def __init__(self, upper=True):
self.version = "27-apr-2016"
if have_ADMIT:
self.table = utils.admit_root() + "/etc/vlsr.tab"
self.cat = read_vlsr(self.table,upper)
logging.debug("VLSR: %s, found %d entries" % (self.table,len(self.cat)))
else:
logging.warning("VLSR: Warning, no ADMIT, empty catalogue")
self.cat = {}
def tab_to_slit(xym, clip=0.0, gamma=1.0):
"""take all values from a map over clip, compute best slit for PV Slice
"""
x = xym[0] # maxposx
y = xym[1] # maxposy
m = xym[2] # max
logging.debug("CLIP %g" % clip)
slit = convert_to_slit(m,x,y,0,0,gamma,expand=2.0)
return (slit,clip)
def peakstats(image, freq, sigma, nsigma, minchan, maxgap, psample, peakfit = False):
""" Go through a cube and find peaks in the spectral dimension
It will gather a table of <peak>,<freq>,<sigma> which can be
optionally used for plotting
"""
if psample < 0: return
cutoff = nsigma * sigma
madata = casautil.getdata(image)
data = madata.data
shape = data.shape
logging.debug("peakstats: shape=%s cutoff=%g" % (str(shape),cutoff))
#print "DATA SHAPE:",shape
#print "cutoff=",cutoff
nx = shape[0]
ny = shape[1]
nz = shape[2]
chan = np.arange(nz)
# prepare the segment finder
# we now have an array data[nx,ny,nz]
sum = 0.0
pval = []
mval = []
wval = []
for x in range(0,nx,psample):
for y in range(0,ny,psample):
s0 = data[x,y,:]
spec = ma.masked_invalid(s0)
sum += spec.sum()
# using abs=True is a bit counter intuitive, but a patch to deal with the confusion in
# ADMITSegmentFinder w.r.t abs usage
asf = ADMITSegmentFinder(pmin=nsigma, minchan=minchan, maxgap=maxgap, freq=freq, spec=spec, abs=True)
#asf = ADMITSegmentFinder(pmin=nsigma, minchan=minchan, maxgap=maxgap, freq=freq, spec=spec, abs=False)
f = asf.line_segments(spec, nsigma*sigma)
for s in f:
if False:
for i in range(s[0],s[1]+1):
print "# ",x,y,i,spec[i]
## area preserving and peak are correlated, 18% difference
## fitgauss1Dm was about 5"
## with fitgauss1D was about 30", and still bad fits
par = utils.fitgauss1Dm(chan[s[0]:s[1]+1], spec[s[0]:s[1]+1], True) # peak from max
#par = utils.fitgauss1Dm(chan[s[0]:s[1]+1], spec[s[0]:s[1]+1], False) # peak from area preserving
if peakfit:
(par,cov) = utils.fitgauss1D (chan[s[0]:s[1]+1], spec[s[0]:s[1]+1],par)
#print "FIND: ",x,y,s,cutoff,0.0,0.0,par[0],par[1],par[2],s[1]-s[0]+1
pval.append(par[0])
mval.append(par[1])
wval.append(par[2])
#print "SUM:",sum
return (np.array(pval),np.array(mval),np.array(wval))
def test_debug(self):
msg = "unit_test_debug_message"
Alogging.debug(msg)
found = False
r = open(self.logfile, 'r')
for line in r.readlines():
if msg in line:
if(self.verbose):
print "\nFound message > ", line
found = True
r.close()
break
self.assertTrue(found)
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("Smooth")
dt.tag("start")
# get the input keys
bmaj = self.getkey("bmaj")
bmin = self.getkey("bmin")
bpa = self.getkey("bpa")
velres = self.getkey("velres")
# take care of potential issues in the unit strings
# @todo if not provided?
bmaj['unit'] = bmaj['unit'].lower()
bmin['unit'] = bmin['unit'].lower()
velres['unit'] = velres['unit'].lower()
taskargs = "bmaj=%s bmin=%s bpa=%s velres=%s" % (bmaj, bmin, bpa,
velres)
bdpnames = []
for ibdp in self._bdp_in:
istem = ibdp.getimagefile(bt.CASA)
image_in = ibdp.baseDir() + istem
bdp_name = self.mkext(istem, 'sim')
image_out = self.dir(bdp_name)
taskinit.ia.open(image_in)
h = casa.imhead(image_in, mode='list')
pix_scale = np.abs(h['cdelt1'] *
206265.0) # pix scale in asec @todo QA ?
CC = 299792458.0 # speed of light @todo somewhere else [utils.c , but in km/s]
rest_freq = h['crval3']
# frequency pixel scale in km/s
vel_scale = np.abs(CC * h['cdelt3'] / rest_freq / 1000.0)
# unit conversion to arcsec (spatial) or km/s
# (velocity) or some flavor of Hz.
if (bmaj['unit'] == 'pixel'):
bmaj = bmaj['value'] * pix_scale
else:
bmaj = bmaj['value']
if (bmin['unit'] == 'pixel'):
bmin = bmin['value'] * pix_scale
else:
bmin = bmin['value']
hertz_input = False
if velres['unit'] == 'pixel':
velres['value'] = velres['value'] * vel_scale
velres['unit'] = 'km/s'
elif velres['unit'] == 'm/s':
velres['value'] = velres['value'] / 1000.0
velres['unit'] = 'km/s'
elif velres['unit'][-2:] == 'hz':
hertz_input = True
elif velres['unit'] == 'km/s':
pass
else:
logging.error("Unknown units in velres=%s" % velres['unit'])
rdata = bmaj
# we smooth in velocity first. if smoothing in velocity
# the cube apparently must be closed afterwards and
# then reopened if spatial smoothing is to be done.
if velres['value'] > 0:
# handle the different units allowed. CASA doesn't
# like lowercase for hz units...
if not hertz_input:
freq_res = str(
velres['value'] * 1000.0 / CC * rest_freq) + 'Hz'
else:
freq_res = str(velres['value'])
# try to convert velres to km/s for debug purposes
velres['value'] = velres['value'] / rest_freq * CC / 1000.0
if (velres['unit'] == 'khz'):
velres['value'] = velres['value'] * 1000.0
velres['unit'] = 'kHz'
elif (velres['unit'] == 'mhz'):
velres['value'] = velres['value'] * 1E6
velres['unit'] = 'MHz'
elif (velres['unit'] == 'ghz'):
velres['value'] = velres['value'] * 1E9
velres['unit'] = 'GHz'
freq_res = freq_res + velres['unit']
# NB: there is apparently a bug in CASA. only smoothing along the frequency
# axis does not work. sepconvolve gives a unit error (says axis unit is radian rather
# than Hz). MUST smooth in 2+ dimensions if you want this to work.
if (velres['value'] < vel_scale):
raise Exception, "Desired velocity resolution %g less than pixel scale %g" % (
velres['value'], vel_scale)
image_tmp = self.dir('tmp.smooth')
im2=taskinit.ia.sepconvolve(outfile=image_tmp,axes=[0,1,2], types=["boxcar","boxcar","gauss"],\
widths=['1pix','1pix',freq_res], overwrite=True)
im2.done()
logging.debug("sepconvolve to %s" % image_out)
# for some reason, doing this in memory does not seem to work, so outfile must be specified.
logging.info(
"Smoothing cube to a velocity resolution of %s km/s" %
str(velres['value']))
logging.info("Smoothing cube to a frequency resolution of %s" %
freq_res)
taskinit.ia.close()
taskinit.ia.open(image_tmp)
dt.tag("sepconvolve")
else:
image_tmp = image_out
# now do the spatial smoothing
convolve_to_min_beam = True # default is to convolve to a min enclosing beam
if bmaj > 0 and bmin > 0:
# form qa objects out of these so that casa can understand
bmaj = taskinit.qa.quantity(bmaj, 'arcsec')
bmin = taskinit.qa.quantity(bmin, 'arcsec')
bpa = taskinit.qa.quantity(bpa, 'deg')
target_res = {}
target_res['major'] = bmaj
target_res['minor'] = bmin
target_res['positionangle'] = bpa
# throw an exception if cannot be convolved
try:
# for whatever reason, if you give convolve2d a beam parameter,
# it complains ...
im2=taskinit.ia.convolve2d(outfile=image_out,major = bmaj,\
minor = bmin, pa = bpa,\
targetres=True,overwrite=True)
im2.done()
logging.info(
"Smoothing cube to a resolution of %s by %s at a PA of %s"
% (str(bmaj['value']), str(
bmin['value']), str(bpa['value'])))
convolve_to_min_beam = False
achieved_res = target_res
except:
# @todo remind what you need ?
logging.error("Warning: Could not convolve to requested resolution of "\
+str(bmaj['value']) + " by " + str(bmin['value']) + \
" at a PA of "+ str(bpa['value']))
raise Exception, "Could not convolve to beam given!"
dt.tag("convolve2d-1")
if convolve_to_min_beam:
restoring_beams = taskinit.ia.restoringbeam()
commonbeam = taskinit.ia.commonbeam()
# for whatever reason, setrestoringbeam does not use the same set of hashes...
commonbeam['positionangle'] = commonbeam['pa']
del commonbeam['pa']
# if there's one beam, apparently the beams keyword does not exist
if 'beams' in restoring_beams:
print "Smoothing cube to a resolution of "+ \
str(commonbeam['major']['value']) +" by "+ \
str(commonbeam['minor']['value'])+" at a PA of "\
+str(commonbeam['pa']['value'])
target_res = commonbeam
im2=taskinit.ia.convolve2d(outfile=image_out,major=commonbeam['major'],\
minor=commonbeam['minor'],\
pa=commonbeam['positionangle'],\
targetres=True,overwrite=True)
im2.done()
achieved_res = commonbeam
dt.tag("convolve2d-2")
else:
print "One beam for all planes. Smoothing to common beam redundant."
achieved_res = commonbeam
if velres['value'] < 0:
taskinit.ia.fromimage(outfile=image_out,
infile=image_in)
# not really doing anything
# else, we've already done what we needed to
taskinit.ia.setrestoringbeam(beam=achieved_res)
rdata = achieved_res['major']['value']
# else do no smoothing and just close the image
taskinit.ia.close()
dt.tag("close")
b1 = SpwCube_BDP(bdp_name)
self.addoutput(b1)
# need to update for multiple images.
b1.setkey("image", Image(images={bt.CASA: bdp_name}))
bdpnames = bdpnames.append(bdp_name)
# and clean up the temp image before the next image
if velres['value'] > 0:
utils.remove(image_tmp)
# thes are task arguments not summary entries.
_bmaj = taskinit.qa.convert(achieved_res['major'], 'rad')['value']
_bmin = taskinit.qa.convert(achieved_res['minor'], 'rad')['value']
_bpa = taskinit.qa.convert(achieved_res['positionangle'],
'deg')['value']
vres = "%.2f %s" % (velres['value'], velres['unit'])
logging.regression("SMOOTH: %f %f" % (rdata, velres['value']))
self._summary["smooth"] = SummaryEntry(
[bdp_name, convolve_to_min_beam, _bmaj, _bmin, _bpa, vres],
"Smooth_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
"""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):
"""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("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
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):
""" 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):
#
self._summary = {} # prepare to make a summary here
dt = utils.Dtime("Ingest") # timer for debugging
do_cbeam = True # enforce a common beam
#
pb = self.getkey('pb')
do_pb = len(pb) > 0
use_pb = self.getkey("usepb")
#
create_mask = self.getkey('mask') # create a new mask ?
box = self.getkey("box") # corners in Z, XY or XYZ
edge = self.getkey("edge") # number of edge channels to remove
restfreq = self.getkey("restfreq") # < 0 means not activated
# smooth= could become deprecated, and/or include a decimation option to make it useful
# again, Smooth_AT() does this also , at the cost of an extra cube to store
smooth = self.getkey("smooth") #
#
vlsr = self.getkey("vlsr") # see also LineID, where this could be given again
# first place a fits file in the admit project directory (symlink)
# this is a bit involved, depending on if an absolute or relative path was
# give to Ingest_AT(file=)
fitsfile = self.getkey('file')
if fitsfile[0] != os.sep:
fitsfile = os.path.abspath(os.getcwd() + os.sep + fitsfile)
logging.debug('FILE=%s' % fitsfile)
if fitsfile[0] != os.sep:
raise Exception,"Bad file=%s, expected absolute name",fitsfile
# now determine if it could have been a CASA (or MIRIAD) image already
# which we'll assume if it's a directory; this is natively supported by CASA
# but there are tools where if you pass it a FITS or MIRIAD
# MIRIAD is not recommended for serious work, especially big files, since there
# is a performance penalty due to tiling.
file_is_casa = casautil.iscasa(fitsfile)
loc = fitsfile.rfind(os.sep) # find the '/'
ffile0 = fitsfile[loc+1:] # basename.fits
basename = self.getkey('basename') # (new) basename allowed (allow no dots?)
if len(basename) == 0:
basename = ffile0[:ffile0.rfind('.')] # basename
logging.info("basename=%s" % basename)
target = self.dir(ffile0)
if not os.path.exists(target) :
cmd = 'ln -s "%s" "%s"' % (fitsfile, target)
logging.debug("CMD: %s" % cmd)
os.system(cmd)
readonly = False
if file_is_casa:
logging.debug("Assuming input %s is a CASA (or MIRIAD) image" % ffile0)
bdpfile = self.mkext(basename,"im")
if bdpfile == ffile0:
logging.warning("No selections allowed on CASA image, since no alias was given")
readonly = True
b1 = SpwCube_BDP(bdpfile)
self.addoutput(b1)
b1.setkey("image", Image(images={bt.CASA:bdpfile}))
# @todo b2 and PB?
else:
# construct the output name and construct the BDP based on the CASA image name
# this also takes care of the behind the scenes alias= substitution
bdpfile = self.mkext(basename,"im")
if bdpfile == basename:
raise Exception,"basename and bdpfile are the same, Ingest_AT needs a fix for this"
b1 = SpwCube_BDP(bdpfile)
self.addoutput(b1)
if do_pb:
print "doing the PB"
bdpfile2 = self.mkext(basename,"pb")
b2 = Image_BDP(bdpfile2)
self.addoutput(b2)
# @todo we should also set readonly=True if no box, no mask etc. and still an alias
# that way it will speed up and not make a copy of the image ?
# fni and fno are full (abspath) filenames, ready for CASA
# fni is the same as fitsfile
fni = self.dir(ffile0)
fno = self.dir(bdpfile)
if do_pb: fno2 = self.dir(bdpfile2)
dt.tag("start")
if file_is_casa:
taskinit.ia.open(fni)
else:
if do_pb and use_pb:
# @todo this needs a fix for the path for pb, only works if abs path is given
# impbcor(im.fits,pb.fits,out.im,overwrite=True,mode='m')
if False:
# this may seem like a nice shortcut, to have the fits->casa conversion be done
# internally in impbcor, but it's a terrible performance for big cubes. (tiling?)
# we keep this code here, perhaps at some future time (mpi?) this performs better
# @todo fno2
impbcor(fni,pb,fno,overwrite=True,mode='m')
dt.tag("impbcor-1")
else:
# the better way is to convert FITS->CASA and then call impbcor()
# the CPU savings are big, but I/O overhead can still be substantial
taskinit.ia.fromfits('_pbcor',fni,overwrite=True)
taskinit.ia.fromfits('_pb',pb,overwrite=True)
dt.tag("impbcor-1f")
if False:
impbcor('_pbcor','_pb',fno,overwrite=True,mode='m')
# @todo fno2
utils.remove('_pbcor')
utils.remove('_pb')
dt.tag("impbcor-2")
else:
# immath appears to be even faster (2x in CPU)
# https://bugs.nrao.edu/browse/CAS-8299
# @todo this needs to be confirmed that impbcor is now good to go (r36078)
casa.immath(['_pbcor','_pb'],'evalexpr',fno,'IM0*IM1')
dt.tag("immath")
if True:
# use the mean of all channels... faster may be to use the middle plane
# barf; edge channels can be with fewer subfields in a mosaic
taskinit.ia.open('_pb')
taskinit.ia.summary()
ia1=taskinit.ia.moments(moments=[-1],drop=True,outfile=fno2)
ia1.done()
taskinit.ia.close()
dt.tag("moments")
utils.remove('_pbcor')
utils.remove('_pb')
dt.tag("impbcor-3")
elif do_pb and not use_pb:
# cheat case: PB was given, but not meant to be used
# not implemented yet
print "cheat case dummy PB not implemented yet"
else:
# no PB given
if True:
# re-running this was more consistently faster in wall clock time
# note that zeroblanks=True will still keep the mask
logging.debug("casa::ia.fromfits(%s) -> %s" % (fni,bdpfile))
taskinit.ia.fromfits(fno,fni,overwrite=True)
#taskinit.ia.fromfits(fno,fni,overwrite=True,zeroblanks=True)
dt.tag("fromfits")
else:
# not working to extend 3D yet, but this would solve the impv() 3D problem
logging.debug("casa::importfits(%s) -> %s" % (fni,bdpfile))
#casa.importfits(fni,fno,defaultaxes=True,defaultaxesvalues=[None,None,None,'I'])
# possible bug: zeroblanks=True has no effect?
casa.importfits(fni,fno,zeroblanks=True)
dt.tag("importfits")
taskinit.ia.open(fno)
if len(smooth) > 0:
# smooth here, but Smooth_AT is another option
# here we only allow pixel smoothing
# spatial: gauss
# spectral: boxcar/hanning (check for flux conservation)
# is the boxcar wrong, not centered, but edged?
# @todo CASA BUG: this will loose the object name (and maybe more?) from header, so VLSR lookup fails
fnos = fno + '.smooth'
taskinit.ia.convolve2d(outfile=fnos, overwrite=True, pa='0deg',
major='%gpix' % smooth[0], minor='%gpix' % smooth[1], type='gaussian')
taskinit.ia.close()
srcname = casa.imhead(fno,mode="get",hdkey="object") # work around CASA bug
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnos,fno)
casa.imhead(fno,mode="put",hdkey="object",hdvalue=srcname) # work around CASA bug
dt.tag("convolve2d")
if len(smooth) > 2 and smooth[2] > 0:
if smooth[2] == 1:
# @todo only 1 channel option
specsmooth(fno,fnos,axis=2,function='hanning',dmethod="")
else:
# @todo may have the wrong center
specsmooth(fno,fnos,axis=2,function='boxcar',dmethod="",width=smooth[2])
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnos,fno)
dt.tag("specsmooth")
taskinit.ia.open(fno)
s = taskinit.ia.summary()
if len(s['shape']) != 4:
logging.warning("Adding dummy STOKES-I axis")
fnot = fno + '_4'
taskinit.ia.adddegaxes(stokes='I',outfile=fnot)
taskinit.ia.close()
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnot,fno)
taskinit.ia.open(fno)
dt.tag("adddegaxes")
else:
logging.info("SHAPE: %s" % str(s['shape']))
s = taskinit.ia.summary()
dt.tag("summary-0")
if s['hasmask'] and create_mask:
logging.warning("no extra mask created because input image already had one")
create_mask = False
# if a box= or edge= was given, only a subset of the cube needs to be ingested
# this however complicates PB correction later on
if len(box) > 0 or len(edge) > 0:
if readonly:
raise Exception,"Cannot use box= or edge=, data is read-only, or use an basename/alias"
if len(edge) == 1: edge.append(edge[0])
nx = s['shape'][0]
ny = s['shape'][1]
nz = s['shape'][2]
logging.info("box=%s edge=%s processing with SHAPE: %s" % (str(box),str(edge),str(s['shape'])))
if len(box) == 2:
# select zrange
if len(edge)>0:
raise Exception,"Cannot use edge= when box=[z1,z2] is used"
r1 = taskinit.rg.box([0,0,box[0]] , [nx-1,ny-1,box[1]])
elif len(box) == 4:
if len(edge) == 0:
# select just an XY box
r1 = taskinit.rg.box([box[0],box[1]] , [box[2],box[3]])
elif len(edge) == 2:
# select an XY box, but remove some edge channels
r1 = taskinit.rg.box([box[0],box[1],edge[0]] , [box[2],box[3],nz-edge[1]-1])
else:
raise Exception,"Bad edge= for len(box)=4"
elif len(box) == 6:
# select an XYZ box
r1 = taskinit.rg.box([box[0],box[1],box[2]] , [box[3],box[4],box[5]])
elif len(edge) == 2:
# remove some edge channels, but keep the whole XY box
r1 = taskinit.rg.box([0,0,edge[0]] , [nx-1,ny-1,nz-edge[1]-1])
else:
raise Exception,"box=%s illegal" % box
logging.debug("BOX/EDGE selection: %s %s" % (str(r1['blc']),str(r1['trc'])))
#if taskinit.ia.isopen(): taskinit.ia.close()
logging.info("SUBIMAGE")
subimage = taskinit.ia.subimage(region=r1,outfile=fno+'.box',overwrite=True)
taskinit.ia.close()
taskinit.ia.done()
subimage.rename(fno,overwrite=True)
subimage.close()
subimage.done()
taskinit.ia.open(fno)
dt.tag("subimage-1")
else:
# the whole cube is passed onto ADMIT
if readonly and create_mask:
raise Exception,"Cannot use mask=True, data read-only, or use an alias"
if file_is_casa and not readonly:
# @todo a miriad file - which should be read only - will also create a useless copy here if no alias used
taskinit.ia.subimage(overwrite=True,outfile=fno)
taskinit.ia.close()
taskinit.ia.open(fno)
dt.tag("subimage-0")
if create_mask:
if readonly:
raise Exception,"Cannot create mask, data read-only, or use an alias"
# also check out the 'fromfits::zeroblanks = False'
# calcmask() will overwrite any previous pixelmask
#taskinit.ia.calcmask('mask("%s") && "%s" != 0.0' % (fno,fno))
taskinit.ia.calcmask('"%s" != 0.0' % fno)
dt.tag("mask")
s = taskinit.ia.summary()
dt.tag("summary-1")
# do a fast statistics (no median or robust)
s0 = taskinit.ia.statistics()
dt.tag("statistics")
if len(s0['npts']) == 0:
raise Exception,"No statistics possible, are there valid data in this cube?"
# There may be multiple beams per plane so we can't
# rely on the BEAM's 'major', 'minor', 'positionangle' being present.
# ia.commonbeam() is guaranteed to return beam parameters
# if present
if do_cbeam and s.has_key('perplanebeams'):
# report on the beam extremities, need to loop over all,
# first and last don't need to be extremes....
n = s['perplanebeams']['nChannels']
ab0 = '*0'
bb0 = s['perplanebeams']['beams'][ab0]['*0']
bmaj0 = bb0['major']['value']
bmin0 = bb0['minor']['value']
beamd = 0.0
for i in range(n):
ab1 = '*%d' % i
bb1 = s['perplanebeams']['beams'][ab1]['*0']
bmaj1 = bb1['major']['value']
bmin1 = bb1['minor']['value']
beamd = max(beamd,abs(bmaj0-bmaj1),abs(bmin0-bmin1))
logging.warning("MAX-BEAMSPREAD %f" % (beamd))
#
if True:
logging.info("Applying a commonbeam from the median beam accross the band")
# imhead is a bit slow; alternatively use ia.summary() at the half point for setrestoringbeam()
h = casa.imhead(fno,mode='list')
b = h['perplanebeams']['median area beam']
taskinit.ia.setrestoringbeam(remove=True)
taskinit.ia.setrestoringbeam(beam=b)
commonbeam = taskinit.ia.commonbeam()
else:
# @todo : this will be VERY slow - code not finished, needs renaming etc.
# this is however formally the better solution
logging.warning("commmonbeam code not finished")
cb = taskinit.ia.commonbeam()
taskinit.ia.convolve2d(outfile='junk-common.im', major=cb['major'], minor=cb['minor'], pa=cb['pa'],
targetres=True, overwrite=True)
dt.tag('convolve2d')
commonbeam = {}
else:
try:
commonbeam = taskinit.ia.commonbeam()
except:
nppb = 4.0
logging.warning("No synthesized beam found, faking one to prevent downstream problems: nppb=%f" % nppb)
s = taskinit.ia.summary()
cdelt2 = abs(s['incr'][0]) * 180.0/math.pi*3600.0
bmaj = nppb * cdelt2 # use a nominal 4 points per (round) beam
bmin = nppb * cdelt2
bpa = 0.0
taskinit.ia.setrestoringbeam(major='%farcsec' % bmaj, minor='%farcsec' % bmin, pa='%fdeg' % bpa)
commonbeam = {}
logging.info("COMMONBEAM[%d] %s" % (len(commonbeam),str(commonbeam)))
first_point = taskinit.ia.getchunk(blc=[0,0,0,0],trc=[0,0,0,0],dropdeg=True)
logging.debug("DATA0*: %s" % str(first_point))
taskinit.ia.close()
logging.info('BASICS: [shape] npts min max: %s %d %f %f' % (s['shape'],s0['npts'][0],s0['min'][0],s0['max'][0]))
logging.info('S/N (all data): %f' % (s0['max'][0]/s0['rms'][0]))
npix = 1
nx = s['shape'][0]
ny = s['shape'][1]
nz = s['shape'][2]
for n in s['shape']:
npix = npix * n
ngood = int(s0['npts'][0])
fgood = (1.0*ngood)/npix
logging.info('GOOD PIXELS: %d/%d (%f%% good or %f%% bad)' % (ngood,npix,100.0*fgood,100.0*(1 - fgood)))
if s['hasmask']:
logging.warning('MASKS: %s' % (str(s['masks'])))
if not file_is_casa:
b1.setkey("image", Image(images={bt.CASA:bdpfile}))
if do_pb:
b2.setkey("image", Image(images={bt.CASA:bdpfile2}))
# cube sanity: needs to be either 4D or 2D. But p-p-v cube
# alternative: ia.subimage(dropdeg = True)
# see also: https://bugs.nrao.edu/browse/CAS-5406
shape = s['shape']
if len(shape)>3:
if shape[3]>1:
# @todo this happens when you ingest a fits or casa image which is ra-dec-pol-freq
if nz > 1:
msg = 'Ingest_AT: cannot deal with real 4D cubes yet'
logging.critical(msg)
raise Exception,msg
else:
# @todo this is not working yet when the input was a casa image, but ok when fits. go figure.
fnot = fno + ".trans"
if True:
# this works
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fno,fnot)
imtrans(fnot,fno,"0132")
utils.remove(fnot)
else:
# this does not work, what the heck
imtrans(fno,fnot,"0132")
#@todo use safer ia.rename() here.
# https://casa.nrao.edu/docs/CasaRef/image.rename.html
utils.rename(fnot,fno)
nz = s['shape'][3]
# get a new summary 's'
taskinit.ia.open(fno)
s = taskinit.ia.summary()
taskinit.ia.close()
logging.warning("Using imtrans, with nz=%d, to fix axis ordering" % nz)
dt.tag("imtrans4")
# @todo ensure first two axes are position, followed by frequency
elif len(shape)==3:
# the current importfits() can do defaultaxes=True,defaultaxesvalues=['', '', '', 'I']
# but then appears to return a ra-dec-pol-freq cube
# this branch probably never happens, since ia.fromfits() will
# properly convert a 3D cube to 4D now !!
# NO: when NAXIS=3 but various AXIS4's are present, that works. But not if it's pure 3D
# @todo box=
logging.warning("patching up a 3D to 4D cube")
raise Exception,"SHOULD NEVER GET HERE"
fnot = fno + ".trans"
casa.importfits(fni,fnot,defaultaxes=True,defaultaxesvalues=['', '', '', 'I'])
utils.remove(fno) # ieck
imtrans(fnot,fno,"0132")
utils.remove(fnot)
dt.tag("imtrans3")
logging.regression('CUBE: %g %g %g %d %d %d %f' % (s0['min'],s0['max'],s0['rms'],nx,ny,nz,100.0*(1 - fgood)))
# if the cube has only 1 plane (e.g. continuum) , create a visual (png or so)
# for 3D cubes, rely on something like CubeSum
if nz == 1:
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot.plotter(rasterfile=bdpfile,figname=bdpfile)
# @todo needs to be registered for the BDP, right now we only have the plot
# ia.summary() doesn't have this easily available, so run the more expensive imhead()
h = casa.imhead(fno,mode='list')
telescope = h['telescope']
# work around CASA's PIPELINE bug/feature? if 'OBJECT' is blank, try 'FIELD'
srcname = h['object']
if srcname == ' ':
logging.warning('FIELD used for OBJECT')
srcname = casa.imhead(fno,mode='get',hdkey='field')
if srcname == False:
# if no FIELD either, we're doomed. yes, this did happen.
srcname = 'Unknown'
casa.imhead(fno,mode="put",hdkey="object",hdvalue=srcname)
h['object'] = srcname
logging.info('TELESCOPE: %s' % telescope)
logging.info('OBJECT: %s' % srcname)
logging.info('REFFREQTYPE: %s' % h['reffreqtype'])
if h['reffreqtype'].find('TOPO')>=0:
msg = 'Ingest_AT: cannot deal with cubes with TOPOCENTRIC frequencies yet - winging it'
logging.warning(msg)
#raise Exception,msg
# Ensure beam parameters are available if there are multiple beams
# If there is just one beam, then we are just overwriting the header
# variables with their identical values.
if len(commonbeam) != 0:
h['beammajor'] = commonbeam['major']
h['beamminor'] = commonbeam['minor']
h['beampa'] = commonbeam['pa']
# cheat add some things that need to be passed to summary....
h['badpixel'] = 1.0-fgood
if vlsr < -999998.0:
vlsr = admit.VLSR().vlsr(h['object'].upper())
h['vlsr'] = vlsr
logging.info("VLSR = %f (from source catalog)" % vlsr)
taskargs = "file=" + fitsfile
if create_mask == True:
taskargs = taskargs + " mask=True"
if len(box) > 0:
taskargs = taskargs + " " + str(box)
if len(edge) > 0:
taskargs = taskargs + " " + str(edge)
r2d = 57.29577951308232
logging.info("RA Axis 1: %f %f %f" % (h['crval1']*r2d,h['cdelt1']*r2d*3600.0,h['crpix1']))
logging.info("DEC Axis 2: %f %f %f" % (h['crval2']*r2d,h['cdelt2']*r2d*3600.0,h['crpix2']))
if nz > 1:
# @todo check if this is really a freq axis (for ALMA it is, but...)
t3 = h['ctype3']
df = h['cdelt3']
fc = h['crval3'] + (0.5*(float(shape[2])-1)-h['crpix3'])*df # center freq; 0 based pixels
if h.has_key('restfreq'):
fr = float(h['restfreq'][0])
else:
fr = fc
fw = df*float(shape[2])
dv = -df/fr*utils.c
logging.info("Freq Axis 3: %g %g %g" % (h['crval3']/1e9,h['cdelt3']/1e9,h['crpix3']))
logging.info("Cube Axis 3: type=%s velocity increment=%f km/s @ fc=%f fw=%f GHz" % (t3,dv,fc/1e9,fw/1e9))
# @todo sort out this restfreq/vlsr
# report 'reffreqtype', 'restfreq' 'telescope'
# if the fits file has ALTRVAL/ALTRPIX, this is lost in CASA?
# but if you do fits->casa->fits , it's back in fits (with some obvious single precision loss of digits)
# @todo ZSOURCE is the proposed VLSR slot in the fits header, but this has frame issues (it's also optical)
#
# Another method to get the vlsr is to override the restfreq (f0) with an AT keyword
# and the 'restfreq' from the header (f) is then used to compute the vlsr: v = c (1 - f/f0)
#
if shape[2] > 1 and h.has_key('restfreq'):
logging.info("RESTFREQ: %g %g %g" % (fr/1e9,h['restfreq'][0]/1e9,restfreq))
if shape[2] > 1:
# v_radio of the center of the window w.r.t. restfreq
c = utils.c # 299792.458 km/s
vlsrc = c*(1-fc/fr) # @todo rel frame?
vlsrw = dv*float(shape[2])
if restfreq > 0:
vlsrf = c*(1-fr/restfreq/1e9)
h['vlsr'] = vlsrf
else:
vlsrf = 0.0
logging.info("VLSRc = %f VLSRw = %f VLSRf = %f VLSR = %f" % (vlsrc, vlsrw, vlsrf, vlsr))
if h['vlsr'] == 0.0: # @todo! This fails if vlsr actually is zero. Need another magic number
h['vlsr'] = vlsrc
logging.warning("Warning: No VLSR found, substituting VLSRc = %f" % vlsrc)
else:
msg = 'Ingest_AT: missing RESTFREQ'
print msg
# @todo LINTRN is the ALMA keyword that designates the expected line transition in a spw
self._summarize(fitsfile, bdpfile, h, shape, taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("Smooth")
dt.tag("start")
# get the input keys
bmaj = self.getkey("bmaj")
bmin = self.getkey("bmin")
bpa = self.getkey("bpa")
velres = self.getkey("velres")
# take care of potential issues in the unit strings
# @todo if not provided?
bmaj['unit'] = bmaj['unit'].lower()
bmin['unit'] = bmin['unit'].lower()
velres['unit'] = velres['unit'].lower()
taskargs = "bmaj=%s bmin=%s bpa=%s velres=%s" % (bmaj,bmin,bpa,velres)
bdpnames=[]
for ibdp in self._bdp_in:
istem = ibdp.getimagefile(bt.CASA)
image_in = ibdp.baseDir() + istem
bdp_name = self.mkext(istem,'sim')
image_out = self.dir(bdp_name)
taskinit.ia.open(image_in)
h = casa.imhead(image_in, mode='list')
pix_scale = np.abs(h['cdelt1'] * 206265.0) # pix scale in asec @todo QA ?
CC = 299792458.0 # speed of light @todo somewhere else [utils.c , but in km/s]
rest_freq = h['crval3']
# frequency pixel scale in km/s
vel_scale = np.abs(CC*h['cdelt3']/rest_freq/1000.0)
# unit conversion to arcsec (spatial) or km/s
# (velocity) or some flavor of Hz.
if(bmaj['unit'] == 'pixel'):
bmaj = bmaj['value']*pix_scale
else:
bmaj = bmaj['value']
if(bmin['unit'] == 'pixel'):
bmin = bmin['value']*pix_scale
else:
bmin = bmin['value']
hertz_input = False
if velres['unit'] == 'pixel':
velres['value'] = velres['value']*vel_scale
velres['unit'] = 'km/s'
elif velres['unit'] == 'm/s':
velres['value'] = velres['value']/1000.0
velres['unit'] = 'km/s'
elif velres['unit'][-2:] == 'hz':
hertz_input = True
elif velres['unit'] == 'km/s':
pass
else:
logging.error("Unknown units in velres=%s" % velres['unit'])
rdata = bmaj
# we smooth in velocity first. if smoothing in velocity
# the cube apparently must be closed afterwards and
# then reopened if spatial smoothing is to be done.
if velres['value'] > 0:
# handle the different units allowed. CASA doesn't
# like lowercase for hz units...
if not hertz_input:
freq_res = str(velres['value']*1000.0/CC *rest_freq )+'Hz'
else:
freq_res = str(velres['value'])
# try to convert velres to km/s for debug purposes
velres['value'] = velres['value']/rest_freq*CC / 1000.0
if(velres['unit'] == 'khz'):
velres['value'] = velres['value']*1000.0
velres['unit'] = 'kHz'
elif(velres['unit']=='mhz'):
velres['value'] = velres['value']*1E6
velres['unit'] = 'MHz'
elif(velres['unit']=='ghz'):
velres['value'] = velres['value']*1E9
velres['unit'] = 'GHz'
freq_res = freq_res + velres['unit']
# NB: there is apparently a bug in CASA. only smoothing along the frequency
# axis does not work. sepconvolve gives a unit error (says axis unit is radian rather
# than Hz). MUST smooth in 2+ dimensions if you want this to work.
if(velres['value'] < vel_scale):
raise Exception,"Desired velocity resolution %g less than pixel scale %g" % (velres['value'],vel_scale)
image_tmp = self.dir('tmp.smooth')
im2=taskinit.ia.sepconvolve(outfile=image_tmp,axes=[0,1,2], types=["boxcar","boxcar","gauss"],\
widths=['1pix','1pix',freq_res], overwrite=True)
im2.done()
logging.debug("sepconvolve to %s" % image_out)
# for some reason, doing this in memory does not seem to work, so outfile must be specified.
logging.info("Smoothing cube to a velocity resolution of %s km/s" % str(velres['value']))
logging.info("Smoothing cube to a frequency resolution of %s" % freq_res)
taskinit.ia.close()
taskinit.ia.open(image_tmp)
dt.tag("sepconvolve")
else:
image_tmp = image_out
# now do the spatial smoothing
convolve_to_min_beam = True # default is to convolve to a min enclosing beam
if bmaj > 0 and bmin > 0:
# form qa objects out of these so that casa can understand
bmaj = taskinit.qa.quantity(bmaj,'arcsec')
bmin = taskinit.qa.quantity(bmin,'arcsec')
bpa = taskinit.qa.quantity(bpa,'deg')
target_res={}
target_res['major'] = bmaj
target_res['minor'] = bmin
target_res['positionangle'] = bpa
# throw an exception if cannot be convolved
try:
# for whatever reason, if you give convolve2d a beam parameter,
# it complains ...
im2=taskinit.ia.convolve2d(outfile=image_out,major = bmaj,\
minor = bmin, pa = bpa,\
targetres=True,overwrite=True)
im2.done()
logging.info("Smoothing cube to a resolution of %s by %s at a PA of %s" %
(str(bmaj['value']), str(bmin['value']), str(bpa['value'])))
convolve_to_min_beam = False
achieved_res = target_res
except:
# @todo remind what you need ?
logging.error("Warning: Could not convolve to requested resolution of "\
+str(bmaj['value']) + " by " + str(bmin['value']) + \
" at a PA of "+ str(bpa['value']))
raise Exception,"Could not convolve to beam given!"
dt.tag("convolve2d-1")
if convolve_to_min_beam:
restoring_beams = taskinit.ia.restoringbeam()
commonbeam = taskinit.ia.commonbeam()
# for whatever reason, setrestoringbeam does not use the same set of hashes...
commonbeam['positionangle']=commonbeam['pa']
del commonbeam['pa']
# if there's one beam, apparently the beams keyword does not exist
if 'beams' in restoring_beams:
print "Smoothing cube to a resolution of "+ \
str(commonbeam['major']['value']) +" by "+ \
str(commonbeam['minor']['value'])+" at a PA of "\
+str(commonbeam['pa']['value'])
target_res = commonbeam
im2=taskinit.ia.convolve2d(outfile=image_out,major=commonbeam['major'],\
minor=commonbeam['minor'],\
pa=commonbeam['positionangle'],\
targetres=True,overwrite=True)
im2.done()
achieved_res = commonbeam
dt.tag("convolve2d-2")
else:
print "One beam for all planes. Smoothing to common beam redundant."
achieved_res = commonbeam
if velres['value'] < 0:
taskinit.ia.fromimage(outfile=image_out, infile=image_in)
# not really doing anything
# else, we've already done what we needed to
taskinit.ia.setrestoringbeam(beam = achieved_res)
rdata = achieved_res['major']['value']
# else do no smoothing and just close the image
taskinit.ia.close()
dt.tag("close")
b1 = SpwCube_BDP(bdp_name)
self.addoutput(b1)
# need to update for multiple images.
b1.setkey("image", Image(images={bt.CASA:bdp_name}))
bdpnames = bdpnames.append(bdp_name)
# and clean up the temp image before the next image
if velres['value'] > 0:
utils.remove(image_tmp)
# thes are task arguments not summary entries.
_bmaj = taskinit.qa.convert(achieved_res['major'],'rad')['value']
_bmin = taskinit.qa.convert(achieved_res['minor'],'rad')['value']
_bpa = taskinit.qa.convert(achieved_res['positionangle'],'deg')['value']
vres = "%.2f %s" % (velres['value'],velres['unit'])
logging.regression("SMOOTH: %f %f" % (rdata,velres['value']))
self._summary["smooth"] = SummaryEntry([bdp_name,convolve_to_min_beam,_bmaj,_bmin,_bpa,vres],"Smooth_AT",self.id(True),taskargs)
dt.tag("done")
dt.end()
def find(self):
""" Method that does the segment finding
Parameters
----------
None
Returns
-------
Tuple containing a list of the segments, the cutoff used, the
noise level, and a mean baseline.
"""
if self.abs:
self.spec = abs(self.spec)
temp = np.zeros(len(self.spec))
#self.see = ma.masked_array(temp, mask=self.spec.mask)
# parameters (some now from the function argument)
logging.debug("MIN/MAX " + str(self.spec.min()) + " / " +\
str(self.spec.max()))
n = len(self.spec) # data and freq assumed to be same size
if self.hanning:
h = np.hanning(5) / 2 # normalize the convolution array
h = np.array([0.25, 0.5, 0.25])
data2 = np.convolve(self.spec, h, 'same')
else:
data2 = self.spec
if len(data2) != len(self.freq):
raise Exception("ulines: data2 and freq not same array")
# do the work
dr = stats.robust(data2, self.f)
noise = dr.std()
logging.debug("ROBUST: (mean/median/noise) " + \
str(dr.mean()) + " / " + str(ma.median(dr)) + " / " + str(noise))
#print "\n\nD2",data2,"\n"
data3 = ma.masked_invalid(data2)
#print "\n\nD3\n",data3,"\n"
ddiff = data3[1:n] - data3[0:n - 1]
logging.debug("DIFF: (mean/stdev) " + str(ddiff.mean()) +\
" / " + str(ddiff.std()))
#print "\n\n",ddiff,"\n",self.f,"\n"
ddr = stats.robust(ddiff, self.f)
logging.debug("RDIFF: (mean/median/stdev) " + \
str(ddr.mean()) + " / " + str(ma.median(ddr)) + " / " + \
str(ddr.std()))
#plt.show()
if self.bottom:
# first remind the classic
dmean1 = dr.mean()
dstd1 = dr.std()
logging.debug("CLASSIC MEAN/SIGMA: " + str(dmean1) + \
" / " + str(dstd1))
# see if we can find a better one?
# k should really depend on nchan, (like an nsigma), 2-3 should be ok for most.
k = 2.5
dmin = dr.min()
dmax = dr.min() + 2 * k * ddr.std() / 1.414214
logging.debug("DMIN/DMAX: " + str(dmin) + " / " + \
str(dmax))
dm = ma.masked_outside(dr, dmin, dmax)
dmean = max(0.0,
dm.mean()) # ensure that the mean is positive or 0.0
dstd = dm.std()
if self.noise is not None:
cutoff = self.pmin * self.noise
elif self.nomean:
cutoff = self.pmin * dstd
else:
cutoff = dmean + self.pmin * dstd
logging.debug("BETTER MEAN/SIGMA: " + str(dmean) + \
" / " + str(dstd))
else:
# classic simple, but fails when robust didn't kill off (enough of) the signal
# sigma will be too high, cutoff too high and you could have no lines if there
# is one strong lines
dmean = dr.mean()
dstd = dr.std()
if self.noise is not None:
cutoff = self.pmin * self.noise
elif self.nomean:
cutoff = self.pmin * dstd
else:
cutoff = dmean + self.pmin * dstd
logging.debug("CLASSIC MEAN/SIGMA: " + str(dmean) + \
" / " + str(dstd))
logging.debug("SEGMENTS: f=%g pmin=%g maxgap=%d minchan=%d" % \
(self.f, self.pmin, self.maxgap, self.minchan))
#print "\nDATA\n\n",data2,"\n\n"
segments = self.line_segments(data2, cutoff)
#print "SEGMENTS",segments
nlines = len(segments)
logging.debug("Found %d segments above cutoff %f" % \
(nlines, cutoff))
segp = []
rmax = data2.max() + 0.1 # + 0.05*(data2.max()-data2.min())
segp.append([self.freq[0], self.freq[n - 1], cutoff, cutoff])
segp.append([self.freq[0], self.freq[n - 1], dmean, dmean])
for (l, s) in zip(range(nlines), segments):
ch0 = s[0]
ch1 = s[1]
sum0 = sum(data2[ch0:ch1 + 1])
sum1 = sum(self.freq[ch0:ch1 + 1] * data2[ch0:ch1 + 1])
sum2 = sum(self.freq[ch0:ch1 + 1] * self.freq[ch0:ch1 + 1] *
data2[ch0:ch1 + 1])
lmean = sum1 / sum0
# this fails for weaker lines, so wrapped it in a abs
lsigma = math.sqrt(abs(sum2 / sum0 - lmean * lmean))
lmax = max(data2[ch0:ch1 + 1])
if self.peak != None:
lpeak = 1000 * max(self.peak[ch0:ch1 + 1])
else:
lpeak = max(self.spec[ch0:ch1 + 1])
# @todo if we ever allow minchan=1 lsigma would be 0.0.... should we adopt channel width?
lfwhm = 2.355 * lsigma / lmean * utils.c
logging.debug(
"Line in %2d channels %4d - %4d @ %.4f GHz +/- %.4f GHz log(S/N) = %.2f FWHM %5.1f km/s %.2f" % \
(ch1 - ch0 + 1, ch0, ch1, lmean, lsigma, lmax, lfwhm, lpeak))
segp.append([self.freq[ch0], self.freq[ch1], rmax, rmax])
segp.append([lmean, lmean, rmax - 0.1, rmax + 0.05])
return Segments.Segments(segments,
nchan=len(self.spec)), cutoff, dstd, dmean
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 find(self):
""" Method that does the segment finding
Parameters
----------
None
Returns
-------
Tuple containing a list of the segments, the cutoff used, the
noise level, and a mean baseline.
"""
if self.abs:
self.spec = abs(self.spec)
temp = np.zeros(len(self.spec))
#self.see = ma.masked_array(temp, mask=self.spec.mask)
# parameters (some now from the function argument)
logging.debug("MIN/MAX " + str(self.spec.min()) + " / " +\
str(self.spec.max()))
n = len(self.spec) # data and freq assumed to be same size
if self.hanning:
h = np.hanning(5) / 2 # normalize the convolution array
h = np.array([0.25, 0.5, 0.25])
data2 = np.convolve(self.spec, h, 'same')
else:
data2 = self.spec
if len(data2) != len(self.freq):
raise Exception("ulines: data2 and freq not same array")
# do the work
dr = stats.robust(data2, self.f)
noise = dr.std()
logging.debug("ROBUST: (mean/median/noise) " + \
str(dr.mean()) + " / " + str(ma.median(dr)) + " / " + str(noise))
#print "\n\nD2",data2,"\n"
data3 = ma.masked_invalid(data2)
#print "\n\nD3\n",data3,"\n"
ddiff = data3[1:n] - data3[0:n-1]
logging.debug("DIFF: (mean/stdev) " + str(ddiff.mean()) +\
" / " + str(ddiff.std()))
#print "\n\n",ddiff,"\n",self.f,"\n"
ddr = stats.robust(ddiff, self.f)
logging.debug("RDIFF: (mean/median/stdev) " + \
str(ddr.mean()) + " / " + str(ma.median(ddr)) + " / " + \
str(ddr.std()))
#plt.show()
if self.bottom:
# first remind the classic
dmean1 = dr.mean()
dstd1 = dr.std()
logging.debug("CLASSIC MEAN/SIGMA: " + str(dmean1) + \
" / " + str(dstd1))
# see if we can find a better one?
# k should really depend on nchan, (like an nsigma), 2-3 should be ok for most.
k = 2.5
dmin = dr.min()
dmax = dr.min() + 2 * k * ddr.std() / 1.414214
logging.debug("DMIN/DMAX: " + str(dmin) + " / " + \
str(dmax))
dm = ma.masked_outside(dr, dmin, dmax)
dmean = max(0.0, dm.mean()) # ensure that the mean is positive or 0.0
dstd = dm.std()
if self.noise is not None:
cutoff = self.pmin * self.noise
elif self.nomean:
cutoff = self.pmin * dstd
else:
cutoff = dmean + self.pmin * dstd
logging.debug("BETTER MEAN/SIGMA: " + str(dmean) + \
" / " + str(dstd))
else:
# classic simple, but fails when robust didn't kill off (enough of) the signal
# sigma will be too high, cutoff too high and you could have no lines if there
# is one strong lines
dmean = dr.mean()
dstd = dr.std()
if self.noise is not None:
cutoff = self.pmin * self.noise
elif self.nomean:
cutoff = self.pmin * dstd
else:
cutoff = dmean + self.pmin * dstd
logging.debug("CLASSIC MEAN/SIGMA: " + str(dmean) + \
" / " + str(dstd))
logging.debug("SEGMENTS: f=%g pmin=%g maxgap=%d minchan=%d" % \
(self.f, self.pmin, self.maxgap, self.minchan))
#print "\nDATA\n\n",data2,"\n\n"
segments = self.line_segments(data2, cutoff)
#print "SEGMENTS",segments
nlines = len(segments)
logging.debug("Found %d segments above cutoff %f" % \
(nlines, cutoff))
segp = []
rmax = data2.max() + 0.1 # + 0.05*(data2.max()-data2.min())
segp.append([self.freq[0], self.freq[n - 1], cutoff, cutoff])
segp.append([self.freq[0], self.freq[n - 1], dmean, dmean])
for (l, s) in zip(range(nlines), segments):
ch0 = s[0]
ch1 = s[1]
sum0 = sum(data2[ch0:ch1+1])
sum1 = sum(self.freq[ch0:ch1+1] * data2[ch0:ch1+1])
sum2 = sum(self.freq[ch0:ch1+1] * self.freq[ch0:ch1+1] * data2[ch0:ch1+1])
lmean = sum1 / sum0
# this fails for weaker lines, so wrapped it in a abs
lsigma = math.sqrt(abs(sum2 / sum0 - lmean * lmean))
lmax = max(data2[ch0:ch1+1])
if self.peak != None:
lpeak = 1000*max(self.peak[ch0:ch1+1])
else:
lpeak = max(self.spec[ch0:ch1+1])
# @todo if we ever allow minchan=1 lsigma would be 0.0.... should we adopt channel width?
lfwhm = 2.355 * lsigma / lmean * utils.c
logging.debug(
"Line in %2d channels %4d - %4d @ %.4f GHz +/- %.4f GHz log(S/N) = %.2f FWHM %5.1f km/s %.2f" % \
(ch1 - ch0 + 1, ch0, ch1, lmean, lsigma, lmax, lfwhm, lpeak))
segp.append([self.freq[ch0], self.freq[ch1], rmax, rmax])
segp.append([lmean, lmean, rmax - 0.1, rmax + 0.05])
return Segments.Segments(segments, nchan=len(self.spec)), cutoff, dstd, dmean
def map_to_slit(fname, clip=0.0, gamma=1.0):
"""take all values from a map over clip, compute best slit for PV Slice
"""
taskinit.ia.open(fname)
imshape = taskinit.ia.shape()
pix = taskinit.ia.getchunk().squeeze() # this should now be a numpy pix[ix][iy] map
pixmax = pix.max()
pixrms = pix.std()
if False:
pix1 = pix.flatten()
rpix = stats.robust(pix1)
logging.debug("stats: mean: %g %g" % (pix1.mean(), rpix.mean()))
logging.debug("stats: rms: %g %g" % (pix1.std(), rpix.std()))
logging.debug("stats: max: %g %g" % (pix1.max(), rpix.max()))
logging.debug('shape: %s %s %s' % (str(pix.shape),str(pix1.shape),str(imshape)))
taskinit.ia.close()
nx = pix.shape[0]
ny = pix.shape[1]
x=np.arange(pix.shape[0]).reshape( (nx,1) )
y=np.arange(pix.shape[1]).reshape( (1,ny) )
if clip > 0.0:
nmax = nx*ny
clip = clip * pixrms
logging.debug("Using initial clip=%g for rms=%g" % (clip,pixrms))
m=ma.masked_less(pix,clip)
while m.count() == 0:
clip = 0.5 * clip
logging.debug("no masking...trying lower clip=%g" % clip)
m=ma.masked_less(pix,clip)
else:
logging.debug("Clip=%g now found %d/%d points" % (clip,m.count(),nmax))
else:
#@ todo sigma-clipping with iterations? see also astropy.stats.sigma_clip()
rpix = stats.robust(pix.flatten())
r_mean = rpix.mean()
r_std = rpix.std()
logging.info("ROBUST MAP mean/std: %f %f" % (r_mean,r_std))
m=ma.masked_less(pix,-clip*r_std)
logging.debug("Found > clip=%g : %g" % (clip,m.count()))
if m.count() == 0:
logging.warning("Returning a dummy slit, no points above clip %g" % clip)
edge = 3.0
#slit = [edge,0.5*ny,nx-1.0-edge,0.5*ny] # @todo file a bug, this failed
# RuntimeError: (/var/rpmbuild/BUILD/casa-test/casa-test-4.5.7/code/imageanalysis/ImageAnalysis/PVGenerator.cc : 334) Failed AlwaysAssert abs( (endPixRot[0] - startPixRot[0]) - sqrt(xdiff*xdiff + ydiff*ydiff) ) < 1e-6
slit = [edge,0.5*ny-0.1,nx-1.0-edge,0.5*ny+0.1]
else:
slit = convert_to_slit(m,x,y,nx,ny,gamma)
return (slit,clip)
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 convert_to_slit(m,x,y,nx,ny,gamma=1.0,expand=1.0):
"""compute best slit for PV Slice from set of points or masked array
using moments of inertia
m=mass (intensity) x,y = positions
"""
# sanity
if len(m) == 0: return []
if type(m) == ma.core.MaskedArray:
if m.count() == 0: return []
# apply gamma factor
logging.debug("Gamma = %f" % gamma)
mw = ma.power(m,gamma)
# first find a rough center
smx = ma.sum(mw*x)
smy = ma.sum(mw*y)
sm = ma.sum(mw)
xm = smx/sm
ym = smy/sm
logging.debug('MOI::center: %f %f' % (xm,ym))
(xpeak,ypeak) = np.unravel_index(mw.argmax(),mw.shape)
logging.debug('PEAK: %f %f' % (xpeak,ypeak))
if True:
# center on peak
# @todo but if (xm,ym) and (xpeak,ypeak) differ too much, e.g.
# outside of the MOI body, something else is wrong
xm = xpeak
ym = ypeak
# take 2nd moments w.r.t. this center
x = x-xm
y = y-ym
mxx=m*x*x
mxy=m*x*y
myy=m*y*y
#
smxx=ma.sum(mxx)/sm
smxy=ma.sum(mxy)/sm
smyy=ma.sum(myy)/sm
# MOI2
moi = np.array([smxx,smxy,smxy,smyy]).reshape(2,2)
w,v = la.eig(moi)
a = math.sqrt(w[0])
b = math.sqrt(w[1])
phi = -math.atan2(v[0][1],v[0][0])
if a < b:
phi = phi + 0.5*np.pi
logging.debug('MOI::a,b,phi(deg): %g %g %g' % (a,b,phi*180.0/np.pi))
# ds9.reg format (image coords)
sinp = np.sin(phi)
cosp = np.cos(phi)
# compute the line take both a and b into account,
# since we don't even know or care which is the bigger one
r = np.sqrt(a*a+b*b)
x0 = xm - expand*r*cosp
y0 = ym - expand*r*sinp
x1 = xm + expand*r*cosp
y1 = ym + expand*r*sinp
# add 1 for ds9, which used 1 based pixels
logging.debug("ds9 short line(%g,%g,%g,%g)" % (x0+1,y0+1,x1+1,y1+1))
if nx > 0:
s = expand_line(x0,y0,x1,y1,nx,ny)
logging.debug("ds9 full line(%g,%g,%g,%g)" % (s[0],s[1],s[2],s[3]))
return [float(s[0]),float(s[1]),float(s[2]),float(s[3])]
else:
return [float(x0),float(y0),float(x1),float(y1)]
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()
