def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
# our BDP's
# b1 = input BDP
# b1s = optional input CubeSpectrum
# b1m = optional input Moment
# b1p = optional input SourceList for positions
# b2 = output BDP
b1 = self._bdp_in[0] # check input SpwCube (or LineCube)
fin = b1.getimagefile(bt.CASA)
if self._bdp_in[0]._type == bt.LINECUBE_BDP:
use_vel = True
else:
use_vel = False
sources = self.getkey("sources")
pos = [
] # blank it first, then try and grab it from the optional bdp_in's
cmean = 0.0
csigma = 0.0
smax = [] # accumulate max in each spectrum for regression
self.spec_description = [] # for summary()
if self._bdp_in[1] != None: # check if CubeStats_BDP
#print "BDP[1] type: ",self._bdp_in[1]._type
if self._bdp_in[1]._type != bt.CUBESTATS_BDP:
raise Exception, "bdp_in[1] not a CubeStats_BDP, should never happen"
# a table (cubestats)
b1s = self._bdp_in[1]
pos.append(b1s.maxpos[0])
pos.append(b1s.maxpos[1])
logging.info('CubeStats::maxpos,val=%s,%f' %
(str(b1s.maxpos), b1s.maxval))
cmean = b1s.mean
csigma = b1s.sigma
dt.tag("CubeStats-pos")
if self._bdp_in[
2] != None: # check if Moment_BDP (probably from CubeSum)
#print "BDP[2] type: ",self._bdp_in[2]._type
if self._bdp_in[2]._type != bt.MOMENT_BDP:
raise Exception, "bdp_in[2] not a Moment_BDP, should never happen"
b1m = self._bdp_in[2]
fim = b1m.getimagefile(bt.CASA)
pos1, maxval = self.maxpos_im(
self.dir(fim)) # compute maxpos, since it is not in bdp (yet)
logging.info('CubeSum::maxpos,val=%s,%f' % (str(pos1), maxval))
pos.append(pos1[0])
pos.append(pos1[1])
dt.tag("Moment-pos")
if self._bdp_in[3] != None: # check if SourceList
#print "BDP[3] type: ",self._bdp_in[3]._type
# a table (SourceList)
b1p = self._bdp_in[3]
ra = b1p.table.getFullColumnByName("RA")
dec = b1p.table.getFullColumnByName("DEC")
peak = b1p.table.getFullColumnByName("Peak")
if sources == []:
# use the whole SourceList
for (r, d, p) in zip(ra, dec, peak):
rdc = convert_sexa(r, d)
pos.append(rdc[0])
pos.append(rdc[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(rdc), p))
else:
# select specific ones from the source list
for ipos in sources:
if ipos < len(ra):
radec = convert_sexa(ra[ipos], dec[ipos])
pos.append(radec[0])
pos.append(radec[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(radec), peak[ipos]))
else:
logging.warning('Skipping illegal source number %d' %
ipos)
dt.tag("SourceList-pos")
# if pos[] still blank, use the AT keyword.
if len(pos) == 0:
pos = self.getkey("pos")
# if still none, try the map center
if len(pos) == 0:
# @todo this could result in a masked pixel and cause further havoc
# @todo could also take the reference pixel, but that could be outside image
taskinit.ia.open(self.dir(fin))
s = taskinit.ia.summary()
pos = [int(s['shape'][0]) / 2, int(s['shape'][1]) / 2]
logging.warning(
"No input positions supplied, map center choosen: %s" %
str(pos))
dt.tag("map-center")
# exhausted all sources where pos[] can be set; if still zero, bail out
if len(pos) == 0:
raise Exception, "No positions found from input BDP's or pos="
# convert this regular list to a list of tuples with duplicates removed
# sadly the order is lost.
pos = list(set(zip(pos[0::2], pos[1::2])))
npos = len(pos)
dt.tag("open")
bdp_name = self.mkext(fin, "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
imval = range(npos) # spectra, one for each pos (placeholder)
planes = range(npos) # labels for the tables (placeholder)
images = {} # png's accumulated
for i in range(npos): # loop over pos, they can have mixed types now
sd = []
caption = "Spectrum"
xpos = pos[i][0]
ypos = pos[i][1]
if type(xpos) != type(ypos):
print "POS:", xpos, ypos
raise Exception, "position pair not of the same type"
if type(xpos) == int:
# for integers, boxes are allowed, even multiple
box = '%d,%d,%d,%d' % (xpos, ypos, xpos, ypos)
# convention for summary is (box)
cbox = '(%d,%d,%d,%d)' % (xpos, ypos, xpos, ypos)
# use extend here, not append, we want individual values in a list
sd.extend([xpos, ypos, cbox])
caption = "Average Spectrum at %s" % cbox
if False:
# this will fail on 3D cubes (see CAS-7648)
imval[i] = casa.imval(self.dir(fin), box=box)
else:
# work around that CAS-7648 bug
# another approach is the ia.getprofile(), see CubeStats, this will
# also integrate over regions, imval will not (!!!)
region = 'centerbox[[%dpix,%dpix],[1pix,1pix]]' % (xpos,
ypos)
caption = "Average Spectrum at %s" % region
imval[i] = casa.imval(self.dir(fin), region=region)
elif type(xpos) == str:
# this is tricky, to stay under 1 pixel , or you get a 2x2 back.
region = 'centerbox[[%s,%s],[1pix,1pix]]' % (xpos, ypos)
caption = "Average Spectrum at %s" % region
sd.extend([xpos, ypos, region])
imval[i] = casa.imval(self.dir(fin), region=region)
else:
print "Data type: ", type(xpos)
raise Exception, "Data type for region not handled"
dt.tag("imval")
flux = imval[i]['data']
if len(flux.shape
) > 1: # rare case if we step on a boundary between cells?
logging.warning(
"source %d has spectrum shape %s: averaging the spectra" %
(i, repr(flux.shape)))
flux = np.average(flux, axis=0)
logging.debug('minmax: %f %f %d' %
(flux.min(), flux.max(), len(flux)))
smax.append(flux.max())
if i == 0: # for first point record few extra things
if len(imval[i]['coords'].shape) == 2: # normal case: 1 pixel
freqs = imval[i]['coords'].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
elif len(imval[i]['coords'].shape
) == 3: # rare case if > 1 point in imval()
freqs = imval[i]['coords'][0].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
else:
logging.fatal(
"bad shape %s in freq return from imval - SHOULD NEVER HAPPEN"
% imval[i]['coords'].shape)
chans = np.arange(len(freqs)) # channels 0..nchans-1
unit = imval[i]['unit']
restfreq = casa.imhead(
self.dir(fin), mode="get",
hdkey="restfreq")['value'] / 1e9 # in GHz
dt.tag("imhead")
vel = (
1 - freqs / restfreq
) * utils.c # @todo : use a function (and what about relativistic?)
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel", "frequency", "flux"]
units = ["number", "GHz", unit]
data = (chans, freqs, flux)
if i == 0:
# plane 0 : we are allowing a multiplane table, so the first plane is special
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
# planes 1,2,3.... are stacked onto the previous one
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
if use_vel:
x = vel
xlab = 'VLSR (km/s)'
else:
x = chans
xlab = 'Channel'
y = [flux]
sd.append(xlab)
if type(xpos) == int:
# grab the RA/DEC... kludgy
h = casa.imstat(self.dir(fin), box=box)
ra = h['blcf'].split(',')[0]
dec = h['blcf'].split(',')[1]
title = '%s %d @ %d,%d = %s,%s' % (bdp_name, i, xpos, ypos, ra,
dec)
else:
title = '%s %d @ %s,%s' % (
bdp_name, i, xpos, ypos
) # or use box, once we allow non-points
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux (%s)' % unit
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x,
y,
title,
p1,
xlab=xlab,
ylab=ylab,
thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno, relative=True)
sd.extend([ii, thumbname, caption, fin])
self.spec_description.append(sd)
logging.regression("CSP: %s" % str(smax))
image = Image(images=images, description="CubeSpectrum")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", csigma) # TODO: not always available
b2.setkey("mean", cmean) # TODO: not always available
if True:
# @todo only first plane due to limitation in exportTable()
islash = bdp_name.find('/')
if islash < 0:
tabname = self.dir("testCubeSpectrum.tab")
else:
tabname = self.dir(bdp_name[:islash] + "/testCubeSpectrum.tab")
table.exportTable(tabname, cols=["frequency", "flux"])
dt.tag("done")
# For a single spectrum this is
# SummaryEntry([[data for spec1]], "CubeSpectrum_AT",taskid)
# For multiple spectra this is
# SummaryEntry([[data for spec1],[data for spec2],...], "CubeSpectrum_AT",taskid)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"CubeSpectrum_AT",
self.id(True))
taskargs = "pos=" + str(pos)
taskargs += ' <span style="background-color:white"> ' + fin.split(
'/')[0] + ' </span>'
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("Smooth")
dt.tag("start")
# get the input keys
bmaj = self.getkey("bmaj")
bmin = self.getkey("bmin")
bpa = self.getkey("bpa")
velres = self.getkey("velres")
# take care of potential issues in the unit strings
# @todo if not provided?
bmaj['unit'] = bmaj['unit'].lower()
bmin['unit'] = bmin['unit'].lower()
velres['unit'] = velres['unit'].lower()
taskargs = "bmaj=%s bmin=%s bpa=%s velres=%s" % (bmaj, bmin, bpa,
velres)
bdpnames = []
for ibdp in self._bdp_in:
istem = ibdp.getimagefile(bt.CASA)
image_in = ibdp.baseDir() + istem
bdp_name = self.mkext(istem, 'sim')
image_out = self.dir(bdp_name)
taskinit.ia.open(image_in)
h = casa.imhead(image_in, mode='list')
pix_scale = np.abs(h['cdelt1'] *
206265.0) # pix scale in asec @todo QA ?
CC = 299792458.0 # speed of light @todo somewhere else [utils.c , but in km/s]
rest_freq = h['crval3']
# frequency pixel scale in km/s
vel_scale = np.abs(CC * h['cdelt3'] / rest_freq / 1000.0)
# unit conversion to arcsec (spatial) or km/s
# (velocity) or some flavor of Hz.
if (bmaj['unit'] == 'pixel'):
bmaj = bmaj['value'] * pix_scale
else:
bmaj = bmaj['value']
if (bmin['unit'] == 'pixel'):
bmin = bmin['value'] * pix_scale
else:
bmin = bmin['value']
hertz_input = False
if velres['unit'] == 'pixel':
velres['value'] = velres['value'] * vel_scale
velres['unit'] = 'km/s'
elif velres['unit'] == 'm/s':
velres['value'] = velres['value'] / 1000.0
velres['unit'] = 'km/s'
elif velres['unit'][-2:] == 'hz':
hertz_input = True
elif velres['unit'] == 'km/s':
pass
else:
logging.error("Unknown units in velres=%s" % velres['unit'])
rdata = bmaj
# we smooth in velocity first. if smoothing in velocity
# the cube apparently must be closed afterwards and
# then reopened if spatial smoothing is to be done.
if velres['value'] > 0:
# handle the different units allowed. CASA doesn't
# like lowercase for hz units...
if not hertz_input:
freq_res = str(
velres['value'] * 1000.0 / CC * rest_freq) + 'Hz'
else:
freq_res = str(velres['value'])
# try to convert velres to km/s for debug purposes
velres['value'] = velres['value'] / rest_freq * CC / 1000.0
if (velres['unit'] == 'khz'):
velres['value'] = velres['value'] * 1000.0
velres['unit'] = 'kHz'
elif (velres['unit'] == 'mhz'):
velres['value'] = velres['value'] * 1E6
velres['unit'] = 'MHz'
elif (velres['unit'] == 'ghz'):
velres['value'] = velres['value'] * 1E9
velres['unit'] = 'GHz'
freq_res = freq_res + velres['unit']
# NB: there is apparently a bug in CASA. only smoothing along the frequency
# axis does not work. sepconvolve gives a unit error (says axis unit is radian rather
# than Hz). MUST smooth in 2+ dimensions if you want this to work.
if (velres['value'] < vel_scale):
raise Exception, "Desired velocity resolution %g less than pixel scale %g" % (
velres['value'], vel_scale)
image_tmp = self.dir('tmp.smooth')
im2=taskinit.ia.sepconvolve(outfile=image_tmp,axes=[0,1,2], types=["boxcar","boxcar","gauss"],\
widths=['1pix','1pix',freq_res], overwrite=True)
im2.done()
logging.debug("sepconvolve to %s" % image_out)
# for some reason, doing this in memory does not seem to work, so outfile must be specified.
logging.info(
"Smoothing cube to a velocity resolution of %s km/s" %
str(velres['value']))
logging.info("Smoothing cube to a frequency resolution of %s" %
freq_res)
taskinit.ia.close()
taskinit.ia.open(image_tmp)
dt.tag("sepconvolve")
else:
image_tmp = image_out
# now do the spatial smoothing
convolve_to_min_beam = True # default is to convolve to a min enclosing beam
if bmaj > 0 and bmin > 0:
# form qa objects out of these so that casa can understand
bmaj = taskinit.qa.quantity(bmaj, 'arcsec')
bmin = taskinit.qa.quantity(bmin, 'arcsec')
bpa = taskinit.qa.quantity(bpa, 'deg')
target_res = {}
target_res['major'] = bmaj
target_res['minor'] = bmin
target_res['positionangle'] = bpa
# throw an exception if cannot be convolved
try:
# for whatever reason, if you give convolve2d a beam parameter,
# it complains ...
im2=taskinit.ia.convolve2d(outfile=image_out,major = bmaj,\
minor = bmin, pa = bpa,\
targetres=True,overwrite=True)
im2.done()
logging.info(
"Smoothing cube to a resolution of %s by %s at a PA of %s"
% (str(bmaj['value']), str(
bmin['value']), str(bpa['value'])))
convolve_to_min_beam = False
achieved_res = target_res
except:
# @todo remind what you need ?
logging.error("Warning: Could not convolve to requested resolution of "\
+str(bmaj['value']) + " by " + str(bmin['value']) + \
" at a PA of "+ str(bpa['value']))
raise Exception, "Could not convolve to beam given!"
dt.tag("convolve2d-1")
if convolve_to_min_beam:
restoring_beams = taskinit.ia.restoringbeam()
commonbeam = taskinit.ia.commonbeam()
# for whatever reason, setrestoringbeam does not use the same set of hashes...
commonbeam['positionangle'] = commonbeam['pa']
del commonbeam['pa']
# if there's one beam, apparently the beams keyword does not exist
if 'beams' in restoring_beams:
print "Smoothing cube to a resolution of "+ \
str(commonbeam['major']['value']) +" by "+ \
str(commonbeam['minor']['value'])+" at a PA of "\
+str(commonbeam['pa']['value'])
target_res = commonbeam
im2=taskinit.ia.convolve2d(outfile=image_out,major=commonbeam['major'],\
minor=commonbeam['minor'],\
pa=commonbeam['positionangle'],\
targetres=True,overwrite=True)
im2.done()
achieved_res = commonbeam
dt.tag("convolve2d-2")
else:
print "One beam for all planes. Smoothing to common beam redundant."
achieved_res = commonbeam
if velres['value'] < 0:
taskinit.ia.fromimage(outfile=image_out,
infile=image_in)
# not really doing anything
# else, we've already done what we needed to
taskinit.ia.setrestoringbeam(beam=achieved_res)
rdata = achieved_res['major']['value']
# else do no smoothing and just close the image
taskinit.ia.close()
dt.tag("close")
b1 = SpwCube_BDP(bdp_name)
self.addoutput(b1)
# need to update for multiple images.
b1.setkey("image", Image(images={bt.CASA: bdp_name}))
bdpnames = bdpnames.append(bdp_name)
# and clean up the temp image before the next image
if velres['value'] > 0:
utils.remove(image_tmp)
# thes are task arguments not summary entries.
_bmaj = taskinit.qa.convert(achieved_res['major'], 'rad')['value']
_bmin = taskinit.qa.convert(achieved_res['minor'], 'rad')['value']
_bpa = taskinit.qa.convert(achieved_res['positionangle'],
'deg')['value']
vres = "%.2f %s" % (velres['value'], velres['unit'])
logging.regression("SMOOTH: %f %f" % (rdata, velres['value']))
self._summary["smooth"] = SummaryEntry(
[bdp_name, convolve_to_min_beam, _bmaj, _bmin, _bpa, vres],
"Smooth_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP.
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("ContinuumSub") # tagging time
self._summary = {} # an ADMIT summary will be created here
contsub = self.getkey("contsub")
pad = self.getkey("pad")
fitorder = self.getkey("fitorder")
# x.im -> x.cim + x.lim
# b1 = input spw BDP
# b1a = optional input {Segment,Line}List
# b1b = optional input Cont Map (now deprecated)
# b2 = output line cube
# b3 = output cont map
b1 = self._bdp_in[0]
f1 = b1.getimagefile(bt.CASA)
b1a = self._bdp_in[1]
# b1b = self._bdp_in[2]
b1b = None # do not allow continuum maps to be input
f2 = self.mkext(f1, 'lim')
f3 = self.mkext(f1, 'cim')
f3a = self.mkext(f1, 'cim3d') # temporary cube name, map is needed
b2 = SpwCube_BDP(f2)
b3 = Image_BDP(f3)
self.addoutput(b2)
self.addoutput(b3)
taskinit.ia.open(self.dir(f1))
s = taskinit.ia.summary()
nchan = s['shape'][
2] # ingest has guarenteed this to the spectral axis
if b1a != None: # if a LineList was given, use that
if len(b1a.table) > 0:
# this section of code actually works for len(ch0)==0 as well
#
ch0 = b1a.table.getFullColumnByName("startchan")
ch1 = b1a.table.getFullColumnByName("endchan")
if pad != 0: # can widen or narrow the segments
if pad > 0:
logging.info("pad=%d to widen the segments" % pad)
else:
logging.info("pad=%d to narrow the segments" % pad)
ch0 = np.where(ch0 - pad < 0, 0, ch0 - pad)
ch1 = np.where(ch1 + pad >= nchan, nchan - 1, ch1 + pad)
s = Segments(ch0, ch1, nchan=nchan)
ch = s.getchannels(
True) # take the complement of lines as the continuum
else:
ch = range(
nchan
) # no lines? take everything as continuum (probably bad)
logging.warning(
"All channels taken as continuum. Are you sure?")
elif len(contsub) > 0: # else if contsub[] was supplied manually
s = Segments(contsub, nchan=nchan)
ch = s.getchannels()
else:
raise Exception, "No contsub= or input LineList given"
if len(ch) > 0:
taskinit.ia.open(self.dir(f1))
taskinit.ia.continuumsub(outline=self.dir(f2),
outcont=self.dir(f3a),
channels=ch,
fitorder=fitorder)
taskinit.ia.close()
dt.tag("continuumsub")
casa.immoments(
self.dir(f3a), -1,
outfile=self.dir(f3)) # mean of the continuum cube (f3a)
utils.remove(self.dir(f3a)) # is the continuum map (f3)
dt.tag("immoments")
if b1b != None:
# this option is now deprecated (see above, by setting b1b = None), no user option allowed
# there is likely a mis-match in the beam, given how they are produced. So it's safer to
# remove this here, and force the flow to smooth manually
print "Adding back in a continuum map"
f1b = b1b.getimagefile(bt.CASA)
f1c = self.mkext(f1, 'sum')
# @todo notice we are not checking for conforming mapsize and WCS
# and let CASA fail out if we've been bad.
casa.immath([self.dir(f3), self.dir(f1b)], 'evalexpr',
self.dir(f1c), 'IM0+IM1')
utils.rename(self.dir(f1c), self.dir(f3))
dt.tag("immath")
else:
raise Exception, "No channels left to determine continuum. pad=%d too large?" % pad
# regression
rdata = casautil.getdata(self.dir(f3)).data
logging.regression("CSUB: %f %f" % (rdata.min(), rdata.max()))
# Create two output images for html and their thumbnails, too
implot = ImPlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
implot.plotter(rasterfile=f3, figname=f3, colorwedge=True)
figname = implot.getFigure(figno=implot.figno, relative=True)
thumbname = implot.getThumbnail(figno=implot.figno, relative=True)
b2.setkey("image", Image(images={bt.CASA: f2}))
b3.setkey("image", Image(images={bt.CASA: f3, bt.PNG: figname}))
dt.tag("implot")
if len(ch) > 0:
taskargs = "pad=%d fitorder=%d contsub=%s" % (pad, fitorder,
str(contsub))
imcaption = "Continuum map"
self._summary["continuumsub"] = SummaryEntry(
[figname, thumbname, imcaption], "ContinuumSub_AT",
self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
seed = self.getkey("seed")
if seed <= 0:
np.random.seed()
else:
np.random.seed(seed)
#print "RANDOM.GET_STATE:",np.random.get_state()
contin = self.getkey("contin")
rms = 1.0 # not a user parameter, we do all spectra in S/N space
f0 = self.getkey("freq") # central frequency in band
df = self.getkey("delta") / 1000.0 # channel width (in GHz)
nspectra = self.getkey("nspectra")
taskargs = " contin=%f freq=%f delta=%f nspectra=%f " % (contin, f0,
df, nspectra)
spec = range(nspectra)
dt.tag("start")
if self.getkey("file") != "":
print "READING spectrum from", self.getkey("file")
(freq, spec[0]) = getspec(self.getkey("file"))
nchan = len(freq)
print "Spectrum %d chans from %f to %f: min/max = %f %f" % (
nchan, freq.min(), freq.max(), spec[0].min(), spec[0].max())
# @todo nspectra>1 not tested
for i in range(1, nspectra):
spec[i] = deepcopy(spec[0])
dt.tag("getspec")
else:
nchan = self.getkey("nchan")
freq = np.arange(nchan, dtype=np.float64)
center = int(nchan / 2)
for i in range(nchan):
freq[i] = f0 + (float((i - center)) * df)
for i in range(nspectra):
spec[i] = np.zeros(nchan)
chans = np.arange(nchan)
taskargs += " nchan = %d" % nchan
for i in range(nspectra):
if seed >= 0:
spec[i] += np.random.normal(contin, rms, nchan)
# print "MEAN/STD",spec[i].mean(),spec[i].std()
lines = self.getkey("lines")
sls = SpectralLineSearch(False)
for item in self.getkey("transitions"):
kw = {
"include_only_nrao": True,
"line_strengths": ["ls1", "ls2"],
"energy_levels": ["el2", "el4"],
"fel": True,
"species": item[0]
}
results = sls.search(item[1][0], item[1][1], "off", **kw)
# look at line strengths
if len(results) > 0:
mx = 0.0
indx = -1
for i in range(len(results)):
if results[i].getkey("linestrength") > mx:
indx = i
mx = results[i].getkey("linestrength")
for res in results:
if mx > 0.0:
lines.append([
item[2] * res.getkey("linestrength") / mx,
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
else:
lines.append([
item[2],
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
for item in lines:
for i in range(nspectra):
spec[i] += utils.gaussian1D(freq, item[0], item[1],
utils.veltofreq(item[2], item[1]))
if self.getkey("hanning"):
for i in range(nspectra):
filter = Filter1D.Filter1D(spec[i], "hanning", **{"width": 3})
spec[i] = filter.run()
dt.tag("hanning")
center = int(nchan / 2)
dt.tag("open")
bdp_name = self.mkext("Genspec", "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
images = {} # png's accumulated
for i in range(nspectra):
sd = []
caption = "Generated Spectrum %d" % i
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel", "frequency", "flux"]
units = ["number", "GHz", ""]
data = (chans, freq, spec[i])
# plane 0 : we are allowing a multiplane table, so the first plane is special
if i == 0:
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
x = chans
xlab = 'Channel'
y = [spec[i]]
sd.append(xlab)
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux'
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x, y, "", p1, xlab=xlab, ylab=ylab, thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno, relative=True)
image = Image(images=images, description="CubeSpectrum")
sd.extend([ii, thumbname, caption])
self.spec_description.append(sd)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"GenerateSpectrum_AT",
self.id(True), taskargs)
dt.tag("table")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", rms)
b2.setkey("mean", contin)
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("SFind2D") # tagging time
self._summary = {}
# get key words that user input
nsigma = self.getkey("numsigma")
sigma = self.getkey("sigma")
region = self.getkey("region")
robust = self.getkey("robust")
snmax = self.getkey("snmax")
nmax = self.getkey("nmax")
ds9 = True # writes a "ds9.reg" file
mpl = True # aplot.map1() plot
dynlog = 20.0 # above this value of dyn range finder chart is log I-scaled
bpatch = True # patch units to Jy/beam for ia.findsources()
# get the input casa image from bdp[0]
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
if mpl:
data = np.flipud(np.rot90(casautil.getdata(self.dir(infile)).data))
# check if there is a 2nd image (which will be a PB)
for i in range(len(self._bdp_in)):
print 'BDP', i, type(self._bdp_in[i])
if self._bdp_in[2] != None:
bdpin_pb = self._bdp_in[1]
bdpin_cst = self._bdp_in[2]
print "Need to process PB"
else:
bdpin_pb = None
bdpin_cst = self._bdp_in[1]
print "No PB given"
# get the output bdp basename
slbase = self.mkext(infile, 'sl')
# make sure it's a 2D map
if not casautil.mapdim(self.dir(infile), 2):
raise Exception, "Input map dimension not 2: %s" % infile
# arguments for imstat call if required
args = {"imagename": self.dir(infile)}
if region != "":
args["region"] = region
dt.tag("start")
# The following code sets the sigma level for searching for sources using
# the sigma and snmax keyword as appropriate
# if no CubeStats BDP was given and no sigma was specified:
# find a noise level via casa.imstat()
# if a CubeStat_BDP is given get it from there.
if bdpin_cst == None:
# get statistics from input image with imstat because no CubeStat_BDP
stat = casa.imstat(**args)
dmin = float(
stat["min"]
[0]) # these would be wrong if robust were used already
dmax = float(stat["max"][0])
args.update(casautil.parse_robust(
robust)) # only now add robust keywords for the sigma
stat = casa.imstat(**args)
if sigma <= 0.0:
sigma = float(stat["sigma"][0])
dt.tag("imstat")
else:
# get statistics from CubeStat_BDP
sigma = bdpin_cst.get("sigma")
dmin = bdpin_cst.get("minval")
dmax = bdpin_cst.get("maxval")
self.setkey("sigma", sigma)
# calculate cutoff based either on RMS or dynamic range limitation
drange = dmax / (nsigma * sigma)
if snmax < 0.0:
snmax = drange
if drange > snmax:
cutoff = 1.0 / snmax
else:
cutoff = 1.0 / drange
logging.info("sigma, dmin, dmax, snmax, cutoff %g %g %g %g %g" %
(sigma, dmin, dmax, snmax, cutoff))
# define arguments for call to findsources
args2 = {"cutoff": cutoff}
args2["nmax"] = nmax
if region != "":
args2["region"] = region
#args2["mask"] = ""
args2["point"] = False
args2["width"] = 5
args2["negfind"] = False
# set-up for SourceList_BDP
slbdp = SourceList_BDP(slbase)
# connect to casa image and call casa ia.findsources tool
ia = taskinit.iatool()
ia.open(self.dir(infile))
# findsources() cannot deal with 'Jy/beam.km/s' ???
# so for the duration of findsources() we patch it
bunit = ia.brightnessunit()
if bpatch and bunit != 'Jy/beam':
logging.warning(
"Temporarely patching your %s units to Jy/beam for ia.findsources()"
% bunit)
ia.setbrightnessunit('Jy/beam')
else:
bpatch = False
atab = ia.findsources(**args2)
if bpatch:
ia.setbrightnessunit(bunit)
taskargs = "nsigma=%4.1f sigma=%g region=%s robust=%s snmax=%5.1f nmax=%d" % (
nsigma, sigma, str(region), str(robust), snmax, nmax)
dt.tag("findsources")
nsources = atab["nelements"]
xtab = []
ytab = []
logscale = False
sumflux = 0.0
if nsources > 0:
# @TODO: Why are Xpix, YPix not stored in the table?
# -> PJT: I left them out since they are connected to an image which may not be available here
# but we should store the frequency of the observation here for later bandmerging
logging.debug("%s" % str(atab['component0']['shape']))
logging.info(
"Right Ascen. Declination X(pix) Y(pix) Peak Flux Major Minor PA SNR"
)
funits = atab['component0']['flux']['unit']
if atab['component0']['shape'].has_key('majoraxis'):
sunits = atab['component0']['shape']['majoraxis']['unit']
aunits = atab['component0']['shape']['positionangle']['unit']
else:
sunits = "n/a"
aunits = "n/a"
punits = ia.summary()['unit']
logging.info(
" %s %s %s %s %s"
% (punits, funits, sunits, sunits, aunits))
#
# @todo future improvement is to look at image coordinates and control output appropriately
#
if ds9:
# @todo variable name
regname = self.mkext(infile, 'ds9.reg')
fp9 = open(self.dir(regname), "w!")
sn0 = -1.0
for i in range(nsources):
c = "component%d" % i
name = "%d" % (i + 1)
r = atab[c]['shape']['direction']['m0']['value']
d = atab[c]['shape']['direction']['m1']['value']
pixel = ia.topixel([r, d])
xpos = pixel['numeric'][0]
ypos = pixel['numeric'][1]
rd = ia.toworld([xpos, ypos], 's')
ra = rd['string'][0][:12]
dec = rd['string'][1][:12]
flux = atab[c]['flux']['value'][0]
sumflux = sumflux + flux
if atab[c]['shape'].has_key('majoraxis'):
smajor = atab[c]['shape']['majoraxis']['value']
sminor = atab[c]['shape']['minoraxis']['value']
sangle = atab[c]['shape']['positionangle']['value']
else:
smajor = 0.0
sminor = 0.0
sangle = 0.0
peakstr = ia.pixelvalue([xpos, ypos, 0, 0])
if len(peakstr) == 0:
logging.warning("Problem with source %d @ %d,%d" %
(i, xpos, ypos))
continue
peakf = peakstr['value']['value']
snr = peakf / sigma
if snr > dynlog:
logscale = True
if snr > sn0:
sn0 = snr
logging.info(
"%s %s %8.2f %8.2f %10.3g %10.3g %7.3f %7.3f %6.1f %6.1f" %
(ra, dec, xpos, ypos, peakf, flux, smajor, sminor, sangle,
snr))
xtab.append(xpos)
ytab.append(ypos)
slbdp.addRow(
[name, ra, dec, flux, peakf, smajor, sminor, sangle])
if ds9:
ras = ra
des = dec.replace('.', ':', 2)
msg = 'ellipse(%s,%s,%g",%g",%g) # text={%s}' % (
ras, des, smajor, sminor, sangle + 90.0, i + 1)
fp9.write("%s\n" % msg)
if ds9:
fp9.close()
logging.info("Wrote ds9.reg")
dt.tag("table")
logging.regression("CONTFLUX: %d %g" % (nsources, sumflux))
summary = ia.summary()
beammaj = summary['restoringbeam']['major']['value']
beammin = summary['restoringbeam']['minor']['value']
beamunit = summary['restoringbeam']['minor']['unit']
beamang = summary['restoringbeam']['positionangle']['value']
angunit = summary['restoringbeam']['positionangle']['unit']
# @todo add to table comments?
logging.info(" Fitted Gaussian size; NOT deconvolved source size.")
logging.info(
" Restoring Beam: Major axis: %10.3g %s , Minor axis: %10.3g %s , PA: %5.1f %s"
% (beammaj, beamunit, beammin, beamunit, beamang, angunit))
# form into a xml table
# output is a table_bdp
self.addoutput(slbdp)
# instantiate a plotter for all plots made herein
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
# make output png with circles marking sources found
if mpl:
circles = []
nx = data.shape[1] # data[] array was already flipud(rot90)'d
ny = data.shape[0] #
for (x, y) in zip(xtab, ytab):
circles.append([x, y, 1])
# @todo variable name
if logscale:
logging.warning("LogScaling applied")
data = data / sigma
data = np.where(data < 0, -np.log10(1 - data),
+np.log10(1 + data))
if nsources == 0:
title = "SFind2D: 0 sources above S/N=%.1f" % (nsigma)
elif nsources == 1:
title = "SFind2D: 1 source (%.1f < S/N < %.1f)" % (nsigma, sn0)
else:
title = "SFind2D: %d sources (%.1f < S/N < %.1f)" % (
nsources, nsigma, sn0)
myplot.map1(data,
title,
slbase,
thumbnail=True,
circles=circles,
zoom=self.getkey("zoom"))
#---------------------------------------------------------
# Get the figure and thumbmail names and create a caption
#---------------------------------------------------------
imname = myplot.getFigure(figno=myplot.figno, relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno, relative=True)
caption = "Image of input map with sources found by SFind2D overlayed in green."
slbdp.table.description = "Table of source locations and sizes (not deconvolved)"
#---------------------------------------------------------
# Add finder image to the BDP
#---------------------------------------------------------
image = Image(images={bt.PNG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG,
description=caption)
slbdp.image.addimage(image, "finderimage")
#-------------------------------------------------------------
# Create the summary entry for the table and image
#-------------------------------------------------------------
self._summary["sources"] = SummaryEntry(
[slbdp.table.serialize(),
slbdp.image.serialize()], "SFind2D_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
ia = taskinit.iatool()
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
ia.open(self.dir("mom0.masked"))
defmask = ia.maskhandler('default')
ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
ia.open(self.dir(imagename))
ia.maskhandler('set', defmask)
ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,
colorwedge=True,zoom=self.getkey("zoom"))
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("CubeSum") # tagging time
self._summary = {} # an ADMIT summary will be created here
numsigma = self.getkey("numsigma") # get the input keys
sigma = self.getkey("sigma")
use_lines = self.getkey("linesum")
pad = self.getkey("pad")
b1 = self._bdp_in[0] # spw image cube
b1a = self._bdp_in[1] # cubestats (optional)
b1b = self._bdp_in[2] # linelist (optional)
f1 = b1.getimagefile(bt.CASA)
taskinit.ia.open(self.dir(f1))
s = taskinit.ia.summary()
nchan = s['shape'][2]
if b1b != None:
ch0 = b1b.table.getFullColumnByName("startchan")
ch1 = b1b.table.getFullColumnByName("endchan")
s = Segments(ch0, ch1, nchan=nchan)
# @todo something isn't merging here as i would have expected,
# e.g. test0.fits [(16, 32), (16, 30), (16, 29)]
if pad > 0:
for (c0, c1) in s.getsegmentsastuples():
s.append([c0 - pad, c0])
s.append([c1, c1 + pad])
s.merge()
s.recalcmask()
# print "PJT segments:",s.getsegmentsastuples()
ns = len(s.getsegmentsastuples())
chans = s.chans(not use_lines)
if use_lines:
msum = s.getmask()
else:
msum = 1 - s.getmask()
logging.info("Read %d segments" % ns)
# print "chans",chans
# print "msum",msum
# from a deprecated keyword, but kept here to pre-smooth the spectrum before clipping
# examples are: ['boxcar',3] ['gaussian',7] ['hanning',5]
smooth = []
sig_const = False # figure out if sigma is taken as constant in the cube
if b1a == None: # if no 2nd BDP was given, sigma needs to be specified
if sigma <= 0.0:
raise Exception, "Neither user-supplied sigma nor CubeStats_BDP input given. One is required."
else:
sig_const = True # and is constant
else:
if sigma > 0:
sigma = b1a.get("sigma")
sig_const = True
if sig_const:
logging.info("Using constant sigma = %f" % sigma)
else:
logging.info("Using varying sigma per plane")
infile = b1.getimagefile(bt.CASA) # ADMIT filename of the image (cube)
bdp_name = self.mkext(
infile, 'csm'
) # morph to the new output name with replaced extension 'csm'
image_out = self.dir(bdp_name) # absolute filename
args = {
"imagename": self.dir(infile)
} # assemble arguments for immoments()
args["moments"] = 0 # only need moments=0 (or [0] is ok as well)
args["outfile"] = image_out # note full pathname
dt.tag("start")
if sig_const:
args["excludepix"] = [-numsigma * sigma,
numsigma * sigma] # single global sigma
if b1b != None:
# print "PJT: ",chans
args["chans"] = chans
else:
# @todo in this section bad channels can cause a fully masked cubesum = bad
# cubestats input
sigma_array = b1a.table.getColumnByName(
"sigma") # channel dependent sigma
sigma_pos = sigma_array[np.where(sigma_array > 0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f" % (smin, smax))
maxval = b1a.get("maxval") # max in cube
nzeros = len(np.where(sigma_array <= 0.0)[0]) # check bad channels
if nzeros > 0:
logging.warning("There are %d NaN channels " % nzeros)
# raise Exception,"need to recode CubeSum or use constant sigma"
dt.tag("grab_sig")
if len(smooth) > 0:
# see also LineID and others
filter = Filter1D.Filter1D(
sigma_array, smooth[0],
**Filter1D.Filter1D.convertargs(smooth))
sigma_array = filter.run()
dt.tag("smooth_sig")
# create a CASA image copy for making the mirror sigma cube to mask against
file = self.dir(infile)
mask = file + "_mask"
taskinit.ia.fromimage(infile=file, outfile=mask)
nx = taskinit.ia.shape()[0]
ny = taskinit.ia.shape()[1]
nchan = taskinit.ia.shape()[2]
taskinit.ia.fromshape(shape=[nx, ny, 1])
plane = taskinit.ia.getchunk(
[0, 0, 0],
[-1, -1, 0]) # convenience plane for masking operation
dt.tag("mask_sig")
taskinit.ia.open(mask)
dt.tag("open_mask")
count = 0
for i in range(nchan):
if sigma_array[i] > 0:
if b1b != None:
if msum[i]:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval,
blc=[0, 0, i, -1])
else:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval, blc=[0, 0, i, -1])
taskinit.ia.close()
logging.info("%d/%d channels used for CubeSum" % (count, nchan))
dt.tag("close_mask")
names = [file, mask]
tmp = file + '.tmp'
if numsigma == 0.0:
# hopefully this will also make use of the mask
exp = "IM0[IM1<%f]" % (0.99 * maxval)
else:
exp = "IM0[abs(IM0/IM1)>%f]" % (numsigma)
# print "PJT: exp",exp
casa.immath(mode='evalexpr',
imagename=names,
expr=exp,
outfile=tmp)
args["imagename"] = tmp
dt.tag("immath")
casa.immoments(**args)
dt.tag("immoments")
if sig_const is False:
# get rid of temporary files
utils.remove(tmp)
utils.remove(mask)
# get the flux
taskinit.ia.open(image_out)
st = taskinit.ia.statistics()
taskinit.ia.close()
dt.tag("statistics")
# report that flux, but there's no way to get the units from casa it seems
# ia.summary()['unit'] is usually 'Jy/beam.km/s' for ALMA
# imstat() does seem to know it.
if st.has_key('flux'):
rdata = [st['flux'][0], st['sum'][0]]
logging.info("Total flux: %f (sum=%f)" % (st['flux'], st['sum']))
else:
rdata = [st['sum'][0]]
logging.info("Sum: %f (beam parameters missing)" % (st['sum']))
logging.regression("CSM: %s" % str(rdata))
# Create two output images for html and their thumbnails, too
implot = ImPlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
implot.plotter(rasterfile=bdp_name, figname=bdp_name, colorwedge=True)
figname = implot.getFigure(figno=implot.figno, relative=True)
thumbname = implot.getThumbnail(figno=implot.figno, relative=True)
dt.tag("implot")
thumbtype = bt.PNG # really should be correlated with self._plot_type!!
# 2. Create a histogram of the map data
# get the data for a histogram
data = casautil.getdata(image_out, zeromask=True).compressed()
dt.tag("getdata")
# get the label for the x axis
bunit = casa.imhead(imagename=image_out, mode="get", hdkey="bunit")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
auxname = bdp_name + "_histo"
auxtype = bt.PNG # really should be correlated with self._plot_type!!
myplot.histogram(columns=data,
figname=auxname,
xlab=bunit,
ylab="Count",
title="Histogram of CubeSum: %s" % (bdp_name),
thumbnail=True)
auxname = myplot.getFigure(figno=myplot.figno, relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno, relative=True)
images = {bt.CASA: bdp_name, bt.PNG: figname}
casaimage = Image(images=images,
auxiliary=auxname,
auxtype=auxtype,
thumbnail=thumbname,
thumbnailtype=thumbtype)
if hasattr(b1, "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(b1, "line"))
if type(line) != type(Line):
line = Line(name="Undetermined")
else:
line = Line(name="Undetermined") # fake a Line if there wasn't one
self.addoutput(
Moment_BDP(xmlFile=bdp_name,
moment=0,
image=deepcopy(casaimage),
line=line))
imcaption = "Integral (moment 0) of all emission in image cube"
auxcaption = "Histogram of cube sum for image cube"
taskargs = "numsigma=%.1f sigma=%g smooth=%s" % (numsigma, sigma,
str(smooth))
self._summary["cubesum"] = SummaryEntry([
figname, thumbname, imcaption, auxname, auxthumb, auxcaption,
bdp_name, infile
], "CubeSum_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()