def test_applyChannelFlagCut(self):
freqs = [0.1, 0.2, 0.3, 0.4]
chans = range(len(freqs))
chan2FreqDict = {chan: freqs[chan] for chan in chans}
bl2SepDict = {(0, 1): np.asarray([45.0, 0.0]), (0, 2): np.asarray([90.0, 0.0])}
reds = uc.uCalReds(freqs, bl2SepDict.keys(), chan2FreqDict, bl2SepDict)
reds.applyChannelFlagCut([0])
self.assertEqual(len(reds.blChanPairs), 1)
reds.applyChannelFlagCut([1])
self.assertEqual(len(reds.blChanPairs), 0)
def test_init(self):
freqs = [0.1, 0.2, 0.3, 0.4]
chans = range(len(freqs))
chan2FreqDict = {chan: freqs[chan] for chan in chans}
bl2SepDict = {(0, 1): np.asarray([45.0, 0.0]), (0, 2): np.asarray([90.0, 0.0])}
reds = uc.uCalReds(freqs, bl2SepDict.keys(), chan2FreqDict, bl2SepDict)
self.assertEqual(reds.blChanPairs[0, (0, 2), 1, (0, 1)][0][0], 9.0)
self.assertEqual(reds.blChanPairs[1, (0, 2), 3, (0, 1)][0][0], 18.0)
self.assertEqual(reds.blChanPairs[0, (0, 2), 1, (0, 1)][0][1], 0)
self.assertEqual(reds.blChanPairs[1, (0, 2), 3, (0, 1)][0][1], 0)
self.assertEqual(len(reds.blChanPairs), 2)
def test_applyChannelFlagCut(self):
freqs = [.1, .2, .3, .4]
chans = range(len(freqs))
chan2FreqDict = {chan: freqs[chan] for chan in chans}
bl2SepDict = {
(0, 1): np.asarray([45.0, 0.0]),
(0, 2): np.asarray([90.0, 0.0])
}
reds = uc.uCalReds(freqs, bl2SepDict.keys(), chan2FreqDict, bl2SepDict)
reds.applyChannelFlagCut([0])
self.assertEqual(len(reds.blChanPairs), 1)
reds.applyChannelFlagCut([1])
self.assertEqual(len(reds.blChanPairs), 0)
def test_init(self):
freqs = [.1, .2, .3, .4]
chans = range(len(freqs))
chan2FreqDict = {chan: freqs[chan] for chan in chans}
bl2SepDict = {
(0, 1): np.asarray([45.0, 0.0]),
(0, 2): np.asarray([90.0, 0.0])
}
reds = uc.uCalReds(freqs, bl2SepDict.keys(), chan2FreqDict, bl2SepDict)
self.assertEqual(reds.blChanPairs[0, (0, 2), 1, (0, 1)][0][0], 9.0)
self.assertEqual(reds.blChanPairs[1, (0, 2), 3, (0, 1)][0][0], 18.0)
self.assertEqual(reds.blChanPairs[0, (0, 2), 1, (0, 1)][0][1], 0)
self.assertEqual(reds.blChanPairs[1, (0, 2), 3, (0, 1)][0][1], 0)
self.assertEqual(len(reds.blChanPairs), 2)
#PAPER 128 Setup
intSeps = np.arange(1,16) #* 15m = baseline lengths
dx = 15.0 / scipy.constants.c * 1e9
chan2FreqDict = {chan: freqs[chan] for chan in chans}
separations = dx*intSeps
if calFile is None: calFile = 'psa6622_v003'
bls = getBaselines(freqs, intSeps, dataFiles[0], calFile=calFile)
bl2SepDict = {bl: np.asarray([sep,0.0]) for bl,sep in zip(bls,separations)}
redundancyDict = blRedundancyDictFromCalFile(calFile=calFile)
pickleFileName = dataFiles[0].replace('.npz', '.uCalDataWithBootstraps.p')
if loadPickle:
uReds, uCal, uCalBootstraps, loadedDataFiles = pickle.load(open(pickleFileName,'rb'))
if not len(uCalBootstraps) == nBootstraps or not loadedDataFiles == dataFiles: raise RuntimeError('Loaded uCalDataWithBootstraps.p does not match current specifications.')
else:
uReds = uc.uCalReds(freqs, bls, chan2FreqDict, bl2SepDict, maxDeltau=maxDeltau, verbose=verbose) #just pass in freqs
uCal = uc.uCalibrator(uReds.getBlChanPairs())
data, samples = loadVisibilitiesAndSamples(dataFiles, pol, bls, redundancyDict)
uCal.computeVisibilityCorrelations(data, samples, verbose=verbose)
uCalBootstraps = [uc.uCalibrator(uReds.getBlChanPairs()) for i in range(nBootstraps)]
dataBootstraps, samplesBootstraps = dataAndSamplesBootstraps(data, samples, bls, nBootstraps)
for i in range(nBootstraps):
if verbose: print 'Now computing visibility correlations for bootstrap ' + str(i) + ' of ' + str(nBootstraps) + '...'
uCalBootstraps[i].computeVisibilityCorrelations(dataBootstraps[i], samplesBootstraps[i], verbose=verbose)
harmonizeChannelFlags(chans, uCal, uCalBootstraps, verbose=verbose)
if verbose: print 'Now saving intermediate data products to ' + pickleFileName + ' for easy rerunning...'
pickle.dump([uReds, uCal, uCalBootstraps, dataFiles], open(pickleFileName, 'wb'))
#%%##########################################
# uCal Functionality
bl2SepDict = {bl: np.asarray([sep, 0.0]) for bl, sep in zip(bls, separations)}
redundancyDict = blRedundancyDictFromCalFile(calFile=calFile)
pickleFileName = dataFiles[0].replace('.npz', '.uCalDataWithBootstraps.p')
if loadPickle:
uReds, uCal, uCalBootstraps, loadedDataFiles = pickle.load(
open(pickleFileName, 'rb'))
if not len(
uCalBootstraps) == nBootstraps or not loadedDataFiles == dataFiles:
raise RuntimeError(
'Loaded uCalDataWithBootstraps.p does not match current specifications.'
)
else:
uReds = uc.uCalReds(freqs,
bls,
chan2FreqDict,
bl2SepDict,
maxDeltau=maxDeltau,
verbose=verbose) #just pass in freqs
uCal = uc.uCalibrator(uReds.getBlChanPairs())
data, samples = loadVisibilitiesAndSamples(dataFiles, pol, bls,
redundancyDict)
uCal.computeVisibilityCorrelations(data, samples, verbose=verbose)
uCalBootstraps = [
uc.uCalibrator(uReds.getBlChanPairs()) for i in range(nBootstraps)
]
dataBootstraps, samplesBootstraps = dataAndSamplesBootstraps(
data, samples, bls, nBootstraps)
for i in range(nBootstraps):
if verbose:
print 'Now computing visibility correlations for bootstrap ' + str(
i) + ' of ' + str(nBootstraps) + '...'
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][data[b]==0] = 0 #TODO: we need a more sophisticated way of handling flagging than this!!!
return data, samples
data, samples = loadVisibilitiesAndSamples(dataFiles, pol, bls, redundancyDict)
#############################################
# uCal Script
#############################################
print '\nNow performing uCal...\n'
if regenerateEverything: #regenerate uReds and data
uReds = uc.uCalReds(freqs, bls, chan2FreqDict, bl2SepDict, maxDeltau=.3) #just pass in freqs
uReds.applyuCut(uMin=25, uMax=150)
uReds.applyChannelFlagCut(flaggedChannels)
uCal = uc.uCalibrator(uReds.getBlChanPairs())
uCal.computeVisibilityCorrelations(data, samples)
pickle.dump([uReds, uCal], open('./Data/uCalData.p', 'wb'))
else:
uReds, uCal = pickle.load(open('./Data/uCalData.p','rb'))
uCal.setupBinning(uBinSize = .72**.5, duBinSize = 5.0/203) #TODO: investigate uBinSize = .5 (nyquist sampling)
#uCal.setupBinning(uBinSize = .5, duBinSize = 5.0/203)
print 'Now performing logcal...'
betasLogcal, SigmasLogcal, DsLogcal = uCal.performLogcal()
noiseCovDiag = uCal.generateNoiseCovariance(betasLogcal)
betas, Sigmas, Ds = betasLogcal.copy(), SigmasLogcal.copy(), DsLogcal.copy()