def __init__(self,xmlFile=None,**keyval):
Image_BDP.__init__(self,xmlFile)
Table_BDP.__init__(self,xmlFile)
self.yourname = ""
self.planet = ""
self.star = ""
self.setkey(keyval)
def __init__(self, xmlFile=None, **keyval):
Image_BDP.__init__(self, xmlFile)
Table_BDP.__init__(self, xmlFile)
self.yourname = ""
self.planet = ""
self.star = ""
self.setkey(keyval)
def __init__(self, xmlFile=None, **keyval):
Table_BDP.__init__(self, xmlFile)
Image_BDP.__init__(self, xmlFile)
self.veltype = "vlsr"
self.ra = ""
self.dec = ""
self.table.setkey("columns", utils.linelist_columns)
self.table.setkey("units", utils.linelist_units)
self.table.description="Identified Spectral Lines"
self.table.data = np.array([], dtype=object)
self.spectra = Table()
self.spectra.setkey("columns", ["channel", "frequency", "intensity",
"mask", "continuum", "noise"])
self.spectra.setkey("units", ["", "GHz", "", "", "", ""])
self.setkey(keyval)
self._version= "0.2.0"
def __init__(self, xmlFile=None, **keyval):
Table_BDP.__init__(self, xmlFile)
Image_BDP.__init__(self, xmlFile)
self.veltype = "vlsr"
self.ra = ""
self.dec = ""
self.table.setkey("columns", utils.linelist_columns)
self.table.setkey("units", utils.linelist_units)
self.table.description = "Identified Spectral Lines"
self.table.data = np.array([], dtype=object)
self.spectra = Table()
self.spectra.setkey("columns", [
"channel", "frequency", "intensity", "mask", "continuum", "noise"
])
self.spectra.setkey("units", ["", "GHz", "", "", "", ""])
self.setkey(keyval)
self._version = "0.2.0"
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):
""" 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):
""" Main program for OverlapIntegral
"""
dt = utils.Dtime("OverlapIntegral")
self._summary = {}
chans =self.getkey("chans")
cmap = self.getkey("cmap")
normalize = self.getkey("normalize")
doCross = True
doCross = False
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
dt.tag("start")
n = len(self._bdp_in)
if n==0:
raise Exception,"Need at least 1 input Image_BDP "
logging.debug("Processing %d input maps" % n)
data = range(n) # array in which each element is placeholder for the data
mdata = range(n) # array to hold the max in each array
summarytable = admit.util.Table()
summarytable.columns = ["File name","Spectral Line ID"]
summarytable.description = "Images used in Overlap Integral"
for i in range(n):
bdpfile = self._bdp_in[i].getimagefile(bt.CASA)
if hasattr(self._bdp_in[i],"line"):
line = getattr(self._bdp_in[i],"line")
logging.info("Map %d: %s" % (i,line.uid))
lineid = line.uid
else:
lineid="no line"
data[i] = casautil.getdata(self.dir(bdpfile),chans)
mdata[i] = data[i].max()
logging.info("shape[%d] = %s with %d good data" % (i,data[i].shape,data[i].count()))
if i==0:
shape = data[i].shape
outfile = self.mkext("testOI","oi")
else:
if shape != data[i].shape:
raise Exception,"Shapes not the same, cannot overlap them"
# collect the file names and line identifications for the summary
summarytable.addRow([bdpfile,lineid])
logging.regression("OI: %s" % str(mdata))
if len(shape)>2 and shape[2] > 1:
raise Exception,"Cannot handle 3D cubes yet"
if doCross:
# debug: produce all cross-corr's of the N input maps (expensive!)
crossn(data, myplot)
dt.tag("crossn")
b1 = Image_BDP(outfile)
self.addoutput(b1)
b1.setkey("image", Image(images={bt.CASA:outfile}))
dt.tag("open")
useClone = True
# to create an output dataset, clone the first input, but using the chans=ch0~ch1
# e.g. using imsubimage(infile,outfile=,chans=
if len(chans) > 0:
# ia.regrid() doesn't have the chans=
taskinit.ia.open(self.dir(self._bdp_in[0].getimagefile(bt.CASA)))
taskinit.ia.regrid(outfile=self.dir(outfile))
taskinit.ia.close()
else:
# 2D for now
if not useClone:
logging.info("OVERLAP out=%s" % outfile)
taskinit.ia.fromimage(infile=self.dir(self._bdp_in[0].getimagefile(bt.CASA)),
outfile=self.dir(outfile), overwrite=True)
taskinit.ia.close()
dt.tag("fromimage")
if n==3:
# RGB
logging.info("RGB mode")
out = rgb1(data[0],data[1],data[2], normalize)
else:
# simple sum
out = data[0]
for i in range(1,n):
out = out + data[i]
if useClone:
casautil.putdata_raw(self.dir(outfile),out,clone=self.dir(self._bdp_in[0].getimagefile(bt.CASA)))
else:
taskinit.ia.open(self.dir(outfile))
s1 = taskinit.ia.shape()
s0 = [0,0,0,0]
r1 = taskinit.rg.box(blc=s0,trc=s1)
pixeldata = out.data
pixelmask = ~out.mask
taskinit.ia.putregion(pixels=pixeldata, pixelmask=pixelmask, region=r1)
taskinit.ia.close()
title = "OverlapIntegral"
pdata = np.rot90(out.squeeze())
logging.info("PDATA: %s" % str(pdata.shape))
myplot.map1(pdata,title,"testOI",thumbnail=True,cmap=cmap)
#-----------------------------
# Populate summary information
#-----------------------------
taskargs = "chans=%s cmap=%s" % (chans, cmap)
imname = ""
thumbnailname = ""
# uncomment when ready.
imname = myplot.getFigure(figno=myplot.figno,relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,relative=True)
#@todo fill in caption with more info - line names, etc.
caption = "Need descriptive caption here"
summaryinfo = [summarytable.serialize(),imname,thumbnailname,caption]
self._summary["overlap"] = SummaryEntry(summaryinfo,
"OverlapIntegral_AT",
self.id(True),taskargs)
#-----------------------------
dt.tag("done")
dt.end()
def run(self):
#
self._summary = {} # prepare to make a summary here
dt = utils.Dtime("Ingest") # timer for debugging
do_cbeam = True # enforce a common beam
#
pb = self.getkey('pb')
do_pb = len(pb) > 0
use_pb = self.getkey("usepb")
#
create_mask = self.getkey('mask') # create a new mask ?
box = self.getkey("box") # corners in Z, XY or XYZ
edge = self.getkey("edge") # number of edge channels to remove
restfreq = self.getkey("restfreq") # < 0 means not activated
# smooth= could become deprecated, and/or include a decimation option to make it useful
# again, Smooth_AT() does this also , at the cost of an extra cube to store
smooth = self.getkey("smooth") #
#
vlsr = self.getkey("vlsr") # see also LineID, where this could be given again
# first place a fits file in the admit project directory (symlink)
# this is a bit involved, depending on if an absolute or relative path was
# give to Ingest_AT(file=)
fitsfile = self.getkey('file')
if fitsfile[0] != os.sep:
fitsfile = os.path.abspath(os.getcwd() + os.sep + fitsfile)
logging.debug('FILE=%s' % fitsfile)
if fitsfile[0] != os.sep:
raise Exception,"Bad file=%s, expected absolute name",fitsfile
# now determine if it could have been a CASA (or MIRIAD) image already
# which we'll assume if it's a directory; this is natively supported by CASA
# but there are tools where if you pass it a FITS or MIRIAD
# MIRIAD is not recommended for serious work, especially big files, since there
# is a performance penalty due to tiling.
file_is_casa = casautil.iscasa(fitsfile)
loc = fitsfile.rfind(os.sep) # find the '/'
ffile0 = fitsfile[loc+1:] # basename.fits
basename = self.getkey('basename') # (new) basename allowed (allow no dots?)
if len(basename) == 0:
basename = ffile0[:ffile0.rfind('.')] # basename
logging.info("basename=%s" % basename)
target = self.dir(ffile0)
if not os.path.exists(target) :
cmd = 'ln -s "%s" "%s"' % (fitsfile, target)
logging.debug("CMD: %s" % cmd)
os.system(cmd)
readonly = False
if file_is_casa:
logging.debug("Assuming input %s is a CASA (or MIRIAD) image" % ffile0)
bdpfile = self.mkext(basename,"im")
if bdpfile == ffile0:
logging.warning("No selections allowed on CASA image, since no alias was given")
readonly = True
b1 = SpwCube_BDP(bdpfile)
self.addoutput(b1)
b1.setkey("image", Image(images={bt.CASA:bdpfile}))
# @todo b2 and PB?
else:
# construct the output name and construct the BDP based on the CASA image name
# this also takes care of the behind the scenes alias= substitution
bdpfile = self.mkext(basename,"im")
if bdpfile == basename:
raise Exception,"basename and bdpfile are the same, Ingest_AT needs a fix for this"
b1 = SpwCube_BDP(bdpfile)
self.addoutput(b1)
if do_pb:
print "doing the PB"
bdpfile2 = self.mkext(basename,"pb")
b2 = Image_BDP(bdpfile2)
self.addoutput(b2)
# @todo we should also set readonly=True if no box, no mask etc. and still an alias
# that way it will speed up and not make a copy of the image ?
# fni and fno are full (abspath) filenames, ready for CASA
# fni is the same as fitsfile
fni = self.dir(ffile0)
fno = self.dir(bdpfile)
if do_pb: fno2 = self.dir(bdpfile2)
dt.tag("start")
if file_is_casa:
taskinit.ia.open(fni)
else:
if do_pb and use_pb:
# @todo this needs a fix for the path for pb, only works if abs path is given
# impbcor(im.fits,pb.fits,out.im,overwrite=True,mode='m')
if False:
# this may seem like a nice shortcut, to have the fits->casa conversion be done
# internally in impbcor, but it's a terrible performance for big cubes. (tiling?)
# we keep this code here, perhaps at some future time (mpi?) this performs better
# @todo fno2
impbcor(fni,pb,fno,overwrite=True,mode='m')
dt.tag("impbcor-1")
else:
# the better way is to convert FITS->CASA and then call impbcor()
# the CPU savings are big, but I/O overhead can still be substantial
taskinit.ia.fromfits('_pbcor',fni,overwrite=True)
taskinit.ia.fromfits('_pb',pb,overwrite=True)
dt.tag("impbcor-1f")
if False:
impbcor('_pbcor','_pb',fno,overwrite=True,mode='m')
# @todo fno2
utils.remove('_pbcor')
utils.remove('_pb')
dt.tag("impbcor-2")
else:
# immath appears to be even faster (2x in CPU)
# https://bugs.nrao.edu/browse/CAS-8299
# @todo this needs to be confirmed that impbcor is now good to go (r36078)
casa.immath(['_pbcor','_pb'],'evalexpr',fno,'IM0*IM1')
dt.tag("immath")
if True:
# use the mean of all channels... faster may be to use the middle plane
# barf; edge channels can be with fewer subfields in a mosaic
taskinit.ia.open('_pb')
taskinit.ia.summary()
ia1=taskinit.ia.moments(moments=[-1],drop=True,outfile=fno2)
ia1.done()
taskinit.ia.close()
dt.tag("moments")
utils.remove('_pbcor')
utils.remove('_pb')
dt.tag("impbcor-3")
elif do_pb and not use_pb:
# cheat case: PB was given, but not meant to be used
# not implemented yet
print "cheat case dummy PB not implemented yet"
else:
# no PB given
if True:
# re-running this was more consistently faster in wall clock time
# note that zeroblanks=True will still keep the mask
logging.debug("casa::ia.fromfits(%s) -> %s" % (fni,bdpfile))
taskinit.ia.fromfits(fno,fni,overwrite=True)
#taskinit.ia.fromfits(fno,fni,overwrite=True,zeroblanks=True)
dt.tag("fromfits")
else:
# not working to extend 3D yet, but this would solve the impv() 3D problem
logging.debug("casa::importfits(%s) -> %s" % (fni,bdpfile))
#casa.importfits(fni,fno,defaultaxes=True,defaultaxesvalues=[None,None,None,'I'])
# possible bug: zeroblanks=True has no effect?
casa.importfits(fni,fno,zeroblanks=True)
dt.tag("importfits")
taskinit.ia.open(fno)
if len(smooth) > 0:
# smooth here, but Smooth_AT is another option
# here we only allow pixel smoothing
# spatial: gauss
# spectral: boxcar/hanning (check for flux conservation)
# is the boxcar wrong, not centered, but edged?
# @todo CASA BUG: this will loose the object name (and maybe more?) from header, so VLSR lookup fails
fnos = fno + '.smooth'
taskinit.ia.convolve2d(outfile=fnos, overwrite=True, pa='0deg',
major='%gpix' % smooth[0], minor='%gpix' % smooth[1], type='gaussian')
taskinit.ia.close()
srcname = casa.imhead(fno,mode="get",hdkey="object") # work around CASA bug
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnos,fno)
casa.imhead(fno,mode="put",hdkey="object",hdvalue=srcname) # work around CASA bug
dt.tag("convolve2d")
if len(smooth) > 2 and smooth[2] > 0:
if smooth[2] == 1:
# @todo only 1 channel option
specsmooth(fno,fnos,axis=2,function='hanning',dmethod="")
else:
# @todo may have the wrong center
specsmooth(fno,fnos,axis=2,function='boxcar',dmethod="",width=smooth[2])
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnos,fno)
dt.tag("specsmooth")
taskinit.ia.open(fno)
s = taskinit.ia.summary()
if len(s['shape']) != 4:
logging.warning("Adding dummy STOKES-I axis")
fnot = fno + '_4'
taskinit.ia.adddegaxes(stokes='I',outfile=fnot)
taskinit.ia.close()
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnot,fno)
taskinit.ia.open(fno)
dt.tag("adddegaxes")
else:
logging.info("SHAPE: %s" % str(s['shape']))
s = taskinit.ia.summary()
dt.tag("summary-0")
if s['hasmask'] and create_mask:
logging.warning("no extra mask created because input image already had one")
create_mask = False
# if a box= or edge= was given, only a subset of the cube needs to be ingested
# this however complicates PB correction later on
if len(box) > 0 or len(edge) > 0:
if readonly:
raise Exception,"Cannot use box= or edge=, data is read-only, or use an basename/alias"
if len(edge) == 1: edge.append(edge[0])
nx = s['shape'][0]
ny = s['shape'][1]
nz = s['shape'][2]
logging.info("box=%s edge=%s processing with SHAPE: %s" % (str(box),str(edge),str(s['shape'])))
if len(box) == 2:
# select zrange
if len(edge)>0:
raise Exception,"Cannot use edge= when box=[z1,z2] is used"
r1 = taskinit.rg.box([0,0,box[0]] , [nx-1,ny-1,box[1]])
elif len(box) == 4:
if len(edge) == 0:
# select just an XY box
r1 = taskinit.rg.box([box[0],box[1]] , [box[2],box[3]])
elif len(edge) == 2:
# select an XY box, but remove some edge channels
r1 = taskinit.rg.box([box[0],box[1],edge[0]] , [box[2],box[3],nz-edge[1]-1])
else:
raise Exception,"Bad edge= for len(box)=4"
elif len(box) == 6:
# select an XYZ box
r1 = taskinit.rg.box([box[0],box[1],box[2]] , [box[3],box[4],box[5]])
elif len(edge) == 2:
# remove some edge channels, but keep the whole XY box
r1 = taskinit.rg.box([0,0,edge[0]] , [nx-1,ny-1,nz-edge[1]-1])
else:
raise Exception,"box=%s illegal" % box
logging.debug("BOX/EDGE selection: %s %s" % (str(r1['blc']),str(r1['trc'])))
#if taskinit.ia.isopen(): taskinit.ia.close()
logging.info("SUBIMAGE")
subimage = taskinit.ia.subimage(region=r1,outfile=fno+'.box',overwrite=True)
taskinit.ia.close()
taskinit.ia.done()
subimage.rename(fno,overwrite=True)
subimage.close()
subimage.done()
taskinit.ia.open(fno)
dt.tag("subimage-1")
else:
# the whole cube is passed onto ADMIT
if readonly and create_mask:
raise Exception,"Cannot use mask=True, data read-only, or use an alias"
if file_is_casa and not readonly:
# @todo a miriad file - which should be read only - will also create a useless copy here if no alias used
taskinit.ia.subimage(overwrite=True,outfile=fno)
taskinit.ia.close()
taskinit.ia.open(fno)
dt.tag("subimage-0")
if create_mask:
if readonly:
raise Exception,"Cannot create mask, data read-only, or use an alias"
# also check out the 'fromfits::zeroblanks = False'
# calcmask() will overwrite any previous pixelmask
#taskinit.ia.calcmask('mask("%s") && "%s" != 0.0' % (fno,fno))
taskinit.ia.calcmask('"%s" != 0.0' % fno)
dt.tag("mask")
s = taskinit.ia.summary()
dt.tag("summary-1")
# do a fast statistics (no median or robust)
s0 = taskinit.ia.statistics()
dt.tag("statistics")
if len(s0['npts']) == 0:
raise Exception,"No statistics possible, are there valid data in this cube?"
# There may be multiple beams per plane so we can't
# rely on the BEAM's 'major', 'minor', 'positionangle' being present.
# ia.commonbeam() is guaranteed to return beam parameters
# if present
if do_cbeam and s.has_key('perplanebeams'):
# report on the beam extremities, need to loop over all,
# first and last don't need to be extremes....
n = s['perplanebeams']['nChannels']
ab0 = '*0'
bb0 = s['perplanebeams']['beams'][ab0]['*0']
bmaj0 = bb0['major']['value']
bmin0 = bb0['minor']['value']
beamd = 0.0
for i in range(n):
ab1 = '*%d' % i
bb1 = s['perplanebeams']['beams'][ab1]['*0']
bmaj1 = bb1['major']['value']
bmin1 = bb1['minor']['value']
beamd = max(beamd,abs(bmaj0-bmaj1),abs(bmin0-bmin1))
logging.warning("MAX-BEAMSPREAD %f" % (beamd))
#
if True:
logging.info("Applying a commonbeam from the median beam accross the band")
# imhead is a bit slow; alternatively use ia.summary() at the half point for setrestoringbeam()
h = casa.imhead(fno,mode='list')
b = h['perplanebeams']['median area beam']
taskinit.ia.setrestoringbeam(remove=True)
taskinit.ia.setrestoringbeam(beam=b)
commonbeam = taskinit.ia.commonbeam()
else:
# @todo : this will be VERY slow - code not finished, needs renaming etc.
# this is however formally the better solution
logging.warning("commmonbeam code not finished")
cb = taskinit.ia.commonbeam()
taskinit.ia.convolve2d(outfile='junk-common.im', major=cb['major'], minor=cb['minor'], pa=cb['pa'],
targetres=True, overwrite=True)
dt.tag('convolve2d')
commonbeam = {}
else:
try:
commonbeam = taskinit.ia.commonbeam()
except:
nppb = 4.0
logging.warning("No synthesized beam found, faking one to prevent downstream problems: nppb=%f" % nppb)
s = taskinit.ia.summary()
cdelt2 = abs(s['incr'][0]) * 180.0/math.pi*3600.0
bmaj = nppb * cdelt2 # use a nominal 4 points per (round) beam
bmin = nppb * cdelt2
bpa = 0.0
taskinit.ia.setrestoringbeam(major='%farcsec' % bmaj, minor='%farcsec' % bmin, pa='%fdeg' % bpa)
commonbeam = {}
logging.info("COMMONBEAM[%d] %s" % (len(commonbeam),str(commonbeam)))
first_point = taskinit.ia.getchunk(blc=[0,0,0,0],trc=[0,0,0,0],dropdeg=True)
logging.debug("DATA0*: %s" % str(first_point))
taskinit.ia.close()
logging.info('BASICS: [shape] npts min max: %s %d %f %f' % (s['shape'],s0['npts'][0],s0['min'][0],s0['max'][0]))
logging.info('S/N (all data): %f' % (s0['max'][0]/s0['rms'][0]))
npix = 1
nx = s['shape'][0]
ny = s['shape'][1]
nz = s['shape'][2]
for n in s['shape']:
npix = npix * n
ngood = int(s0['npts'][0])
fgood = (1.0*ngood)/npix
logging.info('GOOD PIXELS: %d/%d (%f%% good or %f%% bad)' % (ngood,npix,100.0*fgood,100.0*(1 - fgood)))
if s['hasmask']:
logging.warning('MASKS: %s' % (str(s['masks'])))
if not file_is_casa:
b1.setkey("image", Image(images={bt.CASA:bdpfile}))
if do_pb:
b2.setkey("image", Image(images={bt.CASA:bdpfile2}))
# cube sanity: needs to be either 4D or 2D. But p-p-v cube
# alternative: ia.subimage(dropdeg = True)
# see also: https://bugs.nrao.edu/browse/CAS-5406
shape = s['shape']
if len(shape)>3:
if shape[3]>1:
# @todo this happens when you ingest a fits or casa image which is ra-dec-pol-freq
if nz > 1:
msg = 'Ingest_AT: cannot deal with real 4D cubes yet'
logging.critical(msg)
raise Exception,msg
else:
# @todo this is not working yet when the input was a casa image, but ok when fits. go figure.
fnot = fno + ".trans"
if True:
# this works
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fno,fnot)
imtrans(fnot,fno,"0132")
utils.remove(fnot)
else:
# this does not work, what the heck
imtrans(fno,fnot,"0132")
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnot,fno)
nz = s['shape'][3]
# get a new summary 's'
taskinit.ia.open(fno)
s = taskinit.ia.summary()
taskinit.ia.close()
logging.warning("Using imtrans, with nz=%d, to fix axis ordering" % nz)
dt.tag("imtrans4")
# @todo ensure first two axes are position, followed by frequency
elif len(shape)==3:
# the current importfits() can do defaultaxes=True,defaultaxesvalues=['', '', '', 'I']
# but then appears to return a ra-dec-pol-freq cube
# this branch probably never happens, since ia.fromfits() will
# properly convert a 3D cube to 4D now !!
# NO: when NAXIS=3 but various AXIS4's are present, that works. But not if it's pure 3D
# @todo box=
logging.warning("patching up a 3D to 4D cube")
raise Exception,"SHOULD NEVER GET HERE"
fnot = fno + ".trans"
casa.importfits(fni,fnot,defaultaxes=True,defaultaxesvalues=['', '', '', 'I'])
utils.remove(fno) # ieck
imtrans(fnot,fno,"0132")
utils.remove(fnot)
dt.tag("imtrans3")
logging.regression('CUBE: %g %g %g %d %d %d %f' % (s0['min'],s0['max'],s0['rms'],nx,ny,nz,100.0*(1 - fgood)))
# if the cube has only 1 plane (e.g. continuum) , create a visual (png or so)
# for 3D cubes, rely on something like CubeSum
if nz == 1:
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot.plotter(rasterfile=bdpfile,figname=bdpfile)
# @todo needs to be registered for the BDP, right now we only have the plot
# ia.summary() doesn't have this easily available, so run the more expensive imhead()
h = casa.imhead(fno,mode='list')
telescope = h['telescope']
# work around CASA's PIPELINE bug/feature? if 'OBJECT' is blank, try 'FIELD'
srcname = h['object']
if srcname == ' ':
logging.warning('FIELD used for OBJECT')
srcname = casa.imhead(fno,mode='get',hdkey='field')
if srcname == False:
# if no FIELD either, we're doomed. yes, this did happen.
srcname = 'Unknown'
casa.imhead(fno,mode="put",hdkey="object",hdvalue=srcname)
h['object'] = srcname
logging.info('TELESCOPE: %s' % telescope)
logging.info('OBJECT: %s' % srcname)
logging.info('REFFREQTYPE: %s' % h['reffreqtype'])
if h['reffreqtype'].find('TOPO')>=0:
msg = 'Ingest_AT: cannot deal with cubes with TOPOCENTRIC frequencies yet - winging it'
logging.warning(msg)
#raise Exception,msg
# Ensure beam parameters are available if there are multiple beams
# If there is just one beam, then we are just overwriting the header
# variables with their identical values.
if len(commonbeam) != 0:
h['beammajor'] = commonbeam['major']
h['beamminor'] = commonbeam['minor']
h['beampa'] = commonbeam['pa']
# cheat add some things that need to be passed to summary....
h['badpixel'] = 1.0-fgood
if vlsr < -999998.0:
vlsr = admit.VLSR().vlsr(h['object'].upper())
h['vlsr'] = vlsr
logging.info("VLSR = %f (from source catalog)" % vlsr)
taskargs = "file=" + fitsfile
if create_mask == True:
taskargs = taskargs + " mask=True"
if len(box) > 0:
taskargs = taskargs + " " + str(box)
if len(edge) > 0:
taskargs = taskargs + " " + str(edge)
r2d = 57.29577951308232
logging.info("RA Axis 1: %f %f %f" % (h['crval1']*r2d,h['cdelt1']*r2d*3600.0,h['crpix1']))
logging.info("DEC Axis 2: %f %f %f" % (h['crval2']*r2d,h['cdelt2']*r2d*3600.0,h['crpix2']))
if nz > 1:
# @todo check if this is really a freq axis (for ALMA it is, but...)
t3 = h['ctype3']
df = h['cdelt3']
fc = h['crval3'] + (0.5*(float(shape[2])-1)-h['crpix3'])*df # center freq; 0 based pixels
if h.has_key('restfreq'):
fr = float(h['restfreq'][0])
else:
fr = fc
fw = df*float(shape[2])
dv = -df/fr*utils.c
logging.info("Freq Axis 3: %g %g %g" % (h['crval3']/1e9,h['cdelt3']/1e9,h['crpix3']))
logging.info("Cube Axis 3: type=%s velocity increment=%f km/s @ fc=%f fw=%f GHz" % (t3,dv,fc/1e9,fw/1e9))
# @todo sort out this restfreq/vlsr
# report 'reffreqtype', 'restfreq' 'telescope'
# if the fits file has ALTRVAL/ALTRPIX, this is lost in CASA?
# but if you do fits->casa->fits , it's back in fits (with some obvious single precision loss of digits)
# @todo ZSOURCE is the proposed VLSR slot in the fits header, but this has frame issues (it's also optical)
#
# Another method to get the vlsr is to override the restfreq (f0) with an AT keyword
# and the 'restfreq' from the header (f) is then used to compute the vlsr: v = c (1 - f/f0)
#
if shape[2] > 1 and h.has_key('restfreq'):
logging.info("RESTFREQ: %g %g %g" % (fr/1e9,h['restfreq'][0]/1e9,restfreq))
if shape[2] > 1:
# v_radio of the center of the window w.r.t. restfreq
c = utils.c # 299792.458 km/s
vlsrc = c*(1-fc/fr) # @todo rel frame?
vlsrw = dv*float(shape[2])
if restfreq > 0:
vlsrf = c*(1-fr/restfreq/1e9)
h['vlsr'] = vlsrf
else:
vlsrf = 0.0
logging.info("VLSRc = %f VLSRw = %f VLSRf = %f VLSR = %f" % (vlsrc, vlsrw, vlsrf, vlsr))
if h['vlsr'] == 0.0: # @todo! This fails if vlsr actually is zero. Need another magic number
h['vlsr'] = vlsrc
logging.warning("Warning: No VLSR found, substituting VLSRc = %f" % vlsrc)
else:
msg = 'Ingest_AT: missing RESTFREQ'
print msg
# @todo LINTRN is the ALMA keyword that designates the expected line transition in a spw
self._summarize(fitsfile, bdpfile, h, shape, taskargs)
dt.tag("done")
dt.end()