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):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeStats")
#maxvrms = 2.0 # maximum variation in rms allowed (hardcoded for now)
#maxvrms = -1.0 # turn maximum variation in rms allowed off
maxvrms = self.getkey("maxvrms")
psample = -1
psample = self.getkey("psample")
# BDP's used :
# b1 = input BDP
# b2 = output BDP
b1 = self._bdp_in[0]
fin = b1.getimagefile(bt.CASA)
bdp_name = self.mkext(fin,'cst')
b2 = CubeStats_BDP(bdp_name)
self.addoutput(b2)
# PeakPointPlot
use_ppp = self.getkey("ppp")
# peakstats: not enabled for mortal users yet
# peakstats = (psample=1, numsigma=4, minchan=3, maxgap=2, peakfit=False)
pnumsigma = 4
minchan = 3
maxgap = 2
peakfit = False # True will enable a true gaussian fit
# numsigma: adding all signal > numsigma ; not user enabled; for peaksum.
numsigma = -1.0
numsigma = 3.0
# grab the new robust statistics. If this is used, 'rms' will be the RMS,
# else we will use RMS = 1.4826*MAD (MAD does a decent job on outliers as well)
# and was the only method available before CASA 4.4 when robust was implemented
robust = self.getkey("robust")
rargs = casautil.parse_robust(robust)
nrargs = len(rargs)
if nrargs == 0:
sumrargs = "medabsdevmed" # for the summary, indicate the default robust
else:
sumrargs = str(rargs)
self._summary["rmsmethd"] = SummaryEntry([sumrargs,fin],"CubeStats_AT",self.id(True))
#@todo think about using this instead of putting 'fin' in all the SummaryEntry
#self._summary["casaimage"] = SummaryEntry(fin,"CubeStats_AT",self.id(True))
# extra CASA call to get the freq's in GHz, as these are not in imstat1{}
# @todo what if the coordinates are not in FREQ ?
# Note: CAS-7648 bug on 3D cubes
if False:
# csys method
ia.open(self.dir(fin))
csys = ia.coordsys()
spec_axis = csys.findaxisbyname("spectral")
# ieck, we need a valid position, or else it will come back and "Exception: All selected pixels are masked"
#freqs = ia.getprofile(spec_axis, region=rg.box([0,0],[0,0]))['coords']/1e9
#freqs = ia.getprofile(spec_axis)['coords']/1e9
freqs = ia.getprofile(spec_axis,unit="GHz")['coords']
dt.tag("getprofile")
else:
# old imval method
#imval0 = casa.imval(self.dir(fin),box='0,0,0,0') # this fails on 3D
imval0 = casa.imval(self.dir(fin))
freqs = imval0['coords'].transpose()[2]/1e9
dt.tag("imval")
nchan = len(freqs)
chans = np.arange(nchan)
# call CASA to get what we want
# imstat0 is the whole cube, imstat1 the plane based statistics
# warning: certain robust stats (**rargs) on the whole cube are going to be very slow
dt.tag("start")
imstat0 = casa.imstat(self.dir(fin), logfile=self.dir('imstat0.logfile'),append=False,**rargs)
dt.tag("imstat0")
imstat1 = casa.imstat(self.dir(fin),axes=[0,1],logfile=self.dir('imstat1.logfile'),append=False,**rargs)
dt.tag("imstat1")
# imm = casa.immoments(self.dir(fin),axis='spec', moments=8, outfile=self.dir('ppp.im'))
if nrargs > 0:
# need to get the peaks without rubust
imstat10 = casa.imstat(self.dir(fin), logfile=self.dir('imstat0.logfile'),append=True)
dt.tag("imstat10")
imstat11 = casa.imstat(self.dir(fin),axes=[0,1],logfile=self.dir('imstat1.logfile'),append=True)
dt.tag("imstat11")
# grab the relevant plane-based things from imstat1
if nrargs == 0:
mean = imstat1["mean"]
sigma = imstat1["medabsdevmed"]*1.4826 # see also: astropy.stats.median_absolute_deviation()
peakval = imstat1["max"]
minval = imstat1["min"]
else:
mean = imstat1["mean"]
sigma = imstat1["rms"]
peakval = imstat11["max"]
minval = imstat11["min"]
if True:
# work around a bug in imstat(axes=[0,1]) for last channel [CAS-7697]
for i in range(len(sigma)):
if sigma[i] == 0.0:
minval[i] = peakval[i] = 0.0
# too many variations in the RMS ?
sigma_pos = sigma[np.where(sigma>0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f; %d/%d channels ok" % (smin,smax,len(sigma_pos),len(sigma)))
if maxvrms > 0:
if smax/smin > maxvrms:
cliprms = smin * maxvrms
logging.warning("sigma varies too much, going to clip to %g (%g > %g)" % (cliprms, smax/smin, maxvrms))
sigma = np.where(sigma < cliprms, sigma, cliprms)
# @todo (and check again) for foobar.fits all sigma's became 0 when robust was selected
# was this with mask=True/False?
# PeakPointPlot (can be expensive, hence the option)
if use_ppp:
logging.info("Computing MaxPos for PeakPointPlot")
xpos = np.zeros(nchan)
ypos = np.zeros(nchan)
peaksum = np.zeros(nchan)
ia.open(self.dir(fin))
for i in range(nchan):
if sigma[i] > 0.0:
plane = ia.getchunk(blc=[0,0,i,-1],trc=[-1,-1,i,-1],dropdeg=True)
v = ma.masked_invalid(plane)
v_abs = np.absolute(v)
max = np.unravel_index(v_abs.argmax(), v_abs.shape)
xpos[i] = max[0]
ypos[i] = max[1]
if numsigma > 0.0:
peaksum[i] = ma.masked_less(v,numsigma * sigma[i]).sum()
peaksum = np.nan_to_num(peaksum) # put 0's where nan's are found
ia.close()
dt.tag("ppp")
nzeros = len(np.where(sigma<=0.0))
if nzeros > 0:
zeroch = np.where(sigma<=0.0)
logging.warning("There are %d fully masked channels (%s)" % (nzeros,str(zeroch)))
# construct the admit Table for CubeStats_BDP
# note data needs to be a tuple, later to be column_stack'd
if use_ppp:
labels = ["channel" ,"frequency" ,"mean" ,"sigma" ,"max" ,"maxposx" ,"maxposy" ,"min", "peaksum"]
units = ["number" ,"GHz" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam" ,"number" ,"number" ,"Jy/beam", "Jy"]
data = (chans ,freqs ,mean ,sigma ,peakval ,xpos ,ypos ,minval, peaksum)
else:
labels = ["channel" ,"frequency" ,"mean" ,"sigma" ,"max" ,"min"]
units = ["number" ,"GHz" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam"]
data = (chans ,freqs ,mean ,sigma ,peakval ,minval)
table = Table(columns=labels,units=units,data=np.column_stack(data))
b2.setkey("table",table)
# get the full cube statistics, it depends if robust was pre-selected
if nrargs == 0:
mean0 = imstat0["mean"][0]
sigma0 = imstat0["medabsdevmed"][0]*1.4826
peak0 = imstat0["max"][0]
b2.setkey("mean" , float(mean0))
b2.setkey("sigma", float(sigma0))
b2.setkey("minval",float(imstat0["min"][0]))
b2.setkey("maxval",float(imstat0["max"][0]))
b2.setkey("minpos",imstat0["minpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
b2.setkey("maxpos",imstat0["maxpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
logging.info("CubeMax: %f @ %s" % (imstat0["max"][0],str(imstat0["maxpos"])))
logging.info("CubeMin: %f @ %s" % (imstat0["min"][0],str(imstat0["minpos"])))
logging.info("CubeRMS: %f" % sigma0)
else:
mean0 = imstat0["mean"][0]
sigma0 = imstat0["rms"][0]
peak0 = imstat10["max"][0]
b2.setkey("mean" , float(mean0))
b2.setkey("sigma", float(sigma0))
b2.setkey("minval",float(imstat10["min"][0]))
b2.setkey("maxval",float(imstat10["max"][0]))
b2.setkey("minpos",imstat10["minpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
b2.setkey("maxpos",imstat10["maxpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
logging.info("CubeMax: %f @ %s" % (imstat10["max"][0],str(imstat10["maxpos"])))
logging.info("CubeMin: %f @ %s" % (imstat10["min"][0],str(imstat10["minpos"])))
logging.info("CubeRMS: %f" % sigma0)
b2.setkey("robust",robust)
rms_ratio = imstat0["rms"][0]/sigma0
logging.info("RMS Sanity check %f" % rms_ratio)
if rms_ratio > 1.5:
logging.warning("RMS sanity check = %f. Either bad sidelobes, lotsa signal, or both" % rms_ratio)
logging.regression("CST: %f %f" % (sigma0, rms_ratio))
# plots: no plots need to be made when nchan=1 for continuum
# however we could make a histogram, overlaying the "best" gauss so
# signal deviations are clear?
logging.info('mean,rms,S/N=%f %f %f' % (mean0,sigma0,peak0/sigma0))
if nchan == 1:
# for a continuum/1-channel we only need to stuff some numbers into the _summary
self._summary["chanrms"] = SummaryEntry([float(sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["dynrange"] = SummaryEntry([float(peak0)/float(sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["datamean"] = SummaryEntry([float(mean0), fin], "CubeStats_AT", self.id(True))
else:
y1 = np.log10(ma.masked_invalid(peakval))
y2 = np.log10(ma.masked_invalid(sigma))
y3 = y1-y2
y4 = np.log10(ma.masked_invalid(-minval))
y5 = y1-y4
y = [y1,y2,y3,y4]
title = 'CubeStats: ' + bdp_name+'_0'
xlab = 'Channel'
ylab = 'log(Peak,Noise,Peak/Noise)'
labels = ['log(peak)','log(rms noise)','log(peak/noise)','log(|minval|)']
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
segp = [[chans[0],chans[nchan-1],math.log10(sigma0),math.log10(sigma0)]]
myplot.plotter(chans,y,title,bdp_name+"_0",xlab=xlab,ylab=ylab,segments=segp,labels=labels,thumbnail=True)
imfile = myplot.getFigure(figno=myplot.figno,relative=True)
thumbfile = myplot.getThumbnail(figno=myplot.figno,relative=True)
image0 = Image(images={bt.PNG:imfile},thumbnail=thumbfile,thumbnailtype=bt.PNG,description="CubeStats_0")
b2.addimage(image0,"im0")
if use_ppp:
# new trial for Lee
title = 'PeakSum: (numsigma=%.1f)' % (numsigma)
ylab = 'Jy*N_ppb'
myplot.plotter(chans,[peaksum],title,bdp_name+"_00",xlab=xlab,ylab=ylab,thumbnail=False)
if True:
# hack ascii table
y30 = np.where(sigma > 0, np.log10(peakval/sigma), 0.0)
table2 = Table(columns=["freq","log(P/N)"],data=np.column_stack((freqs,y30)))
table2.exportTable(self.dir("testCubeStats.tab"))
del table2
# the "box" for the "spectrum" is all pixels. Don't know how to
# get this except via shape.
ia.open(self.dir(fin))
s = ia.summary()
ia.close()
if 'shape' in s:
specbox = (0,0,s['shape'][0],s['shape'][1])
else:
specbox = ()
caption = "Emission characteristics as a function of channel, as derived by CubeStats_AT "
caption += "(cyan: global rms,"
caption += " green: noise per channel,"
caption += " blue: peak value per channel,"
caption += " red: peak/noise per channel)."
self._summary["spectra"] = SummaryEntry([0, 0, str(specbox), 'Channel', imfile, thumbfile , caption, fin], "CubeStats_AT", self.id(True))
self._summary["chanrms"] = SummaryEntry([float(sigma0), fin], "CubeStats_AT", self.id(True))
# @todo Will imstat["max"][0] always be equal to s['datamax']? If not, why not?
if 'datamax' in s:
self._summary["dynrange"] = SummaryEntry([float(s['datamax']/sigma0), fin], "CubeStats_AT", self.id(True))
else:
self._summary["dynrange"] = SummaryEntry([float(imstat0["max"][0]/sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["datamean"] = SummaryEntry([imstat0["mean"][0], fin], "CubeStats_AT", self.id(True))
title = bdp_name + "_1"
xlab = 'log(Peak,Noise,P/N)'
myplot.histogram([y1,y2,y3],title,bdp_name+"_1",xlab=xlab,thumbnail=True)
imfile = myplot.getFigure(figno=myplot.figno,relative=True)
thumbfile = myplot.getThumbnail(figno=myplot.figno,relative=True)
image1 = Image(images={bt.PNG:imfile},thumbnail=thumbfile,thumbnailtype=bt.PNG,description="CubeStats_1")
b2.addimage(image1,"im1")
# note that the 'y2' can have been clipped, which can throw off stats.robust()
# @todo should set a mask for those.
title = bdp_name + "_2"
xlab = 'log(Noise))'
n = len(y2)
ry2 = stats.robust(y2)
y2_mean = ry2.mean()
y2_std = ry2.std()
if n>9: logging.debug("NORMALTEST2: %s" % str(scipy.stats.normaltest(ry2)))
myplot.hisplot(y2,title,bdp_name+"_2",xlab=xlab,gauss=[y2_mean,y2_std],thumbnail=True)
title = bdp_name + "_3"
xlab = 'log(diff[Noise])'
n = len(y2)
# dy2 = y2[0:-2] - y2[1:-1]
dy2 = ma.masked_equal(y2[0:-2] - y2[1:-1],0.0).compressed()
rdy2 = stats.robust(dy2)
dy2_mean = rdy2.mean()
dy2_std = rdy2.std()
if n>9: logging.debug("NORMALTEST3: %s" % str(scipy.stats.normaltest(rdy2)))
myplot.hisplot(dy2,title,bdp_name+"_3",xlab=xlab,gauss=[dy2_mean,dy2_std],thumbnail=True)
title = bdp_name + "_4"
xlab = 'log(Signal/Noise))'
n = len(y3)
ry3 = stats.robust(y3)
y3_mean = ry3.mean()
y3_std = ry3.std()
if n>9: logging.debug("NORMALTEST4: %s" % str(scipy.stats.normaltest(ry3)))
myplot.hisplot(y3,title,bdp_name+"_4",xlab=xlab,gauss=[y3_mean,y3_std],thumbnail=True)
title = bdp_name + "_5"
xlab = 'log(diff[Signal/Noise)])'
n = len(y3)
dy3 = y3[0:-2] - y3[1:-1]
rdy3 = stats.robust(dy3)
dy3_mean = rdy3.mean()
dy3_std = rdy3.std()
if n>9: logging.debug("NORMALTEST5: %s" % str(scipy.stats.normaltest(rdy3)))
myplot.hisplot(dy3,title,bdp_name+"_5",xlab=xlab,gauss=[dy3_mean,dy3_std],thumbnail=True)
title = bdp_name + "_6"
xlab = 'log(Peak+Min)'
n = len(y1)
ry5 = stats.robust(y5)
y5_mean = ry5.mean()
y5_std = ry5.std()
if n>9: logging.debug("NORMALTEST6: %s" % str(scipy.stats.normaltest(ry5)))
myplot.hisplot(y5,title,bdp_name+"_6",xlab=xlab,gauss=[y5_mean,y5_std],thumbnail=True)
logging.debug("LogPeak: m,s= %f %f min/max %f %f" % (y1.mean(),y1.std(),y1.min(),y1.max()))
logging.debug("LogNoise: m,s= %f %f %f %f min/max %f %f" % (y2.mean(),y2.std(),y2_mean,y2_std,y2.min(),y2.max()))
logging.debug("LogDeltaNoise: RMS/sqrt(2)= %f %f " % (dy2.std()/math.sqrt(2),dy2_std/math.sqrt(2)))
logging.debug("LogDeltaP/N: RMS/sqrt(2)= %f %f" % (dy3.std()/math.sqrt(2),dy3_std/math.sqrt(2)))
logging.debug("LogPeak+Min: robust m,s= %f %f" % (y5_mean,y5_std))
# compute two ratios that should both be near 1.0 if noise is 'normal'
ratio = y2.std()/(dy2.std()/math.sqrt(2))
ratio2 = y2_std/(dy2_std/math.sqrt(2))
logging.info("RMS BAD VARIATION RATIO: %f %f" % (ratio,ratio2))
# making PPP plot
if nchan > 1 and use_ppp:
smax = 10
gamma = 0.75
z0 = peakval/peakval.max()
# point sizes
s = np.pi * ( smax * (z0**gamma) )**2
cmds = ["grid", "axis equal"]
title = "Peak Points per channel"
pppimage = bdp_name + '_ppp'
myplot.scatter(xpos,ypos,title=title,figname=pppimage,size=s,color=chans,cmds=cmds,thumbnail=True)
pppimage = myplot.getFigure(figno=myplot.figno,relative=True)
pppthumbnail = myplot.getThumbnail(figno=myplot.figno,relative=True)
caption = "Peak point plot: Locations of per-channel peaks in the image cube " + fin
self._summary["peakpnt"] = SummaryEntry([pppimage, pppthumbnail, caption, fin], "CubeStats_AT", self.id(True))
dt.tag("plotting")
# making PeakStats plot
if nchan > 1 and psample > 0:
logging.info("Computing peakstats")
# grab peak,mean and width values for all peaks
(pval,mval,wval) = peakstats(self.dir(fin),freqs,sigma0,pnumsigma,minchan,maxgap,psample,peakfit)
title = "PeakStats: cutoff = %g" % (sigma0*pnumsigma)
xlab = 'Peak value'
ylab = 'FWHM (channels)'
pppimage = bdp_name + '_peakstats'
cval = mval
myplot.scatter(pval,wval,title=title,xlab=xlab,ylab=ylab,color=cval,figname=pppimage,thumbnail=False)
dt.tag("peakstats")
# myplot.final() # pjt debug
# all done!
dt.tag("done")
taskargs = "robust=" + sumrargs
if use_ppp:
taskargs = taskargs + " ppp=True"
else:
taskargs = taskargs + " ppp=False"
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
class PVCorr_AT(AT):
"""PV correllation in a PVSlice map.
PVCorr_AT computes a cross-correlation of a feature in a PVSlice with the
whole PVSlice, looking for repeated patterns to detect spectral lines. Much
like the output from CubeStats_AT and CubeSpectrum_AT, this table can then be
given to LineID_AT to attempt a line identification.
See also :ref:`PVCorr-AT-Design` for the design document.
**Keywords**
**numsigma**: float
Minimum intensity, in terms of sigma, above which a selected portion
of the spectrum will be used for cross-correlation.
Default: 3.0.
**range**: integer list
If given, it has to be a list with 2 channel numbers, the first and last channel
(0-based channels) of the range which to use for the cross-correlation.
Default: [].
**nchan**: integer
The number of channels (in case range= was not used) that defines the line.
The line is centers on the strongest point in the input PV-map.
If 0 is given, it will watershed down from the strongest line.
Default: 0.
**Input BDPs**
**PVSlice_BDP**: count: 1
Input PV Slice. As created with e.g. PVSlice_AT.
**CubeStats_BDP**: count: 1
Input cube statistics from which the RMS is taken.
**Output BDPs**
**PVCorr_BDP**: count: 1
Output table.
"""
def __init__(self, **keyval):
keys = {
"numsigma": 3.0, # N-sigma
"range": [], # optional channel range
"nchan":
0, # number of channels around the channel where the peak is
}
AT.__init__(self, keys, keyval)
self._version = "1.0.1"
self.set_bdp_in([
(Image_BDP, 1, bt.REQUIRED),
# @todo optional 2nd PVSlice can be used to draw the template from
(CubeStats_BDP, 1, bt.REQUIRED)
])
self.set_bdp_out([(PVCorr_BDP, 1)])
def summary(self):
"""Returns the summary dictionary from the AT, for merging
into the ADMIT Summary object.
PVCorr_AT adds the following to ADMIT summary:
.. table::
:class: borderless
+----------+----------+-----------------------------------+
| Key | type | Description |
+==========+==========+===================================+
| pvcorr | list | correlation diagram |
+----------+----------+-----------------------------------+
Parameters
----------
None
Returns
-------
dict
Dictionary of SummaryEntry
"""
if hasattr(self, "_summary"):
return self._summary
else:
return {}
def run(self):
dt = utils.Dtime("PVCorr")
self._summary = {}
numsigma = self.getkey("numsigma")
mode = 1 # PV corr mode (1,2,3)
normalize = True
# normalize = False
b1 = self._bdp_in[0] # PVSlice_BDP
fin = b1.getimagefile(bt.CASA) # CASA image
data = casautil.getdata_raw(
self.dir(fin)) # grab the data as a numpy array
self.myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
#print 'DATA[0,0]:',data[0,0]
#print 'pv shape: ',data.shape
npos = data.shape[0]
nvel = data.shape[1]
dt.tag("getdata")
b2 = self._bdp_in[1] # CubeStats_BDP
sigma = b2.sigma # global sigma in the cube
cutoff = numsigma * sigma
freq = b2.table.getColumnByName("frequency")
chans = self.getkey("range") # range of channels, if used
if len(chans) > 0:
if len(chans) != 2:
logging.fatal("range=%s" % chans)
raise Exception, "range= needs two values, left and right (inclusive) channel"
ch0 = chans[0]
ch1 = chans[1]
else:
nchan = self.getkey("nchan")
imstat0 = casa.imstat(self.dir(fin)) # @todo can use data[] now
xmaxpos = imstat0['maxpos'][0]
ymaxpos = imstat0['maxpos'][1]
logging.info("MAXPOS-VEL %s %g" %
(str(imstat0['maxpos']), imstat0['max'][0]))
if nchan > 0:
# expand around it, later ch0,ch1 will be checked for running off the edge
ch0 = ymaxpos - nchan / 2
ch1 = ymaxpos + nchan / 2
else:
# watershed down to find ch0 and ch1 ?
# this doesn't work well in crowded areas
ch0 = ymaxpos
ch1 = ymaxpos
spmax = data.max(axis=0)
k = spmax.argmax()
n = len(spmax)
logging.debug('spmax %s %d %g' %
(str(spmax.shape), k, spmax[k]))
# find lower cutoff
for i in range(n):
ch0 = ymaxpos - i
if ch0 < 0: break
if spmax[ch0] < cutoff: break
ch0 = ch0 + 1
# find higher cutoff
for i in range(n):
ch1 = ymaxpos + i
if ch1 == n: break
if spmax[ch1] < cutoff: break
ch1 = ch1 - 1
dt.tag("imstat")
bdp_name = self.mkext(fin, "pvc") # output PVCorr_BDP
b3 = PVCorr_BDP(bdp_name)
self.addoutput(b3)
if ch0 < 0 or ch1 >= nvel:
# this probably only happens to small cubes (problematic for PVCorr)
# or when the strongest line is really close to the edge of the band
# (which is probably ok)
if ch0 < 0 and ch1 >= nvel:
logging.warning("Serious issues with the size of this cube")
if ch0 < 0:
logging.warning("Resetting ch0 edge to 0")
ch0 = 0
if ch1 >= nvel:
ch1 = nvel - 1
logging.warning("Resetting ch1 edge to the maximum")
if ch0 > ch1:
logging.warning("Sanity swapping ch0,1 due to likely noisy data")
ch0, ch1 = ch1, ch0
if mode == 1:
out, rms = mode1(data, ch0, ch1, cutoff, normalize)
corr = out
elif mode == 2:
out, rms = mode2(data, ch0, ch1, cutoff) # slower 2D version
corr = out[
npos /
2, :] # center cut, but could also try feature detection
elif mode == 3:
out, rms = self.mode3(data, ch0, ch1,
cutoff) # Doug's faster 2D version
# get the peak of each column
corr = np.amax(out, axis=0)
# print "PVCORR SHAPE ",corr.shape," mode", mode
if len(corr) > 0:
# print "SHAPE out:",out.shape,corr.shape,npos/2
ch = range(len(corr))
if len(corr) != len(freq):
logging.fatal("ch (%d) and freq (%d) do not have same size" %
(len(corr), len(freq)))
raise Exception, "ch and freq do not have same dimension"
dt.tag("mode")
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
data = (ch, freq, corr)
table = Table(columns=labels,
units=units,
data=np.column_stack(data))
else:
# still construct a table, but with no rows
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
table = Table(columns=labels, units=units)
b3.setkey("table", table)
b3.setkey("sigma", float(rms))
dt.tag("table")
if len(corr) > 0:
table.exportTable(self.dir("testPVCorr.tab"),
cols=['frequency', 'pvcorr'])
test_single(ch, freq, corr)
logging.regression("PVC: %f %f" % (corr.min(), corr.max()))
title = 'PVCorr mode=%d [%d,%d] %g' % (mode, ch0, ch1, cutoff)
x = ch
xlab = 'Channel'
y = [corr]
ylab = 'PV Correlation'
p1 = "%s_%d" % (bdp_name, 0)
segp = []
segp.append([0, len(ch), 0.0, 0.0])
segp.append([0, len(ch), 3.0 * rms, 3.0 * rms])
# @todo: in principle we know with given noise and size of box, what the sigma in pvcorr should be
self.myplot.plotter(x,
y,
title,
figname=p1,
xlab=xlab,
ylab=ylab,
segments=segp,
thumbnail=True)
#out1 = np.rot90 (data.reshape((nvel,npos)) )
if mode > 1:
self.myplot.map1(data=out,
title="testing PVCorr_AT: mode%d" % mode,
figname='testPVCorr',
thumbnail=True)
taskargs = "numsigma=%.1f range=[%d,%d]" % (numsigma, ch0, ch1)
caption = "Position-velocity correlation plot"
thumbname = self.myplot.getThumbnail(figno=self.myplot.figno,
relative=True)
figname = self.myplot.getFigure(figno=self.myplot.figno,
relative=True)
image = Image(images={bt.PNG: figname},
thumbnail=thumbname,
thumbnailtype=bt.PNG,
description=caption)
b3.image.addimage(image, "pvcorr")
self._summary["pvcorr"] = SummaryEntry(
[figname, thumbname, caption, fin], "PVCorr_AT", self.id(True),
taskargs)
else:
self._summary["pvcorr"] = None
logging.warning("No summary")
logging.regression("PVC: -1")
dt.tag("done")
dt.end()
def mode3(self, data, v0, v1, dmin=0.0):
""" v0..v1 (both inclusive) are channel selections
threshold on dmin
@todo the frequency axis is not properly calibrated here
@todo a full 2D is slow, we only need the 1D version
"""
print "PVCorr mode3: v0,1=", v0, v1
smin = data.min()
#s = data[v0:v1+1,:]
s = data[:, v0:v1 + 1]
if dmin == 0.0:
logging.warning("Using all data in crosscorr")
f = s
else:
f = np.where(s > dmin, s, 0)
# find out where the zeros are
temp = np.amax(f, axis=1)
nz = np.nonzero(temp)
# trim the kernel in the y direction, removing rows that are all 0.0
f = f[nz[0][0]:nz[0][-1], :]
f0 = np.where(s > smin, 1, 0)
f1 = np.where(s > dmin, 1, 0)
fmax = f.max()
print "PVCorr mode3:", f1.sum(), '/', f0.sum(), 'min/max', smin, fmax
out = scipy.signal.correlate2d(data, f, mode='same')
self.myplot.map1(data=f,
title="PVCorr 2D Kernel",
figname='PVCorrKernel',
thumbnail=True)
print 'PVCorr min/max:', out.min(), out.max()
n1, m1, s1, n2, m2, s2 = stats.mystats(out.flatten())
print "PVCorr stats", n1, m1, s1, n2, m2, s2
rms_est = s2 / np.sqrt(f1.sum())
return out, rms_est
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
seed = self.getkey("seed")
if seed <= 0:
np.random.seed()
else:
np.random.seed(seed)
#print "RANDOM.GET_STATE:",np.random.get_state()
contin = self.getkey("contin")
rms = 1.0 # not a user parameter, we do all spectra in S/N space
f0 = self.getkey("freq") # central frequency in band
df = self.getkey("delta") / 1000.0 # channel width (in GHz)
nspectra = self.getkey("nspectra")
taskargs = " contin=%f freq=%f delta=%f nspectra=%f " % (contin, f0,
df, nspectra)
spec = range(nspectra)
dt.tag("start")
if self.getkey("file") != "":
print "READING spectrum from", self.getkey("file")
(freq, spec[0]) = getspec(self.getkey("file"))
nchan = len(freq)
print "Spectrum %d chans from %f to %f: min/max = %f %f" % (
nchan, freq.min(), freq.max(), spec[0].min(), spec[0].max())
# @todo nspectra>1 not tested
for i in range(1, nspectra):
spec[i] = deepcopy(spec[0])
dt.tag("getspec")
else:
nchan = self.getkey("nchan")
freq = np.arange(nchan, dtype=np.float64)
center = int(nchan / 2)
for i in range(nchan):
freq[i] = f0 + (float((i - center)) * df)
for i in range(nspectra):
spec[i] = np.zeros(nchan)
chans = np.arange(nchan)
taskargs += " nchan = %d" % nchan
for i in range(nspectra):
if seed >= 0:
spec[i] += np.random.normal(contin, rms, nchan)
# print "MEAN/STD",spec[i].mean(),spec[i].std()
lines = self.getkey("lines")
sls = SpectralLineSearch(False)
for item in self.getkey("transitions"):
kw = {
"include_only_nrao": True,
"line_strengths": ["ls1", "ls2"],
"energy_levels": ["el2", "el4"],
"fel": True,
"species": item[0]
}
results = sls.search(item[1][0], item[1][1], "off", **kw)
# look at line strengths
if len(results) > 0:
mx = 0.0
indx = -1
for i in range(len(results)):
if results[i].getkey("linestrength") > mx:
indx = i
mx = results[i].getkey("linestrength")
for res in results:
if mx > 0.0:
lines.append([
item[2] * res.getkey("linestrength") / mx,
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
else:
lines.append([
item[2],
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
for item in lines:
for i in range(nspectra):
spec[i] += utils.gaussian1D(freq, item[0], item[1],
utils.veltofreq(item[2], item[1]))
if self.getkey("hanning"):
for i in range(nspectra):
filter = Filter1D.Filter1D(spec[i], "hanning", **{"width": 3})
spec[i] = filter.run()
dt.tag("hanning")
center = int(nchan / 2)
dt.tag("open")
bdp_name = self.mkext("Genspec", "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
images = {} # png's accumulated
for i in range(nspectra):
sd = []
caption = "Generated Spectrum %d" % i
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel", "frequency", "flux"]
units = ["number", "GHz", ""]
data = (chans, freq, spec[i])
# plane 0 : we are allowing a multiplane table, so the first plane is special
if i == 0:
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
x = chans
xlab = 'Channel'
y = [spec[i]]
sd.append(xlab)
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux'
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x, y, "", p1, xlab=xlab, ylab=ylab, thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno, relative=True)
image = Image(images=images, description="CubeSpectrum")
sd.extend([ii, thumbname, caption])
self.spec_description.append(sd)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"GenerateSpectrum_AT",
self.id(True), taskargs)
dt.tag("table")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", rms)
b2.setkey("mean", contin)
dt.tag("done")
dt.end()
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
# our BDP's
# b1 = input BDP
# b1s = optional input CubeSpectrum
# b1m = optional input Moment
# b1p = optional input SourceList for positions
# b2 = output BDP
b1 = self._bdp_in[0] # check input SpwCube (or LineCube)
fin = b1.getimagefile(bt.CASA)
if self._bdp_in[0]._type == bt.LINECUBE_BDP:
use_vel = True
else:
use_vel = False
sources = self.getkey("sources")
pos = [
] # blank it first, then try and grab it from the optional bdp_in's
cmean = 0.0
csigma = 0.0
smax = [] # accumulate max in each spectrum for regression
self.spec_description = [] # for summary()
if self._bdp_in[1] != None: # check if CubeStats_BDP
#print "BDP[1] type: ",self._bdp_in[1]._type
if self._bdp_in[1]._type != bt.CUBESTATS_BDP:
raise Exception, "bdp_in[1] not a CubeStats_BDP, should never happen"
# a table (cubestats)
b1s = self._bdp_in[1]
pos.append(b1s.maxpos[0])
pos.append(b1s.maxpos[1])
logging.info('CubeStats::maxpos,val=%s,%f' %
(str(b1s.maxpos), b1s.maxval))
cmean = b1s.mean
csigma = b1s.sigma
dt.tag("CubeStats-pos")
if self._bdp_in[
2] != None: # check if Moment_BDP (probably from CubeSum)
#print "BDP[2] type: ",self._bdp_in[2]._type
if self._bdp_in[2]._type != bt.MOMENT_BDP:
raise Exception, "bdp_in[2] not a Moment_BDP, should never happen"
b1m = self._bdp_in[2]
fim = b1m.getimagefile(bt.CASA)
pos1, maxval = self.maxpos_im(
self.dir(fim)) # compute maxpos, since it is not in bdp (yet)
logging.info('CubeSum::maxpos,val=%s,%f' % (str(pos1), maxval))
pos.append(pos1[0])
pos.append(pos1[1])
dt.tag("Moment-pos")
if self._bdp_in[3] != None: # check if SourceList
#print "BDP[3] type: ",self._bdp_in[3]._type
# a table (SourceList)
b1p = self._bdp_in[3]
ra = b1p.table.getFullColumnByName("RA")
dec = b1p.table.getFullColumnByName("DEC")
peak = b1p.table.getFullColumnByName("Peak")
if sources == []:
# use the whole SourceList
for (r, d, p) in zip(ra, dec, peak):
rdc = convert_sexa(r, d)
pos.append(rdc[0])
pos.append(rdc[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(rdc), p))
else:
# select specific ones from the source list
for ipos in sources:
if ipos < len(ra):
radec = convert_sexa(ra[ipos], dec[ipos])
pos.append(radec[0])
pos.append(radec[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(radec), peak[ipos]))
else:
logging.warning('Skipping illegal source number %d' %
ipos)
dt.tag("SourceList-pos")
# if pos[] still blank, use the AT keyword.
if len(pos) == 0:
pos = self.getkey("pos")
# if still none, try the map center
if len(pos) == 0:
# @todo this could result in a masked pixel and cause further havoc
# @todo could also take the reference pixel, but that could be outside image
taskinit.ia.open(self.dir(fin))
s = taskinit.ia.summary()
pos = [int(s['shape'][0]) / 2, int(s['shape'][1]) / 2]
logging.warning(
"No input positions supplied, map center choosen: %s" %
str(pos))
dt.tag("map-center")
# exhausted all sources where pos[] can be set; if still zero, bail out
if len(pos) == 0:
raise Exception, "No positions found from input BDP's or pos="
# convert this regular list to a list of tuples with duplicates removed
# sadly the order is lost.
pos = list(set(zip(pos[0::2], pos[1::2])))
npos = len(pos)
dt.tag("open")
bdp_name = self.mkext(fin, "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
imval = range(npos) # spectra, one for each pos (placeholder)
planes = range(npos) # labels for the tables (placeholder)
images = {} # png's accumulated
for i in range(npos): # loop over pos, they can have mixed types now
sd = []
caption = "Spectrum"
xpos = pos[i][0]
ypos = pos[i][1]
if type(xpos) != type(ypos):
print "POS:", xpos, ypos
raise Exception, "position pair not of the same type"
if type(xpos) == int:
# for integers, boxes are allowed, even multiple
box = '%d,%d,%d,%d' % (xpos, ypos, xpos, ypos)
# convention for summary is (box)
cbox = '(%d,%d,%d,%d)' % (xpos, ypos, xpos, ypos)
# use extend here, not append, we want individual values in a list
sd.extend([xpos, ypos, cbox])
caption = "Average Spectrum at %s" % cbox
if False:
# this will fail on 3D cubes (see CAS-7648)
imval[i] = casa.imval(self.dir(fin), box=box)
else:
# work around that CAS-7648 bug
# another approach is the ia.getprofile(), see CubeStats, this will
# also integrate over regions, imval will not (!!!)
region = 'centerbox[[%dpix,%dpix],[1pix,1pix]]' % (xpos,
ypos)
caption = "Average Spectrum at %s" % region
imval[i] = casa.imval(self.dir(fin), region=region)
elif type(xpos) == str:
# this is tricky, to stay under 1 pixel , or you get a 2x2 back.
region = 'centerbox[[%s,%s],[1pix,1pix]]' % (xpos, ypos)
caption = "Average Spectrum at %s" % region
sd.extend([xpos, ypos, region])
imval[i] = casa.imval(self.dir(fin), region=region)
else:
print "Data type: ", type(xpos)
raise Exception, "Data type for region not handled"
dt.tag("imval")
flux = imval[i]['data']
if len(flux.shape
) > 1: # rare case if we step on a boundary between cells?
logging.warning(
"source %d has spectrum shape %s: averaging the spectra" %
(i, repr(flux.shape)))
flux = np.average(flux, axis=0)
logging.debug('minmax: %f %f %d' %
(flux.min(), flux.max(), len(flux)))
smax.append(flux.max())
if i == 0: # for first point record few extra things
if len(imval[i]['coords'].shape) == 2: # normal case: 1 pixel
freqs = imval[i]['coords'].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
elif len(imval[i]['coords'].shape
) == 3: # rare case if > 1 point in imval()
freqs = imval[i]['coords'][0].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
else:
logging.fatal(
"bad shape %s in freq return from imval - SHOULD NEVER HAPPEN"
% imval[i]['coords'].shape)
chans = np.arange(len(freqs)) # channels 0..nchans-1
unit = imval[i]['unit']
restfreq = casa.imhead(
self.dir(fin), mode="get",
hdkey="restfreq")['value'] / 1e9 # in GHz
dt.tag("imhead")
vel = (
1 - freqs / restfreq
) * utils.c # @todo : use a function (and what about relativistic?)
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel", "frequency", "flux"]
units = ["number", "GHz", unit]
data = (chans, freqs, flux)
if i == 0:
# plane 0 : we are allowing a multiplane table, so the first plane is special
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
# planes 1,2,3.... are stacked onto the previous one
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
if use_vel:
x = vel
xlab = 'VLSR (km/s)'
else:
x = chans
xlab = 'Channel'
y = [flux]
sd.append(xlab)
if type(xpos) == int:
# grab the RA/DEC... kludgy
h = casa.imstat(self.dir(fin), box=box)
ra = h['blcf'].split(',')[0]
dec = h['blcf'].split(',')[1]
title = '%s %d @ %d,%d = %s,%s' % (bdp_name, i, xpos, ypos, ra,
dec)
else:
title = '%s %d @ %s,%s' % (
bdp_name, i, xpos, ypos
) # or use box, once we allow non-points
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux (%s)' % unit
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x,
y,
title,
p1,
xlab=xlab,
ylab=ylab,
thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno, relative=True)
sd.extend([ii, thumbname, caption, fin])
self.spec_description.append(sd)
logging.regression("CSP: %s" % str(smax))
image = Image(images=images, description="CubeSpectrum")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", csigma) # TODO: not always available
b2.setkey("mean", cmean) # TODO: not always available
if True:
# @todo only first plane due to limitation in exportTable()
islash = bdp_name.find('/')
if islash < 0:
tabname = self.dir("testCubeSpectrum.tab")
else:
tabname = self.dir(bdp_name[:islash] + "/testCubeSpectrum.tab")
table.exportTable(tabname, cols=["frequency", "flux"])
dt.tag("done")
# For a single spectrum this is
# SummaryEntry([[data for spec1]], "CubeSpectrum_AT",taskid)
# For multiple spectra this is
# SummaryEntry([[data for spec1],[data for spec2],...], "CubeSpectrum_AT",taskid)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"CubeSpectrum_AT",
self.id(True))
taskargs = "pos=" + str(pos)
taskargs += ' <span style="background-color:white"> ' + fin.split(
'/')[0] + ' </span>'
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
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, locates lines, attempts to identify them, and
creates the BDP
Parameters
----------
None
Returns
-------
None
"""
if not self.boxcar:
logging.info("Boxcar smoothing turned off.")
self._summary = {}
self.freq = []
self.chan = []
dt = utils.Dtime("LineSegment") # timer for debugging
spec_description = []
taskargs = self._taskargs()
statbdp = None # for the CubeStats BDP
specbdp = None # for the CubeSpectrum BDP
specs = [] # to hold the input CubeSpectrum based spectra
statspec = [] # to hold the input CubeStats based spectrum
statseg = [] # to hold the detected segments from statspec
specseg = [] # to hold the detected segments from specs
#statcutoff = [] # cutoff for statspec line finding
#speccutoff = [] # cutoff for specs line finding
infile = ""
if self.getkey("minchan") < 1:
raise Exception("minchan must eb a positive value.")
elif self.getkey("minchan") == 1 and self.getkey("iterate"):
logging.info(
"iterate=True is not allowed for minchan=1, setting iterate to False"
)
self.setkey("iterate", False)
vlsr = 0.0
# get the input bdp
if self._bdp_in[0] is not None:
specbdp = self._bdp_in[0]
infile = specbdp.xmlFile
if self._bdp_in[1] is not None:
statbdp = self._bdp_in[1]
infile = statbdp.xmlFile
# still need to do this check since all are optional inputs
if specbdp == statbdp is None:
raise Exception("No input BDP's found.")
imbase = self.mkext(infile, 'lseg')
# grab any optional references overplotted on the "ll" plots
# instantiate a plotter for all plots made herein
self._plot_type = admit.util.PlotControl.SVG
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
dt.tag("start")
############################################################################
# Smoothing and continuum (baseline) subtraction of input spectra #
############################################################################
# get and smooth all input spectra
basicsegment = {
"method": self.getkey("segment"),
"minchan": self.getkey("minchan"),
"maxgap": self.getkey("maxgap"),
"numsigma": self.getkey("numsigma"),
"iterate": self.getkey("iterate"),
"nomean": True
}
segargsforcont = {
"name": "Line_Segment.%i.asap" % self.id(True),
"pmin": self.getkey("numsigma"),
"minchan": self.getkey("minchan"),
"maxgap": self.getkey("maxgap")
}
if specbdp is not None:
# get the spectrum
specs = specutil.getspectrum(specbdp, vlsr, self.getkey("smooth"),
self.getkey("recalcnoise"),
basicsegment)
# remove the continuum, if requested
if self.getkey("csub")[1] is not None:
logging.info(
"Attempting Continuum Subtraction for Input Spectra")
order = self.getkey("csub")[1]
specutil.contsub(self.id(True),
specs,
self.getkey("segment"),
segargsforcont,
algorithm="PolyFit",
**{"deg": order})
else:
for spec in specs:
spec.set_contin(np.zeros(len(spec)))
for spec in specs:
self.freq, self.chan = specutil.mergefreq(
self.freq, self.chan, spec.freq(False), spec.chans(False))
# get any input cubestats
if statbdp is not None:
statspec = specutil.getspectrum(statbdp, vlsr,
self.getkey("smooth"),
self.getkey("recalcnoise"),
basicsegment)
# remove the continuum
if self.getkey("csub")[0] is not None:
logging.info(
"Attempting Continuum Subtraction for Input CubeStats Spectra"
)
order = self.getkey("csub")[0]
specutil.contsub(self.id(True),
statspec,
self.getkey("segment"),
segargsforcont,
algorithm="PolyFit",
**{"deg": order})
# The 'min' spectrum is inverted for segment finding.
# Doesn't this mean it will also be plotted upside down?
if len(statspec) > 0: statspec[1].invert()
for spec in statspec:
self.freq, self.chan = specutil.mergefreq(
self.freq, self.chan, spec.freq(False), spec.chans(False))
dt.tag("getspectrum")
if isinstance(self.freq, np.ndarray):
self.freq = self.freq.tolist()
if isinstance(self.chan, np.ndarray):
self.chan = self.chan.tolist()
# search for segments of spectral line emission
#NB: this is repetitive with basicsegment above.
method = self.getkey("segment")
minchan = self.getkey("minchan")
maxgap = self.getkey("maxgap")
numsigma = self.getkey("numsigma")
iterate = self.getkey("iterate")
if specbdp is not None:
logging.info("Detecting segments in CubeSpectrum based data")
values = specutil.findsegments(specs, method, minchan, maxgap,
numsigma, iterate)
for i, t in enumerate(values):
specseg.append(t[0])
specs[i].set_noise(t[2])
if statbdp is not None:
logging.info("Detecting segments in CubeStats based data")
values = specutil.findsegments(statspec, method, minchan, maxgap,
numsigma, iterate)
for i, t in enumerate(values):
statseg.append(t[0])
# print ("MWP LINESEGMENT %d Setting noise=%f minchan=%d",(i,t[2],minchan))
statspec[i].set_noise(t[2])
#statcutoff.append(t[1])
dt.tag("segment finder")
lsbdp = LineSegment_BDP(imbase)
finalsegs = utils.mergesegments([statseg, specseg], len(self.freq))
lines = specutil.linedatafromsegments(self.freq, self.chan, finalsegs,
specs, statspec)
llist = []
for l in lines:
lsbdp.addRow(l)
llist.append(l)
rdata = []
# create the output
label = ["Peak/Noise", "Minimum/Noise"]
caption = [
"Potential lines overlaid on peak intensity plot from CubeStats_BDP.",
"Potential lines overlaid on minimum intensity plot from CubeStats_BDP."
]
xlabel = "Frequency (GHz)"
for i, spec in enumerate(statspec):
freqs = []
for ch in statseg[i]:
frq = [
min(spec.freq()[ch[0]],
spec.freq()[ch[1]]),
max(spec.freq()[ch[0]],
spec.freq()[ch[1]])
]
freqs.append(frq)
rdata.append(frq)
#print("Stats segment, peak, ratio, fwhm ",lname,peak,ratio,fwhm)
mult = 1.
if i == 1:
mult = -1.
# print("MWP statspec plot cutoff[%d] = %f, contin=%f" % (i, (statspec[i].contin() + mult*(statspec[i].noise() * self.getkey("numsigma")))[0], statspec[i].contin()[0] ) )
myplot.segplotter(
spec.freq(),
spec.spec(csub=False),
title="Detected Line Segments",
xlab=xlabel,
ylab=label[i],
figname=imbase + "_statspec%i" % i,
segments=freqs,
cutoff=(spec.contin() + mult *
(spec.noise() * self.getkey("numsigma"))),
continuum=spec.contin(),
thumbnail=True)
imname = myplot.getFigure(figno=myplot.figno, relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,
relative=True)
image = Image(images={bt.SVG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG,
description=caption[i])
lsbdp.image.addimage(image, "statspec%i" % i)
spec_description.append([
lsbdp.ra, lsbdp.dec, "", xlabel, imname, thumbnailname,
caption[i], infile
])
for i in range(len(specs)):
freqs = []
for ch in specseg[i]:
frq = [
min(specs[i].freq()[ch[0]], specs[i].freq()[ch[1]]),
max(specs[i].freq()[ch[0]], specs[i].freq()[ch[1]])
]
freqs.append(frq)
rdata.append(frq)
myplot.segplotter(specs[i].freq(),
specs[i].spec(csub=False),
title="Detected Line Segments",
xlab=xlabel,
ylab="Intensity",
figname=imbase + "_spec%03d" % i,
segments=freqs,
cutoff=specs[i].contin() +
(specs[i].noise() * self.getkey("numsigma")),
continuum=specs[i].contin(),
thumbnail=True)
imname = myplot.getFigure(figno=myplot.figno, relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,
relative=True)
caption = "Detected line segments from input spectrum #%i." % (i)
image = Image(images={bt.SVG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG,
description=caption)
lsbdp.image.addimage(image, "spec%03d" % i)
spec_description.append([
lsbdp.ra, lsbdp.dec, "", xlabel, imname, thumbnailname,
caption, infile
])
caption = "Merged segments overlaid on CubeStats spectrum"
myplot.summaryspec(statspec, specs, None, imbase + "_summary", llist)
imname = myplot.getFigure(figno=myplot.figno, relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno, relative=True)
caption = "Identified segments overlaid on Signal/Noise plot of all spectra."
image = Image(images={bt.SVG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG,
description=caption)
lsbdp.image.addimage(image, "summary")
spec_description.append([
lsbdp.ra, lsbdp.dec, "", "Signal/Noise", imname, thumbnailname,
caption, infile
])
self._summary["segments"] = SummaryEntry(lsbdp.table.serialize(),
"LineSegment_AT",
self.id(True), taskargs)
self._summary["spectra"] = [
SummaryEntry(spec_description, "LineSegment_AT", self.id(True),
taskargs)
]
self.addoutput(lsbdp)
logging.regression("LINESEG: %s" % str(rdata))
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
ia = taskinit.iatool()
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
ia.open(self.dir("mom0.masked"))
defmask = ia.maskhandler('default')
ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
ia.open(self.dir(imagename))
ia.maskhandler('set', defmask)
ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,
colorwedge=True,zoom=self.getkey("zoom"))
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("SFind2D") # tagging time
self._summary = {}
# get key words that user input
nsigma = self.getkey("numsigma")
sigma = self.getkey("sigma")
region = self.getkey("region")
robust = self.getkey("robust")
snmax = self.getkey("snmax")
nmax = self.getkey("nmax")
ds9 = True # writes a "ds9.reg" file
mpl = True # aplot.map1() plot
dynlog = 20.0 # above this value of dyn range finder chart is log I-scaled
bpatch = True # patch units to Jy/beam for ia.findsources()
# get the input casa image from bdp[0]
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
if mpl:
data = np.flipud(np.rot90(casautil.getdata(self.dir(infile)).data))
# check if there is a 2nd image (which will be a PB)
for i in range(len(self._bdp_in)):
print 'BDP', i, type(self._bdp_in[i])
if self._bdp_in[2] != None:
bdpin_pb = self._bdp_in[1]
bdpin_cst = self._bdp_in[2]
print "Need to process PB"
else:
bdpin_pb = None
bdpin_cst = self._bdp_in[1]
print "No PB given"
# get the output bdp basename
slbase = self.mkext(infile, 'sl')
# make sure it's a 2D map
if not casautil.mapdim(self.dir(infile), 2):
raise Exception, "Input map dimension not 2: %s" % infile
# arguments for imstat call if required
args = {"imagename": self.dir(infile)}
if region != "":
args["region"] = region
dt.tag("start")
# The following code sets the sigma level for searching for sources using
# the sigma and snmax keyword as appropriate
# if no CubeStats BDP was given and no sigma was specified:
# find a noise level via casa.imstat()
# if a CubeStat_BDP is given get it from there.
if bdpin_cst == None:
# get statistics from input image with imstat because no CubeStat_BDP
stat = casa.imstat(**args)
dmin = float(
stat["min"]
[0]) # these would be wrong if robust were used already
dmax = float(stat["max"][0])
args.update(casautil.parse_robust(
robust)) # only now add robust keywords for the sigma
stat = casa.imstat(**args)
if sigma <= 0.0:
sigma = float(stat["sigma"][0])
dt.tag("imstat")
else:
# get statistics from CubeStat_BDP
sigma = bdpin_cst.get("sigma")
dmin = bdpin_cst.get("minval")
dmax = bdpin_cst.get("maxval")
self.setkey("sigma", sigma)
# calculate cutoff based either on RMS or dynamic range limitation
drange = dmax / (nsigma * sigma)
if snmax < 0.0:
snmax = drange
if drange > snmax:
cutoff = 1.0 / snmax
else:
cutoff = 1.0 / drange
logging.info("sigma, dmin, dmax, snmax, cutoff %g %g %g %g %g" %
(sigma, dmin, dmax, snmax, cutoff))
# define arguments for call to findsources
args2 = {"cutoff": cutoff}
args2["nmax"] = nmax
if region != "":
args2["region"] = region
#args2["mask"] = ""
args2["point"] = False
args2["width"] = 5
args2["negfind"] = False
# set-up for SourceList_BDP
slbdp = SourceList_BDP(slbase)
# connect to casa image and call casa ia.findsources tool
ia = taskinit.iatool()
ia.open(self.dir(infile))
# findsources() cannot deal with 'Jy/beam.km/s' ???
# so for the duration of findsources() we patch it
bunit = ia.brightnessunit()
if bpatch and bunit != 'Jy/beam':
logging.warning(
"Temporarely patching your %s units to Jy/beam for ia.findsources()"
% bunit)
ia.setbrightnessunit('Jy/beam')
else:
bpatch = False
atab = ia.findsources(**args2)
if bpatch:
ia.setbrightnessunit(bunit)
taskargs = "nsigma=%4.1f sigma=%g region=%s robust=%s snmax=%5.1f nmax=%d" % (
nsigma, sigma, str(region), str(robust), snmax, nmax)
dt.tag("findsources")
nsources = atab["nelements"]
xtab = []
ytab = []
logscale = False
sumflux = 0.0
if nsources > 0:
# @TODO: Why are Xpix, YPix not stored in the table?
# -> PJT: I left them out since they are connected to an image which may not be available here
# but we should store the frequency of the observation here for later bandmerging
logging.debug("%s" % str(atab['component0']['shape']))
logging.info(
"Right Ascen. Declination X(pix) Y(pix) Peak Flux Major Minor PA SNR"
)
funits = atab['component0']['flux']['unit']
if atab['component0']['shape'].has_key('majoraxis'):
sunits = atab['component0']['shape']['majoraxis']['unit']
aunits = atab['component0']['shape']['positionangle']['unit']
else:
sunits = "n/a"
aunits = "n/a"
punits = ia.summary()['unit']
logging.info(
" %s %s %s %s %s"
% (punits, funits, sunits, sunits, aunits))
#
# @todo future improvement is to look at image coordinates and control output appropriately
#
if ds9:
# @todo variable name
regname = self.mkext(infile, 'ds9.reg')
fp9 = open(self.dir(regname), "w!")
sn0 = -1.0
for i in range(nsources):
c = "component%d" % i
name = "%d" % (i + 1)
r = atab[c]['shape']['direction']['m0']['value']
d = atab[c]['shape']['direction']['m1']['value']
pixel = ia.topixel([r, d])
xpos = pixel['numeric'][0]
ypos = pixel['numeric'][1]
rd = ia.toworld([xpos, ypos], 's')
ra = rd['string'][0][:12]
dec = rd['string'][1][:12]
flux = atab[c]['flux']['value'][0]
sumflux = sumflux + flux
if atab[c]['shape'].has_key('majoraxis'):
smajor = atab[c]['shape']['majoraxis']['value']
sminor = atab[c]['shape']['minoraxis']['value']
sangle = atab[c]['shape']['positionangle']['value']
else:
smajor = 0.0
sminor = 0.0
sangle = 0.0
peakstr = ia.pixelvalue([xpos, ypos, 0, 0])
if len(peakstr) == 0:
logging.warning("Problem with source %d @ %d,%d" %
(i, xpos, ypos))
continue
peakf = peakstr['value']['value']
snr = peakf / sigma
if snr > dynlog:
logscale = True
if snr > sn0:
sn0 = snr
logging.info(
"%s %s %8.2f %8.2f %10.3g %10.3g %7.3f %7.3f %6.1f %6.1f" %
(ra, dec, xpos, ypos, peakf, flux, smajor, sminor, sangle,
snr))
xtab.append(xpos)
ytab.append(ypos)
slbdp.addRow(
[name, ra, dec, flux, peakf, smajor, sminor, sangle])
if ds9:
ras = ra
des = dec.replace('.', ':', 2)
msg = 'ellipse(%s,%s,%g",%g",%g) # text={%s}' % (
ras, des, smajor, sminor, sangle + 90.0, i + 1)
fp9.write("%s\n" % msg)
if ds9:
fp9.close()
logging.info("Wrote ds9.reg")
dt.tag("table")
logging.regression("CONTFLUX: %d %g" % (nsources, sumflux))
summary = ia.summary()
beammaj = summary['restoringbeam']['major']['value']
beammin = summary['restoringbeam']['minor']['value']
beamunit = summary['restoringbeam']['minor']['unit']
beamang = summary['restoringbeam']['positionangle']['value']
angunit = summary['restoringbeam']['positionangle']['unit']
# @todo add to table comments?
logging.info(" Fitted Gaussian size; NOT deconvolved source size.")
logging.info(
" Restoring Beam: Major axis: %10.3g %s , Minor axis: %10.3g %s , PA: %5.1f %s"
% (beammaj, beamunit, beammin, beamunit, beamang, angunit))
# form into a xml table
# output is a table_bdp
self.addoutput(slbdp)
# instantiate a plotter for all plots made herein
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
# make output png with circles marking sources found
if mpl:
circles = []
nx = data.shape[1] # data[] array was already flipud(rot90)'d
ny = data.shape[0] #
for (x, y) in zip(xtab, ytab):
circles.append([x, y, 1])
# @todo variable name
if logscale:
logging.warning("LogScaling applied")
data = data / sigma
data = np.where(data < 0, -np.log10(1 - data),
+np.log10(1 + data))
if nsources == 0:
title = "SFind2D: 0 sources above S/N=%.1f" % (nsigma)
elif nsources == 1:
title = "SFind2D: 1 source (%.1f < S/N < %.1f)" % (nsigma, sn0)
else:
title = "SFind2D: %d sources (%.1f < S/N < %.1f)" % (
nsources, nsigma, sn0)
myplot.map1(data,
title,
slbase,
thumbnail=True,
circles=circles,
zoom=self.getkey("zoom"))
#---------------------------------------------------------
# Get the figure and thumbmail names and create a caption
#---------------------------------------------------------
imname = myplot.getFigure(figno=myplot.figno, relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno, relative=True)
caption = "Image of input map with sources found by SFind2D overlayed in green."
slbdp.table.description = "Table of source locations and sizes (not deconvolved)"
#---------------------------------------------------------
# Add finder image to the BDP
#---------------------------------------------------------
image = Image(images={bt.PNG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG,
description=caption)
slbdp.image.addimage(image, "finderimage")
#-------------------------------------------------------------
# Create the summary entry for the table and image
#-------------------------------------------------------------
self._summary["sources"] = SummaryEntry(
[slbdp.table.serialize(),
slbdp.image.serialize()], "SFind2D_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" 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):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
# our BDP's
# b1 = input BDP
# b1s = optional input CubeSpectrum
# b1m = optional input Moment
# b1p = optional input SourceList for positions
# b2 = output BDP
b1 = self._bdp_in[0] # check input SpwCube (or LineCube)
fin = b1.getimagefile(bt.CASA)
if self._bdp_in[0]._type == bt.LINECUBE_BDP:
use_vel = True
else:
use_vel = False
sources = self.getkey("sources")
pos = [] # blank it first, then try and grab it from the optional bdp_in's
cmean = 0.0
csigma = 0.0
smax = [] # accumulate max in each spectrum for regression
self.spec_description = [] # for summary()
if self._bdp_in[1] != None: # check if CubeStats_BDP
#print "BDP[1] type: ",self._bdp_in[1]._type
if self._bdp_in[1]._type != bt.CUBESTATS_BDP:
raise Exception,"bdp_in[1] not a CubeStats_BDP, should never happen"
# a table (cubestats)
b1s = self._bdp_in[1]
pos.append(b1s.maxpos[0])
pos.append(b1s.maxpos[1])
logging.info('CubeStats::maxpos,val=%s,%f' % (str(b1s.maxpos),b1s.maxval))
cmean = b1s.mean
csigma = b1s.sigma
dt.tag("CubeStats-pos")
if self._bdp_in[2] != None: # check if Moment_BDP (probably from CubeSum)
#print "BDP[2] type: ",self._bdp_in[2]._type
if self._bdp_in[2]._type != bt.MOMENT_BDP:
raise Exception,"bdp_in[2] not a Moment_BDP, should never happen"
b1m = self._bdp_in[2]
fim = b1m.getimagefile(bt.CASA)
pos1,maxval = self.maxpos_im(self.dir(fim)) # compute maxpos, since it is not in bdp (yet)
logging.info('CubeSum::maxpos,val=%s,%f' % (str(pos1),maxval))
pos.append(pos1[0])
pos.append(pos1[1])
dt.tag("Moment-pos")
if self._bdp_in[3] != None: # check if SourceList
#print "BDP[3] type: ",self._bdp_in[3]._type
# a table (SourceList)
b1p = self._bdp_in[3]
ra = b1p.table.getFullColumnByName("RA")
dec = b1p.table.getFullColumnByName("DEC")
peak = b1p.table.getFullColumnByName("Peak")
if sources == []:
# use the whole SourceList
for (r,d,p) in zip(ra,dec,peak):
rdc = convert_sexa(r,d)
pos.append(rdc[0])
pos.append(rdc[1])
logging.info('SourceList::maxpos,val=%s,%f' % (str(rdc),p))
else:
# select specific ones from the source list
for ipos in sources:
if ipos < len(ra):
radec = convert_sexa(ra[ipos],dec[ipos])
pos.append(radec[0])
pos.append(radec[1])
logging.info('SourceList::maxpos,val=%s,%f' % (str(radec),peak[ipos]))
else:
logging.warning('Skipping illegal source number %d' % ipos)
dt.tag("SourceList-pos")
# if pos[] still blank, use the AT keyword.
if len(pos) == 0:
pos = self.getkey("pos")
# if still none, try the map center
if len(pos) == 0:
# @todo this could result in a masked pixel and cause further havoc
# @todo could also take the reference pixel, but that could be outside image
taskinit.ia.open(self.dir(fin))
s = taskinit.ia.summary()
pos = [int(s['shape'][0])/2, int(s['shape'][1])/2]
logging.warning("No input positions supplied, map center choosen: %s" % str(pos))
dt.tag("map-center")
# exhausted all sources where pos[] can be set; if still zero, bail out
if len(pos) == 0:
raise Exception,"No positions found from input BDP's or pos="
# convert this regular list to a list of tuples with duplicates removed
# sadly the order is lost.
pos = list(set(zip(pos[0::2],pos[1::2])))
npos = len(pos)
dt.tag("open")
bdp_name = self.mkext(fin,"csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
imval = range(npos) # spectra, one for each pos (placeholder)
planes = range(npos) # labels for the tables (placeholder)
images = {} # png's accumulated
for i in range(npos): # loop over pos, they can have mixed types now
sd = []
caption = "Spectrum"
xpos = pos[i][0]
ypos = pos[i][1]
if type(xpos) != type(ypos):
print "POS:",xpos,ypos
raise Exception,"position pair not of the same type"
if type(xpos)==int:
# for integers, boxes are allowed, even multiple
box = '%d,%d,%d,%d' % (xpos,ypos,xpos,ypos)
# convention for summary is (box)
cbox = '(%d,%d,%d,%d)' % (xpos,ypos,xpos,ypos)
# use extend here, not append, we want individual values in a list
sd.extend([xpos,ypos,cbox])
caption = "Average Spectrum at %s" % cbox
if False:
# this will fail on 3D cubes (see CAS-7648)
imval[i] = casa.imval(self.dir(fin),box=box)
else:
# work around that CAS-7648 bug
# another approach is the ia.getprofile(), see CubeStats, this will
# also integrate over regions, imval will not (!!!)
region = 'centerbox[[%dpix,%dpix],[1pix,1pix]]' % (xpos,ypos)
caption = "Average Spectrum at %s" % region
imval[i] = casa.imval(self.dir(fin),region=region)
elif type(xpos)==str:
# this is tricky, to stay under 1 pixel , or you get a 2x2 back.
region = 'centerbox[[%s,%s],[1pix,1pix]]' % (xpos,ypos)
caption = "Average Spectrum at %s" % region
sd.extend([xpos,ypos,region])
imval[i] = casa.imval(self.dir(fin),region=region)
else:
print "Data type: ",type(xpos)
raise Exception,"Data type for region not handled"
dt.tag("imval")
flux = imval[i]['data']
if len(flux.shape) > 1: # rare case if we step on a boundary between cells?
logging.warning("source %d has spectrum shape %s: averaging the spectra" % (i,repr(flux.shape)))
flux = np.average(flux,axis=0)
logging.debug('minmax: %f %f %d' % (flux.min(),flux.max(),len(flux)))
smax.append(flux.max())
if i==0: # for first point record few extra things
if len(imval[i]['coords'].shape) == 2: # normal case: 1 pixel
freqs = imval[i]['coords'].transpose()[2]/1e9 # convert to GHz @todo: input units ok?
elif len(imval[i]['coords'].shape) == 3: # rare case if > 1 point in imval()
freqs = imval[i]['coords'][0].transpose()[2]/1e9 # convert to GHz @todo: input units ok?
else:
logging.fatal("bad shape %s in freq return from imval - SHOULD NEVER HAPPEN" % imval[i]['coords'].shape)
chans = np.arange(len(freqs)) # channels 0..nchans-1
unit = imval[i]['unit']
restfreq = casa.imhead(self.dir(fin),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
dt.tag("imhead")
vel = (1-freqs/restfreq)*utils.c # @todo : use a function (and what about relativistic?)
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel" ,"frequency" ,"flux" ]
units = ["number" ,"GHz" ,unit ]
data = (chans ,freqs ,flux )
if i==0:
# plane 0 : we are allowing a multiplane table, so the first plane is special
table = Table(columns=labels,units=units,data=np.column_stack(data),planes=["0"])
else:
# planes 1,2,3.... are stacked onto the previous one
table.addPlane(np.column_stack(data),"%d" % i)
# example plot , one per position for now
if use_vel:
x = vel
xlab = 'VLSR (km/s)'
else:
x = chans
xlab = 'Channel'
y = [flux]
sd.append(xlab)
if type(xpos)==int:
# grab the RA/DEC... kludgy
h = casa.imstat(self.dir(fin),box=box)
ra = h['blcf'].split(',')[0]
dec = h['blcf'].split(',')[1]
title = '%s %d @ %d,%d = %s,%s' % (bdp_name,i,xpos,ypos,ra,dec)
else:
title = '%s %d @ %s,%s' % (bdp_name,i,xpos,ypos) # or use box, once we allow non-points
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode, abspath=self.dir())
ylab = 'Flux (%s)' % unit
p1 = "%s_%d" % (bdp_name,i)
myplot.plotter(x,y,title,p1,xlab=xlab,ylab=ylab,thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno,relative=True)
sd.extend([ii, thumbname, caption, fin])
self.spec_description.append(sd)
logging.regression("CSP: %s" % str(smax))
image = Image(images=images, description="CubeSpectrum")
b2.setkey("image",image)
b2.setkey("table",table)
b2.setkey("sigma",csigma) # TODO: not always available
b2.setkey("mean",cmean) # TODO: not always available
if True:
# @todo only first plane due to limitation in exportTable()
islash = bdp_name.find('/')
if islash < 0:
tabname = self.dir("testCubeSpectrum.tab")
else:
tabname = self.dir(bdp_name[:islash] + "/testCubeSpectrum.tab")
table.exportTable(tabname,cols=["frequency" ,"flux"])
dt.tag("done")
# For a single spectrum this is
# SummaryEntry([[data for spec1]], "CubeSpectrum_AT",taskid)
# For multiple spectra this is
# SummaryEntry([[data for spec1],[data for spec2],...], "CubeSpectrum_AT",taskid)
self._summary["spectra"] = SummaryEntry(self.spec_description,"CubeSpectrum_AT",self.id(True))
taskargs = "pos="+str(pos)
taskargs += ' <span style="background-color:white"> ' + fin.split('/')[0] + ' </span>'
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
seed = self.getkey("seed")
if seed <= 0:
np.random.seed()
else:
np.random.seed(seed)
#print "RANDOM.GET_STATE:",np.random.get_state()
contin = self.getkey("contin")
rms = 1.0 # not a user parameter, we do all spectra in S/N space
f0 = self.getkey("freq") # central frequency in band
df = self.getkey("delta") / 1000.0 # channel width (in GHz)
nspectra = self.getkey("nspectra")
taskargs = " contin=%f freq=%f delta=%f nspectra=%f " % (contin,f0,df,nspectra)
spec = range(nspectra)
dt.tag("start")
if self.getkey("file") != "":
print "READING spectrum from",self.getkey("file")
(freq, spec[0]) = getspec(self.getkey("file"))
nchan = len(freq)
print "Spectrum %d chans from %f to %f: min/max = %f %f" % (nchan, freq.min(), freq.max(), spec[0].min(), spec[0].max())
# @todo nspectra>1 not tested
for i in range(1,nspectra):
spec[i] = deepcopy(spec[0])
dt.tag("getspec")
else:
nchan = self.getkey("nchan")
freq = np.arange(nchan, dtype=np.float64)
center = int(nchan/2)
for i in range(nchan):
freq[i] = f0 + (float((i - center)) * df)
for i in range(nspectra):
spec[i] = np.zeros(nchan)
chans = np.arange(nchan)
taskargs += " nchan = %d" % nchan
for i in range(nspectra):
if seed >= 0:
spec[i] += np.random.normal(contin, rms, nchan)
# print "MEAN/STD",spec[i].mean(),spec[i].std()
lines = self.getkey("lines")
sls = SpectralLineSearch(False)
for item in self.getkey("transitions"):
kw = {"include_only_nrao" : True,
"line_strengths": ["ls1", "ls2"],
"energy_levels" : ["el2", "el4"],
"fel" : True,
"species" : item[0]
}
results = sls.search(item[1][0], item[1][1], "off", **kw)
# look at line strengths
if len(results) > 0:
mx = 0.0
indx = -1
for i in range(len(results)):
if results[i].getkey("linestrength") > mx:
indx = i
mx = results[i].getkey("linestrength")
for res in results:
if mx > 0.0:
lines.append([item[2] * res.getkey("linestrength") / mx, res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")), item[3]])
else:
lines.append([item[2], res.getkey("frequency") + utils.veltofreq(item[4],
res.getkey("frequency")), item[3]])
for item in lines:
for i in range(nspectra):
spec[i] += utils.gaussian1D(freq, item[0], item[1], utils.veltofreq(item[2], item[1]))
if self.getkey("hanning"):
for i in range(nspectra):
filter = Filter1D.Filter1D(spec[i], "hanning", **{"width" : 3})
spec[i] = filter.run()
dt.tag("hanning")
center = int(nchan/2)
dt.tag("open")
bdp_name = self.mkext("Genspec","csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
images = {} # png's accumulated
for i in range(nspectra):
sd = []
caption = "Generated Spectrum %d" % i
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel" ,"frequency" ,"flux" ]
units = ["number" ,"GHz" ,"" ]
data = (chans ,freq ,spec[i] )
# plane 0 : we are allowing a multiplane table, so the first plane is special
if i==0:
table = Table(columns=labels,units=units,data=np.column_stack(data),planes=["0"])
else:
table.addPlane(np.column_stack(data),"%d" % i)
# example plot , one per position for now
x = chans
xlab = 'Channel'
y = [spec[i]]
sd.append(xlab)
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode, abspath=self.dir())
ylab = 'Flux'
p1 = "%s_%d" % (bdp_name,i)
myplot.plotter(x,y,"",p1,xlab=xlab,ylab=ylab,thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno,relative=True)
image = Image(images=images, description="CubeSpectrum")
sd.extend([ii, thumbname, caption])
self.spec_description.append(sd)
self._summary["spectra"] = SummaryEntry(self.spec_description,"GenerateSpectrum_AT",self.id(True), taskargs)
dt.tag("table")
b2.setkey("image",image)
b2.setkey("table",table)
b2.setkey("sigma",rms)
b2.setkey("mean",contin)
dt.tag("done")
dt.end()
class PVCorr_AT(AT):
"""PV correllation in a PVSlice map.
PVCorr_AT computes a cross-correlation of a feature in a PVSlice with the
whole PVSlice, looking for repeated patterns to detect spectral lines. Much
like the output from CubeStats_AT and CubeSpectrum_AT, this table can then be
given to LineID_AT to attempt a line identification.
See also :ref:`PVCorr-AT-Design` for the design document.
**Keywords**
**numsigma**: float
Minimum intensity, in terms of sigma, above which a selected portion
of the spectrum will be used for cross-correlation.
Default: 3.0.
**range**: integer list
If given, it has to be a list with 2 channel numbers, the first and last channel
(0-based channels) of the range which to use for the cross-correlation.
Default: [].
**nchan**: integer
The number of channels (in case range= was not used) that defines the line.
The line is centers on the strongest point in the input PV-map.
If 0 is given, it will watershed down from the strongest line.
Default: 0.
**Input BDPs**
**PVSlice_BDP**: count: 1
Input PV Slice. As created with e.g. PVSlice_AT.
**CubeStats_BDP**: count: 1
Input cube statistics from which the RMS is taken.
**Output BDPs**
**PVCorr_BDP**: count: 1
Output table.
"""
def __init__(self,**keyval):
keys = {"numsigma" : 3.0, # N-sigma
"range" : [], # optional channel range
"nchan" : 0, # number of channels around the channel where the peak is
}
AT.__init__(self,keys,keyval)
self._version = "1.0.1"
self.set_bdp_in([(Image_BDP,1,bt.REQUIRED),
# @todo optional 2nd PVSlice can be used to draw the template from
(CubeStats_BDP,1,bt.REQUIRED)])
self.set_bdp_out([(PVCorr_BDP,1)])
def summary(self):
"""Returns the summary dictionary from the AT, for merging
into the ADMIT Summary object.
PVCorr_AT adds the following to ADMIT summary:
.. table::
:class: borderless
+----------+----------+-----------------------------------+
| Key | type | Description |
+==========+==========+===================================+
| pvcorr | list | correlation diagram |
+----------+----------+-----------------------------------+
Parameters
----------
None
Returns
-------
dict
Dictionary of SummaryEntry
"""
if hasattr(self,"_summary"):
return self._summary
else:
return {}
def run(self):
dt = utils.Dtime("PVCorr")
self._summary = {}
numsigma = self.getkey("numsigma")
mode = 1 # PV corr mode (1,2,3)
normalize = True
# normalize = False
b1 = self._bdp_in[0] # PVSlice_BDP
fin = b1.getimagefile(bt.CASA) # CASA image
data = casautil.getdata_raw(self.dir(fin)) # grab the data as a numpy array
self.myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
#print 'DATA[0,0]:',data[0,0]
#print 'pv shape: ',data.shape
npos = data.shape[0]
nvel = data.shape[1]
dt.tag("getdata")
b2 = self._bdp_in[1] # CubeStats_BDP
sigma = b2.sigma # global sigma in the cube
cutoff = numsigma * sigma
freq = b2.table.getColumnByName("frequency")
chans = self.getkey("range") # range of channels, if used
if len(chans) > 0:
if len(chans) != 2:
logging.fatal("range=%s" % chans)
raise Exception,"range= needs two values, left and right (inclusive) channel"
ch0 = chans[0]
ch1 = chans[1]
else:
nchan = self.getkey("nchan")
imstat0 = casa.imstat(self.dir(fin)) # @todo can use data[] now
xmaxpos = imstat0['maxpos'][0]
ymaxpos = imstat0['maxpos'][1]
logging.info("MAXPOS-VEL %s %g" % (str(imstat0['maxpos']),imstat0['max'][0]))
if nchan > 0:
# expand around it, later ch0,ch1 will be checked for running off the edge
ch0 = ymaxpos - nchan/2
ch1 = ymaxpos + nchan/2
else:
# watershed down to find ch0 and ch1 ?
# this doesn't work well in crowded areas
ch0 = ymaxpos
ch1 = ymaxpos
spmax = data.max(axis=0)
k = spmax.argmax()
n = len(spmax)
logging.debug('spmax %s %d %g' % (str(spmax.shape),k,spmax[k]))
# find lower cutoff
for i in range(n):
ch0 = ymaxpos - i
if ch0<0: break
if spmax[ch0] < cutoff: break
ch0 = ch0 + 1
# find higher cutoff
for i in range(n):
ch1 = ymaxpos + i
if ch1==n: break
if spmax[ch1] < cutoff: break
ch1 = ch1 - 1
dt.tag("imstat")
bdp_name = self.mkext(fin,"pvc") # output PVCorr_BDP
b3 = PVCorr_BDP(bdp_name)
self.addoutput(b3)
if ch0<0 or ch1>=nvel:
# this probably only happens to small cubes (problematic for PVCorr)
# or when the strongest line is really close to the edge of the band
# (which is probably ok)
if ch0<0 and ch1>=nvel:
logging.warning("Serious issues with the size of this cube")
if ch0<0:
logging.warning("Resetting ch0 edge to 0")
ch0=0
if ch1>=nvel:
ch1=nvel-1
logging.warning("Resetting ch1 edge to the maximum")
if ch0 > ch1:
logging.warning("Sanity swapping ch0,1 due to likely noisy data")
ch0,ch1 = ch1,ch0
if mode == 1:
out,rms = mode1(data, ch0, ch1, cutoff, normalize)
corr = out
elif mode == 2:
out,rms = mode2(data, ch0, ch1, cutoff) # slower 2D version
corr = out[npos/2,:] # center cut, but could also try feature detection
elif mode == 3:
out,rms = self.mode3(data, ch0, ch1, cutoff) # Doug's faster 2D version
# get the peak of each column
corr = np.amax(out,axis=0)
# print "PVCORR SHAPE ",corr.shape," mode", mode
if len(corr) > 0:
# print "SHAPE out:",out.shape,corr.shape,npos/2
ch = range(len(corr))
if len(corr) != len(freq):
logging.fatal("ch (%d) and freq (%d) do not have same size" % (len(corr),len(freq)))
raise Exception,"ch and freq do not have same dimension"
dt.tag("mode")
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
data = (ch, freq, corr)
table = Table(columns=labels,units=units,data=np.column_stack(data))
else:
# still construct a table, but with no rows
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
table = Table(columns=labels,units=units)
b3.setkey("table",table)
b3.setkey("sigma",float(rms))
dt.tag("table")
if len(corr) > 0:
table.exportTable(self.dir("testPVCorr.tab"),cols=['frequency','pvcorr'])
test_single(ch,freq,corr)
logging.regression("PVC: %f %f" % (corr.min(),corr.max()))
title = 'PVCorr mode=%d [%d,%d] %g' % (mode,ch0,ch1,cutoff)
x = ch
xlab = 'Channel'
y = [corr]
ylab = 'PV Correlation'
p1 = "%s_%d" % (bdp_name,0)
segp = []
segp.append( [0,len(ch),0.0,0.0] )
segp.append( [0,len(ch),3.0*rms, 3.0*rms] )
# @todo: in principle we know with given noise and size of box, what the sigma in pvcorr should be
self.myplot.plotter(x,y,title,figname=p1,xlab=xlab,ylab=ylab,segments=segp, thumbnail=True)
#out1 = np.rot90 (data.reshape((nvel,npos)) )
if mode > 1:
self.myplot.map1(data=out,title="testing PVCorr_AT: mode%d"%mode,figname='testPVCorr', thumbnail=True)
taskargs = "numsigma=%.1f range=[%d,%d]" % (numsigma,ch0,ch1)
caption = "Position-velocity correlation plot"
thumbname = self.myplot.getThumbnail(figno=self.myplot.figno,relative=True)
figname = self.myplot.getFigure(figno=self.myplot.figno,relative=True)
image = Image(images={bt.PNG: figname}, thumbnail=thumbname, thumbnailtype=bt.PNG,
description=caption)
b3.image.addimage(image, "pvcorr")
self._summary["pvcorr"] = SummaryEntry([figname,thumbname,caption,fin],"PVCorr_AT",self.id(True),taskargs)
else:
self._summary["pvcorr"] = None
logging.warning("No summary")
logging.regression("PVC: -1")
dt.tag("done")
dt.end()
def mode3(self, data, v0, v1, dmin=0.0):
""" v0..v1 (both inclusive) are channel selections
threshold on dmin
@todo the frequency axis is not properly calibrated here
@todo a full 2D is slow, we only need the 1D version
"""
print "PVCorr mode3: v0,1=",v0,v1
smin = data.min()
#s = data[v0:v1+1,:]
s = data[:,v0:v1+1]
if dmin==0.0:
logging.warning("Using all data in crosscorr")
f = s
else:
f = np.where(s>dmin,s,0)
# find out where the zeros are
temp = np.amax(f,axis=1)
nz = np.nonzero(temp)
# trim the kernel in the y direction, removing rows that are all 0.0
f = f[nz[0][0]:nz[0][-1],:]
f0 = np.where(s>smin,1,0)
f1 = np.where(s>dmin,1,0)
fmax = f.max()
print "PVCorr mode3:",f1.sum(),'/',f0.sum(),'min/max',smin,fmax
out = scipy.signal.correlate2d(data,f,mode='same')
self.myplot.map1(data=f,title="PVCorr 2D Kernel",figname='PVCorrKernel', thumbnail=True)
print 'PVCorr min/max:',out.min(),out.max()
n1,m1,s1,n2,m2,s2 = stats.mystats(out.flatten())
print "PVCorr stats", n1,m1,s1,n2,m2,s2
rms_est = s2/np.sqrt(f1.sum())
return out,rms_est
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 creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("SFind2D") # tagging time
self._summary = {}
# get key words that user input
nsigma = self.getkey("numsigma")
sigma = self.getkey("sigma")
region = self.getkey("region")
robust = self.getkey("robust")
snmax = self.getkey("snmax")
ds9 = True # writes a "ds9.reg" file
mpl = True # aplot.map1() plot
dynlog = 20.0 # above this value of dyn range finder chart is log I-scaled
bpatch = True # patch units to Jy/beam for ia.findsources()
# get the input casa image from bdp[0]
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
if mpl:
data = np.flipud(np.rot90(casautil.getdata(self.dir(infile)).data))
# check if there is a 2nd image (which will be a PB)
for i in range(len(self._bdp_in)):
print 'BDP',i,type(self._bdp_in[i])
if self._bdp_in[2] != None:
bdpin_pb = self._bdp_in[1]
bdpin_cst = self._bdp_in[2]
print "Need to process PB"
else:
bdpin_pb = None
bdpin_cst = self._bdp_in[1]
print "No PB given"
# get the output bdp basename
slbase = self.mkext(infile,'sl')
# make sure it's a 2D map
if not casautil.mapdim(self.dir(infile),2):
raise Exception,"Input map dimension not 2: %s" % infile
# arguments for imstat call if required
args = {"imagename" : self.dir(infile)}
if region != "":
args["region"] = region
dt.tag("start")
# The following code sets the sigma level for searching for sources using
# the sigma and snmax keyword as appropriate
# if no CubeStats BDP was given and no sigma was specified:
# find a noise level via casa.imstat()
# if a CubeStat_BDP is given get it from there.
if bdpin_cst == None:
# get statistics from input image with imstat because no CubeStat_BDP
stat = casa.imstat(**args)
dmin = float(stat["min"][0]) # these would be wrong if robust were used already
dmax = float(stat["max"][0])
args.update(casautil.parse_robust(robust)) # only now add robust keywords for the sigma
stat = casa.imstat(**args)
if sigma <= 0.0 :
sigma = float(stat["sigma"][0])
dt.tag("imstat")
else:
# get statistics from CubeStat_BDP
sigma = bdpin_cst.get("sigma")
dmin = bdpin_cst.get("minval")
dmax = bdpin_cst.get("maxval")
self.setkey("sigma",sigma)
# calculate cutoff based either on RMS or dynamic range limitation
drange = dmax/(nsigma*sigma)
if snmax < 0.0 :
snmax = drange
if drange > snmax :
cutoff = 1.0/snmax
else:
cutoff = 1.0/drange
logging.info("sigma, dmin, dmax, snmax, cutoff %g %g %g %g %g" % (sigma, dmin, dmax, snmax, cutoff))
# define arguments for call to findsources
args2 = {"cutoff" : cutoff}
args2["nmax"] = 30
if region != "" :
args2["region"] = region
#args2["mask"] = ""
args2["point"] = False
args2["width"] = 5
args2["negfind"] = False
# set-up for SourceList_BDP
slbdp = SourceList_BDP(slbase)
# connect to casa image and call casa ia.findsources tool
taskinit.ia.open(self.dir(infile))
# findsources() cannot deal with 'Jy/beam.km/s' ???
# so for the duration of findsources() we patch it
bunit = taskinit.ia.brightnessunit()
if bpatch and bunit != 'Jy/beam':
logging.warning("Temporarely patching your %s units to Jy/beam for ia.findsources()" % bunit)
taskinit.ia.setbrightnessunit('Jy/beam')
else:
bpatch = False
atab = taskinit.ia.findsources(**args2)
if bpatch:
taskinit.ia.setbrightnessunit(bunit)
taskargs = "nsigma=%4.1f sigma=%g region=%s robust=%s snmax=%5.1f" % (nsigma,sigma,str(region),str(robust),snmax)
dt.tag("findsources")
nsources = atab["nelements"]
xtab = []
ytab = []
logscale = False
sumflux = 0.0
if nsources > 0:
# @TODO: Why are Xpix, YPix not stored in the table?
# -> PJT: I left them out since they are connected to an image which may not be available here
# but we should store the frequency of the observation here for later bandmerging
logging.debug("%s" % str(atab['component0']['shape']))
logging.info("Right Ascen. Declination X(pix) Y(pix) Peak Flux Major Minor PA SNR")
funits = atab['component0']['flux']['unit']
if atab['component0']['shape'].has_key('majoraxis'):
sunits = atab['component0']['shape']['majoraxis']['unit']
aunits = atab['component0']['shape']['positionangle']['unit']
else:
sunits = "n/a"
aunits = "n/a"
punits = taskinit.ia.summary()['unit']
logging.info(" %s %s %s %s %s" % (punits,funits,sunits,sunits,aunits))
#
# @todo future improvement is to look at image coordinates and control output appropriately
#
if ds9:
# @todo variable name
regname = self.mkext(infile,'ds9.reg')
fp9 = open(self.dir(regname),"w!")
for i in range(nsources):
c = "component%d" % i
name = "%d" % (i+1)
r = atab[c]['shape']['direction']['m0']['value']
d = atab[c]['shape']['direction']['m1']['value']
pixel = taskinit.ia.topixel([r,d])
xpos = pixel['numeric'][0]
ypos = pixel['numeric'][1]
rd = taskinit.ia.toworld([xpos,ypos],'s')
ra = rd['string'][0][:12]
dec = rd['string'][1][:12]
flux = atab[c]['flux']['value'][0]
sumflux = sumflux + flux
if atab[c]['shape'].has_key('majoraxis'):
smajor = atab[c]['shape']['majoraxis']['value']
sminor = atab[c]['shape']['minoraxis']['value']
sangle = atab[c]['shape']['positionangle']['value']
else:
smajor = 0.0
sminor = 0.0
sangle = 0.0
peakstr = taskinit.ia.pixelvalue([xpos,ypos,0,0])
if len(peakstr) == 0:
logging.warning("Problem with source %d @ %d,%d" % (i,xpos,ypos))
continue
peakf = peakstr['value']['value']
snr = peakf/sigma
if snr > dynlog:
logscale = True
logging.info("%s %s %8.2f %8.2f %10.3g %10.3g %7.3f %7.3f %6.1f %6.1f" % (ra,dec,xpos,ypos,peakf,flux,smajor,sminor,sangle,snr))
xtab.append(xpos)
ytab.append(ypos)
slbdp.addRow([name,ra,dec,flux,peakf,smajor,sminor,sangle])
if ds9:
ras = ra
des = dec.replace('.',':',2)
msg = 'ellipse(%s,%s,%g",%g",%g) # text={%s}' % (ras,des,smajor,sminor,sangle+90.0,i+1)
fp9.write("%s\n" % msg)
if ds9:
fp9.close()
logging.info("Wrote ds9.reg")
dt.tag("table")
logging.regression("CONTFLUX: %d %g" % (nsources,sumflux))
summary = taskinit.ia.summary()
beammaj = summary['restoringbeam']['major']['value']
beammin = summary['restoringbeam']['minor']['value']
beamunit = summary['restoringbeam']['minor']['unit']
beamang = summary['restoringbeam']['positionangle']['value']
angunit = summary['restoringbeam']['positionangle']['unit']
# @todo add to table comments?
logging.info(" Fitted Gaussian size; NOT deconvolved source size.")
logging.info(" Restoring Beam: Major axis: %10.3g %s , Minor axis: %10.3g %s , PA: %5.1f %s" % (beammaj, beamunit, beammin, beamunit, beamang, angunit))
# form into a xml table
# output is a table_bdp
self.addoutput(slbdp)
# instantiate a plotter for all plots made herein
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
# make output png with circles marking sources found
if mpl:
circles=[]
nx = data.shape[1] # data[] array was already flipud(rot90)'d
ny = data.shape[0] #
for (x,y) in zip(xtab,ytab):
circles.append([x,y,1])
# @todo variable name
if logscale:
logging.warning("LogScaling applied")
data = data/sigma
data = np.where(data<0,-np.log10(1-data),+np.log10(1+data))
title = "SFind2D: %d sources" % nsources
myplot.map1(data,title,slbase,thumbnail=True,circles=circles)
#---------------------------------------------------------
# Get the figure and thumbmail names and create a caption
#---------------------------------------------------------
imname = myplot.getFigure(figno=myplot.figno,relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,relative=True)
caption = "Image of input map with sources found by SFind2D overlayed in green."
slbdp.table.description="Table of source locations and sizes (not deconvolved)"
#---------------------------------------------------------
# Add finder image to the BDP
#---------------------------------------------------------
image = Image(images={bt.PNG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG, description=caption)
slbdp.image.addimage(image, "finderimage")
#-------------------------------------------------------------
# Create the summary entry for the table and image
#-------------------------------------------------------------
self._summary["sources"] = SummaryEntry([slbdp.table.serialize(),
slbdp.image.serialize()],
"SFind2D_AT",
self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
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()