def tigger_src(src, idx):
name = "SRC%d" % idx
flux = ModelClasses.Polarization(float(src["int_flux"]), 0, 0, 0,
I_err=float(src["err_int_flux"]))
ra, ra_err = map(numpy.deg2rad, (float(src["ra"]), float(src["err_ra"])))
dec, dec_err = map(numpy.deg2rad, (float(src["dec"]),
float(src["err_dec"])))
pos = ModelClasses.Position(ra, dec, ra_err=ra_err, dec_err=dec_err)
ex, ex_err = map(numpy.deg2rad, (float(src["a"]), float(src["err_a"])))
ey, ey_err = map(numpy.deg2rad, (float(src["b"]), float(src["err_b"])))
pa, pa_err = map(numpy.deg2rad, (float(src["pa"]), float(src["err_pa"])))
if ex and ey:
shape = ModelClasses.Gaussian(
ex, ey, pa, ex_err=ex_err, ey_err=ey_err, pa_err=pa_err)
else:
shape = None
source = SkyModel.Source(name, pos, flux, shape=shape)
# Adding source peak flux (error) as extra flux attributes for sources,
# and to avoid null values for point sources I_peak = src["Total_flux"]
if shape:
source.setAttribute("I_peak", float(src["peak_flux"]))
source.setAttribute("I_peak_err", float(src["err_peak_flux"]))
else:
source.setAttribute("I_peak", float(src["int_flux"]))
source.setAttribute("I_peak_err", float(src["err_int_flux"]))
return source
def tigger_src(src, idx):
name = "SRC%d" % idx
flux = ModelClasses.Polarization(src["Total_flux"],
0,
0,
0,
I_err=src["E_Total_flux"])
ra, ra_err = map(numpy.deg2rad, (src["RA"], src["E_RA"]))
dec, dec_err = map(numpy.deg2rad, (src["DEC"], src["E_DEC"]))
pos = ModelClasses.Position(ra, dec, ra_err=ra_err, dec_err=dec_err)
ex, ex_err = map(numpy.deg2rad, (src["DC_Maj"], src["E_DC_Maj"]))
ey, ey_err = map(numpy.deg2rad, (src["DC_Min"], src["E_DC_Min"]))
pa, pa_err = map(numpy.deg2rad, (src["PA"], src["E_PA"]))
if ex and ey:
shape = ModelClasses.Gaussian(ex,
ey,
pa,
ex_err=ex_err,
ey_err=ey_err,
pa_err=pa_err)
else:
shape = None
source = SkyModel.Source(name, pos, flux, shape=shape)
# Adding source peak flux (error) as extra flux attributes for sources,
# and to avoid null values for point sources I_peak = src["Total_flux"]
if shape:
source.setAttribute("I_peak", src["Peak_flux"])
source.setAttribute("I_peak_err", src["E_peak_flux"])
else:
source.setAttribute("I_peak", src["Total_flux"])
source.setAttribute("I_peak_err", src["E_Total_flux"])
if spi_do:
# Check if start frequency is provided if not provided raise error.
# It is used to define tigger source spectrum index frequency
if freq0:
spi, spi_err = (src['Spec_Indx'], src['E_Spec_Indx'])
source.spectrum = ModelClasses.SpectralIndex(spi, freq0)
source.setAttribute('spi_error', spi_err)
else:
raise RuntimeError("No start frequency (freq0) provided.")
return source
def tigger_src(src, idx):
name = "SRC%d" % idx
flux = ModelClasses.Polarization(src["f_int"], 0, 0, 0)
ra = numpy.deg2rad(src["ra"])
dec = numpy.deg2rad(src["dec"])
pos = ModelClasses.Position(ra, dec)
ex = numpy.deg2rad(src["ell_maj"])
ey = numpy.deg2rad(src["ell_min"])
pa = numpy.deg2rad(src["ell_pa"])
if ex and ey:
shape = ModelClasses.Gaussian(ex, ey, pa)
else:
shape = None
source = SkyModel.Source(name, pos, flux, shape=shape)
# Adding source peak flux (error) as extra flux attributes for sources,
# and to avoid null values for point sources I_peak = src["Total_flux"]
if shape:
source.setAttribute("I_peak", float(src["f_peak"]))
else:
source.setAttribute("I_peak", float(src["f_int"]))
return source
def load(filename, import_src=True, import_cc=True, min_extent=0, **kw):
"""Imports a NEWSTAR MDL file.
min_extent is minimal source extent (in radians), above which a source will be treated as a Gaussian rather than a point component.
import_src=False causes source components to be omitted
import_cc=False causes clean components to be omitted
"""
srclist = []
dprint(1, "importing NEWSTAR file", filename)
# build the LSM from a NewStar .MDL model file
# if only_cleancomp=True, only clean components are used to build the LSM
# if no_cleancomp=True, no clean components are used to build the LSM
ff = open(filename, mode="rb")
### read GFH and MDH headers -- 512 bytes
try:
gfh = numpy.fromfile(ff, dtype=numpy.uint8, count=512)
mdh = numpy.fromfile(ff, dtype=numpy.uint8, count=64)
# parse headers
ftype, fhlen, fver, crdate, crtime, rrdate, rrtime, rcount, nname = parseGFH(
gfh)
if ftype != ".MDL":
raise TypeError
maxlin, modptr, nsources, mtype, mepoch, ra0, dec0, freq0 = parseMDH(
mdh)
beam_const = 65 * 1e-9 * freq0
## temp dict to hold unique nodenames
unamedict = {}
### Models -- 56 bytes
for ii in range(0, nsources):
mdl = numpy.fromfile(ff, dtype=numpy.uint8, count=56)
### source parameters
sI, ll, mm, id, sQ, sU, sV, eX, eY, eP, SI, RM = struct.unpack(
'fffiffffffff', mdl[0:48])
### type bits
bit1, bit2 = struct.unpack('BB', mdl[52:54])
# convert fluxes
sI *= 0.005 # convert from WU to Jy (1WU=5mJy)
sQ *= sI
sU *= sI
sV *= sI
# Interpret bitflags 1: bit 0= extended; bit 1= Q|U|V <>0 and no longer used according to Wim
fl_ext = bit1 & 1
# Interpret bitflags 2: bit 0= clean component; bit 3= beamed
fl_cc = bit2 & 1
fl_beamed = bit2 & 8
### extended source params: in arcsec, so multiply by ???
if fl_ext:
## the procedure is NMOEXT in nscan/nmoext.for
if eP == 0 and eX == eY:
r0 = 0
else:
r0 = .5 * math.atan2(-eP, eY - eX)
r1 = math.sqrt(eP * eP + (eX - eY) * (eX - eY))
r2 = eX + eY
eX = 2 * math.sqrt(abs(0.5 * (r2 + r1)))
eY = 2 * math.sqrt(abs(0.5 * (r2 - r1)))
eP = r0
# NEWSTAR MDL lists might have same source twice if they are
# clean components, so make a unique name for them
bname = 'N' + str(id)
if bname in unamedict:
uniqname = bname + '_' + str(unamedict[bname])
unamedict[bname] += 1
else:
uniqname = bname
unamedict[bname] = 1
# compose source information
pos = ModelClasses.Position(*lm_ncp_to_radec(ra0, dec0, ll, mm))
flux = ModelClasses.PolarizationWithRM(sI, sQ, sU, sV, RM, freq0)
spectrum = ModelClasses.SpectralIndex(SI, freq0)
tags = {}
# work out beam gain and apparent flux
tags['_lm_ncp'] = (ll, mm)
tags['_newstar_r'] = tags['r'] = r = math.sqrt(ll * ll + mm * mm)
tags['newstar_beamgain'] = bg = max(
math.cos(beam_const * r)**6, .01)
tags['newstar_id'] = id
if fl_beamed:
tags['Iapp'] = sI * bg
tags['newstar_beamed'] = True
tags['flux_intrinsic'] = True
else:
tags['flux_apparent'] = True
# make some tags based on model flags
if fl_cc:
tags['newstar_cc'] = True
# make shape if extended
if fl_ext and max(eX, eY) >= min_extent:
shape = ModelClasses.Gaussian(eX, eY, eP)
else:
shape = None
# compute apparent flux
src = SkyModel.Source(uniqname,
pos,
flux,
shape=shape,
spectrum=spectrum,
**tags)
srclist.append(src)
except:
traceback.print_exc()
raise TypeError("%s does not appear to be a valid NEWSTAR MDL file" %
filename)
dprintf(2, "imported %d sources from file %s\n", len(srclist), filename)
return ModelClasses.SkyModel(ra0=ra0,
dec0=dec0,
freq0=freq0,
pbexp='max(cos(65*1e-9*fq*r)**6,.01)',
*srclist)
def load(filename, format=None, freq0=None, center_on_brightest=False, min_extent=0, verbose=0, **kw):
"""Imports an ASCII table
The 'format' argument can be either a dict (such as the DefaultDMSFormat dict above), or a string such as DefaultDMSFormatString.
(Other possible field names are "ra_d", "ra_rad", "dec_rad", "dec_sign".)
If None is specified, DefaultDMSFormat is used.
The 'freq0' argument supplies a default reference frequency (if one is not contained in the file.)
If 'center_on_brightest' is True, the mpodel field center will be set to the brightest source.
'min_extent' is minimal source extent (in radians), above which a source will be treated as a Gaussian rather than a point component.
"""
srclist = []
dprint(1, "importing ASCII DMS file", filename)
# brightest source and its coordinates
maxbright = 0
brightest_name = radec0 = None
# Get column number associated with field from format dict, as well as the error
# column number. Returns tuple of indices, with None index indicating no such column
def get_field(name):
return format.get(name, None), format.get(name + "_err", None)
# Get column number associated with field from format dict, as well as the error
# column number. Field is an angle thus will be suffixed with _{rad,d,h,m,s}.
# Returns tuple of
# column,scale,err_column,err_scale
# with None index indicating no such column. Scale is scaling factor to convert
# quantity in column to radians
def get_ang_field(name, units=ANGULAR_UNITS):
column = err_column = colunit = errunit = None
units = units or ANGULAR_UNITS
for unit, scale in units.items():
if column is None:
column = format.get("%s_%s" % (name, unit))
if column is not None:
colunit = scale
if err_column is None:
err_column = format.get("%s_err_%s" % (name, unit))
if err_column is not None:
errunit = scale
return column, colunit, err_column, errunit
# helper function: returns element #num from the fields list, multiplied by scale, or None if no such field
def getval(num, scale=1):
return None if (num is None or len(fields) <= num) else float(fields[num]) * scale
# now process file line-by-line
linenum = 0
format_str = ''
for line in open(filename):
# for the first line, figure out the file format
if not linenum:
if not format and line.startswith("#format:"):
format = line[len("#format:"):].strip()
dprint(1, "file contains format header:", format)
# set default format
if format is None:
format = DefaultDMSFormatString
# is the format a string rather than a dict? Turn it into a dict then
if isinstance(format, str):
format_str = format
# make list of fieldname,fieldnumber tuples
fields = [(field, i) for i, field in enumerate(format.split())]
if not fields:
raise ValueError("illegal format string in file: '%s'" % format)
# last fieldname can end with ... to indicate that it absorbs the rest of the line
if fields[-1][0].endswith('...'):
fields[-1] = (fields[-1][0][:-3], slice(fields[-1][1], None))
# make format dict
format = dict(fields)
elif not isinstance(format, dict):
raise TypeError("invalid 'format' argument of type %s" % (type(format)))
# nf = max(format.itervalues())+1
# fields = ['---']*nf
# for field,number in format.iteritems():
# fields[number] = field
# format_str = " ".join(fields)
# get list of custom attributes from format
custom_attrs = []
for name, col in format.items():
if name.startswith(":"):
m = re.match("^:(bool|int|float|complex|str):([\w]+)$", name)
if not m:
raise TypeError("invalid field specification '%s' in format string" % name)
custom_attrs.append((eval(m.group(1)), m.group(2), col))
# get minimum necessary fields from format
name_field = format.get('name', None)
# flux
i_field, i_err_field = get_field("i")
if i_field is None:
raise ValueError("ASCII format specification lacks mandatory flux field ('i')")
# main RA field
ra_field, ra_scale, ra_err_field, ra_err_scale = get_ang_field('ra', ANGULAR_UNITS_RA)
if ra_field is None:
raise ValueError("ASCII format specification lacks mandatory Right Ascension field ('ra_h', 'ra_d' or 'ra_rad')")
# main Dec field
dec_field, dec_scale, dec_err_field, dec_err_scale = get_ang_field('dec', ANGULAR_UNITS_DEC)
if dec_field is None:
raise ValueError("ASCII format specification lacks mandatory Declination field ('dec_d' or 'dec_rad')")
# polarization as QUV
quv_fields = [get_field(x) for x in ['q', 'u', 'v']]
# linear polarization as fraction and angle
polfrac_field = format.get('pol_frac', None)
if polfrac_field is not None:
polpa_field, polpa_scale = format.get('pol_pa_d', None), (math.pi / 180)
if not polpa_field is not None:
polpa_field, polpa_scale = format.get('pol_pa_rad', None), 1
# fields for extent parameters
extent_fields = [get_ang_field(x, ANGULAR_UNITS) for x in ('emaj', 'emin', 'pa')]
# all three must be present, else ignore
if any([x[0] is None for x in extent_fields]):
extent_fields = None
# fields for reference freq and RM and SpI
freq0_field = format.get('freq0', None)
rm_field, rm_err_field = get_field('rm')
spi_fields = [get_field('spi')] + [get_field('spi%d' % i) for i in range(2, 10)]
tags_slice = format.get('tags', None)
# now go on to process the line
linenum += 1
try:
# strip whitespace
line = line.strip()
dprintf(4, "%s:%d: read line '%s'\n", filename, linenum, line)
# skip empty or commented lines
if not line or line[0] == '#':
continue
# split (at whitespace) into fields
fields = line.split()
# get name
name = fields[name_field] if name_field is not None else str(len(srclist) + 1)
i = getval(i_field)
i_err = getval(i_err_field)
# get position: RA
ra = getval(ra_field)
ra_err = getval(ra_err_field, ra_scale)
if 'ra_m' in format:
ra += float(fields[format['ra_m']]) / 60.
if 'ra_s' in format:
ra += float(fields[format['ra_s']]) / 3600.
ra *= ra_scale
# position: Dec. Separate treatment of sign
dec = abs(getval(dec_field))
dec_err = getval(dec_err_field, dec_scale)
if 'dec_m' in format:
dec += float(fields[format['dec_m']]) / 60.
if 'dec_s' in format:
dec += float(fields[format['dec_s']]) / 3600.
if fields[format.get('dec_sign', dec_field)][0] == '-':
dec = -dec
dec *= dec_scale
# for up position object
pos = ModelClasses.Position(ra, dec, ra_err=ra_err, dec_err=dec_err)
# see if we have freq0
# Use explicitly provided reference frequency for this source if available
f0 = None
if freq0_field is not None:
try:
f0 = float(fields[freq0_field])
# If no default reference frequency for the model was supplied,
# initialise from first source with a reference frequency
if freq0 is None:
freq0 = f0
dprint(0, "Set default freq0 to %s "
"from source on line %s." % (f0, linenum))
except IndexError:
f0 = None
# Otherwise use default reference frequency (derived from args
# or first reference frequency found in source)
if f0 is None and freq0 is not None:
f0 = freq0
# see if we have Q/U/V
(q, q_err), (u, u_err), (v, v_err) = [(getval(x), getval(x_err)) for x, x_err in quv_fields]
if polfrac_field is not None:
pf = fields[polfrac_field]
pf = float(pf[:-1]) / 100 if pf.endswith("%") else float(pf)
ppa = float(fields[polpa_field]) * polpa_scale if polpa_field is not None else 0
q = i * pf * math.cos(2 * ppa)
u = i * pf * math.sin(2 * ppa)
v = 0
# see if we have RM as well. Create flux object (unpolarized, polarized, polarized w/RM)
rm, rm_err = getval(rm_field), getval(rm_err_field)
if q is None:
flux = ModelClasses.Polarization(i, 0, 0, 0, I_err=i_err)
elif f0 is None or rm is None:
flux = ModelClasses.Polarization(i, q, u, v, I_err=i_err, Q_err=q_err, U_err=u_err, V_err=v_err)
else:
flux = ModelClasses.PolarizationWithRM(i, q, u, v, rm, f0, I_err=i_err, Q_err=q_err, U_err=u_err,
V_err=v_err, rm_err=rm_err)
# see if we have a spectral index
if f0 is None:
spectrum = None
else:
spi = [getval(x) for x, xerr in spi_fields]
spi_err = [getval(xerr) for x, xerr in spi_fields]
dprint(4, name, "spi is", spi, "err is", spi_err)
# if any higher-order spectral terms are specified, include them here but trim off all trailing zeroes
while spi and not spi[-1]:
del spi[-1]
del spi_err[-1]
if not spi:
spectrum = None
elif len(spi) == 1:
spectrum = ModelClasses.SpectralIndex(spi[0], f0)
if spi_err[0] is not None:
spectrum.spi_err = spi_err[0]
else:
spectrum = ModelClasses.SpectralIndex(spi, f0)
if any([x is not None for x in spi_err]):
spectrum.spi_err = spi_err
# see if we have extent parameters
ex = ey = pa = 0
if extent_fields:
ex, ey, pa = [(getval(x[0], x[1]) or 0) for x in extent_fields]
extent_errors = [getval(x[2], x[3]) for x in extent_fields]
# form up shape object
if (ex or ey) and max(ex, ey) >= min_extent:
shape = ModelClasses.Gaussian(ex, ey, pa)
for ifield, field in enumerate(['ex', 'ey', 'pa']):
if extent_errors[ifield] is not None:
shape.setAttribute(field + "_err", extent_errors[ifield])
else:
shape = None
# get tags
tagdict = {}
if tags_slice:
try:
tags = fields[tags_slice]
except IndexError:
pass
for tagstr1 in tags:
for tagstr in tagstr1.split(","):
if tagstr[0] == "+":
tagname, value = tagstr[1:], True
elif tagstr[0] == "-":
tagname, value = tagstr[1:], False
elif "=" in tagstr:
tagname, value = tagstr.split("=", 1)
if value[0] in "'\"" and value[-1] in "'\"":
value = value[1:-1]
else:
try:
value = float(value)
except:
continue
else:
tagname, value = tagstr, True
tagdict[tagname] = value
# OK, now form up the source object
# now create a source object
dprint(3, name, ra, dec, i, q, u, v)
src = SkyModel.Source(name, pos, flux, shape=shape, spectrum=spectrum, **tagdict)
# get custom attributes
for type_, attr, column in custom_attrs:
if column is not None and len(fields) > column:
src.setAttribute(attr, type_(fields[column]))
# add to source list
srclist.append(src)
# check if it's the brightest
brightness = src.brightness()
if brightness > maxbright:
maxbright = brightness
brightest_name = src.name
radec0 = ra, dec
except:
dprintf(0, "%s:%d: %s, skipping\n", filename, linenum, str(sys.exc_info()[1]))
if verbose:
raise
dprintf(2, "imported %d sources from file %s\n", len(srclist), filename)
# create model
model = ModelClasses.SkyModel(*srclist)
if freq0 is not None:
model.setRefFreq(freq0)
# set model format
model.setAttribute("ASCII_Format", format_str)
# setup model center
if center_on_brightest and radec0:
dprintf(2, "brightest source is %s (%g Jy) at %f,%f\n", brightest_name, maxbright, *radec0)
model.setFieldCenter(*radec0)
# setup radial distances
projection = Coordinates.Projection.SinWCS(*model.fieldCenter())
for src in model.sources:
l, m = projection.lm(src.pos.ra, src.pos.dec)
src.setAttribute('r', math.sqrt(l * l + m * m))
return model
def params(self, modelfits):
# reads in source finder output
with pyfits.open(modelfits) as hdu:
data = hdu[1].data
tfile = tempfile.NamedTemporaryFile(suffix=".txt")
tfile.flush()
# writes a catalogue in a temporaty txt file
with open(tfile.name, "w") as std:
std.write("#format:name ra_rad dec_rad i emaj_r emin_r pa_r\n")
model = Tigger.load(tfile.name) # open a tmp. file
peak, total, area, loc, corr = [], [], [], [], []
for i in range(len(data)):
flux = data["Total_flux"][i]
dc_emaj, dc_emin = data["DC_Maj"][i], data["DC_Min"][i]
ra, dec = data["RA"][i], data["DEC"][i]
pa = data["DC_PA"][i]
name = "SRC%d" % i
peak_flux = data["Peak_flux"][i]
posrd = ModelClasses.Position(numpy.deg2rad(ra),
numpy.deg2rad(dec))
flux_I = ModelClasses.Polarization(flux, 0, 0, 0)
if dc_emaj == 0 and dc_emin == 0:
shape = None
else:
shape = ModelClasses.Gaussian(numpy.deg2rad(dc_emaj),
numpy.deg2rad(dc_emin),
numpy.deg2rad(pa))
srs = SkyModel.Source(name, posrd, flux_I, shape=shape)
# using convolved maj and min for reliability estimate
emaj, emin = data["Maj"][i], data["Min"][i]
# area: find ex and ey if are 0 assign beam size
if emaj or emin == 0:
srcarea = math.pi * (numpy.rad2deg(self.bmaj)) * pow(3600.0, 2) *\
(numpy.rad2deg(self.bmin))
if emaj and emin > 0:
srcarea = emaj * emin * math.pi * pow(3600.0, 2) # arcsecond
# only accepts sources with flux > 0 and not nan RA and DEC
# and local variance
pos = [self.wcs.wcs2pix(*(ra, dec))
][0] #positions from deg to pixel
with pyfits.open(self.negimage) as hdu:
negdata = utils.image_data(hdu[0].data)
if flux > 0 and peak_flux > 0 and not math.isnan(float(ra))\
and not math.isnan(float(dec)):
local = utils.compute_local_variance(negdata, pos,
self.locstep)
srs.setAttribute("local_variance", local)
if not math.isnan(float(local)) or local > 0:
if self.psfname:
pdata, psf = utils.compute_psf_correlation(
self.imagename, self.psfname, pos, self.cfstep)
if len(pdata) == len(psf):
c_region = numpy.corrcoef((pdata, psf))
cf = (numpy.diag((numpy.rot90(c_region))**
2).sum())**0.5 / 2**0.5
srs.setAttribute("correlation_factor", cf)
corr.append(cf)
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
else:
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
labels = dict(size=(0, "Log$_{10}$(Source area)"),
peak=(1, "Log$_{10}$( Peak flux [Jy] )"),
tot=(2, "Log$_{10}$( Total flux [Jy] )"))
if self.do_psf_corr:
labels.update({"coeff": (len(labels), "Log$_{10}$ (CF)")})
if self.do_local_var:
labels.update(
{"local": (len(labels), "Log$_{10}$(Local Variance)")})
if self.nearsources:
labels.update({"near": (len(labels), "Log$_{10}$(Near Sources)")})
nsrc = len(model.sources)
out = numpy.zeros([nsrc, len(labels)])
# returning parameters
for i, src in enumerate(model.sources):
ra, dec = src.pos.ra, src.pos.dec
near = model.getSourcesNear(ra, dec, 5 * self.bmaj)
nonear = len(near)
if self.nearsources:
src.setAttribute("neibours", nonear)
if self.do_psf_corr and self.do_local_var and self.nearsources:
out[i,
...] = area[i], peak[i], total[i], corr[i], loc[i], nonear
elif self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], corr[i], loc[i]
elif self.do_psf_corr and self.nearsources and not self.do_local_var:
out[i, ...] = area[i], peak[i], total[i], corr[i], nonear
elif not self.do_psf_corr and self.do_local_var and self.nearsources:
out[i, ...] = area[i], peak[i], total[i], loc[i], nonear
elif self.do_psf_corr and not self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], corr[i]
elif not self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], loc[i]
elif not self.do_psf_corr and not self.do_local_var and self.nearsources:
out[i, ...] = area[i], peak[i], total[i], nonear
else:
out[i, ...] = area[i], peak[i], total[i]
# removes the rows with 0s
removezeros = (out == 0).sum(1)
output = out[removezeros <= 0, :]
return model, numpy.log10(output), labels
def load(filename, freq0=None, center_on_brightest=False, **kw):
"""Imports an BBS catalog file
The 'format' argument can be either a dict (such as the DefaultDMSFormat dict above), or a string such as DefaultDMSFormatString.
(Other possible field names are "ra_d", "ra_rad", "dec_rad", "dec_sign".)
If None is specified, DefaultDMSFormat is used.
The 'freq0' argument supplies a default reference frequency (if one is not contained in the file.)
If 'center_on_brightest' is True, the mpodel field center will be set to the brightest source,
else to the center of the first patch.
"""
srclist = []
dprint(1, "importing BBS source table", filename)
# read file
ff = open(filename)
# first line must be a format string: extract it
line0 = ff.readline().strip()
match = re.match("#\s*\((.+)\)\s*=\s*format", line0)
if not match:
raise ValueError("line 1 is not a valid format specification")
format_str = match.group(1)
# create format parser from this string
parser = CatalogParser(format_str)
# check for mandatory fields
for field in "Name", "Type":
if not parser.defines(field):
raise ValueError("Table lacks mandatory field '%s'" % field)
maxbright = 0
patches = []
ref_freq = freq0
# now process file line-by-line
linenum = 1
for line in ff:
linenum += 1
try:
# parse one line
dprint(4, "read line:", line)
catline = parser.parse(line, linenum)
if not catline:
continue
dprint(5, "line %d: " % linenum, catline.__dict__)
# is it a patch record?
patchname = getattr(catline, 'Patch', '')
if not catline.Name:
dprintf(2, "%s:%d: patch %s\n", filename, linenum, patchname)
patches.append((patchname, catline.ra_rad, catline.dec_rad))
continue
# form up name
name = "%s:%s" % (patchname, catline.Name) if patchname else catline.Name
# check source type
stype = catline.Type.upper()
if stype not in ("POINT", "GAUSSIAN"):
raise ValueError("unsupported source type %s" % stype)
# see if we have freq0
if freq0:
f0 = freq0
elif hasattr(catline, 'ReferenceFrequency'):
f0 = float(catline.ReferenceFrequency or '0')
else:
f0 = None
# set model refrence frequency
if f0 is not None and ref_freq is None:
ref_freq = f0
# see if we have Q/U/V
i, q, u, v = [float(getattr(catline, stokes, '0') or '0') for stokes in "IQUV"]
# see if we have RM as well. Create flux object (unpolarized, polarized, polarized w/RM)
if f0 is not None and hasattr(catline, 'RotationMeasure'):
flux = ModelClasses.PolarizationWithRM(i, q, u, v, float(catline.RotationMeasure or '0'), f0)
else:
flux = ModelClasses.Polarization(i, q, u, v)
# see if we have a spectral index
if f0 is not None and hasattr(catline, 'SpectralIndex:0'):
spectrum = ModelClasses.SpectralIndex(float(getattr(catline, 'SpectralIndex:0') or '0'), f0)
else:
spectrum = None
# see if we have extent parameters
if stype == "GAUSSIAN":
ex = float(getattr(catline, "MajorAxis", "0") or "0")
ey = float(getattr(catline, "MinorAxis", "0") or "0")
pa = float(getattr(catline, "Orientation", "0") or "0")
shape = ModelClasses.Gaussian(ex, ey, pa)
else:
shape = None
# create tags
tags = {}
for field in "Patch", "Category":
if hasattr(catline, field):
tags['BBS_%s' % field] = getattr(catline, field)
# OK, now form up the source object
# position
pos = ModelClasses.Position(catline.ra_rad, catline.dec_rad)
# now create a source object
src = SkyModel.Source(name, pos, flux, shape=shape, spectrum=spectrum, **tags)
srclist.append(src)
# check if it's the brightest
brightness = src.brightness()
if brightness > maxbright:
maxbright = brightness
brightest_name = src.name
radec0 = catline.ra_rad, catline.dec_rad
except:
dprintf(0, "%s:%d: %s, skipping\n", filename, linenum, str(sys.exc_info()[1]))
dprintf(2, "imported %d sources from file %s\n", len(srclist), filename)
# create model
model = ModelClasses.SkyModel(*srclist)
if ref_freq is not None:
model.setRefFreq(ref_freq)
# setup model center
if center_on_brightest and radec0:
dprintf(2, "setting model centre to brightest source %s (%g Jy) at %f,%f\n", brightest_name, maxbright,
*radec0)
model.setFieldCenter(*radec0)
elif patches:
name, ra, dec = patches[0]
dprintf(2, "setting model centre to first patch %s at %f,%f\n", name, ra, dec)
model.setFieldCenter(ra, dec)
# map patches to model tags
model.setAttribute("BBS_Patches", patches)
model.setAttribute("BBS_Format", format_str)
# setup radial distances
projection = Coordinates.Projection.SinWCS(*model.fieldCenter())
for src in model.sources:
l, m = projection.lm(src.pos.ra, src.pos.dec)
src.setAttribute('r', math.sqrt(l * l + m * m))
return model
def load(filename,
format=None,
freq0=None,
center_on_brightest=False,
min_extent=0,
**kw):
"""Imports an ASCII table
The 'format' argument can be either a dict (such as the DefaultDMSFormat dict above), or a string such as DefaultDMSFormatString.
(Other possible field names are "ra_d", "ra_rad", "dec_rad", "dec_sign".)
If None is specified, DefaultDMSFormat is used.
The 'freq0' argument supplies a default reference frequency (if one is not contained in the file.)
If 'center_on_brightest' is True, the mpodel field center will be set to the brightest source.
'min_extent' is minimal source extent (in radians), above which a source will be treated as a Gaussian rather than a point component.
"""
srclist = []
dprint(1, "importing ASCII DMS file", filename)
# brightest source and its coordinates
maxbright = 0
brightest_name = radec0 = None
# now process file line-by-line
linenum = 0
format_str = ''
for line in file(filename):
# for the first line, firgure out the file format
if not linenum:
if not format and line.startswith("#format:"):
format = line[len("#format:"):].strip()
dprint(1, "file contains format header:", format)
# set default format
if format is None:
format = DefaultDMSFormatString
# is the format a string rather than a dict? Turn it into a dict then
if isinstance(format, str):
format_str = format
# make list of fieldname,fieldnumber tuples
fields = [(field, i) for i, field in enumerate(format.split())]
if not fields:
raise ValueError, "illegal format string in file: '%s'" % format
# last fieldname can end with ... to indicate that it absorbs the rest of the line
if fields[-1][0].endswith('...'):
fields[-1] = (fields[-1][0][:-3],
slice(fields[-1][1], None))
# make format dict
format = dict(fields)
elif not isinstance(format, dict):
raise TypeError, "invalid 'format' argument of type %s" % (
type(format))
nf = max(format.itervalues()) + 1
fields = ['---'] * nf
for field, number in format.iteritems():
fields[number] = field
format_str = " ".join(fields)
# get minimum necessary fields from format
name_field = format.get('name', None)
# flux
try:
i_field = format['i']
except KeyError:
raise ValueError, "ASCII format specification lacks mandatory flux field ('i')"
# main RA field
if 'ra_h' in format:
ra_field, ra_scale = format['ra_h'], (math.pi / 12)
elif 'ra_d' in format:
ra_field, ra_scale = format['ra_d'], (math.pi / 180)
elif 'ra_rad' in format:
ra_field, ra_scale = format['ra_rad'], 1.
else:
raise ValueError, "ASCII format specification lacks mandatory Right Ascension field ('ra_h', 'ra_d' or 'ra_rad')"
# main Dec field
if 'dec_d' in format:
dec_field, dec_scale = format['dec_d'], (math.pi / 180)
elif 'dec_rad' in format:
dec_field, dec_scale = format['dec_rad'], 1.
else:
raise ValueError, "ASCII format specification lacks mandatory Declination field ('dec_d' or 'dec_rad')"
# polarization as QUV
try:
quv_fields = [format[x] for x in ['q', 'u', 'v']]
except KeyError:
quv_fields = None
# linear polarization as fraction and angle
polfrac_field = format.get('pol_frac', None)
if polfrac_field is not None:
polpa_field, polpa_scale = format.get('pol_pa_d',
None), (math.pi / 180)
if not polpa_field is not None:
polpa_field, polpa_scale = format.get('pol_pa_rad',
None), 1
# fields for extent parameters
ext_fields = []
for ext in 'emaj', 'emin', 'pa':
for field, scale in (ext, 1.), (ext + "_rad",
1.), (ext + '_d',
DEG), (ext + '_m', DEG /
60), (ext + '_s',
DEG / 3600):
if field in format:
ext_fields.append((format[field], scale))
break
# if not all three accumulated, ignore
if len(ext_fields) != 3:
ext_fields = None
# fields for reference freq and RM and SpI
freq0_field = format.get('freq0', None)
rm_field = format.get('rm', None)
spi_field = format.get('spi', None)
spi2_field = [format.get('spi%d' % i, None) for i in range(2, 10)]
tags_slice = format.get('tags', None)
# now go on to process the line
linenum += 1
try:
# strip whitespace
line = line.strip()
dprintf(4, "%s:%d: read line '%s'\n", filename, linenum, line)
# skip empty or commented lines
if not line or line[0] == '#':
continue
# split (at whitespace) into fields
fields = line.split()
# get name
name = fields[name_field] if name_field is not None else str(
len(srclist) + 1)
i = float(fields[i_field])
# get position: RA
ra = float(fields[ra_field])
if 'ra_m' in format:
ra += float(fields[format['ra_m']]) / 60.
if 'ra_s' in format:
ra += float(fields[format['ra_s']]) / 3600.
ra *= ra_scale
# position: Dec. Separate treatment of sign
dec = abs(float(fields[dec_field]))
if 'dec_m' in format:
dec += float(fields[format['dec_m']]) / 60.
if 'dec_s' in format:
dec += float(fields[format['dec_s']]) / 3600.
if fields[format.get('dec_sign', dec_field)][0] == '-':
dec = -dec
dec *= dec_scale
# see if we have freq0
try:
f0 = freq0 or (freq0_field and float(fields[freq0_field]))
except IndexError:
f0 = None
# set model refrence frequency
if f0 is not None and freq0 is None:
freq0 = f0
# see if we have Q/U/V
q = u = v = None
if quv_fields:
try:
q, u, v = map(float, [fields[x] for x in quv_fields])
except IndexError:
pass
if polfrac_field is not None:
pf = fields[polfrac_field]
pf = float(pf[:-1]) / 100 if pf.endswith("%") else float(pf)
ppa = float(fields[polpa_field]
) * polpa_scale if polpa_field is not None else 0
q = i * pf * math.cos(2 * ppa)
u = i * pf * math.sin(2 * ppa)
v = 0
# see if we have RM as well. Create flux object (unpolarized, polarized, polarized w/RM)
if q is None:
flux = ModelClasses.Polarization(i, 0, 0, 0)
elif f0 is None or rm_field is None or rm_field >= len(fields):
flux = ModelClasses.Polarization(i, q, u, v)
else:
flux = ModelClasses.PolarizationWithRM(i, q, u, v,
float(fields[rm_field]),
f0)
# see if we have a spectral index
if f0 is None or spi_field is None or spi_field >= len(fields):
spectrum = None
else:
spi = [ float(fields[spi_field]) ] + \
[ (float(fields[x]) if x is not None else 0) for x in spi2_field ]
# if any higher-order spectral terms are specified, include them here
# but trim off all trailing zeroes
while len(spi) > 1 and not spi[-1]:
del spi[-1]
if len(spi) == 1:
spi = spi[0]
spectrum = ModelClasses.SpectralIndex(spi, f0)
# see if we have extent parameters
ex = ey = pa = 0
if ext_fields:
try:
ex, ey, pa = [
float(fields[num]) * scale for num, scale in ext_fields
]
except IndexError:
pass
# form up shape object
if (ex or ey) and max(ex, ey) >= min_extent:
shape = ModelClasses.Gaussian(ex, ey, pa)
else:
shape = None
# get tags
tagdict = {}
if tags_slice:
try:
tags = fields[tags_slice]
except IndexError:
pass
for tagstr1 in tags:
for tagstr in tagstr1.split(","):
if tagstr[0] == "+":
tagname, value = tagstr[1:], True
elif tagstr[0] == "-":
tagname, value = tagstr[1:], False
elif "=" in tagstr:
tagname, value = tagstr.split("=", 1)
if value[0] in "'\"" and value[-1] in "'\"":
value = value[1:-1]
else:
try:
value = float(value)
except:
continue
else:
tagname, value = tagstr, True
tagdict[tagname] = value
# OK, now form up the source object
# position
pos = ModelClasses.Position(ra, dec)
# now create a source object
dprint(3, name, ra, dec, i, q, u, v)
src = SkyModel.Source(name,
pos,
flux,
shape=shape,
spectrum=spectrum,
**tagdict)
srclist.append(src)
# check if it's the brightest
brightness = src.brightness()
if brightness > maxbright:
maxbright = brightness
brightest_name = src.name
radec0 = ra, dec
except:
dprintf(0, "%s:%d: %s, skipping\n", filename, linenum,
str(sys.exc_info()[1]))
dprintf(2, "imported %d sources from file %s\n", len(srclist), filename)
# create model
model = ModelClasses.SkyModel(*srclist)
if freq0 is not None:
model.setRefFreq(freq0)
# set model format
model.setAttribute("ASCII_Format", format_str)
# setup model center
if center_on_brightest and radec0:
dprintf(2, "brightest source is %s (%g Jy) at %f,%f\n", brightest_name,
maxbright, *radec0)
model.setFieldCenter(*radec0)
# setup radial distances
projection = Coordinates.Projection.SinWCS(*model.fieldCenter())
for src in model.sources:
l, m = projection.lm(src.pos.ra, src.pos.dec)
src.setAttribute('r', math.sqrt(l * l + m * m))
return model