def reduce(telescope, obskey, tstart, tend, nchan, ngate, ntbin, ntw_min,
rfi_filter_raw=None, fref=None, dedisperse=None,
do_waterfall=True, do_foldspec=True, verbose=True):
comm = MPI.COMM_WORLD
if verbose > 3 and comm.rank == 0:
print(telescope, obskey, tstart, tend, nchan, ngate, ntbin, ntw_min,
rfi_filter_raw, fref, dedisperse,
do_waterfall, do_foldspec, verbose)
if dedisperse is not None and fref is None:
raise ValueError("Need to give reference frequency to dedisperse to")
Obs = obsdata()
if verbose > 3 and comm.rank == 0:
print(Obs)
# find nearest observation to 'date',
# warning if > 1s off requested start time of observation
if obskey not in Obs[telescope]:
# assume it is a date, which may be only good to nearest second
obskey = Obs[telescope].nearest_observation(obskey)
# target of this observation
psr = Obs[telescope][obskey]['src']
assert psr in Obs['psrs'].keys()
dm = Obs['psrs'][psr]['dm']
files = Obs[telescope].file_list(obskey)
setup = Obs[telescope].get('setup', {})
setup.update(Obs[telescope][obskey].get('setup', {}))
for k, v in setup.iteritems():
if k in ('P', 'S', 'channels'):
setup[k] = [int(_v) for _v in v]
else:
setup[k] = eval(v)
if telescope == 'kairo':
raise ValueError("Kairo not yet set up!")
elif telescope == 'lofar':
GenericOpen = LOFARdata_Pcombined
elif telescope == 'gmrt':
GenericOpen = GMRTdata
elif telescope == 'aro':
GenericOpen = AROdata
elif telescope == 'arochime':
GenericOpen = AROCHIMEData
elif telescope == 'jbdada':
GenericOpen = DADAData
elif telescope == 'mark4':
GenericOpen = Mark4Data
if verbose and comm.rank == 0:
print("Attempting to open files {0}".format(files))
print("GenericOpen={0}\n setup={1}".format(GenericOpen, setup))
with GenericOpen(*files, comm=comm, **setup) as fh:
if verbose and comm.rank == 0:
print("Opened all files")
# nchan = None means data is channelized already, so we get this
# property directly from the file
if nchan is None:
nchan = fh.nchan
# ensure requested number of channels is integer multiple of
# existing channels
if nchan % getattr(fh, 'nchan', 1) != 0:
raise ValueError("Can only channelize data to an integer multiple "
"of the number of input channels (={0})."
.format(fh.nchan))
time0 = fh.time0
tstart = time0 if tstart is None else Time(tstart, scale='utc')
if tend is None:
tend = Obs[telescope][obskey]['tend']
try:
tend = Time(tend, scale='utc')
dt = tend - tstart
except ValueError:
dt = TimeDelta(float(tend), format='sec')
tend = tstart + dt
if verbose and comm.rank == 0:
print("Requested time span: {0} to {1}".format(tstart.isot,
tend.isot))
phasepol = Obs[telescope][obskey].get_phasepol(time0)
nt = fh.ntimebins(tstart, tend)
ntint = fh.ntint(nchan)
# number of samples to combine for waterfall
ntw = min(ntw_min, nt*ntint)
# number of records to skip
if verbose and comm.rank == 0:
print("Using start time {0} and phase polynomial {1}"
.format(time0.isot, phasepol))
fh.seek(tstart)
if verbose and comm.rank == 0:
print("Skipped {0} blocks and will fold {1} blocks to cover "
"time span {2} to {3}"
.format(fh.offset/fh.blocksize, nt, fh.time().isot,
fh.time(fh.offset + nt*fh.blocksize).isot))
# set the default parameters to fold
# Note, some parameters may be in fh's HDUs, or fh.__getitem__
# but these are overwritten if explicitly sprecified in Folder
folder = Folder(fh, nchan=nchan,
nt=nt, ntint=ntint, ngate=ngate,
ntbin=ntbin, ntw=ntw, dm=dm, fref=fref,
phasepol=phasepol, dedisperse=dedisperse,
do_waterfall=do_waterfall, do_foldspec=do_foldspec,
verbose=verbose, progress_interval=1,
rfi_filter_raw=rfi_filter_raw,
rfi_filter_power=None)
myfoldspec, myicount, mywaterfall = folder(fh, comm=comm)
# end with
print("Rank {0} exited with statement".format(comm.rank))
savepref = "{0}{1}_{2}chan{3}ntbin".format(telescope, psr, nchan, ntbin)
if do_waterfall:
waterfall = np.zeros_like(mywaterfall)
comm.Reduce(mywaterfall, waterfall, op=MPI.SUM, root=0)
if comm.rank == 0:
# waterfall = normalize_counts(waterfall)
np.save("{0}waterfall_{1}+{2:08}sec.npy"
.format(savepref, tstart.isot, dt.sec), waterfall)
if do_foldspec:
foldspec = np.zeros_like(myfoldspec) if comm.rank == 0 else None
print("Rank {0} is entering comm.Reduce".format(comm.rank))
comm.Reduce(myfoldspec, foldspec, op=MPI.SUM, root=0)
del myfoldspec # save memory on node 0
icount = np.zeros_like(myicount) if comm.rank == 0 else None
comm.Reduce(myicount, icount, op=MPI.SUM, root=0)
del myicount # save memory on node 0
if comm.rank == 0:
fname = ("{0}foldspec_{1}+{2:08}sec.npy")
np.save(fname.format(savepref, tstart.isot, dt.sec), foldspec)
iname = ("{0}icount_{1}+{2:08}sec.npy")
np.save(iname.format(savepref, tstart.isot, dt.sec), icount)
if comm.rank == 0:
if do_foldspec and foldspec.ndim == 3:
# sum over time slices -> pulse profile vs channel
foldspec1 = normalize_counts(foldspec.sum(0).astype(np.float64),
icount.sum(0).astype(np.int64))
# sum over channels -> pulse profile vs time
foldspec3 = normalize_counts(foldspec.sum(1).astype(np.float64),
icount.sum(1).astype(np.int64))
fluxes = foldspec1.sum(axis=0)
with open('{0}flux_{1}+{2:08}sec.dat'
.format(savepref, tstart.isot, dt.sec), 'w') as f:
for i, flux in enumerate(fluxes):
f.write('{0:12d} {1:12.9g}\n'.format(i+1, flux))
# ratio'd flux only if file will not be ridiculously large
if ntbin*ngate < 10000:
foldspec2 = normalize_counts(foldspec, icount)
plots = True
if plots and do_waterfall and waterfall.ndim == 2: # no polarizations
w = waterfall.copy()
pmap('{0}waterfall_{1}+{2:08}sec.pgm'
.format(savepref, tstart.isot, dt.sec), w, 1, verbose=True)
if plots and do_foldspec and foldspec.ndim == 3:
pmap('{0}folded_{1}+{2:08}sec.pgm'
.format(savepref, tstart.isot, dt.sec), foldspec1, 0, verbose)
pmap('{0}folded3_{1}+{2:08}sec.pgm'
.format(savepref, tstart.isot, dt.sec),
foldspec3.T, 0, verbose)
# ratio'd flux only if file will not be ridiculously large
if ntbin*ngate < 10000:
pmap('{0}foldedbin_{1}+{2:08}sec.pgm'
.format(savepref, tstart.isot, dt.sec),
foldspec2.transpose(1,2,0).reshape(nchan,-1), 1, verbose)
def main_correlate(tel1, date1, tel2, date2, nchan, tstart, tend, dedisperse,
do_foldspec, ntbin, ngate,
do_waterfall, ntw_min,
save_xcorr, verbose=0):
"""
the main cross-correlation routine
"""
comm = MPI.COMM_WORLD
if comm.size > 1 and save_xcorr:
if comm.rank == 0:
print("Warning, h5py mpio is sometimes slow. Consider disabling save_xcorr")
# save_xcorr = False
# observing parameters
t0 = Time(tstart, scale='utc')
t1 = Time(tend, scale='utc')
Obs = obsdata()
obskey1 = Obs[tel1].nearest_observation(date1)
obskey2 = Obs[tel2].nearest_observation(date2)
psr1 = Obs[tel1][obskey1]['src']
psr2 = Obs[tel2][obskey2]['src']
files1 = Obs[tel1].file_list(obskey1)
files2 = Obs[tel2].file_list(obskey2)
assert psr1 == psr2
if comm.rank == 0:
print("forming visibilities from (telescope, observation_key) = \n"
"\t ({0}, {1}) and ({2}, {3}), source {4}".format(tel1, obskey1, tel2, obskey2, psr1))
dm = Obs['psrs'][psr1]['dm']
with LOFARdata_Pcombined(*files1, comm=comm) as fh1,\
GMRTdata(*files2, comm=comm) as fh2:
phasepol1 = Obs['lofar'][obskey1].get_phasepol(fh1.time0, rphase=None)
phasepol2 = Obs['gmrt'][obskey2].get_phasepol(fh2.time0, rphase=None)
nt = min(fh1.ntimebins(t0, t1), fh2.ntimebins(t0, t1))
# out = (foldspec, icount, waterfall)
out = correlate.correlate(fh1, fh2, dm=dm, nchan=nchan, ngate=ngate,
ntbin=ntbin, nt=nt, ntw=ntw_min,
t0=t0, t1=t1, dedisperse=dedisperse,
phasepol=(phasepol1, phasepol2),
do_waterfall=do_waterfall,
do_foldspec=do_foldspec,
save_xcorr=save_xcorr,
comm=comm)
myfoldspec = out[0]
myicount = out[1]
mywaterfall = out[2]
savepref = "{0}{1}_{2}chan{3}ntbin".format(tel1[0], tel2[0], nchan, ntbin)
dt = t1 - t0
if do_waterfall:
waterfall = np.zeros_like(mywaterfall)
comm.Reduce(mywaterfall, waterfall, op=MPI.SUM, root=0)
if comm.rank == 0:
# waterfall = normalize_counts(waterfall)
np.save("{0}waterfall_{1}+{2:08}sec.npy"
.format(savepref, t0, dt.sec), waterfall)
if do_foldspec:
foldspec = np.zeros_like(myfoldspec)
icount = np.zeros_like(myicount)
comm.Reduce(myfoldspec, foldspec, op=MPI.SUM, root=0)
comm.Reduce(myicount, icount, op=MPI.SUM, root=0)
if comm.rank == 0:
fname = ("{0}foldspec_{1}+{2:08}sec.npy")
iname = ("{0}icount_{1}+{2:08}sec.npy")
np.save(fname.format(savepref, t0, dt.sec), foldspec)
np.save(iname.format(savepref, t0, dt.sec), icount)
# get normalized flux in each bin (where any were added)
f2 = normalize_counts(foldspec, icount)
foldspec1 = f2.sum(axis=2)
fluxes = foldspec1.sum(axis=0)
foldspec3 = f2.sum(axis=0)
with open('{0}flux_{1}+{2:08}sec.dat'
.format(savepref, t0, dt.sec), 'w') as f:
for i, flux in enumerate(fluxes):
f.write('{0:12d} {1:12.9g}\n'.format(i + 1, flux))
plots = True
if plots and comm.rank == 0:
if do_waterfall:
w = waterfall.copy()
try:
pmap('{0}waterfall_{1}+{2:08}sec.pgm'
.format(savepref, t0, dt.sec), w, 1, verbose=True)
except:
pass
if do_foldspec:
pmap('{0}folded_{1}+{2:08}sec.pgm'
.format(savepref, t0, dt.sec), foldspec1, 0, verbose)
# TODO: Note, I (aaron) don't think this works for LOFAR data
# since nchan=20, but we concatenate several subband files
# together, so f2.nchan = N_concat * nchan
# It should work for my "new" LOFAR_Pconcate file class
pmap('{0}foldedbin_{1}+{2:08}sec.pgm'
.format(savepref, t0, dt.sec),
f2.transpose(0, 2, 1).reshape(nchan, -1), 1, verbose)
pmap('{0}folded3_{1}+{2:08}sec.pgm'
.format(savepref, t0, dt.sec), foldspec3, 0, verbose)
def reduce(telescope,
obskey,
tstart,
tend,
nchan,
ngate,
ntbin,
ntw_min,
rfi_filter_raw=None,
fref=None,
dedisperse=None,
do_waterfall=True,
do_foldspec=True,
verbose=True):
comm = MPI.COMM_WORLD
if verbose > 3 and comm.rank == 0:
print(telescope, obskey, tstart, tend, nchan, ngate, ntbin, ntw_min,
rfi_filter_raw, fref, dedisperse, do_waterfall, do_foldspec,
verbose)
if dedisperse is not None and fref is None:
raise ValueError("Need to give reference frequency to dedisperse to")
Obs = obsdata()
# find nearest observation to 'date',
# warning if > 1s off requested start time of observation
if obskey not in Obs[telescope]:
# assume it is a date, which may be only good to nearest second
obskey = Obs[telescope].nearest_observation(obskey)
# target of this observation
psr = Obs[telescope][obskey]['src']
assert psr in Obs['psrs'].keys()
dm = Obs['psrs'][psr]['dm']
files = Obs[telescope].file_list(obskey)
setup = Obs[telescope].get('setup', {})
setup.update(Obs[telescope][obskey].get('setup', {}))
setup = {k: eval(v) for k, v in setup.iteritems()}
if telescope == 'kairo':
raise ValueError("Kairo not yet set up!")
elif telescope == 'lofar':
GenericOpen = LOFARdata_Pcombined
elif telescope == 'gmrt':
GenericOpen = GMRTdata
elif telescope == 'aro':
GenericOpen = AROdata
elif telescope == 'arochime':
GenericOpen = AROCHIMEData
if verbose and comm.rank == 0:
print("Attempting to open files {0}".format(files))
print("GenericOpen={0}\n setup={1}".format(GenericOpen, setup))
with GenericOpen(*files, comm=comm, **setup) as fh:
if verbose and comm.rank == 0:
print("Opened all files")
# nchan = None means data is channelized already, so we get this
# property directly from the file
if nchan is None:
nchan = fh.nchan
# ensure requested number of channels is integer multiple of
# existing channels
if nchan % getattr(fh, 'nchan', 1) != 0:
raise ValueError("Can only channelize data to an integer multiple "
"of the number of input channels (={0}).".format(
fh.nchan))
time0 = fh.time0
tstart = time0 if tstart is None else Time(tstart, scale='utc')
if tend is None:
tend = Obs[telescope][obskey]['tend']
try:
tend = Time(tend, scale='utc')
dt = tend - tstart
except ValueError:
dt = TimeDelta(float(tend), format='sec')
tend = tstart + dt
if verbose and comm.rank == 0:
print("Requested time span: {0} to {1}".format(
tstart.isot, tend.isot))
phasepol = Obs[telescope][obskey].get_phasepol(time0)
nt = fh.ntimebins(tstart, tend)
ntint = fh.ntint(nchan)
# number of samples to combine for waterfall
ntw = min(ntw_min, nt * ntint)
# number of records to skip
if verbose and comm.rank == 0:
print("Using start time {0} and phase polynomial {1}".format(
time0.isot, phasepol))
fh.seek(tstart)
if verbose and comm.rank == 0:
print("Skipped {0} blocks and will fold {1} blocks to cover "
"time span {2} to {3}".format(
fh.offset / fh.blocksize, nt,
fh.time().isot,
fh.time(fh.offset + nt * fh.blocksize).isot))
# set the default parameters to fold
# Note, some parameters may be in fh's HDUs, or fh.__getitem__
# but these are overwritten if explicitly sprecified in Folder
folder = Folder(fh,
nchan=nchan,
nt=nt,
ntint=ntint,
ngate=ngate,
ntbin=ntbin,
ntw=ntw,
dm=dm,
fref=fref,
phasepol=phasepol,
dedisperse=dedisperse,
do_waterfall=do_waterfall,
do_foldspec=do_foldspec,
verbose=verbose,
progress_interval=1,
rfi_filter_raw=rfi_filter_raw,
rfi_filter_power=None)
myfoldspec, myicount, mywaterfall = folder(fh, comm=comm)
# end with
savepref = "{0}{1}_{2}chan{3}ntbin".format(telescope, psr, nchan, ntbin)
if do_waterfall:
waterfall = np.zeros_like(mywaterfall)
comm.Reduce(mywaterfall, waterfall, op=MPI.SUM, root=0)
if comm.rank == 0:
# waterfall = normalize_counts(waterfall)
np.save(
"{0}waterfall_{1}+{2:08}sec.npy".format(
savepref, tstart.isot, dt.sec), waterfall)
if do_foldspec:
foldspec = np.zeros_like(myfoldspec) if comm.rank == 0 else None
comm.Reduce(myfoldspec, foldspec, op=MPI.SUM, root=0)
del myfoldspec # save memory on node 0
icount = np.zeros_like(myicount) if comm.rank == 0 else None
comm.Reduce(myicount, icount, op=MPI.SUM, root=0)
del myicount # save memory on node 0
if comm.rank == 0:
fname = ("{0}foldspec_{1}+{2:08}sec.npy")
np.save(fname.format(savepref, tstart.isot, dt.sec), foldspec)
iname = ("{0}icount_{1}+{2:08}sec.npy")
np.save(iname.format(savepref, tstart.isot, dt.sec), icount)
if comm.rank == 0:
if do_foldspec and foldspec.ndim == 3:
# sum over time slices -> pulse profile vs channel
foldspec1 = normalize_counts(
foldspec.sum(0).astype(np.float64),
icount.sum(0).astype(np.int64))
# sum over channels -> pulse profile vs time
foldspec3 = normalize_counts(
foldspec.sum(1).astype(np.float64),
icount.sum(1).astype(np.int64))
fluxes = foldspec1.sum(axis=0)
with open(
'{0}flux_{1}+{2:08}sec.dat'.format(savepref, tstart.isot,
dt.sec), 'w') as f:
for i, flux in enumerate(fluxes):
f.write('{0:12d} {1:12.9g}\n'.format(i + 1, flux))
# ratio'd flux only if file will not be ridiculously large
if ntbin * ngate < 10000:
foldspec2 = normalize_counts(foldspec, icount)
plots = True
if plots and do_waterfall and waterfall.ndim == 2: # no polarizations
w = waterfall.copy()
pmap('{0}waterfall_{1}+{2:08}sec.pgm'.format(
savepref, tstart.isot, dt.sec),
w,
1,
verbose=True)
if plots and do_foldspec and foldspec.ndim == 3:
pmap(
'{0}folded_{1}+{2:08}sec.pgm'.format(savepref, tstart.isot,
dt.sec), foldspec1, 0,
verbose)
pmap(
'{0}folded3_{1}+{2:08}sec.pgm'.format(savepref, tstart.isot,
dt.sec), foldspec3.T, 0,
verbose)
# ratio'd flux only if file will not be ridiculously large
if ntbin * ngate < 10000:
pmap(
'{0}foldedbin_{1}+{2:08}sec.pgm'.format(
savepref, tstart.isot, dt.sec),
foldspec2.transpose(1, 2, 0).reshape(nchan, -1), 1,
verbose)
def reduce(telescope,
obsdate,
tstart,
tend,
nchan,
ngate,
ntbin,
ntw_min=10200,
fref=_fref,
rfi_filter_raw=None,
do_waterfall=True,
do_foldspec=True,
dedisperse=None,
verbose=True):
comm = MPI.COMM_WORLD
Obs = obsdata()
# find nearest observation to 'date',
# warning if > 1s off requested start time of observation
obskey = Obs[telescope].nearest_observation(obsdate)
# target of this observation
psr = Obs[telescope][obskey]['src']
assert psr in Obs['psrs'].keys()
dm = Obs['psrs'][psr]['dm']
files = Obs[telescope].file_list(obskey)
if telescope == 'aro':
GenericOpen = AROdata
elif telescope == 'lofar':
GenericOpen = LOFARdata
GenericOpen = LOFARdata_Pcombined
elif telescope == 'gmrt':
GenericOpen = GMRTdata
with GenericOpen(*files, comm=comm) as fh:
# nchan = None means data is channelized already, so we get this
# property directly from the file
if nchan is None or telescope == 'lofar':
if comm.rank == 0:
print("LOFAR data already channelized: overriding nchan to {0}"
"\n\t as configured in observations.conf".format(
fh.nchan))
nchan = fh.nchan
time0 = fh.time0
phasepol = Obs[telescope][obskey].get_phasepol(time0)
nt = fh.ntimebins(tstart, tend)
ntint = fh.ntint(nchan)
# number of samples to combine for waterfall
ntw = min(ntw_min, nt * ntint)
# number of records to skip
tstart = Time(tstart, scale='utc')
tend = Time(tend, scale='utc')
dt = tend - tstart
fh.seek(tstart)
if verbose and comm.rank == 0:
print("Using start time {0} and phase polynomial {1}".format(
time0, phasepol))
print("Skipping {0} blocks and folding {1} blocks to cover "
"time span {2} to {3}".format(
fh.offset / fh.blocksize, nt, fh.time(),
fh.time(fh.offset + nt * fh.blocksize)))
# set the default parameters to fold
# Note, some parameters may be in fh's HDUs, or fh.__getitem__
# but these are overwritten if explicitly sprecified in Folder
folder = Folder(fh,
nchan=nchan,
nt=nt,
ntint=ntint,
ngate=ngate,
ntbin=ntbin,
ntw=ntw,
dm=dm,
fref=fref,
phasepol=phasepol,
dedisperse=dedisperse,
do_waterfall=do_waterfall,
do_foldspec=do_foldspec,
verbose=verbose,
progress_interval=1,
rfi_filter_raw=rfi_filter_raw,
rfi_filter_power=None)
myfoldspec, myicount, mywaterfall, subint_table = folder(fh, comm=comm)
# end with
savepref = "{0}{1}_{2}chan{3}ntbin".format(telescope, psr, nchan, ntbin)
if do_waterfall:
waterfall = np.zeros_like(mywaterfall)
comm.Reduce(mywaterfall, waterfall, op=MPI.SUM, root=0)
if comm.rank == 0:
# waterfall = normalize_counts(waterfall)
np.save(
"{0}waterfall_{1}+{2:08}sec.npy".format(
savepref, tstart, dt.sec), waterfall)
if do_foldspec:
foldspec = np.zeros_like(myfoldspec)
icount = np.zeros_like(myicount)
comm.Reduce(myfoldspec, foldspec, op=MPI.SUM, root=0)
comm.Reduce(myicount, icount, op=MPI.SUM, root=0)
if comm.rank == 0:
fname = ("{0}foldspec_{1}+{2:08}sec.npy")
iname = ("{0}icount_{1}+{2:08}sec.npy")
np.save(fname.format(savepref, tstart, dt.sec), foldspec)
np.save(iname.format(savepref, tstart, dt.sec), icount)
# get normalized flux in each bin (where any were added)
f2 = normalize_counts(foldspec, icount)
foldspec1 = f2.sum(axis=2)
fluxes = foldspec1.sum(axis=0)
foldspec3 = f2.sum(axis=0)
with open(
'{0}flux_{1}+{2:08}sec.dat'.format(savepref, tstart,
dt.sec), 'w') as f:
for i, flux in enumerate(fluxes):
f.write('{0:12d} {1:12.9g}\n'.format(i + 1, flux))
plots = True
if plots and comm.rank == 0:
if do_waterfall:
w = waterfall.copy()
pmap('{0}waterfall_{1}+{2:08}sec.pgm'.format(
savepref, tstart, dt.sec),
w,
1,
verbose=True)
if do_foldspec:
pmap(
'{0}folded_{1}+{2:08}sec.pgm'.format(savepref, tstart, dt.sec),
foldspec1, 0, verbose)
# TODO: Note, I (aaron) don't think this works for LOFAR data
# since nchan=20, but we concatenate several subband files
# together, so f2.nchan = N_concat * nchan
# It should work for my "new" LOFAR_Pconcate file class
pmap(
'{0}foldedbin_{1}+{2:08}sec.pgm'.format(
savepref, tstart, dt.sec),
f2.transpose(0, 2, 1).reshape(nchan, -1), 1, verbose)
pmap(
'{0}folded3_{1}+{2:08}sec.pgm'.format(savepref, tstart,
dt.sec), foldspec3, 0,
verbose)
savefits = False
if savefits and comm.rank == 0:
print("Saving FITS files...")
# assign the folded data ( [f2] = [nchan, ngate, ntbin]
# want [ntbin, npol, nchan, ngate]
# TODO: figure out lofar and aro data, which concatenates the channels
# so f2 = len(range(P))*nchan
if telescope == 'lofar':
f2 = f2[-nchan:]
elif telescope == 'aro':
f2 = f2[0:nchan]
nchan, ngate, ntbin = f2.shape
f2 = f2.transpose(2, 0, 1)
f2 = f2.reshape(ntbin, np.newaxis, nchan, ngate)
std = f2.std(axis=0)
subint_table[1].data.field('DATA')[:] = (2**16 * f2 / std).astype(
np.int16)
fout = '{0}folded3_{1}+{2:08}sec.fits'.format(savepref, tstart, dt.sec)
# Note: output_verify != 'ignore' resets the cards for some reason
try:
subint_table.writeto(fout, output_verify='ignore', clobber=True)
except ValueError:
print("FITS writings is a work in progress: "
"need to come to terms with array shapes")
print("f2.shape, subint_table[1].data.field('DATA')[:].shape = ",
f2.shape, subint_table[1].data.field('DATA')[:].shape)
def main_correlate(tel1,
date1,
tel2,
date2,
nchan,
tstart,
tend,
dedisperse,
do_foldspec,
ntbin,
ngate,
do_waterfall,
ntw_min,
save_xcorr,
verbose=0):
"""
the main cross-correlation routine
"""
comm = MPI.COMM_WORLD
if comm.size > 1 and save_xcorr:
if comm.rank == 0:
print(
"Warning, h5py mpio is sometimes slow. Consider disabling save_xcorr"
)
# save_xcorr = False
# observing parameters
t0 = Time(tstart, scale='utc')
t1 = Time(tend, scale='utc')
Obs = obsdata()
obskey1 = Obs[tel1].nearest_observation(date1)
obskey2 = Obs[tel2].nearest_observation(date2)
psr1 = Obs[tel1][obskey1]['src']
psr2 = Obs[tel2][obskey2]['src']
files1 = Obs[tel1].file_list(obskey1)
files2 = Obs[tel2].file_list(obskey2)
assert psr1 == psr2
if comm.rank == 0:
print("forming visibilities from (telescope, observation_key) = \n"
"\t ({0}, {1}) and ({2}, {3}), source {4}".format(
tel1, obskey1, tel2, obskey2, psr1))
dm = Obs['psrs'][psr1]['dm']
with LOFARdata_Pcombined(*files1, comm=comm) as fh1,\
GMRTdata(*files2, comm=comm) as fh2:
phasepol1 = Obs['lofar'][obskey1].get_phasepol(fh1.time0, rphase=None)
phasepol2 = Obs['gmrt'][obskey2].get_phasepol(fh2.time0, rphase=None)
nt = min(fh1.ntimebins(t0, t1), fh2.ntimebins(t0, t1))
# out = (foldspec, icount, waterfall)
out = correlate.correlate(fh1,
fh2,
dm=dm,
nchan=nchan,
ngate=ngate,
ntbin=ntbin,
nt=nt,
ntw=ntw_min,
t0=t0,
t1=t1,
dedisperse=dedisperse,
phasepol=(phasepol1, phasepol2),
do_waterfall=do_waterfall,
do_foldspec=do_foldspec,
save_xcorr=save_xcorr,
comm=comm)
myfoldspec = out[0]
myicount = out[1]
mywaterfall = out[2]
savepref = "{0}{1}_{2}chan{3}ntbin".format(tel1[0], tel2[0], nchan, ntbin)
dt = t1 - t0
if do_waterfall:
waterfall = np.zeros_like(mywaterfall)
comm.Reduce(mywaterfall, waterfall, op=MPI.SUM, root=0)
if comm.rank == 0:
# waterfall = normalize_counts(waterfall)
np.save(
"{0}waterfall_{1}+{2:08}sec.npy".format(savepref, t0, dt.sec),
waterfall)
if do_foldspec:
foldspec = np.zeros_like(myfoldspec)
icount = np.zeros_like(myicount)
comm.Reduce(myfoldspec, foldspec, op=MPI.SUM, root=0)
comm.Reduce(myicount, icount, op=MPI.SUM, root=0)
if comm.rank == 0:
fname = ("{0}foldspec_{1}+{2:08}sec.npy")
iname = ("{0}icount_{1}+{2:08}sec.npy")
np.save(fname.format(savepref, t0, dt.sec), foldspec)
np.save(iname.format(savepref, t0, dt.sec), icount)
# get normalized flux in each bin (where any were added)
f2 = normalize_counts(foldspec, icount)
foldspec1 = f2.sum(axis=2)
fluxes = foldspec1.sum(axis=0)
foldspec3 = f2.sum(axis=0)
with open('{0}flux_{1}+{2:08}sec.dat'.format(savepref, t0, dt.sec),
'w') as f:
for i, flux in enumerate(fluxes):
f.write('{0:12d} {1:12.9g}\n'.format(i + 1, flux))
plots = True
if plots and comm.rank == 0:
if do_waterfall:
w = waterfall.copy()
try:
pmap('{0}waterfall_{1}+{2:08}sec.pgm'.format(
savepref, t0, dt.sec),
w,
1,
verbose=True)
except:
pass
if do_foldspec:
pmap('{0}folded_{1}+{2:08}sec.pgm'.format(savepref, t0, dt.sec),
foldspec1, 0, verbose)
# TODO: Note, I (aaron) don't think this works for LOFAR data
# since nchan=20, but we concatenate several subband files
# together, so f2.nchan = N_concat * nchan
# It should work for my "new" LOFAR_Pconcate file class
pmap('{0}foldedbin_{1}+{2:08}sec.pgm'.format(savepref, t0, dt.sec),
f2.transpose(0, 2, 1).reshape(nchan, -1), 1, verbose)
pmap('{0}folded3_{1}+{2:08}sec.pgm'.format(savepref, t0, dt.sec),
foldspec3, 0, verbose)
