def createmask(dir_data, imagename, thres, outmask="mask1.image", pixelmin=5.):
#create mask with thres
outfile = dir_data + outmask
os.system("rm -rf " + outfile)
immath(imagename=dir_data + imagename,
mode="evalexpr",
expr="iif(IM0 >= " + str(thres) + ", 1.0, 0.0)",
outfile=outfile)
imhead(imagename=outfile, mode="del", hdkey="beammajor")
makemask(mode="copy",
inpimage=outfile,
inpmask=outfile,
output=outfile + ":mask0",
overwrite=True)
savemodel='none',
scales=[0, 3, 9],
nterms=2,
selectdata=True,
)
makefits(imagename)
dirtyimage = imagename + '.image.tt0'
ia.open(dirtyimage)
ia.calcmask(mask=dirtyimage + " > 0.0025",
name='dirty_mask_{0}'.format(field_nospace))
ia.close()
makemask(mode='copy',
inpimage=dirtyimage,
inpmask=dirtyimage + ":dirty_mask_{0}".format(field_nospace),
output='dirty_mask_{0}.mask'.format(field_nospace),
overwrite=True)
mask = 'dirty_mask_{0}.mask'.format(field_nospace)
exportfits(mask, mask + '.fits', dropdeg=True, overwrite=True)
exportfits(dirtyimage, dirtyimage + ".fits", overwrite=True)
reg = pyregion.open('cleanbox_regions_{0}.reg'.format(field_nospace))
imghdu = fits.open(dirtyimage + ".pbcor.fits")[0]
imghdu2 = fits.open(dirtyimage + ".fits")[0]
mask = reg.get_mask(imghdu)[None, None, :, :]
imghdu2.data = mask.astype('int16')
imghdu2.header['BITPIX'] = 16
imghdu2.writeto('cleanbox_mask_{0}.fits'.format(field_nospace),
clobber=True)
importfits(fitsimage='cleanbox_mask_{0}.fits'.format(field_nospace),
def moments_to_ratio(dir_data, galname, suffix, threesigma_co10,
threesigma_co21, threesigma8_co10, threesigma8_co21):
"""
"""
print("###########################")
print("### running moments_to_ratio")
print("###########################")
### setup
dir_data1 = dir_data + galname + "/"
im_co10 = glob.glob(dir_data1 + galname + "*co10*" + suffix +
"*moment0")[0]
im_co21 = glob.glob(dir_data1 + galname + "*co21*" + suffix +
"*moment0")[0]
outmask_co10 = im_co10.replace(".moment0", "_mom.mask")
outmask_co21 = im_co21.replace(".moment0", "_mom.mask")
m8_co10 = glob.glob(dir_data1 + galname + "*co10*" + suffix +
"*moment8")[0]
m8_co21 = glob.glob(dir_data1 + galname + "*co21*" + suffix +
"*moment8")[0]
outmask8_co10 = m8_co10.replace(".moment8", "_mom8.mask")
outmask8_co21 = m8_co21.replace(".moment8", "_mom8.mask")
### create a combined mask
# mom-0
peak = imstat(im_co10)["max"][0]
createmask(im_co10, threesigma_co10, outmask_co10)
peak = imstat(im_co21)["max"][0]
createmask(im_co21, threesigma_co21, outmask_co21)
outfile = dir_data1 + galname + "_r21_" + suffix + ".mask"
os.system("rm -rf " + outfile)
immath(imagename=[outmask_co10, outmask_co21],
mode="evalexpr",
expr="IM0*IM1",
outfile=outfile)
makemask(mode="copy",
inpimage=outfile,
inpmask=outfile,
output=outfile + ":mask0",
overwrite=True)
#mom-8
peak = imstat(m8_co10)["max"][0]
createmask(m8_co10, threesigma8_co10, outmask8_co10)
peak = imstat(im_co21)["max"][0]
createmask(m8_co21, threesigma8_co21, outmask8_co21)
outfile = dir_data1 + galname + "_r21_" + suffix + "_m8.mask"
os.system("rm -rf " + outfile)
immath(imagename=[outmask8_co10, outmask8_co21],
mode="evalexpr",
expr="IM0*IM1",
outfile=outfile)
makemask(mode="copy",
inpimage=outfile,
inpmask=outfile,
output=outfile + ":mask0",
overwrite=True)
### create line ratio map
#mom-0
outfile = dir_data1 + galname + "_r21_" + suffix + ".image"
mask = dir_data1 + galname + "_r21_" + suffix + ".mask"
line_ratio(dir_data="",
im1=im_co21,
im2=im_co10,
outfile=outfile,
diff="4.",
mask=mask)
#mom-8
outfile = dir_data1 + galname + "_r21_" + suffix + "_m8.image"
mask = dir_data1 + galname + "_r21_" + suffix + "_m8.mask"
line_ratio(dir_data="",
im1=m8_co21,
im2=m8_co10,
outfile=outfile,
diff="4.",
mask=mask)
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
ia = taskinit.iatool()
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
ia.open(self.dir("mom0.masked"))
defmask = ia.maskhandler('default')
ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
ia.open(self.dir(imagename))
ia.maskhandler('set', defmask)
ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,
colorwedge=True,zoom=self.getkey("zoom"))
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
taskinit.ia.open(self.dir("mom0.masked"))
defmask = taskinit.ia.maskhandler('default')
taskinit.ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
taskinit.ia.open(self.dir(imagename))
taskinit.ia.maskhandler('set', defmask)
taskinit.ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,colorwedge=True)
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()