def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
labels = itensor['labels']
if labels[-1] != 'time' or labels[-2] != 'freq':
raise KeyError("Expected axes [..., 'freq', 'time'], got %s" %
labels)
nchan = itensor['shape'][-2]
f0_, df_ = itensor['scales'][-2]
t0_, dt_ = itensor['scales'][-1]
# Units must match self.kdm
f0 = convert_units(f0_, itensor['units'][-2], 'MHz')
df = convert_units(df_, itensor['units'][-2], 'MHz')
dt = convert_units(dt_, itensor['units'][-1], 's')
if self.max_mode == 'diagonal':
max_diagonal = self.max_value
self.max_mode = 'delay'
self.max_value = int(math.ceil(nchan * max_diagonal))
if self.max_mode == 'dm':
max_dm = self.max_value
rel_delay = (self.kdm / dt * max_dm * (f0**-2 -
(f0 + nchan * df)**-2))
self.max_delay = int(math.ceil(abs(rel_delay)))
elif self.max_mode == 'delay':
self.max_delay = self.max_value
fac = (f0**-2 - (f0 + nchan * df)**-2)
max_dm = self.max_delay * dt / (self.kdm * abs(fac))
else:
raise ValueError("Unknown max mode: %s" % self.max_mode)
if self.negative_delays:
max_dm = -max_dm
self.dm_step = max_dm / self.max_delay
self.fdmt.init(nchan, self.max_delay, f0, df, self.exponent,
self.space)
ohdr = deepcopy(ihdr)
if 'refdm' in ihdr:
refdm = convert_units(ihdr['refdm'], ihdr['refdm_units'],
self.dm_units)
else:
refdm = 0.
# Update transformed axis info
ohdr['_tensor']['dtype'] = 'f32'
ohdr['_tensor']['shape'][-2] = self.max_delay
ohdr['_tensor']['labels'][-2] = 'dispersion'
ohdr['_tensor']['scales'][-2] = (refdm, self.dm_step)
ohdr['_tensor']['units'][-2] = self.dm_units
# Add some new metadata
ohdr['max_dm'] = max_dm
ohdr['max_dm_units'] = self.dm_units
ohdr['cfreq'] = f0_ + 0.5 * (nchan - 1) * df_
ohdr['cfreq_units'] = itensor['units'][-2]
ohdr['bw'] = nchan * df_
ohdr['bw_units'] = itensor['units'][-2]
gulp_nframe = self.gulp_nframe or ihdr['gulp_nframe']
return ohdr
def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
# TODO: Assert that axis labels match expected (and/or allow more flexibility in which axes are used)
nchan = itensor['shape'][-2]
npol = itensor['shape'][-3]
f0_, df_ = itensor['scales'][-2]
t0_, dt_ = itensor['scales'][-1]
# Units must match self.kdm
f0 = convert_units(f0_, itensor['units'][-2], 'MHz')
df = convert_units(df_, itensor['units'][-2], 'MHz')
dt = convert_units(dt_, itensor['units'][-1], 's')
rel_delay = self.kdm / dt * self.max_dm * (f0**-2 -
(f0 + nchan * df)**-2)
self.max_delay = int(math.ceil(abs(rel_delay)))
self.dm_step = self.max_dm / self.max_delay
if self.negative_delays:
self.dm_step *= -1
self.fdmt.init(nchan, self.max_delay, f0, df, self.exponent,
self.space)
ohdr = deepcopy(ihdr)
if 'refdm' in ihdr:
refdm = convert_units(ihdr['refdm'], ihdr['refdm_units'],
self.dm_units)
else:
refdm = 0.
# Update transformed axis info
ohdr['_tensor']['dtype'] = 'f32'
ohdr['_tensor']['shape'][-2] = self.max_delay
ohdr['_tensor']['labels'][-2] = 'dispersion'
ohdr['_tensor']['scales'][-2] = (refdm, self.dm_step)
ohdr['_tensor']['units'][-2] = self.dm_units
# Add some new metadata
ohdr['max_dm'] = self.max_dm
ohdr['max_dm_units'] = self.dm_units
ohdr['cfreq'] = 0.5 * (f0_ + (nchan - 1) * df_)
ohdr['cfreq_units'] = itensor['units'][-2]
ohdr['bw'] = nchan * df_
ohdr['bw_units'] = itensor['units'][-2]
gulp_nframe = self.gulp_nframe or ihdr['gulp_nframe']
return ohdr, slice(0, gulp_nframe + self.max_delay, gulp_nframe)
def header_transform(hdr):
axis1 = merge_axes.axis1
axis2 = merge_axes.axis2
label = merge_axes.label
tensor = hdr['_tensor']
if isinstance(axis1, basestring):
axis1 = tensor['labels'].index(axis1)
if isinstance(axis2, basestring):
axis2 = tensor['labels'].index(axis2)
axis1, axis2 = sorted([axis1, axis2])
if axis2 != axis1 + 1:
raise ValueError("Merge axes must be adjacent")
n = tensor['shape'][axis2]
if n == -1:
# Axis2 is frame axis
raise ValueError("Second merge axis cannot be frame axis")
elif tensor['shape'][axis1] == -1:
# Axis1 is frame axis
tensor['gulp_nframe'] *= n
else:
# Neither axis is frame axis
tensor['shape'][axis1] *= n
del tensor['shape'][axis2]
if 'scales' in tensor and 'units' in tensor:
scale1 = tensor['scales'][axis1][1]
scale2 = tensor['scales'][axis2][1]
units1 = tensor['units'][axis1]
units2 = tensor['units'][axis2]
scale2 = convert_units(scale2, units2, units1)
if not isclose(scale1, n * scale2):
raise ValueError("Scales of merge axes do not line up: "
"%f != %f" % (scale1, n * scale2))
tensor['scales'][axis1][1] = scale2
del tensor['scales'][axis2]
if 'labels' in tensor:
if label is not None:
tensor['labels'][axis1] = label
del tensor['labels'][axis2]
return hdr
def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
axnames = tuple(itensor['labels'])
shape = itensor['shape']
scales = itensor['scales']
units = itensor['units']
ndim = len(shape)
dtype = DataType(itensor['dtype'])
nchan = shape[-1]
sample_time = convert_units(scales[-2][1], units[-2], 's')
sample_rate = int(round(1. / sample_time))
frame_nbyte = nchan * dtype.itemsize
ohdr = {
'audio_fmt': 1, # 1 => PCM (linear quantization, uncompressed)
'nchan': nchan,
'sample_rate': sample_rate,
'byte_rate': sample_rate * frame_nbyte,
'block_align': frame_nbyte,
'nbit': dtype.itemsize_bits
}
filename = os.path.join(self.path, ihdr['name'])
if ndim == 2 and axnames[-2] == 'time':
self.ofile = open(filename + '.wav', 'wb')
wav_write_header(self.ofile, ohdr)
elif ndim == 3 and axnames[-2] == 'time':
nfile = shape[-3]
filenames = [filename + '.%09i.tim' % i for i in range(nfile)]
self.ofiles = [open(fname + '.wav', 'wb') for fname in filenames]
for ofile in self.ofiles:
wav_write_header(ofile, ohdr)
else:
raise ValueError("Incompatible axes: " + str(axnames))
def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
axnames = list(itensor['labels'])
shape = list(itensor['shape'])
scales = list(itensor['scales'])
units = list(itensor['units'])
ndim = len(shape)
dtype = DataType(itensor['dtype'])
sigproc_hdr = {}
_copy_item_if_exists(sigproc_hdr, ihdr, 'source_name')
_copy_item_if_exists(sigproc_hdr, ihdr, 'rawdatafile')
_copy_item_if_exists(sigproc_hdr, ihdr, 'az_start')
_copy_item_if_exists(sigproc_hdr, ihdr, 'za_start')
_copy_item_if_exists(sigproc_hdr, ihdr, 'raj', 'src_raj')
_copy_item_if_exists(sigproc_hdr, ihdr, 'dej', 'src_dej')
if 'telescope' in ihdr:
sigproc_hdr['telescope_id'] = sigproc.telescope2id(
ihdr['telescope'])
if 'machine' in ihdr:
sigproc_hdr['machine_id'] = sigproc.machine2id(ihdr['machine'])
_copy_item_if_exists(sigproc_hdr, ihdr, 'telescope_id')
_copy_item_if_exists(sigproc_hdr, ihdr, 'machine_id')
_copy_item_if_exists(sigproc_hdr, ihdr, 'ibeam')
_copy_item_if_exists(sigproc_hdr, ihdr, 'nbeams')
sigproc_hdr['nbits'] = dtype.itemsize_bits
_copy_item_if_exists(sigproc_hdr, ihdr, 'barycentric')
_copy_item_if_exists(sigproc_hdr, ihdr, 'pulsarcentric')
if dtype.is_integer and dtype.is_signed:
sigproc_hdr['signed'] = True
if 'coord_frame' in ihdr:
coord_frame = ihdr['coord_frame']
else:
coord_frame = None
sigproc_hdr['pulsarcentric'] = (coord_frame == 'pulsarcentric')
sigproc_hdr['barycentric'] = (coord_frame == 'barycentric')
filename = os.path.join(self.path, ihdr['name'])
if ndim >= 3 and axnames[-3:] == ['time', 'pol', 'freq']:
self.data_format = 'filterbank'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 1
sigproc_hdr['nifs'] = shape[-2]
sigproc_hdr['nchans'] = shape[-1]
sigproc_hdr['tstart'] = _unix2mjd(scales[-3][0])
sigproc_hdr['tsamp'] = convert_units(scales[-3][1], units[-3], 's')
sigproc_hdr['fch1'] = convert_units(scales[-1][0], units[-1],
'MHz')
sigproc_hdr['foff'] = convert_units(scales[-1][1], units[-1],
'MHz')
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(ihdr['refdm'],
ihdr['refdm_units'],
'pc cm^-3')
if ndim == 3:
filename += '.fil'
self.ofile = open(filename, 'wb')
sigproc.write_header(sigproc_hdr, self.ofile)
elif ndim == 4:
if axnames[-4] != 'beam':
raise ValueError("Expected first axis to be 'beam'"
" got '%s'" % axnames[-4])
nbeam = shape[-4]
sigproc_hdr['nbeams'] = nbeam
filenames = [
filename + '.%06iof.%06i.fil' % (b + 1, nbeam)
for b in range(nbeam)
]
self.ofiles = [open(fname, 'wb') for fname in filenames]
for b in range(nbeam):
sigproc_hdr['ibeam'] = b
sigproc.write_header(sigproc_hdr, self.ofiles[b])
else:
raise ValueError("Too many dimensions")
elif ndim >= 2 and 'time' in axnames and 'pol' in axnames:
pol_axis = axnames.index('pol')
if pol_axis != ndim - 1:
# Need to move pol axis
# Note: We support this because it tends to be convenient
# for rest of the pipeline to operate with pol being
# the first dim, and doing the transpose on the fly
# inside this block is unlikely to cost much relative
# to disk perf (and it's free if npol==1).
axnames.append(axnames[pol_axis])
del axnames[pol_axis]
shape.append(shape[pol_axis])
del shape[pol_axis]
scales.append(scales[pol_axis])
del scales[pol_axis]
units.append(units[pol_axis])
del units[pol_axis]
self.pol_axis = pol_axis
self.data_format = 'timeseries'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 2
sigproc_hdr['nchans'] = 1
sigproc_hdr['nifs'] = shape[-2]
sigproc_hdr['tstart'] = _unix2mjd(scales[-2][0])
sigproc_hdr['tsamp'] = convert_units(scales[-2][1], units[-2], 's')
if 'cfreq' in ihdr and 'bw' in ihdr:
sigproc_hdr['fch1'] = convert_units(ihdr['cfreq'],
ihdr['cfreq_units'], 'MHz')
sigproc_hdr['foff'] = convert_units(ihdr['bw'],
ihdr['bw_units'], 'MHz')
# TODO: Write ndim separate output files, each with its own refdm
if ndim == 2:
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(
ihdr['refdm'], ihdr['refdm_units'], 'pc cm^-3')
filename += '.tim'
self.ofile = open(filename, 'wb')
sigproc.write_header(sigproc_hdr, self.ofile)
elif ndim == 3:
if axnames[-3] != 'dispersion':
raise ValueError("Expected first axis to be 'dispersion'"
" got '%s'" % axnames[-3])
ndm = shape[-3]
dm0 = scales[-3][0]
ddm = scales[-3][1]
dms = [dm0 + ddm * d for d in range(ndm)]
dms = [convert_units(dm, units[-3], 'pc cm^-3') for dm in dms]
filenames = [filename + '.%09.2f.tim' % dm for dm in dms]
self.ofiles = [open(fname, 'wb') for fname in filenames]
for d, dm in enumerate(dms):
sigproc_hdr['refdm'] = dm
sigproc.write_header(sigproc_hdr, self.ofiles[d])
else:
raise ValueError("Too many dimensions")
elif ndim == 4 and axnames[-3:] == ['pol', 'freq', 'phase']:
self.data_format = 'pulseprofile'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 2
sigproc_hdr['nifs'] = shape[-3]
sigproc_hdr['nchans'] = shape[-2]
sigproc_hdr['nbins'] = shape[-1]
sigproc_hdr['tstart'] = _unix2mjd(scales[-4][0])
sigproc_hdr['tsamp'] = convert_units(scales[-4][1], units[-4], 's')
sigproc_hdr['fch1'] = convert_units(scales[-2][0], units[-2],
'MHz')
sigproc_hdr['foff'] = convert_units(scales[-2][1], units[-2],
'MHz')
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(ihdr['refdm'],
ihdr['refdm_units'],
'pc cm^-3')
_copy_item_if_exists(sigproc_hdr, ihdr, 'npuls')
self.filename = filename
self.sigproc_hdr = sigproc_hdr
self.t0 = scales[-4][0]
self.dt = scales[-4][1]
else:
raise ValueError(
"Axis labels do not correspond to a known data format: " +
str(axnames) + "\nKnown formats are:" +
"\n [time, pol, freq]\n [beam, time, pol]\n" +
" [time, pol]\n [dispersion, time, pol]\n" +
" [pol, freq, phase]")
def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
axnames = tuple(itensor['labels'])
shape = itensor['shape']
scales = itensor['scales']
units = itensor['units']
ndim = len(shape)
dtype = DataType(itensor['dtype'])
sigproc_hdr = {}
copy_item_if_exists(sigproc_hdr, ihdr, 'source_name')
copy_item_if_exists(sigproc_hdr, ihdr, 'rawdatafile')
copy_item_if_exists(sigproc_hdr, ihdr, 'az_start')
copy_item_if_exists(sigproc_hdr, ihdr, 'za_start')
copy_item_if_exists(sigproc_hdr, ihdr, 'raj', 'src_raj')
copy_item_if_exists(sigproc_hdr, ihdr, 'dej', 'src_dej')
if 'telescope' in ihdr:
sigproc_hdr['telescope_id'] = sigproc.telescope2id(
ihdr['telescope'])
if 'machine' in ihdr:
sigproc_hdr['machine_id'] = sigproc.machine2id(ihdr['machine'])
copy_item_if_exists(sigproc_hdr, ihdr, 'telescope_id')
copy_item_if_exists(sigproc_hdr, ihdr, 'machine_id')
copy_item_if_exists(sigproc_hdr, ihdr, 'ibeam')
copy_item_if_exists(sigproc_hdr, ihdr, 'nbeams')
sigproc_hdr['nbits'] = dtype.itemsize_bits
copy_item_if_exists(sigproc_hdr, ihdr, 'barycentric')
copy_item_if_exists(sigproc_hdr, ihdr, 'pulsarcentric')
if dtype.is_integer and dtype.is_signed:
sigproc_hdr['signed'] = True
if 'coord_frame' in ihdr:
coord_frame = ihdr['coord_frame']
else:
coord_frame = None
sigproc_hdr['pulsarcentric'] = (coord_frame == 'pulsarcentric')
sigproc_hdr['barycentric'] = (coord_frame == 'barycentric')
filename = os.path.join(self.path, ihdr['name'])
if ndim >= 3 and axnames[-3:] == ('time', 'pol', 'freq'):
self.data_format = 'filterbank'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 1
sigproc_hdr['nifs'] = shape[-2]
sigproc_hdr['nchans'] = shape[-1]
sigproc_hdr['tstart'] = unix2mjd(scales[-3][0])
sigproc_hdr['tsamp'] = convert_units(scales[-3][1], units[-3], 's')
sigproc_hdr['fch1'] = convert_units(scales[-1][0], units[-1],
'MHz')
sigproc_hdr['foff'] = convert_units(scales[-1][1], units[-1],
'MHz')
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(ihdr['refdm'],
ihdr['refdm_units'],
'pc cm^-3')
if ndim == 3:
filename += '.fil'
self.ofile = open(filename, 'wb')
sigproc.write_header(sigproc_hdr, self.ofile)
elif ndim == 4:
if axnames[-4] != 'beam':
raise ValueError("Expected first axis to be 'beam'")
nbeam = shape[-4]
sigproc_hdr['nbeams'] = nbeam
filenames = [
filename + '.%06iof.%06i.fil' % (b + 1, nbeam)
for b in xrange(nbeam)
]
self.ofiles = [open(fname, 'wb') for fname in filenames]
for b in xrange(nbeam):
sigproc_hdr['ibeam'] = b
sigproc.write_header(sigproc_hdr, self.ofiles[b])
else:
raise ValueError("Too many dimensions")
elif ndim >= 2 and axnames[-2:] == ('time', 'pol'):
self.data_format = 'timeseries'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 2
sigproc_hdr['nchans'] = 1
sigproc_hdr['nifs'] = shape[-2]
sigproc_hdr['tstart'] = unix2mjd(scales[-2][0])
sigproc_hdr['tsamp'] = convert_units(scales[-2][1], units[-2], 's')
if 'cfreq' in ihdr and 'bw' in ihdr:
sigproc_hdr['fch1'] = convert_units(ihdr['cfreq'],
ihdr['cfreq_units'], 'MHz')
sigproc_hdr['foff'] = convert_units(ihdr['bw'],
ihdr['bw_units'], 'MHz')
# TODO: Write ndim separate output files, each with its own refdm
if ndim == 2:
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(
ihdr['refdm'], ihdr['refdm_units'], 'pc cm^-3')
filename += '.tim'
self.ofile = open(filename, 'wb')
sigproc.write_header(sigproc_hdr, self.ofile)
elif ndim == 3:
if axnames[-3] != 'dispersion measure':
raise ValueError(
"Expected first axis to be 'dispersion measure'")
ndm = shape[-3]
dm0 = scales[-3][0]
ddm = scales[-3][1]
dms = [dm0 + ddm * d for d in xrange(ndm)]
dms = [convert_units(dm, units[-3], 'pc cm^-3') for dm in dms]
filenames = [filename + '.%09.2f.tim' % dm for dm in dms]
self.ofiles = [open(fname, 'wb') for fname in filenames]
for d, dm in enumerate(dms):
sigproc_hdr['refdm'] = dm
sigproc.write_header(sigproc_hdr, self.ofiles[d])
else:
raise ValueError("Too many dimensions")
elif ndim == 4 and axnames[-3:] == ('pol', 'freq', 'phase'):
self.data_format = 'pulseprofile'
assert (dtype.is_real)
sigproc_hdr['data_type'] = 2
sigproc_hdr['nifs'] = shape[-3]
sigproc_hdr['nchans'] = shape[-2]
sigproc_hdr['nbins'] = shape[-1]
sigproc_hdr['tstart'] = unix2mjd(scales[-4][0])
sigproc_hdr['tsamp'] = convert_units(scales[-4][1], units[-4], 's')
sigproc_hdr['fch1'] = convert_units(scales[-2][0], units[-2],
'MHz')
sigproc_hdr['foff'] = convert_units(scales[-2][1], units[-2],
'MHz')
if 'refdm' in ihdr:
sigproc_hdr['refdm'] = convert_units(ihdr['refdm'],
ihdr['refdm_units'],
'pc cm^-3')
copy_item_if_exists(sigproc_hdr, ihdr, 'npuls')
self.filename = filename
self.sigproc_hdr = sigproc_hdr
self.t0 = scales[-4][0]
self.dt = scales[-4][1]
else:
raise ValueError(
"Axis labels do not correspond to a known data format: " +
str(axnames))
