covers the
# whole file.
obsList = {}
if config['metadata'] is not None:
sdf = metabundle.getSessionDefinition(config['metadata'])
sdfBeam = sdf.sessions[0].drxBeam
spcSetup = sdf.sessions[0].spcSetup
if sdfBeam != beam:
raise RuntimeError(
"Metadata is for beam #%i, but data is from beam #%i" %
(sdfBeam, beam))
for i, obs in enumerate(sdf.sessions[0].observations):
sdfStart = mcs.mjdmpm2datetime(obs.mjd, obs.mpm)
sdfStop = mcs.mjdmpm2datetime(obs.mjd, obs.mpm + obs.dur)
obsDur = obs.dur / 1000.0
obsSR = drx.filterCodes[obs.filter]
obsList[i + 1] = (sdfStart, sdfStop, obsDur, obsSR)
print "Observations:"
for i in sorted(obsList.keys()):
obs = obsList[i]
print " #%i: %s to %s (%.3f s) at %.3f MHz" % (
i, obs[0], obs[1], obs[2], obs[3] / 1e6)
print " "
hdfData.fillFromMetabundle(f, config['metadata'])
else:
obsList[1] = (datetime.utcfromtimestamp(t1),
datetime(2222, 12, 31, 23, 59,
59), config['duration'], srate)
hdfData.fillMinimum(f, 1, beam, srate)
if config['linear']:
dataProducts = ['XX', 'YY']
else:
dataProducts = ['I', 'Q', 'U', 'V']
for o in sorted(obsList.keys()):
for t in (1, 2):
hdfData.createDataSets(
f,
o,
t,
numpy.arange(LFFT -
1 if float(fxc.__version__) < 0.8 else LFFT,
dtype=numpy.float32),
int(round(obsList[o][2] / config['average'])),
dataProducts,
dataOut=config['return'])
f.attrs['FileGenerator'] = 'hdfWaterfall.py'
f.attrs['InputData'] = os.path.basename(filename)
# Create the various HDF group holders
ds = {}
for o in sorted(obsList.keys()):
obs = hdfData.getObservationSet(f, o)
ds['obs%i' % o] = obs
ds['obs%i-time' % o] = obs.create_dataset(
'time', (int(round(obsList[o][2] / config['average'])), ), 'f8')
for t in (1, 2):
ds['obs%i-freq%i' % (o, t)] = hdfData.getDataSet(f, o, t, 'freq')
for p in dataProducts:
if (config['return'] == 'PSD'):
ds["obs%i-%s%i" % (o, p, t)] = hdfData.getDataSet(
f, o, t, p)
else:
ds["obs%i-%s%imag" % (o, p, t)] = hdfData.getDataSet(
f, o, t, p + 'mag')
ds["obs%i-%s%iphase" % (o, p, t)] = hdfData.getDataSet(
f, o, t, p + 'phase')
ds['obs%i-Saturation%i' % (o, t)] = hdfData.getDataSet(
f, o, t, 'Saturation')
# Load in the correct analysis function
if config['linear']:
processDataBatch = processDataBatchLinear
else:
processDataBatch = processDataBatchStokes
# Go!
for o in sorted(obsList.keys()):
try:
processDataBatch(fh,
antennas,
obsList[o][0],
obsList[o][2],
obsList[o][3],
config,
ds,
obsID=o,
clip1=clip1,
clip2=clip2)
except RuntimeError, e:
print "Observation #%i: %s, abandoning this observation" % (o,
str(e))
def processDataBatchStokes(fh,
antennas,
tStart,
duration,
sampleRate,
config,
dataSets,
obsID=1,
clip1=0,
clip2=0):
"""
Process a chunk of data in a raw DRX file into Stokes parameters and
add the contents to an HDF5 file.
"""
# Length of the FFT
LFFT = config['LFFT']
# Find the start of the observation
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
fh.seek(-drx.FrameSize, 1)
print 'Looking for #%i at %s with sample rate %.1f Hz...' % (obsID, tStart,
sampleRate)
while datetime.utcfromtimestamp(t0) < tStart or srate != sampleRate:
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
print '... Found #%i at %s with sample rate %.1f Hz' % (
obsID, datetime.utcfromtimestamp(t0), srate)
tDiff = datetime.utcfromtimestamp(t0) - tStart
try:
duration = duration - tDiff.total_seconds()
except:
duration = duration - (tDiff.seconds + tDiff.microseconds / 1e6)
beam, tune, pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
# 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 beampols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(1.0 * config['maxFrames'] / beampols * 4096 /
float(LFFT)) * LFFT / 4096 * beampols
# Number of frames to integrate over
print "Line 455: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols
nFramesAvg = int(round(config['average'] * srate / 4096 * beampols))
nFramesAvg = int(1.0 * nFramesAvg / beampols * 4096 /
float(LFFT)) * LFFT / 4096 * beampols
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
print "Line 460: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
nChunks = int(round(duration / config['average']))
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg * nChunks
print "Line 468: config['average']", config[
'average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg, " nChunks ", nChunks
# Date & Central Frequency
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b, t, p = junkFrame.parseID()
if p == 0 and t == 1:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags >> 32) &
(2**32 - 1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags >> 32) &
(2**32 - 1)) / 2**32
else:
pass
fh.seek(-4 * drx.FrameSize, 1)
freq = numpy.fft.fftshift(numpy.fft.fftfreq(LFFT, d=1 / srate))
if float(fxc.__version__) < 0.8:
freq = freq[1:]
dataSets['obs%i-freq1' % obsID][:] = freq + centralFreq1
dataSets['obs%i-freq2' % obsID][:] = freq + centralFreq2
obs = dataSets['obs%i' % obsID]
obs.attrs['tInt'] = config['average']
obs.attrs['tInt_Unit'] = 's'
obs.attrs['LFFT'] = LFFT
obs.attrs['nChan'] = LFFT - 1 if float(fxc.__version__) < 0.8 else LFFT
obs.attrs['RBW'] = freq[1] - freq[0]
obs.attrs['RBW_Units'] = 'Hz'
dataProducts = ['I', 'Q', 'U', 'V']
done = False
for i in xrange(nChunks):
# Find out how many frames remain in the file. If this number is larger
# than the maximum of frames we can work with at a time (maxFrames),
# only deal with that chunk
framesRemaining = nFrames - i * maxFrames
if framesRemaining > maxFrames:
framesWork = maxFrames
else:
framesWork = framesRemaining
print "Working on chunk %i, %i frames remaining" % (i + 1,
framesRemaining)
count = {0: 0, 1: 0, 2: 0, 3: 0}
data = numpy.zeros((4, framesWork * 4096 / beampols),
dtype=numpy.csingle)
# If there are fewer frames than we need to fill an FFT, skip this chunk
if data.shape[1] < LFFT:
break
# Inner loop that actually reads the frames into the data array
print "Working on %.1f ms of data" % (
(framesWork * 4096 / beampols / srate) * 1000.0)
for j in xrange(framesWork):
# Read in the next frame and anticipate any problems that could occur
try:
cFrame = drx.readFrame(fh, Verbose=False)
except errors.eofError:
done = True
break
except errors.syncError:
continue
beam, tune, pol = cFrame.parseID()
aStand = 2 * (tune - 1) + pol
if j is 0:
cTime = cFrame.getTime()
try:
data[aStand, count[aStand] * 4096:(count[aStand] + 1) *
4096] = cFrame.data.iq
count[aStand] += 1
except ValueError:
raise RuntimeError("Invalid Shape")
# Save out some easy stuff
dataSets['obs%i-time' % obsID][i] = cTime
if config['countSats']:
sats = ((data.real**2 + data.imag**2) >= 49).sum(axis=1)
dataSets['obs%i-Saturation1' % obsID][i, :] = sats[0:2]
dataSets['obs%i-Saturation2' % obsID][i, :] = sats[2:4]
else:
dataSets['obs%i-Saturation1' % obsID][i, :] = -1
dataSets['obs%i-Saturation2' % obsID][i, :] = -1
# 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.
if clip1 == clip2:
freq, tempSpec1 = fxc.StokesMaster(data,
antennas,
LFFT=LFFT,
window=config['window'],
verbose=config['verbose'],
SampleRate=srate,
ClipLevel=clip1)
for t in (1, 2):
for l, p in enumerate(dataProducts):
dataSets['obs%i-%s%i' %
(obsID, p, t)][i, :] = tempSpec1[l, t - 1, :]
else:
freq, tempSpec1 = fxc.StokesMaster(data[:2, :],
antennas[:2],
LFFT=LFFT,
window=config['window'],
verbose=config['verbose'],
SampleRate=srate,
ClipLevel=clip1)
freq, tempSpec2 = fxc.StokesMaster(data[2:, :],
antennas[2:],
LFFT=LFFT,
window=config['window'],
verbose=config['verbose'],
SampleRate=srate,
ClipLevel=clip2)
for l, p in enumerate(dataProducts):
dataSets['obs%i-%s%i' % (obsID, p, 1)][i, :] = tempSpec1[l,
0, :]
dataSets['obs%i-%s%i' % (obsID, p, 2)][i, :] = tempSpec2[l,
0, :]
# We don't really need the data array anymore, so delete it
del (data)
# Are we done yet?
if done:
break
return True
def main(args):
filenames = args.filenames
filenames.sort()
times = []
for filename in filenames:
dataDict = numpy.load(filename)
tStart = datetime.utcfromtimestamp(dataDict['tStart'])
tInt = dataDict['tInt']
try:
srate = dataDict['srate']
except KeyError:
srate = 19.6e6
freq1 = dataDict['freq1']
freq2 = dataDict['freq2']
stand1, stand2 = dataDict['stands']
times.append( tStart)
print("Got %i files from %s to %s (%s)" % (len(filenames), times[0].strftime("%Y/%m/%d %H:%M:%S"), times[-1].strftime("%Y/%m/%d %H:%M:%S"), (times[-1]-times[0])))
iTimes = []
for i in xrange(1, len(times)):
dt = times[i] - times[i-1]
iTimes.append(dt.days*24*3600 + dt.seconds + dt.microseconds/1e6)
iTimes = numpy.array(iTimes)
print(" -> Interval: %.3f +/- %.3f seconds (%.3f to %.3f seconds)" % (iTimes.mean(), iTimes.std(), iTimes.min(), iTimes.max()))
print("Number of frequency channels: %i (~%.1f Hz/channel)" % (len(freq1)+1, freq1[1]-freq1[0]))
# Build up the station
if args.lwasv:
site = stations.lwasv
else:
site = stations.lwa1
rawAntennas = site.antennas
antennas = []
for ant in rawAntennas:
if ant.stand.id == stand1 and ant.pol == 0:
antennas.append(ant)
for ant in rawAntennas:
if ant.stand.id == stand2 and ant.pol == 0:
antennas.append(ant)
if len(antennas) != 2:
raise RuntimeError("Can only find stand %i, %i and %i found in the NPZ files" % (antennas[0].stand.id, stand1, stand2))
# Create the simulated array
refJD = unix_to_utcjd(timegm(times[0].timetuple()))
aa1 = simVis.build_sim_array(site, antennas, freq1/1e9, jd=refJD)
aa2 = simVis.build_sim_array(site, antennas, freq2/1e9, jd=refJD)
# Build the model times and range.
jdList = []
dTimes = []
for i in xrange(len(times)):
tNow = timegm(times[i].timetuple())
jdNow = unix_to_utcjd(tNow)
jdList.append(jdNow)
dTimes.append( (times[i]-times[0]).seconds )
# Actually run the simulations
simDict1 = simVis.build_sim_data(aa1, simVis.SOURCES, jd=jdList, pols=['xx',], verbose=False)
simDict2 = simVis.build_sim_data(aa2, simVis.SOURCES, jd=jdList, pols=['xx',], verbose=False)
# Plot
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
vis1 = []
for vis in simDict1['vis']['xx']:
vis1.append( vis )
vis2 = []
for vis in simDict2['vis']['xx']:
vis2.append( vis )
vis1 = numpy.array(vis1)
vis1 = numpy.ma.array(vis1, mask=~numpy.isfinite(vis1))
vis2 = numpy.array(vis2)
vis2 = numpy.ma.array(vis2, mask=~numpy.isfinite(vis2))
data = numpy.abs(vis1)
data = data.ravel()
data.sort()
vmin1 = data[int(round(0.15*len(data)))]
vmax1 = data[int(round(0.85*len(data)))]
print('Plot range for tuning 1:', vmin1, vmax1)
data = numpy.abs(vis2)
data = data.ravel()
data.sort()
vmin2 = data[int(round(0.15*len(data)))]
vmax2 = data[int(round(0.85*len(data)))]
print('Plot range for tuning 2:', vmin2, vmax2)
ax1.imshow(numpy.abs(vis1), extent=(freq1[0], freq1[-1], dTimes[0], dTimes[-1]), origin='lower',
vmin=vmin1, vmax=vmax1)
ax2.imshow(numpy.abs(vis2), extent=(freq1[0], freq1[-1], dTimes[0], dTimes[-1]), origin='lower',
vmin=vmin2, vmax=vmax2)
ax1.axis('auto')
ax2.axis('auto')
fig.suptitle("%s to %s UTC" % (times[0].strftime("%Y/%m/%d %H:%M"), times[-1].strftime("%Y/%m/%d %H:%M")))
ax1.set_xlabel('Frequency [MHz]')
ax2.set_xlabel('Frequency [MHz]')
ax1.set_ylabel('Elapsed Time [s]')
ax2.set_ylabel('Elapsed Time [s]')
plt.show()
def jd(self):
"""
JD as a floating point value.
"""
return unix_to_utcjd(self)
def main(args):
nChunks = 1000 #the temporal shape of a file.
LFFT = 4096*16 #Length of the FFT.
nFramesAvg = 1*4*LFFT/4096 # the intergration time under LFFT, 4 = beampols = 2X + 2Y (high and low tunes)
saveftint = 1 # if =1 save the frequency channel and tInt info
config = parseOptions(args)#Parse command line options
#for offset_i in range(4306, 4309):# one offset = nChunks*nFramesAvg skiped
for offset_i in range(0, 4):# one offset = nChunks*nFramesAvg skiped
offset = nChunks*nFramesAvg*offset_i
# Build the DRX file
try:
#drxFile = drsu.getFileByName(config['args'][0], config['args'][1])
fh = open(config['args'][0], "rb")
nFramesFile = os.path.getsize(config['args'][0]) / drx.FrameSize #drx.FrameSize = 4128
except:
print config['args'],' not found'
sys.exit(1)
try:
junkFrame = drx.readFrame(fh)
try:
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
pass
except ZeroDivisionError:
print 'zero division error'
break
except errors.syncError:
fh.seek(-drx.FrameSize+1, 1)
fh.seek(-drx.FrameSize, 1)
beam,tune,pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
config['offset'] = offset/srate/beampols*4096
if offset != 0:
fh.seek(offset*drx.FrameSize, 1)
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
# nChunks = int(round(config['duration'] / config['average']))
config['duration']=nChunks*config['average']
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg*nChunks
# Date & Central Frequnecy
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b,t,p = junkFrame.parseID()
if p == 0 and t == 0:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags[0]>>32) & (2**32-1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags[0]>>32) & (2**32-1)) / 2**32
else:
pass
fh.seek(-4*drx.FrameSize, 1)
config['freq1'] = centralFreq1
config['freq2'] = centralFreq2
# File summary
print "Filename: %s" % config['args']
print "Date of First Frame: %s" % str(beginDate)
print "Beams: %i" % beams
print "Tune/Pols: %i %i %i %i" % tunepols
print "Sample Rate: %i Hz" % srate
print "Tuning Frequency: %.3f Hz (1); %.3f Hz (2)" % (centralFreq1, centralFreq2)
print "Frames: %i (%.3f s)" % (nFramesFile, 1.0 * nFramesFile / beampols * 4096 / srate)
print "---"
print "Offset: %.3f s (%i frames)" % (config['offset'], offset)
print "Integration: %.4f s (%i frames; %i frames per beam/tune/pol)" % (config['average'], nFramesAvg, nFramesAvg / beampols)
print 'beampols', beampols
print "Duration: %.4f s (%i frames; %i frames per beam/tune/pol)" % (config['average']*nChunks, nFrames, nFrames / beampols)
#break
# Sanity check
if nFrames > (nFramesFile - offset):
raise RuntimeError("Requested integration time + offset is greater than file length")
# Master loop over all of the file chunks
#masterSpectra = numpy.zeros((nChunks, 2, LFFT-1))
masterSpectra = numpy.zeros((nChunks, 4, LFFT-1))
masterTimes = numpy.zeros(nChunks)
for i in xrange(nChunks):
# Find out how many frames remain in the file. If this number is larger
# than the maximum of frames we can work with at a time (maxFrames),
# only deal with that chunk
framesRemaining = nFrames - i*maxFrames
if framesRemaining > maxFrames:
framesWork = maxFrames
else:
framesWork = framesRemaining
if framesRemaining%(nFrames/10)==0:
print "Working on chunk %i, %i frames remaining" % (i, framesRemaining)
count = {0:0, 1:0, 2:0, 3:0}
data = numpy.zeros((4,framesWork*4096/beampols), dtype=numpy.csingle)
#print 'data.shape', data.shape
# If there are fewer frames than we need to fill an FFT, skip this chunk
if data.shape[1] < LFFT:
print 'data.shape[1]< LFFT, break'
break
# Inner loop that actually reads the frames into the data array
#if framesRemaining%(nFrames/10)==0:
# print "Working on %.1f ms of data" % ((framesWork*4096/beampols/srate)*1000.0)
for j in xrange(framesWork):
# Read in the next frame and anticipate any problems that could occur
try:
cFrame = drx.readFrame(fh, Verbose=False)
except errors.eofError:
print "EOF Error"
break
except errors.syncError:
print "Sync Error"
continue
beam,tune,pol = cFrame.parseID()
if tune == 0:
tune += 1
aStand = 2*(tune-1) + pol
if j is 0:
cTime = cFrame.getTime()
data[aStand, count[aStand]*4096:(count[aStand]+1)*4096] = cFrame.data.iq
count[aStand] += 1
# 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.
#print 'data.shape',data.shape
#continue
#freq, tempSpec1 = fxc.SpecMaster(data[:2,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate)
tempSpec1 = numpy.abs(numpy.fft.fft2(data[:2,:]))[:,1:]/2.
tempSpec1 = numpy.fft.fftshift(tempSpec1)**2./LFFT*2
freq = numpy.fft.fftfreq(LFFT, d = 1.0/srate)
freq = numpy.fft.fftshift(freq)
#freq, tempSpec2 = fxc.SpecMaster(data[2:,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate)
tempSpec2 = numpy.abs(numpy.fft.fft2(data[2:,:]))[:,1:]/2.
tempSpec2 = numpy.fft.fftshift(tempSpec2)**2./LFFT*2
#print 'tempSpec.shape', tempSpec1.shape
# Save the results to the various master arrays
masterTimes[i] = cTime
masterSpectra[i,0,:] = tempSpec1[0,:]
masterSpectra[i,1,:] = tempSpec1[1,:]
masterSpectra[i,2,:] = tempSpec2[0,:]
masterSpectra[i,3,:] = tempSpec2[1,:]
# We don't really need the data array anymore, so delete it
del(data)
#continue
#drxFile.close()
# Now that we have read through all of the chunks, perform the final averaging by
# dividing by all of the chunks
outname = "%s_%i_fft_offset_%.9i_LFFT_%.6i_frames" % (config['args'][0][-16:], beam,offset,LFFT)
# numpy.savez(outname, freq=freq, freq1=freq+config['freq1'], freq2=freq+config['freq2'], times=masterTimes, spec=masterSpectra, tInt=(maxFrames*4096/beampols/srate), srate=srate, standMapper=[4*(beam-1) + i for i in xrange(masterSpectra.shape[1])])
if saveftint == 1:
numpy.save('freq1', freq+config['freq1'])
numpy.save('freq2', freq+config['freq2'])
numpy.save('tInt', maxFrames*4096/beampols/srate)
saveftint = 0
#print 'fInt = ',(freq+config['freq1'])[1]-(freq+config['freq1'])[0]
#print 'tInt = ',maxFrames*4096/beampols/srate
#print 'tInt = ', 1.0*LFFT/srate
masterSpectra[:,0,:]=masterSpectra[:,0:2,:].mean(1)
masterSpectra[:,1,:]=masterSpectra[:,2:4,:].mean(1)
#numpy.save(outname[-46:], masterSpectra[:,0:2,:])
#print 'spectrogram shape', masterSpectra[:,0:2,:].shape
numpy.save(outname, masterSpectra[:,0:2,:])
def main(args):
# Break out the files we need
ssmif = args.ssmif
filenames = args.filename
# Setup the LWA station information
station = parse_ssmif(ssmif)
antennas = station.antennas
# Get an observer reader for calculations
obs = station.get_observer()
# Setup the beamformer gain and delay variables
course = numpy.zeros(520)
fine = numpy.zeros(520)
gains = numpy.zeros((260, 4))
gains[:, 0] = 1.0
gains[:, 3] = 1.0
for ant in antennas:
if ant.combined_status != 33:
stand = (ant.digitizer - 1) / 2
gains[stand, :] = 0.0
# Setup the beamformer itself
dp = SoftwareDP(mode='DRX', filter=7, central_freq=74e6)
# Find the target azimuth/elevation to use
idf = TBWFile(filenames[0])
tStart = datetime.utcfromtimestamp(idf.get_info('start_time'))
idf.close()
obs.date = tStart.strftime("%Y/%m/%d %H:%M:%S")
tTransit = obs.next_transit(args.source)
obs.date = tTransit
args.source.compute(obs)
targetAz = args.source.az * 180 / numpy.pi
targetEl = args.source.alt * 180 / numpy.pi
# Preliminary report
print("Working on %i TBW files using SSMIF '%s'" %
(len(filenames), os.path.basename(ssmif)))
print(" Source: '%s'" % args.source.name)
print(" Transit time: %s" % str(tTransit))
print(" Transit azimuth: %.2f degrees" % targetAz)
print(" Transet elevation: %.2f degrees" % targetEl)
print(" ")
# Loop over input files
unx, lst, pwrX, pwrY = [], [], [], []
for filename in filenames:
## Get the file reader
idf = TBWFile(filename)
## Pull out some metadata and update the observer
jd = astro.unix_to_utcjd(idf.get_info('start_time'))
obs.date = ephem.Date(jd - astro.DJD_OFFSET)
sample_rate = idf.get_info('sample_rate')
nInts = int(
round(idf.get_info('nframe') / (30000.0 * len(antennas) / 2)))
transitOffset = (obs.date - tTransit) * 86400.0
## Metadata report
print("Filename: %s" % os.path.basename(filename))
print(" Data type: %s" % type(idf))
print(" Captures in file: %i (%.3f s)" %
(nInts, nInts * 30000 * 400 / sample_rate))
print(" Station: %s" % station.name)
print(" Date observed: %s" % str(obs.date))
print(" MJD: %.5f" % (jd - astro.MJD_OFFSET, ))
print(" LST: %s" % str(obs.sidereal_time()))
print(" %.1f s %s transit" %
(abs(transitOffset), 'before' if transitOffset < 0 else 'after'))
print(" ")
## Load in the data
readT, t, data = idf.read(time_in_samples=True)
## Build up a time array
t = t + numpy.arange(data.shape[1], dtype=numpy.int64)
## Update the beamformer delays for the pointing center(s)
unx.append(idf.get_info('start_time'))
lst.append(obs.sidereal_time() * 12 / numpy.pi)
pwrX.append([])
pwrY.append([])
for offset in (-1, 0, 1):
### Compute
delays = beamformer.calc_delay(antennas,
freq=74.0e6,
azimuth=targetAz,
elevation=targetEl + offset)
delays *= fS * 16
delays = delays.max() - delays
### Decompose into FIFO and FIR
course = (delays // 16)
fine = (delays % 16)
## Form the beams for both polarizations
beamX, beamY = dp.form_beam(antennas, t, data, course, fine, gains)
## Compute the integrated spectra
### Convert to int16
beam = numpy.zeros((2, beamX.size), dtype=numpy.int16)
beam[0, :] = (numpy.round(beamX)).astype(data.dtype)
beam[1, :] = (numpy.round(beamY)).astype(data.dtype)
### Move into the frequency domain
freq, spec = fxc.SpecMaster(beam,
LFFT=8192,
window=fxc.null_window,
verbose=False,
sample_rate=fS,
clip_level=0)
## Save
pwrX[-1].append(spec[0, :])
pwrY[-1].append(spec[1, :])
## Done
idf.close()
# Convert to arrays
unx, lst = numpy.array(unx), numpy.array(lst)
pwrX, pwrY = numpy.array(pwrX), numpy.array(pwrY)
# Save for later (needed for debugging)
outname = "estimateSEFD-%s-%04i%02i%02i.npz" % (os.path.splitext(
os.path.basename(ssmif))[0], tTransit.tuple()[0], tTransit.tuple()[1],
tTransit.tuple()[2])
print("Saving intermediate data to '%s'" % outname)
print(" ")
numpy.savez(outname,
source=args.source.name,
freq=freq,
unx=unx,
lst=lst,
pwrX=pwrX,
pwrY=pwrY)
# Report
print("%s" % (args.source.name, ))
for i in xrange(lst.size):
print("%s: %s %s" %
(str(ephem.hours(str(lst[i]))), pwrX[i, :], pwrY[i, :]))
# Plot
if args.plots:
fig = plt.figure()
ax = fig.gca()
ax.plot(lst, pwrX, linestyle='-', marker='+')
ax.plot(lst, pwrY, linestyle='-', marker='x')
plt.show()
def processDataBatchStokes(fh, antennas, tStart, duration, sampleRate, config, dataSets, obsID=1, clip1=0, clip2=0):
"""
Process a chunk of data in a raw DRX file into Stokes parameters and
add the contents to an HDF5 file.
"""
# Length of the FFT
LFFT = config['LFFT']
# Find the start of the observation
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
fh.seek(-drx.FrameSize, 1)
print 'Looking for #%i at %s with sample rate %.1f Hz...' % (obsID, tStart, sampleRate)
while datetime.utcfromtimestamp(t0) < tStart or srate != sampleRate:
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
print '... Found #%i at %s with sample rate %.1f Hz' % (obsID, datetime.utcfromtimestamp(t0), srate)
tDiff = datetime.utcfromtimestamp(t0) - tStart
try:
duration = duration - tDiff.total_seconds()
except:
duration = duration - (tDiff.seconds + tDiff.microseconds/1e6)
beam,tune,pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
# 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 beampols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(1.0*config['maxFrames']/beampols*4096/float(LFFT))*LFFT/4096*beampols
# Number of frames to integrate over
print "Line 455: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols
nFramesAvg = int(round(config['average'] * srate / 4096 * beampols))
nFramesAvg = int(1.0 * nFramesAvg / beampols*4096/float(LFFT))*LFFT/4096*beampols
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
print "Line 460: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
nChunks = int(round(duration / config['average']))
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg*nChunks
print "Line 468: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg, " nChunks ", nChunks
# Date & Central Frequency
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b,t,p = junkFrame.parseID()
if p == 0 and t == 1:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
else:
pass
fh.seek(-4*drx.FrameSize, 1)
freq = numpy.fft.fftshift(numpy.fft.fftfreq(LFFT, d=1/srate))
if float(fxc.__version__) < 0.8:
freq = freq[1:]
dataSets['obs%i-freq1' % obsID][:] = freq + centralFreq1
dataSets['obs%i-freq2' % obsID][:] = freq + centralFreq2
obs = dataSets['obs%i' % obsID]
obs.attrs['tInt'] = config['average']
obs.attrs['tInt_Unit'] = 's'
obs.attrs['LFFT'] = LFFT
obs.attrs['nChan'] = LFFT-1 if float(fxc.__version__) < 0.8 else LFFT
obs.attrs['RBW'] = freq[1]-freq[0]
obs.attrs['RBW_Units'] = 'Hz'
dataProducts = ['I', 'Q', 'U', 'V']
done = False
for i in xrange(nChunks):
# Find out how many frames remain in the file. If this number is larger
# than the maximum of frames we can work with at a time (maxFrames),
# only deal with that chunk
framesRemaining = nFrames - i*maxFrames
if framesRemaining > maxFrames:
framesWork = maxFrames
else:
framesWork = framesRemaining
print "Working on chunk %i, %i frames remaining" % (i+1, framesRemaining)
count = {0:0, 1:0, 2:0, 3:0}
data = numpy.zeros((4,framesWork*4096/beampols), dtype=numpy.csingle)
# If there are fewer frames than we need to fill an FFT, skip this chunk
if data.shape[1] < LFFT:
break
# Inner loop that actually reads the frames into the data array
print "Working on %.1f ms of data" % ((framesWork*4096/beampols/srate)*1000.0)
for j in xrange(framesWork):
# Read in the next frame and anticipate any problems that could occur
try:
cFrame = drx.readFrame(fh, Verbose=False)
except errors.eofError:
done = True
break
except errors.syncError:
continue
beam,tune,pol = cFrame.parseID()
aStand = 2*(tune-1) + pol
if j is 0:
cTime = cFrame.getTime()
try:
data[aStand, count[aStand]*4096:(count[aStand]+1)*4096] = cFrame.data.iq
count[aStand] += 1
except ValueError:
raise RuntimeError("Invalid Shape")
# Save out some easy stuff
dataSets['obs%i-time' % obsID][i] = cTime
if config['countSats']:
sats = ((data.real**2 + data.imag**2) >= 49).sum(axis=1)
dataSets['obs%i-Saturation1' % obsID][i,:] = sats[0:2]
dataSets['obs%i-Saturation2' % obsID][i,:] = sats[2:4]
else:
dataSets['obs%i-Saturation1' % obsID][i,:] = -1
dataSets['obs%i-Saturation2' % obsID][i,:] = -1
# 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.
if clip1 == clip2:
freq, tempSpec1 = fxc.StokesMaster(data, antennas, LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate, ClipLevel=clip1)
for t in (1,2):
for l,p in enumerate(dataProducts):
dataSets['obs%i-%s%i' % (obsID, p, t)][i,:] = tempSpec1[l,t-1,:]
else:
freq, tempSpec1 = fxc.StokesMaster(data[:2,:], antennas[:2], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate, ClipLevel=clip1)
freq, tempSpec2 = fxc.StokesMaster(data[2:,:], antennas[2:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate, ClipLevel=clip2)
for l,p in enumerate(dataProducts):
dataSets['obs%i-%s%i' % (obsID, p, 1)][i,:] = tempSpec1[l,0,:]
dataSets['obs%i-%s%i' % (obsID, p, 2)][i,:] = tempSpec2[l,0,:]
# We don't really need the data array anymore, so delete it
del(data)
# Are we done yet?
if done:
break
return True
def set_unixtime(self, timestamp):
"""
Set the array time using a UNIX timestamp (epoch 1970).
"""
self.set_jultime(astro.unix_to_utcjd(timestamp))
def main(args):
# Parse command line options
config = parseOptions(args)
# Length of the FFT
LFFT = config['LFFT']
# Open the file and find good data (not spectrometer data)
filename = config['args'][0]
fh = open(filename, "rb")
nFramesFile = os.path.getsize(filename) / drx.FrameSize
try:
for i in xrange(5):
junkFrame = drspec.readFrame(fh)
raise RuntimeError("ERROR: '%s' appears to be a DR spectrometer file, not a raw DRX file" % filename)
except errors.syncError:
fh.seek(0)
while True:
try:
junkFrame = drx.readFrame(fh)
try:
srate = junkFrame.getSampleRate()
t0 = junkFrame.getTime()
break
except ZeroDivisionError:
pass
except errors.syncError:
fh.seek(-drx.FrameSize+1, 1)
fh.seek(-drx.FrameSize, 1)
beam,tune,pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
# Offset in frames for beampols beam/tuning/pol. sets
inoffset = config['offset']
offset = int(config['offset'] * srate / 4096 * beampols)
offset = int(1.0 * offset / beampols) * beampols
fh.seek(offset*drx.FrameSize, 1)
# Iterate on the offsets until we reach the right point in the file. This
# is needed to deal with files that start with only one tuning and/or a
# different sample rate.
while True:
## Figure out where in the file we are and what the current tuning/sample
## rate is
junkFrame = drx.readFrame(fh)
srate = junkFrame.getSampleRate()
t1 = junkFrame.getTime()
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
fh.seek(-drx.FrameSize, 1)
## See how far off the current frame is from the target
tDiff = t1 - (t0 + config['offset'])
## Half that to come up with a new seek parameter
tCorr = -tDiff / 2.0
cOffset = int(tCorr * srate / 4096 * beampols)
cOffset = int(1.0 * cOffset / beampols) * beampols
offset += cOffset
## If the offset is zero, we are done. Otherwise, apply the offset
## and check the location in the file again/
if cOffset is 0:
break
fh.seek(cOffset*drx.FrameSize, 1)
# Update the offset actually used
config['offset'] = t1 - t0
offset = int(round(config['offset'] * srate / 4096 * beampols))
offset = int(1.0 * offset / beampols) * beampols
# 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 beampols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(1.0*config['maxFrames']/beampols*4096/float(LFFT))*LFFT/4096*beampols
# Number of frames to integrate over
print "Line 673: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols
nFramesAvg = int(config['average'] * srate / 4096 * beampols)
if( nFramesAvg == 0):
nFramesAvg = 1 * beampols
else:
nFramesAvg = int(1.0 * nFramesAvg / beampols*4096/float(LFFT))*LFFT/4096*beampols
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
print "Line 678: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
if config['metadata'] is not None:
config['duration'] = 0
if config['duration'] == 0:
config['duration'] = 1.0 * nFramesFile / beampols * 4096 / srate
else:
config['duration'] = int(round(config['duration'] * srate * beampols / 4096) / beampols * 4096 / srate)
nChunks = int(round(config['duration'] / config['average']))
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg*nChunks
print "Line 693: config['average']", config['average'], ' sample rate ', srate, ' beampols ', beampols, " nFramesAvg ", nFramesAvg, " nChunks ", nChunks
# Date & Central Frequency
t1 = junkFrame.getTime()
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b,t,p = junkFrame.parseID()
if p == 0 and t == 1:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
else:
pass
fh.seek(-4*drx.FrameSize, 1)
config['freq1'] = centralFreq1
config['freq2'] = centralFreq2
# File summary
print "Filename: %s" % filename
print "Date of First Frame: %s" % str(beginDate)
print "Beams: %i" % beams
print "Tune/Pols: %i %i %i %i" % tunepols
print "Sample Rate: %i Hz" % srate
print "Tuning Frequency: %.3f Hz (1); %.3f Hz (2)" % (centralFreq1, centralFreq2)
print "Frames: %i (%.3f s)" % (nFramesFile, 1.0 * nFramesFile / beampols * 4096 / srate)
print "---"
print "Offset: %.3f s (%i frames)" % (config['offset'], offset)
print "Integration: %.6f s (%i frames; %i frames per beam/tune/pol)" % (config['average'], nFramesAvg, nFramesAvg / beampols)
print "Duration: %.3f s (%i frames; %i frames per beam/tune/pol)" % (config['average']*nChunks, nFrames, nFrames / beampols)
print "Chunks: %i" % nChunks
print " "
# Estimate clip level (if needed)
if config['estimate']:
clip1, clip2 = estimateClipLevel(fh, beampols)
else:
clip1 = config['clip']
clip2 = config['clip']
# Make the pseudo-antennas for Stokes calculation
antennas = []
for i in xrange(4):
if i / 2 == 0:
newAnt = stations.Antenna(1)
else:
newAnt = stations.Antenna(2)
if i % 2 == 0:
newAnt.pol = 0
else:
newAnt.pol = 1
antennas.append(newAnt)
# Setup the output file
outname = os.path.split(filename)[1]
outname = os.path.splitext(outname)[0]
if( config['return'] == 'FFT' ):
outname = '%s-%d-waterfall-complex.hdf5' %(outname, inoffset)
else:
outname = '%s-waterfall.hdf5' % outname
if os.path.exists(outname):
#yn = raw_input("WARNING: '%s' exists, overwrite? [Y/n] " % outname)
#if yn not in ('n', 'N'):
# os.unlink(outname)
#else:
raise RuntimeError("Output file '%s' already exists" % outname)
f = hdfData.createNewFile(outname)
# Look at the metadata and come up with a list of observations. If
# there are no metadata, create a single "observation" that covers the
# whole file.
obsList = {}
if config['metadata'] is not None:
sdf = metabundle.getSessionDefinition(config['metadata'])
sdfBeam = sdf.sessions[0].drxBeam
spcSetup = sdf.sessions[0].spcSetup
if sdfBeam != beam:
raise RuntimeError("Metadata is for beam #%i, but data is from beam #%i" % (sdfBeam, beam))
for i,obs in enumerate(sdf.sessions[0].observations):
sdfStart = mcs.mjdmpm2datetime(obs.mjd, obs.mpm)
sdfStop = mcs.mjdmpm2datetime(obs.mjd, obs.mpm + obs.dur)
obsDur = obs.dur/1000.0
obsSR = drx.filterCodes[obs.filter]
obsList[i+1] = (sdfStart, sdfStop, obsDur, obsSR)
print "Observations:"
for i in sorted(obsList.keys()):
obs = obsList[i]
print " #%i: %s to %s (%.3f s) at %.3f MHz" % (i, obs[0], obs[1], obs[2], obs[3]/1e6)
print " "
hdfData.fillFromMetabundle(f, config['metadata'])
else:
obsList[1] = (datetime.utcfromtimestamp(t1), datetime(2222,12,31,23,59,59), config['duration'], srate)
hdfData.fillMinimum(f, 1, beam, srate)
if config['linear']:
dataProducts = ['XX', 'YY']
else:
dataProducts = ['I', 'Q', 'U', 'V']
for o in sorted(obsList.keys()):
for t in (1,2):
hdfData.createDataSets(f, o, t, numpy.arange(LFFT-1 if float(fxc.__version__) < 0.8 else LFFT, dtype=numpy.float32), int(round(obsList[o][2]/config['average'])), dataProducts, dataOut=config['return'])
f.attrs['FileGenerator'] = 'hdfWaterfall.py'
f.attrs['InputData'] = os.path.basename(filename)
# Create the various HDF group holders
ds = {}
for o in sorted(obsList.keys()):
obs = hdfData.getObservationSet(f, o)
ds['obs%i' % o] = obs
ds['obs%i-time' % o] = obs.create_dataset('time', (int(round(obsList[o][2]/config['average'])),), 'f8')
for t in (1,2):
ds['obs%i-freq%i' % (o, t)] = hdfData.getDataSet(f, o, t, 'freq')
for p in dataProducts:
if( config['return']=='PSD'):
ds["obs%i-%s%i" % (o, p, t)] = hdfData.getDataSet(f, o, t, p)
else:
ds["obs%i-%s%imag" % (o, p, t)] = hdfData.getDataSet(f, o, t, p+'mag')
ds["obs%i-%s%iphase" % (o, p, t)] = hdfData.getDataSet(f, o, t, p+'phase')
ds['obs%i-Saturation%i' % (o, t)] = hdfData.getDataSet(f, o, t, 'Saturation')
# Load in the correct analysis function
if config['linear']:
processDataBatch = processDataBatchLinear
else:
processDataBatch = processDataBatchStokes
# Go!
for o in sorted(obsList.keys()):
try:
processDataBatch(fh, antennas, obsList[o][0], obsList[o][2], obsList[o][3], config, ds, obsID=o, clip1=clip1, clip2=clip2)
except RuntimeError, e:
print "Observation #%i: %s, abandoning this observation" % (o, str(e))
def main(args):
t0=time.time()
#nChunks = 10000#10000, the size of a file.
#nFramesAvg = 1*4#200, 50 frames per pol, the subintergration time.
nChunks = 3000 #10000 #the size of a file.
nFramesAvg = 4*16 #the intergration time.
fcl = 6000+7000
fch = fcl + 3343
for offset_i in range(0, 1409):# one offset = nChunks*nFramesAvg skiped
#for offset_i in range(1500*2, 1500*3):# one offset = nChunks*nFramesAvg skiped
#for offset_i in range(1500*4, 1500*5):# one offset = nChunks*nFramesAvg skiped
offset = nChunks*nFramesAvg*offset_i
# Parse command line options
config = parseOptions(args)
# Length of the FFT
#LFFT = config['LFFT']
LFFT = 4096*16
# Build the DRX file
try:
#drxFile = drsu.getFileByName(config['args'][0], config['args'][1])
fh = open(config['args'][0], "rb")
nFramesFile = os.path.getsize(config['args'][0]) / drx.FrameSize
except:
print config['args']
sys.exit(1)
#drxFile.open()
#nFramesFile = drxFile.size / drx.FrameSize
while True:
try:
junkFrame = drx.readFrame(fh)
try:
srate = junkFrame.getSampleRate()
#t0 = junkFrame.getTime()
break
except ZeroDivisionError:
pass
except errors.syncError:
fh.seek(-drx.FrameSize+1, 1)
fh.seek(-drx.FrameSize, 1)
beam,tune,pol = junkFrame.parseID()
beams = drx.getBeamCount(fh)
tunepols = drx.getFramesPerObs(fh)
tunepol = tunepols[0] + tunepols[1] + tunepols[2] + tunepols[3]
beampols = tunepol
config['offset'] = offset/srate/beampols*4096
if offset != 0:
fh.seek(offset*drx.FrameSize, 1)
# 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 beampols in the data, the FFT length,
# and the number of samples per frame.
maxFrames = int(1.0*config['maxFrames']/beampols*4096/float(LFFT))*LFFT/4096*beampols
# Number of frames to integrate over
# nFramesAvg = int(config['average'] * srate / 4096 * beampols)
# nFramesAvg = int(1.0 * nFramesAvg / beampols*4096/float(LFFT))*LFFT/4096*beampols
config['average'] = 1.0 * nFramesAvg / beampols * 4096 / srate
maxFrames = nFramesAvg
# Number of remaining chunks (and the correction to the number of
# frames to read in).
# nChunks = int(round(config['duration'] / config['average']))
config['duration']=nChunks*config['average']
if nChunks == 0:
nChunks = 1
nFrames = nFramesAvg*nChunks
# Date & Central Frequnecy
beginDate = ephem.Date(unix_to_utcjd(junkFrame.getTime()) - DJD_OFFSET)
centralFreq1 = 0.0
centralFreq2 = 0.0
for i in xrange(4):
junkFrame = drx.readFrame(fh)
b,t,p = junkFrame.parseID()
if p == 0 and t == 1:
try:
centralFreq1 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq1 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
elif p == 0 and t == 2:
try:
centralFreq2 = junkFrame.getCentralFreq()
except AttributeError:
from lsl.common.dp import fS
centralFreq2 = fS * ((junkFrame.data.flags>>32) & (2**32-1)) / 2**32
else:
pass
fh.seek(-4*drx.FrameSize, 1)
config['freq1'] = centralFreq1
config['freq2'] = centralFreq2
# File summary
print "Filename: %s" % config['args']
print "Date of First Frame: %s" % str(beginDate)
print "Beams: %i" % beams
print "Tune/Pols: %i %i %i %i" % tunepols
print "Sample Rate: %i Hz" % srate
print "Tuning Frequency: %.3f Hz (1); %.3f Hz (2)" % (centralFreq1, centralFreq2)
print "Frames: %i (%.3f s)" % (nFramesFile, 1.0 * nFramesFile / beampols * 4096 / srate)
print "---"
print "Offset: %.3f s (%i frames)" % (config['offset'], offset)
print "Integration: %.3f s (%i frames; %i frames per beam/tune/pol)" % (config['average'], nFramesAvg, nFramesAvg / beampols)
print "Duration: %.3f s (%i frames; %i frames per beam/tune/pol)" % (config['average']*nChunks, nFrames, nFrames / beampols)
print "Chunks: %i" % nChunks
#sys.exit()
# Sanity check
if nFrames > (nFramesFile - offset):
raise RuntimeError("Requested integration time+offset is greater than file length")
# Estimate clip level (if needed)
if config['estimate']:
filePos = fh.tell()
# Read in the first 100 frames for each tuning/polarization
count = {0:0, 1:0, 2:0, 3:0}
data = numpy.zeros((4, 4096*100), dtype=numpy.csingle)
for i in xrange(4*100):
try:
cFrame = drx.readFrame(fh, Verbose=False)
except errors.eofError:
break
except errors.syncError:
continue
beam,tune,pol = cFrame.parseID()
aStand = 2*(tune-1) + pol
data[aStand, count[aStand]*4096:(count[aStand]+1)*4096] = cFrame.data.iq
count[aStand] += 1
# Go back to where we started
fh.seek(filePos)
# Compute the robust mean and standard deviation for I and Q for each
# tuning/polarization
meanI = []
meanQ = []
stdsI = []
stdsQ = []
#for i in xrange(4):
for i in xrange(2):
meanI.append( robust.mean(data[i,:].real) )
meanQ.append( robust.mean(data[i,:].imag) )
stdsI.append( robust.std(data[i,:].real) )
stdsQ.append( robust.std(data[i,:].imag) )
# Come up with the clip levels based on 4 sigma
clip1 = (meanI[0] + meanI[1] + meanQ[0] + meanQ[1]) / 4.0
#clip2 = (meanI[2] + meanI[3] + meanQ[2] + meanQ[3]) / 4.0
clip2 = 0
clip1 += 5*(stdsI[0] + stdsI[1] + stdsQ[0] + stdsQ[1]) / 4.0
#clip2 += 5*(stdsI[2] + stdsI[3] + stdsQ[2] + stdsQ[3]) / 4.0
clip2 += 0
clip1 = int(round(clip1))
clip2 = int(round(clip2))
# Report again
else:
clip1 = config['clip']
clip2 = config['clip']
# Master loop over all of the file chunks
#masterSpectra = numpy.zeros((nChunks, 4, LFFT-1))
masterSpectra = numpy.zeros((nChunks, 4, fch-fcl))
masterTimes = numpy.zeros(nChunks)
for i in xrange(nChunks):
# Find out how many frames remain in the file. If this number is larger
# than the maximum of frames we can work with at a time (maxFrames),
# only deal with that chunk
framesRemaining = nFrames - i*maxFrames
if framesRemaining > maxFrames:
framesWork = maxFrames
else:
framesWork = framesRemaining
if framesRemaining%(nFrames/10)==0:
print "Working on chunk %i, %i frames remaining" % (i, framesRemaining)
count = {0:0, 1:0, 2:0, 3:0}
data = numpy.zeros((4,framesWork*4096/beampols), dtype=numpy.csingle)
# If there are fewer frames than we need to fill an FFT, skip this chunk
if data.shape[1] < LFFT:
break
# Inner loop that actually reads the frames into the data array
if framesRemaining%(nFrames/10)==0:
print "Working on %.1f ms of data" % ((framesWork*4096/beampols/srate)*1000.0)
for j in xrange(framesWork):
# Read in the next frame and anticipate any problems that could occur
try:
cFrame = drx.readFrame(fh, Verbose=False)
except errors.eofError:
print "EOF Error"
break
except errors.syncError:
print "Sync Error"
continue
beam,tune,pol = cFrame.parseID()
aStand = 2*(tune-1) + pol
if j is 0:
cTime = cFrame.getTime()
data[aStand, count[aStand]*4096:(count[aStand]+1)*4096] = cFrame.data.iq
count[aStand] += 1
# 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, tempSpec1 = fxc.SpecMaster(data[:2,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate, ClipLevel=clip1)
freq, tempSpec1 = fxc.SpecMaster(data[:2,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate)
#freq, tempSpec2 = fxc.SpecMaster(data[2:,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate, ClipLevel=clip2)
freq, tempSpec2 = fxc.SpecMaster(data[2:,:], LFFT=LFFT, window=config['window'], verbose=config['verbose'], SampleRate=srate)
# Save the results to the various master arrays
masterTimes[i] = cTime
masterSpectra[i,0,:] = tempSpec1[0,fcl:fch]
masterSpectra[i,1,:] = tempSpec1[1,fcl:fch]
masterSpectra[i,2,:] = tempSpec2[0,fcl:fch]
masterSpectra[i,3,:] = tempSpec2[1,fcl:fch]
# We don't really need the data array anymore, so delete it
del(data)
#drxFile.close()
# Now that we have read through all of the chunks, perform the final averaging by
# dividing by all of the chunks
outname = "%s_%i_fft_offset_%.9i_frames" % (config['args'][0], beam,offset)
# numpy.savez(outname, freq=freq, freq1=freq+config['freq1'], freq2=freq+config['freq2'], times=masterTimes, spec=masterSpectra, tInt=(maxFrames*4096/beampols/srate), srate=srate, standMapper=[4*(beam-1) + i for i in xrange(masterSpectra.shape[1])])
#print 'fInt = ',(freq+config['freq1'])[1]-(freq+config['freq1'])[0]
#print 'tInt = ',maxFrames*4096/beampols/srate
masterSpectra[:,0,:]=masterSpectra[:,0:2,:].mean(1)
masterSpectra[:,1,:]=masterSpectra[:,2:4,:].mean(1)
numpy.save(outname[-46:], masterSpectra[:,1,:])
#numpy.save(outname[-46:], masterSpectra[:,0:2,:])
#numpy.save(outname[-46:], masterSpectra)
# spec = numpy.squeeze( (masterWeight*masterSpectra).sum(axis=0) / masterWeight.sum(axis=0) )
# offset_i = offset_i + 1
print time.time()-t0
def utc_dp(self, value):
if not isinstance(value, (int, float)):
raise TypeError("value must be type int or float")
self._time = astro.unix_to_utcjd(float(value) / fS)
