def _sky_frequencies(spectra, df):
# The spectra array dimensions are now:
# sigref, windows, polarizations, channels
nsig, nwin, npol, nchan = spectra.shape
# ---------------------- calculate frequencies
lo1, offset, iffile_info = filedata.info_from_files(df.project_id, df.scan_number)
# NB 'BANK_A' is a column name that could hold ANY bank value
# and not necessarily 'A'
# The port number is (I think) a unique identifier for polarization
# but it shouldn't matter because frequencies should be the same
# for both polarizations. So, I just grab the first. port=1.
sampler_table = _sampler_table(df)
port, bank = 1, sampler_table['BANK_A'][0]
_, sff_sb, sff_multi, sff_offset = iffile_info[(port, bank)]
# The CRVAL1 and CDELT1 value differ with subband, so collect
# all of them for later reference to determine frequencies.
crval1 = []
cdelt1 = []
for sb in df.subband:
mask = sampler_table['SUBBAND'] == sb
crval1.append(sampler_table[mask]['CRVAL1'][0])
cdelt1.append(sampler_table[mask]['CDELT1'][0])
display_sky_frequencies = []
for count in range(nwin * npol):
ifval = np.array(range(1, df.number_channels+1))
# Below I use df.number_channels/2 instead of df.crpix1 because crpix1
# is currently holding the incorrect value of 0.
# That is a bug in the protobuf Data key sent in the stream from
# the manager.
ifval = crval1[count] + cdelt1[count] * (ifval - df.number_channels/2)
skyfreq = sff_sb * ifval + sff_multi * lo1 + sff_offset
# only return NCHANS numbers of frequencies for each subband
reduced_skyfreqs = (skyfreq[::df.number_channels/cfg.NCHANS]/1e9).tolist()
display_sky_frequencies.extend(reduced_skyfreqs + offset[0]/1e9) # 0 represents the frequency signal
if nsig > 1:
# If we have a 2nd switching state, create another frequency vector with the shift.
# offset[1] is the offset for the reference frequency.
display_sky_frequencies.extend([_ + offset[1]/1e9 for _ in display_sky_frequencies])
return display_sky_frequencies
def _handle_data(sock, key):
"""Do something with the response from a VEGAS bank.
Args:
sock(socket): A bank socket object.
key(str): Either state or data key.
Returns:
If it's data, a list containing project, scan number, integration
number and a spectrum.
If it's state info, we get the state string.
If there is an error, None.
"""
rl = sock.recv_multipart()
el = len(rl)
if el == 1: # Got an error
if rl[0] == "E_NOKEY":
logging.info("No key/value pair found: {}".format(key))
return None
elif el > 1:
# first element is the key
# the following elements are the values
if not rl[0].endswith("Data"):
# This should only happen when we get state info.
df = PBDataField()
df.ParseFromString(rl[1])
return df
else:
# We should only be here if we got a data response.
df = pbVegasData()
df.ParseFromString(rl[1])
ff = array.array('f') # 'f' is typecode for C float
ff.fromstring(df.data_blob)
n_sig_states = len(set(df.sig_ref_state))
n_cal_states = len(set(df.cal_state))
n_subbands = len(set(df.subband))
logging.debug("Project ID is: {}".format(df.project_id))
logging.debug("Scan number is: {}".format(df.scan_number))
logging.debug("Polarization is: {}".format(df.polarization))
logging.debug("Number of sub-bands is: {}".format(n_subbands))
logging.debug("Number of sig switching states is: {}".format(n_sig_states))
logging.debug("Number of cal states is: {}".format(n_cal_states))
logging.debug("Number of polarizations: {}".format(df.number_stokes))
problem = False
if n_subbands < 1:
problem = True
if n_sig_states < 1:
problem = True
if n_cal_states < 1:
problem = True
if problem:
return None
n_chans, n_samplers, n_states = df.data_dims
full_res_spectra = np.array(ff)
logging.debug('full_res_spectra {}'.format(full_res_spectra[:10]))
# change the dimensions of the spectra to be
# STATES (sigref, cal), SAMPLERS (windows, polarizations), CHANNELS
full_res_spectra = full_res_spectra.reshape(df.data_dims[::-1])
# estimate the number of polarizations used to grab the first
# of each subband
n_pols = (n_samplers/n_subbands)
logging.debug('polarization estimate: {}'.format(n_pols))
# Reshape the spectra into these dimensions:
# sigref, cals, windows, polarizations, channels
full_res_spectra = full_res_spectra.reshape((n_sig_states, n_cal_states, n_subbands, n_pols, n_chans))
# If we have more than two polarizations, only keep the first two.
full_res_spectra = full_res_spectra[:,:,:,:2,:]
if n_pols > 2:
n_pols = 2
# average the calon/caloff pairs
# this reduces the state dimension by 1/2
# i.e. 2,14,1024 becomes 1,14,1024 or 14,1024
# TODO this assumes only cal and sig switching
# if other switching is added in the future, it could
# be an issue. n_states should == 2.
myspectra = np.mean(full_res_spectra, axis=1)
if n_sig_states == 2:
logging.debug('SIG SWITCHING')
# The spectra array dimensions are now:
# sigref, windows, polarizations, channels
files_available = True
try:
sky_freqs = _sky_frequencies(myspectra, df)
except:
logging.debug('Frequency information unavailable. Substituting with dummy freq. data.')
files_available = False
sky_freqs = _make_dummy_frequencies(n_sig_states, n_subbands, n_pols)
# Reduce the number of channels in all spectra to make them easier to display.
# This is also where we remove the center spike.
sampled_spectra = _trim_spectra(myspectra)
# Join the frequencies to the spectra.
spectrum = np.array(zip(sky_freqs, sampled_spectra))
# The 2 in the following line accounts for the data and frequency axes.
spectrum = spectrum.reshape((n_sig_states, n_subbands, n_pols, cfg.NCHANS, 2)).tolist()
# sort each spectrum by frequency
for sigidx in range(n_sig_states):
for winidx in range(n_subbands):
for polidx in range(n_pols):
spectrum[sigidx][winidx][polidx] = sorted(spectrum[sigidx][winidx][polidx])
project = str(df.project_id)
scan = int(df.scan_number)
integration = int(df.integration)
# collect the polarization names to display, e.g. "L" or "R"
sampler_table = _sampler_table(df)
polname = []
if files_available:
_, _, iffile_info = filedata.info_from_files(project, scan)
for pnum in range(1, n_pols+1):
port, bank = pnum, sampler_table['BANK_A'][0]
pname, _, _, _ = iffile_info[(pnum, bank)]
polname.append(pname*2) # double the string, e.g. 'R' -> 'RR'
else:
for pnum in range(1, n_pols+1):
polname.append(str(pnum))
response = (project, scan, integration, polname, np.array(spectrum))
return response
else:
return None