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()
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("CubeSum") # tagging time
self._summary = {} # an ADMIT summary will be created here
numsigma = self.getkey("numsigma") # get the input keys
sigma = self.getkey("sigma")
use_lines = self.getkey("linesum")
pad = self.getkey("pad")
b1 = self._bdp_in[0] # spw image cube
b1a = self._bdp_in[1] # cubestats (optional)
b1b = self._bdp_in[2] # linelist (optional)
f1 = b1.getimagefile(bt.CASA)
taskinit.ia.open(self.dir(f1))
s = taskinit.ia.summary()
nchan = s['shape'][2]
if b1b != None:
ch0 = b1b.table.getFullColumnByName("startchan")
ch1 = b1b.table.getFullColumnByName("endchan")
s = Segments(ch0, ch1, nchan=nchan)
# @todo something isn't merging here as i would have expected,
# e.g. test0.fits [(16, 32), (16, 30), (16, 29)]
if pad > 0:
for (c0, c1) in s.getsegmentsastuples():
s.append([c0 - pad, c0])
s.append([c1, c1 + pad])
s.merge()
s.recalcmask()
# print "PJT segments:",s.getsegmentsastuples()
ns = len(s.getsegmentsastuples())
chans = s.chans(not use_lines)
if use_lines:
msum = s.getmask()
else:
msum = 1 - s.getmask()
logging.info("Read %d segments" % ns)
# print "chans",chans
# print "msum",msum
# from a deprecated keyword, but kept here to pre-smooth the spectrum before clipping
# examples are: ['boxcar',3] ['gaussian',7] ['hanning',5]
smooth = []
sig_const = False # figure out if sigma is taken as constant in the cube
if b1a == None: # if no 2nd BDP was given, sigma needs to be specified
if sigma <= 0.0:
raise Exception, "Neither user-supplied sigma nor CubeStats_BDP input given. One is required."
else:
sig_const = True # and is constant
else:
if sigma > 0:
sigma = b1a.get("sigma")
sig_const = True
if sig_const:
logging.info("Using constant sigma = %f" % sigma)
else:
logging.info("Using varying sigma per plane")
infile = b1.getimagefile(bt.CASA) # ADMIT filename of the image (cube)
bdp_name = self.mkext(
infile, 'csm'
) # morph to the new output name with replaced extension 'csm'
image_out = self.dir(bdp_name) # absolute filename
args = {
"imagename": self.dir(infile)
} # assemble arguments for immoments()
args["moments"] = 0 # only need moments=0 (or [0] is ok as well)
args["outfile"] = image_out # note full pathname
dt.tag("start")
if sig_const:
args["excludepix"] = [-numsigma * sigma,
numsigma * sigma] # single global sigma
if b1b != None:
# print "PJT: ",chans
args["chans"] = chans
else:
# @todo in this section bad channels can cause a fully masked cubesum = bad
# cubestats input
sigma_array = b1a.table.getColumnByName(
"sigma") # channel dependent sigma
sigma_pos = sigma_array[np.where(sigma_array > 0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f" % (smin, smax))
maxval = b1a.get("maxval") # max in cube
nzeros = len(np.where(sigma_array <= 0.0)[0]) # check bad channels
if nzeros > 0:
logging.warning("There are %d NaN channels " % nzeros)
# raise Exception,"need to recode CubeSum or use constant sigma"
dt.tag("grab_sig")
if len(smooth) > 0:
# see also LineID and others
filter = Filter1D.Filter1D(
sigma_array, smooth[0],
**Filter1D.Filter1D.convertargs(smooth))
sigma_array = filter.run()
dt.tag("smooth_sig")
# create a CASA image copy for making the mirror sigma cube to mask against
file = self.dir(infile)
mask = file + "_mask"
taskinit.ia.fromimage(infile=file, outfile=mask)
nx = taskinit.ia.shape()[0]
ny = taskinit.ia.shape()[1]
nchan = taskinit.ia.shape()[2]
taskinit.ia.fromshape(shape=[nx, ny, 1])
plane = taskinit.ia.getchunk(
[0, 0, 0],
[-1, -1, 0]) # convenience plane for masking operation
dt.tag("mask_sig")
taskinit.ia.open(mask)
dt.tag("open_mask")
count = 0
for i in range(nchan):
if sigma_array[i] > 0:
if b1b != None:
if msum[i]:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval,
blc=[0, 0, i, -1])
else:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval, blc=[0, 0, i, -1])
taskinit.ia.close()
logging.info("%d/%d channels used for CubeSum" % (count, nchan))
dt.tag("close_mask")
names = [file, mask]
tmp = file + '.tmp'
if numsigma == 0.0:
# hopefully this will also make use of the mask
exp = "IM0[IM1<%f]" % (0.99 * maxval)
else:
exp = "IM0[abs(IM0/IM1)>%f]" % (numsigma)
# print "PJT: exp",exp
casa.immath(mode='evalexpr',
imagename=names,
expr=exp,
outfile=tmp)
args["imagename"] = tmp
dt.tag("immath")
casa.immoments(**args)
dt.tag("immoments")
if sig_const is False:
# get rid of temporary files
utils.remove(tmp)
utils.remove(mask)
# get the flux
taskinit.ia.open(image_out)
st = taskinit.ia.statistics()
taskinit.ia.close()
dt.tag("statistics")
# report that flux, but there's no way to get the units from casa it seems
# ia.summary()['unit'] is usually 'Jy/beam.km/s' for ALMA
# imstat() does seem to know it.
if st.has_key('flux'):
rdata = [st['flux'][0], st['sum'][0]]
logging.info("Total flux: %f (sum=%f)" % (st['flux'], st['sum']))
else:
rdata = [st['sum'][0]]
logging.info("Sum: %f (beam parameters missing)" % (st['sum']))
logging.regression("CSM: %s" % str(rdata))
# Create two output images for html and their thumbnails, too
implot = ImPlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
implot.plotter(rasterfile=bdp_name, figname=bdp_name, colorwedge=True)
figname = implot.getFigure(figno=implot.figno, relative=True)
thumbname = implot.getThumbnail(figno=implot.figno, relative=True)
dt.tag("implot")
thumbtype = bt.PNG # really should be correlated with self._plot_type!!
# 2. Create a histogram of the map data
# get the data for a histogram
data = casautil.getdata(image_out, zeromask=True).compressed()
dt.tag("getdata")
# get the label for the x axis
bunit = casa.imhead(imagename=image_out, mode="get", hdkey="bunit")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
auxname = bdp_name + "_histo"
auxtype = bt.PNG # really should be correlated with self._plot_type!!
myplot.histogram(columns=data,
figname=auxname,
xlab=bunit,
ylab="Count",
title="Histogram of CubeSum: %s" % (bdp_name),
thumbnail=True)
auxname = myplot.getFigure(figno=myplot.figno, relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno, relative=True)
images = {bt.CASA: bdp_name, bt.PNG: figname}
casaimage = Image(images=images,
auxiliary=auxname,
auxtype=auxtype,
thumbnail=thumbname,
thumbnailtype=thumbtype)
if hasattr(b1, "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(b1, "line"))
if type(line) != type(Line):
line = Line(name="Undetermined")
else:
line = Line(name="Undetermined") # fake a Line if there wasn't one
self.addoutput(
Moment_BDP(xmlFile=bdp_name,
moment=0,
image=deepcopy(casaimage),
line=line))
imcaption = "Integral (moment 0) of all emission in image cube"
auxcaption = "Histogram of cube sum for image cube"
taskargs = "numsigma=%.1f sigma=%g smooth=%s" % (numsigma, sigma,
str(smooth))
self._summary["cubesum"] = SummaryEntry([
figname, thumbname, imcaption, auxname, auxthumb, auxcaption,
bdp_name, infile
], "CubeSum_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
ia = taskinit.iatool()
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
ia.open(self.dir("mom0.masked"))
defmask = ia.maskhandler('default')
ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
ia.open(self.dir(imagename))
ia.maskhandler('set', defmask)
ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,
colorwedge=True,zoom=self.getkey("zoom"))
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
def run(self):
""" 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()