def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
pvslicesummary = []
sumslicetype = 'slice'
sliceargs = []
dt = utils.Dtime("PVSlice")
# import here, otherwise sphinx cannot parse
from impv import impv
from imsmooth import imsmooth
pvslice = self.getkey('slice') # x_s,y_s,x_e,y_e (start and end of line)
pvslit = self.getkey('slit') # x_c,y_c,len,pa (center, length and PA of line)
# BDP's used :
# b10 = input BDP
# b11 = input BDP (moment)
# b12 = input BDP (new style cubestats w/ maxpos)
# b2 = output BDP
b10 = self._bdp_in[0] # input SpwCube
fin = b10.getimagefile(bt.CASA) # input name
b11 = self._bdp_in[1] #
b12 = self._bdp_in[2]
clip = self.getkey('clip') # clipping to data for Moment-of-Inertia
gamma = self.getkey('gamma') # gamma factor to data for Moment-of-Inertia
if b11 != None and len(pvslice) == 0 and len(pvslit) == 0:
# if a map (e.g. cubesum ) given, and no slice/slit, get a best pvslice from that
(pvslice,clip) = map_to_slit(self.dir(b11.getimagefile(bt.CASA)),clip=clip,gamma=gamma)
elif b12 != None and len(pvslice) == 0 and len(pvslit) == 0:
# PPP doesn't seem to work too well yet
logging.debug("testing new slice computation from a PPP")
max = b12.table.getColumnByName("max")
maxposx = b12.table.getColumnByName("maxposx")
maxposy = b12.table.getColumnByName("maxposy")
if maxposx == None:
raise Exception,"PPP was not enabled in your CubeStats"
(pvslice,clip) = tab_to_slit([maxposx,maxposy,max],clip=clip,gamma=gamma)
sliceargs = deepcopy(pvslice)
if len(sliceargs)==0:
logging.warning("no slice for plot yet")
# ugh, this puts single quotes around the numbers
formattedslice = str(["%.2f" % a for a in sliceargs])
taskargs = "slice="+formattedslice
dt.tag("slice")
pvname = self.mkext(fin,'pv') # output image name
b2 = PVSlice_BDP(pvname)
self.addoutput(b2)
width = self.getkey('width') # @todo also: "4arcsec" (can't work since it's a single keyword)
if len(pvslice) == 4:
start = pvslice[:2] # @todo also allow: ["14h20m20.5s","-30d45m25.4s"]
end = pvslice[2:]
impv(self.dir(fin), self.dir(pvname),"coords",start=start,end=end,width=width,overwrite=True)
elif len(pvslit) == 4:
sumslicetype = 'slit'
sliceargs = deepcopy(pvslit)
formattedslice = str(["%.2f" % a for a in sliceargs])
taskargs = "slit="+formattedslice
# length="40arcsec" same as {"value": 40, "unit": "arcsec"})
center = pvslit[:2] # @todo also: ["14h20m20.5s","-30d45m25.4s"].
length = pvslit[2] # @todo also: "40arcsec", {"value": 40, "unit": "arcsec"})
if type(pvslit[3]) is float or type(pvslit[3]) is int:
pa = "%gdeg" % pvslit[3]
else:
pa = pvslit[3]
impv(self.dir(fin), self.dir(pvname),"length",center=center,length=length,pa=pa,width=width,overwrite=True)
else:
raise Exception,"no valid input slit= or slice= or bad Moment_BDP input"
sliceargs.append(width)
taskargs = taskargs + " width=%d" % width
dt.tag("impv")
smooth = self.getkey('pvsmooth')
if len(smooth) > 0:
if len(smooth) == 1:
smooth.append(smooth[0])
major = '%dpix' % smooth[0]
minor = '%dpix' % smooth[1]
logging.info("imsmooth PV slice: %s %s" % (major,minor))
imsmooth(self.dir(pvname), outfile=self.dir(pvname)+'.smooth',kernel='boxcar',major=major,minor=minor)
dt.tag("imsmooth")
# utils.rename(self.dir(pvname)+'.smooth',self.dir(pvname))
# @todo we will keep the smooth PVslice for inspection, no further flow work
# get some statistics
data = casautil.getdata_raw(self.dir(pvname))
rpix = stats.robust(data.flatten())
r_mean = rpix.mean()
r_std = rpix.std()
r_max = rpix.max()
logging.info("PV stats: mean/std/max %f %f %f" % (r_mean, r_std, r_max))
logging.regression("PVSLICE: %f %f %f" % (r_mean, r_std, r_max))
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
# hack to get a slice on a mom0map
# @todo if pmode is not png, can viewer handle this?
figname = pvname + ".png"
slicename = self.dir(figname)
overlay = "pvoverlay"
if b11 != None:
f11 = b11.getimagefile(bt.CASA)
taskinit.tb.open(self.dir(f11))
data = taskinit.tb.getcol('map')
nx = data.shape[0]
ny = data.shape[1]
taskinit.tb.close()
d1 = np.flipud(np.rot90 (data.reshape((nx,ny))))
if len(pvslice) == 4:
segm = [[pvslice[0],pvslice[2],pvslice[1],pvslice[3]]]
pa = np.arctan2(pvslice[2]-pvslice[0],pvslice[1]-pvslice[3])*180.0/np.pi
title = "PV Slice location : slice PA=%.1f" % pa
elif len(pvslit) == 4:
# can only do this now if using pixel coordinates
xcen = pvslit[0]
ycen = ny-pvslit[1]-1
slen = pvslit[2]
pard = pvslit[3]*np.pi/180.0
cosp = np.cos(pard)
sinp = np.sin(pard)
halflen = 0.5*slen
segm = [[xcen-halflen*sinp,xcen+halflen*sinp,ycen-halflen*cosp,ycen+halflen*cosp]]
pa = pvslit[3]
title = "PV Slice location : slit PA=%g" % pa
else:
# bogus, some error in pvslice
logging.warning("bogus segm since pvslice=%s" % str(pvslice))
segm = [[10,20,10,20]]
pa = -999.999
title = "PV Slice location - bad PA"
logging.info("MAP1 segm %s %s" % (str(segm),str(pvslice)))
if d1.max() < clip:
logging.warning("datamax=%g, clip=%g" % (d1.max(), clip))
title = title + ' (no signal over %g?)' % clip
myplot.map1(d1,title,overlay,segments=segm,thumbnail=True)
else:
myplot.map1(d1,title,overlay,segments=segm,range=[clip],thumbnail=True)
dt.tag("plot")
overlayname = myplot.getFigure(figno=myplot.figno,relative=True)
overlaythumbname = myplot.getThumbnail(figno=myplot.figno,relative=True)
Qover = True
else:
Qover = False
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot.plotter(rasterfile=pvname, figname=pvname, colorwedge=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
figname = implot.getFigure(figno=implot.figno,relative=True)
if False:
# debug:
#
# @todo tmp1 is ok, tmp2 is not displaying the whole thing
# old style: viewer() seems to plot full image, but imview() wants square pixels?
casa.viewer(infile=self.dir(pvname), outfile=self.dir('tmp1.pv.png'), gui=False, outformat="png")
casa.imview(raster={'file':self.dir(pvname), 'colorwedge' : True, 'scaling':-1},
axes={'y':'Declination'},
out=self.dir('tmp2.pv.png'))
#
# -> this one works, axes= should be correct
# imview(raster={'file':'x.pv', 'colorwedge' : True, 'scaling':-1},axes={'y':'Frequency'})
#
# @TODO big fixme, we're going to reuse 'tmp1.pv.png' because implot give a broken view
figname = 'tmp1.pv.png'
# @todo technically we don't know what map it was overlay'd on.... CubeSum/Moment0
overlaycaption = "Location of position-velocity slice overlaid on a CubeSum map"
pvcaption = "Position-velocity diagram through emission centroid"
pvimage = Image(images={bt.CASA : pvname, bt.PNG : figname},thumbnail=thumbname,thumbnailtype=bt.PNG, description=pvcaption)
b2.setkey("image",pvimage)
b2.setkey("mean",float(r_mean))
b2.setkey("sigma",float(r_std))
if Qover:
thispvsummary = [sumslicetype,sliceargs,figname,thumbname,pvcaption,overlayname,overlaythumbname,overlaycaption,pvname,fin]
else:
thispvsummary = [sumslicetype,sliceargs,figname,thumbname,pvcaption,pvname,fin]
# Yes, this is a nested list. Against the day when PVSLICE can
# compute multiple slices per map.
pvslicesummary.append(thispvsummary)
self._summary["pvslices"] = SummaryEntry(pvslicesummary,"PVSlice_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):
""" 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("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()
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):
#
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()
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()
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
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
taskinit.ia.open(self.dir("mom0.masked"))
defmask = taskinit.ia.maskhandler('default')
taskinit.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]))
taskinit.ia.open(self.dir(imagename))
taskinit.ia.maskhandler('set', defmask)
taskinit.ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,colorwedge=True)
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()