def make_SBimage(image='',cellsize=None,beamsize=None,distance=None,
extension='.image'):
from casa import immath,imhead
beamarea = 1.13*beamsize**2/cellsize**2
massCoeff = 2.36e5 * distance**2 / beamarea
pcPerArcsec = distance *1e6 / 206265.
pcPerPixel = cellsize * pcPerArcsec
pixSB = massCoeff / pcPerPixel**2 # units in Msun per pixel
immath(imagename=image + extension,
outfile=image + '.sb.image',
mode='evalexpr',
expr='IM0*' + str(pixSB))
imhead(imagename=image + '.sb.image',
mode='put',
hdkey='bunit',
hdvalue='',
hdcomment='Units Msun/pc^-2')
def make_SBimage(image='',
cellsize=None,
beamsize=None,
distance=None,
extension='.image'):
from casa import immath, imhead
beamarea = 1.13 * beamsize**2 / cellsize**2
massCoeff = 2.36e5 * distance**2 / beamarea
pcPerArcsec = distance * 1e6 / 206265.
pcPerPixel = cellsize * pcPerArcsec
pixSB = massCoeff / pcPerPixel**2 # units in Msun per pixel
immath(imagename=image + extension,
outfile=image + '.sb.image',
mode='evalexpr',
expr='IM0*' + str(pixSB))
imhead(imagename=image + '.sb.image',
mode='put',
hdkey='bunit',
hdvalue='',
hdcomment='Units Msun/pc^-2')
def nppb(image):
"""work out the flux correction, number of points per beam """
if True:
# more expensive, but works for non-ALMA data
# needs to be done on the MOM0 map
s = casa.imstat(image)
if s.has_key('flux'):
beamarea = s['sum'][0]/s['flux'][0]
else:
beamarea = 1.0
return beamarea
else:
h = casa.imhead(image, mode='list')
# @todo should we not use the casa units for this?
try:
bmin = h['beamminor']['value'] # beam in arcsec (not always true)
bmaj = h['beammajor']['value']
cdelt1 = h['cdelt1'] * 206265.0 # cdelt in radians
cdelt2 = h['cdelt2'] * 206265.0
return abs(1.13309 * bmaj * bmin / (cdelt1 * cdelt2))
except:
return 1.0
def nppb(image):
"""work out the flux correction, number of points per beam """
if True:
# more expensive, but works for non-ALMA data
# needs to be done on the MOM0 map
s = casa.imstat(image)
if s.has_key('flux'):
beamarea = s['sum'][0]/s['flux'][0]
else:
beamarea = 1.0
return beamarea
else:
h = casa.imhead(image, mode='list')
# @todo should we not use the casa units for this?
try:
bmin = h['beamminor']['value'] # beam in arcsec (not always true)
bmaj = h['beammajor']['value']
cdelt1 = h['cdelt1'] * 206265.0 # cdelt in radians
cdelt2 = h['cdelt2'] * 206265.0
return abs(1.13309 * bmaj * bmin / (cdelt1 * cdelt2))
except:
return 1.0
def run(self):
""" The run method, creates the slices, regrids if requested, and
creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("LineCube")
self._summary = {}
# look for an input noise level, either through keyword or input
# CubeStats BDP or calculate it if needed
pad = self.getkey("pad")
fpad = self.getkey("fpad")
equalize = self.getkey("equalize")
minchan = 0
linelist = self._bdp_in[1]
if linelist == None or len(linelist) == 0:
logging.info("No lines found in input LineList_BDP, exiting.")
return
spw = self._bdp_in[0]
# get the columns from the table
cols = linelist.table.getHeader()
# get the casa image
imagename = spw.getimagefile(bt.CASA)
imh = imhead(self.dir(imagename), mode='list')
# set the overall parameters for imsubimage
args = {"imagename": self.dir(imagename), "overwrite": True}
dt.tag("start")
if pad != 0 or fpad > 0:
nchan = imh['shape'][2]
dt.tag("pad")
# if equal size cubes are requested, this will honor the requested pad
if equalize:
start = linelist.table.getColumnByName("startchan")
end = linelist.table.getColumnByName("endchan")
# look for the widest line
for i in range(len(start)):
diff = end[i] - start[i] + 1
if fpad > 0:
minchan = max(minchan, diff * int(1 + 2 * fpad))
else:
minchan = max(minchan, diff + (2 * pad))
dt.tag("equalize")
# get all of the rows in the table
rows = linelist.getall()
delrow = set()
procblend = [0]
# search through looking for blended lines, leave only the strongest from each blend
# in the list
for i, row in enumerate(rows):
if row.blend in procblend:
continue
strongest = -100.
index = -1
indexes = []
blend = row.blend
for j in range(i, len(rows)):
if rows[j].blend != blend:
continue
indexes.append(j)
if rows[j].linestrength > strongest:
strongest = rows[j].linestrength
index = j
indexes.remove(index)
delrow = delrow | set(indexes)
procblend.append(blend)
dr = list(delrow)
dr.sort()
dr.reverse()
for row in dr:
del rows[row]
# check on duplicate UID's, since those are the directory names here
uid1 = []
for row in rows:
uid1.append(row.getkey("uid"))
uid2 = set(uid1)
if len(uid1) != len(uid2):
print "LineList:", uid1
logging.warning("There are duplicate names in the LineList")
#raise Exception,"There are duplicate names in the LineList"
# Create Summary table
lc_description = admit.util.Table()
lc_description.columns = [
"Line Name", "Start Channel", "End Channel", "Output Cube"
]
lc_description.units = ["", "int", "int", ""]
lc_description.description = "Parameters of Line Cubes"
# loop over all entries in the line list
rdata = []
for row in rows:
uid = row.getkey("uid")
cdir = self.mkext(imagename, uid)
self.mkdir(cdir)
basefl = uid
lcd = [basefl]
outfl = cdir + os.sep + "lc.im"
args["outfile"] = self.dir(outfl)
start = row.getkey("startchan")
end = row.getkey("endchan")
diff = end - start + 1
startch = 0
if diff < minchan:
add = int(math.ceil(float(minchan - diff) / 2.0))
start -= add
end += add
startch += add
if start < 0:
logging.info(
"%s is too close to the edge to encompass with the " +
"requested channels, start=%d resetting to 0" %
(uid, start))
startch += abs(start)
start = 0
if end >= nchan:
logging.info(
"%s is too close to the edge to encompass with the " +
"requested channels, end=%d resetting to %d" %
(uid, end, nchan - 1))
end = nchan - 1
#print "\n\nDIFF ",startch,"\n\n"
if not equalize:
if fpad > 0:
diff = end - start + 1
start -= int(fpad * diff)
end += int(fpad * diff)
if start < 0:
logging.warning(
"fpad=%d too large, start=%d resetting to 0" %
(int(fpad * diff), start))
startch += abs(start)
start = 0
else:
startch += int(fpad * diff)
if end >= nchan:
logging.warning(
"fpad=%d too large, end=%d resetting to %d" %
(int(fpad * diff), end, nchan - 1))
end = nchan - 1
elif pad > 0:
start -= pad
end += pad
if start < 0:
logging.warning(
"pad=%d too large, start=%d resetting to 0" %
(pad, start))
startch += abs(start)
start = 0
else:
startch += pad
if end >= nchan:
logging.warning(
"pad=%d too large, end=%d resetting to %d" %
(pad, end, nchan - 1))
end = nchan - 1
elif pad < 0:
mid = (start + end) / 2
start = mid + pad / 2
end = mid - pad / 2 - 1
if start < 0:
logging.warning(
"pad=%d too large, start=%d resetting to 0" %
(pad, start))
startch += abs(start)
start = 0
else:
startch += abs(start)
if end >= nchan:
logging.warning(
"pad=%d too large, end=%d resetting to %d" %
(pad, end, nchan - 1))
end = nchan - 1
endch = startch + diff
args["chans"] = "%i~%i" % (start, end)
rdata.append(start)
rdata.append(end)
# for the summmary, which will be a table of
# Line name, start channel, end channel, output image
lc_description.addRow([basefl, start, end, outfl])
# create the slices
imsubimage(**args)
line = row.converttoline()
# set the restfrequency ouf the output cube
imhead(imagename=args["outfile"],
mode="put",
hdkey="restfreq",
hdvalue="%fGHz" % (row.getkey("frequency")))
# set up the output BDP
images = {bt.CASA: outfl}
casaimage = Image(images=images)
# note that Summary.getLineFluxes() implicitly relies on the BDP out order
# being the same order as in the line list table. If this is ever not
# true, then Summary.getLineFluxes mismatch BDPs and flux values.
#self.addoutput(LineCube_BDP(xmlFile=cdir + os.sep + basefl + ".lc",
self.addoutput(
LineCube_BDP(xmlFile=outfl,
image=casaimage,
line=line,
linechans="%i~%i" % (startch, endch)))
dt.tag("trans-%s" % cdir)
logging.regression("LC: %s" % str(rdata))
taskargs = "pad=%s fpad=%g equalize=%s" % (pad, fpad, equalize)
self._summary["linecube"] = SummaryEntry(lc_description.serialize(),
"LineCube_AT", self.id(True),
taskargs)
dt.tag("done")
dt.end()
# cl.addcomponent(dir=NewDir2, flux=7e9, fluxunit='Jy', freq=freq, shape='point')
# cl.addcomponent(dir=NewDir2,
# flux=1e10, fluxunit='Jy', freq=freq,
# shape="Gaussian", majoraxis=qa.toangle(str(2)+'deg'), minoraxis=qa.toangle(str(1)+'deg'), positionangle=qa.toangle(str(45)+'deg'))
cs=ia.coordsys()
cs.setunits(['rad','rad','','Hz'])
cell_rad=dres #qa.convert(qa.quantity("1arcmin"),"rad")['value']
cs.setincrement([-cell_rad,cell_rad],'direction')
cs.setreferencevalue([phaseCenter['m0']['value'], phaseCenter['m1']['value']],
type="direction")
cs.setrestfrequency(freq)
# add important header keywords
imhead(imagename=imageName,mode="put",hdkey="object",hdvalue="DRAGN")
imhead(imagename=imageName,mode="put",hdkey="imtype",hdvalue='Intensity')
imhead(imagename=imageName,
mode="put",hdkey="observer",hdvalue="simulation")
imhead(imagename=imageName,
mode="put",hdkey="date-obs",hdvalue="2023/03/15/00:00:00")
imhead(imagename=imageName,mode="put",hdkey="reffreqtype",hdvalue='TOPO')
imhead(imagename=imageName,
mode="put",hdkey="restfreq",hdvalue=str(freq))
imhead(imagename=imageName,mode='list')
cs.setreferencevalue(str(freq)+'Hz', 'spectral')
Telescope='VLA' #or else it breaks and whines
cs.settelescope(Telescope)
ia.setcoordsys(cs.torecord())
ia.setbrightnessunit("Jy/pixel")
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("CubeSum") # tagging time
self._summary = {} # an ADMIT summary will be created here
numsigma = self.getkey("numsigma") # get the input keys
sigma = self.getkey("sigma")
use_lines = self.getkey("linesum")
pad = self.getkey("pad")
b1 = self._bdp_in[0] # spw image cube
b1a = self._bdp_in[1] # cubestats (optional)
b1b = self._bdp_in[2] # linelist (optional)
f1 = b1.getimagefile(bt.CASA)
taskinit.ia.open(self.dir(f1))
s = taskinit.ia.summary()
nchan = s['shape'][2]
if b1b != None:
ch0 = b1b.table.getFullColumnByName("startchan")
ch1 = b1b.table.getFullColumnByName("endchan")
s = Segments(ch0,ch1,nchan=nchan)
# @todo something isn't merging here as i would have expected,
# e.g. test0.fits [(16, 32), (16, 30), (16, 29)]
if pad > 0:
for (c0,c1) in s.getsegmentsastuples():
s.append([c0-pad,c0])
s.append([c1,c1+pad])
s.merge()
s.recalcmask()
# print "PJT segments:",s.getsegmentsastuples()
ns = len(s.getsegmentsastuples())
chans = s.chans(not use_lines)
if use_lines:
msum = s.getmask()
else:
msum = 1 - s.getmask()
logging.info("Read %d segments" % ns)
# print "chans",chans
# print "msum",msum
# from a deprecated keyword, but kept here to pre-smooth the spectrum before clipping
# examples are: ['boxcar',3] ['gaussian',7] ['hanning',5]
smooth= []
sig_const = False # figure out if sigma is taken as constant in the cube
if b1a == None: # if no 2nd BDP was given, sigma needs to be specified
if sigma <= 0.0:
raise Exception,"Neither user-supplied sigma nor CubeStats_BDP input given. One is required."
else:
sig_const = True # and is constant
else:
if sigma > 0:
sigma = b1a.get("sigma")
sig_const = True
if sig_const:
logging.info("Using constant sigma = %f" % sigma)
else:
logging.info("Using varying sigma per plane")
infile = b1.getimagefile(bt.CASA) # ADMIT filename of the image (cube)
bdp_name = self.mkext(infile,'csm') # morph to the new output name with replaced extension 'csm'
image_out = self.dir(bdp_name) # absolute filename
args = {"imagename" : self.dir(infile)} # assemble arguments for immoments()
args["moments"] = 0 # only need moments=0 (or [0] is ok as well)
args["outfile"] = image_out # note full pathname
dt.tag("start")
if sig_const:
args["excludepix"] = [-numsigma*sigma, numsigma*sigma] # single global sigma
if b1b != None:
# print "PJT: ",chans
args["chans"] = chans
else:
# @todo in this section bad channels can cause a fully masked cubesum = bad
# cubestats input
sigma_array = b1a.table.getColumnByName("sigma") # channel dependent sigma
sigma_pos = sigma_array[np.where(sigma_array>0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f" % (smin,smax))
maxval = b1a.get("maxval") # max in cube
nzeros = len(np.where(sigma_array<=0.0)[0]) # check bad channels
if nzeros > 0:
logging.warning("There are %d NaN channels " % nzeros)
# raise Exception,"need to recode CubeSum or use constant sigma"
dt.tag("grab_sig")
if len(smooth) > 0:
# see also LineID and others
filter = Filter1D.Filter1D(sigma_array,smooth[0],**Filter1D.Filter1D.convertargs(smooth))
sigma_array = filter.run()
dt.tag("smooth_sig")
# create a CASA image copy for making the mirror sigma cube to mask against
file = self.dir(infile)
mask = file+"_mask"
taskinit.ia.fromimage(infile=file, outfile=mask)
nx = taskinit.ia.shape()[0]
ny = taskinit.ia.shape()[1]
nchan = taskinit.ia.shape()[2]
taskinit.ia.fromshape(shape=[nx,ny,1])
plane = taskinit.ia.getchunk([0,0,0],[-1,-1,0]) # convenience plane for masking operation
dt.tag("mask_sig")
taskinit.ia.open(mask)
dt.tag("open_mask")
count = 0
for i in range(nchan):
if sigma_array[i] > 0:
if b1b != None:
if msum[i]:
taskinit.ia.putchunk(plane*0+sigma_array[i],blc=[0,0,i,-1])
count = count + 1
else:
taskinit.ia.putchunk(plane*0+maxval,blc=[0,0,i,-1])
else:
taskinit.ia.putchunk(plane*0+sigma_array[i],blc=[0,0,i,-1])
count = count + 1
else:
taskinit.ia.putchunk(plane*0+maxval,blc=[0,0,i,-1])
taskinit.ia.close()
logging.info("%d/%d channels used for CubeSum" % (count,nchan))
dt.tag("close_mask")
names = [file, mask]
tmp = file + '.tmp'
if numsigma == 0.0:
# hopefully this will also make use of the mask
exp = "IM0[IM1<%f]" % (0.99*maxval)
else:
exp = "IM0[abs(IM0/IM1)>%f]" % (numsigma)
# print "PJT: exp",exp
casa.immath(mode='evalexpr', imagename=names, expr=exp, outfile=tmp)
args["imagename"] = tmp
dt.tag("immath")
casa.immoments(**args)
dt.tag("immoments")
if sig_const is False:
# get rid of temporary files
utils.remove(tmp)
utils.remove(mask)
# get the flux
taskinit.ia.open(image_out)
st = taskinit.ia.statistics()
taskinit.ia.close()
dt.tag("statistics")
# report that flux, but there's no way to get the units from casa it seems
# ia.summary()['unit'] is usually 'Jy/beam.km/s' for ALMA
# imstat() does seem to know it.
if st.has_key('flux'):
rdata = [st['flux'][0],st['sum'][0]]
logging.info("Total flux: %f (sum=%f)" % (st['flux'],st['sum']))
else:
rdata = [st['sum'][0]]
logging.info("Sum: %f (beam parameters missing)" % (st['sum']))
logging.regression("CSM: %s" % str(rdata))
# Create two output images for html and their thumbnails, too
implot = ImPlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
implot.plotter(rasterfile=bdp_name,figname=bdp_name,colorwedge=True)
figname = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
dt.tag("implot")
thumbtype = bt.PNG # really should be correlated with self._plot_type!!
# 2. Create a histogram of the map data
# get the data for a histogram
data = casautil.getdata(image_out,zeromask=True).compressed()
dt.tag("getdata")
# get the label for the x axis
bunit = casa.imhead(imagename=image_out, mode="get", hdkey="bunit")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
auxname = bdp_name + "_histo"
auxtype = bt.PNG # really should be correlated with self._plot_type!!
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of CubeSum: %s" % (bdp_name),
thumbnail=True)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
images = {bt.CASA : bdp_name, bt.PNG : figname}
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
if hasattr(b1,"line"): # SpwCube doesn't have Line
line = deepcopy(getattr(b1,"line"))
if type(line) != type(Line):
line = Line(name="Undetermined")
else:
line = Line(name="Undetermined") # fake a Line if there wasn't one
self.addoutput(Moment_BDP(xmlFile=bdp_name,moment=0,image=deepcopy(casaimage),line=line))
imcaption = "Integral (moment 0) of all emission in image cube"
auxcaption = "Histogram of cube sum for image cube"
taskargs = "numsigma=%.1f sigma=%g smooth=%s" % (numsigma, sigma, str(smooth))
self._summary["cubesum"] = SummaryEntry([figname,thumbname,imcaption,auxname,auxthumb,auxcaption,bdp_name,infile],"CubeSum_AT",self.id(True),taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("Regrid")
dt.tag("start")
do_spatial_regrid = self.getkey("do_spatial_regrid")
pix_scale = self.getkey("pix_scale")
do_freq_regrid = self.getkey("do_freq_regrid")
chan_width = self.getkey("chan_width")
pix_size = []
chan_size = []
im_size = []
pix_wc_x = []
pix_wc_y = []
pix_wc_nu = []
src_dec = []
RADPERARCSEC = 4.848137E-6
for ibdp in self._bdp_in:
# Convert input CASA images to numpy arrays.
istem = ibdp.getimagefile(bt.CASA)
ifile = ibdp.baseDir() + istem
h = casa.imhead(ifile, mode='list')
pix_size.append(np.abs(
h['cdelt1'])) # pix scale in asec @todo QA ?
chan_size.append(np.abs(h['cdelt3']))
# grab the pixels
pix_x = h['shape'][0]
pix_y = h['shape'][1]
pix_nu = h['shape'][2]
taskinit.ia.open(ifile)
mycs = taskinit.ia.coordsys(axes=[0, 1, 2])
# getting all four corners handles the case of images where
# x-y axis not aligned with RA-dec
for xpix in [0, pix_x]:
for ypix in [0, pix_y]:
x = mycs.toworld([xpix, ypix])['numeric'][0]
y = mycs.toworld([xpix, ypix])['numeric'][1]
pix_wc_x.append(x)
pix_wc_y.append(y)
nu = mycs.toworld([pix_x, pix_y, 0])['numeric'][2]
pix_wc_nu.append(nu)
nu = mycs.toworld([pix_x, pix_y, pix_nu])['numeric'][2]
pix_wc_nu.append(nu)
taskinit.ia.close()
min_ra = np.min(pix_wc_x)
max_ra = np.max(pix_wc_x)
min_dec = np.min(pix_wc_y)
max_dec = np.max(pix_wc_y)
mean_ra = 0.5 * (min_ra + max_ra)
mean_dec = 0.5 * (min_dec + max_dec)
if (pix_scale < 0):
pix_scale = np.min(pix_size)
else:
pix_scale = pix_scale * RADPERARCSEC
npix_ra = int((max_ra - min_ra) / pix_scale * np.cos(mean_dec))
npix_dec = (max_dec - min_dec) / pix_scale
npix_dec = (int(npix_dec)
if npix_dec == int(npix_dec) else int(npix_dec) + 1)
min_nu = np.min(pix_wc_nu)
max_nu = np.max(pix_wc_nu)
mean_nu = 0.5 * (min_nu + max_nu)
if (chan_width < 0):
chan_width = np.min(chan_size)
npix_nu = int((max_nu - min_nu) / chan_width) + 1
# now regrid everything
innames = []
outnames = []
incdelt = []
outcdelt = []
#=========================================================
#@todo - check if bdp_ins refer to same input file.
# If so, the current code will fail because the output
# file name is fixed to $INPUT_regrid. A valid use case
# is "regrid the same input file different ways" -- which
# is not supported in the current code but should be
#=========================================================
for ibdp in self._bdp_in:
istem = ibdp.getimagefile(bt.CASA)
ifile = ibdp.baseDir() + istem
ostem = "%s_regrid/" % (istem)
ofile = self.baseDir() + ostem
# save the input/output file names
innames.append(istem)
outnames.append(ostem)
header = casa.imregrid(imagename=ifile, template='get')
# save the input cdelt1,2,3 for summary table
incdelt.append(
(header['csys']['direction0']['cdelt'][0] / RADPERARCSEC,
header['csys']['direction0']['cdelt'][1] / RADPERARCSEC,
utils.freqtovel(mean_nu,
header['csys']['spectral2']['wcs']['cdelt'])))
if (do_spatial_regrid):
header['csys']['direction0']['cdelt'] = [
-1 * pix_scale, pix_scale
]
header['csys']['direction0']['crval'] = [mean_ra, mean_dec]
header['shap'][0] = npix_ra
header['shap'][1] = npix_dec
header['csys']['direction0']['crpix'] = [
npix_ra / 2, npix_dec / 2
]
if (do_freq_regrid):
header['csys']['spectral2']['wcs']['crval'] = min_nu
header['shap'][2] = npix_nu
chan_size = np.abs(header['csys']['spectral2']['wcs']['cdelt'])
header['csys']['spectral2']['wcs']['cdelt'] = chan_width
flux_correction = chan_width / chan_size
casa.imregrid(imagename=ifile, output=ofile, template=header)
# save the output cdelt1,2,3 for summary table
newhead = casa.imregrid(imagename=ofile, template='get')
outcdelt.append(
(newhead['csys']['direction0']['cdelt'][0] / RADPERARCSEC,
newhead['csys']['direction0']['cdelt'][1] / RADPERARCSEC,
utils.freqtovel(
mean_nu, newhead['csys']['spectral2']['wcs']['cdelt'])))
if (do_freq_regrid):
taskinit.ia.open(ofile)
print flux_correction
taskinit.ia.calc(pixels=ofile.replace(r"/", r"\/") + '*' +
str(flux_correction))
taskinit.ia.done()
obdp = admit.SpwCube_BDP(ostem)
self.addoutput(obdp)
# make a table for summary
atable = admit.util.Table()
atable.columns = [
"Input Image", "cdelt1", "cdelt2", "cdelt3", "Regridded Image",
"cdelt1", "cdelt2", "cdelt3"
]
atable.units = [
"", "arcsec", "arcsec", "km/s", "", "arcsec", "arcsec", "km/s"
]
for i in range(len(innames)):
atable.addRow([
innames[i], incdelt[i][0], incdelt[i][1], incdelt[i][2],
outnames[i], outcdelt[i][0], outcdelt[i][1], outcdelt[i][2]
])
#keys = "pixsize=%.4g naxis1=%d naxis2=%d mean_ra=%0.4f mean_dec=%0.4f reffreq=%g chan_width=%g" % (pix_scale/(4.848137E-6), npix_ra, npix_dec, mean_ra,mean_dec,min_nu,chan_width)
taskargs = "pix_scale = " + str(
self.getkey("pix_scale")) + " chan_width = " + str(
self.getkey("chan_width"))
self._summary["regrid"] = SummaryEntry(atable.serialize(), "Regrid_AT",
self.id(True), 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 run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
# our BDP's
# b1 = input BDP
# b1s = optional input CubeSpectrum
# b1m = optional input Moment
# b1p = optional input SourceList for positions
# b2 = output BDP
b1 = self._bdp_in[0] # check input SpwCube (or LineCube)
fin = b1.getimagefile(bt.CASA)
if self._bdp_in[0]._type == bt.LINECUBE_BDP:
use_vel = True
else:
use_vel = False
sources = self.getkey("sources")
pos = [
] # blank it first, then try and grab it from the optional bdp_in's
cmean = 0.0
csigma = 0.0
smax = [] # accumulate max in each spectrum for regression
self.spec_description = [] # for summary()
if self._bdp_in[1] != None: # check if CubeStats_BDP
#print "BDP[1] type: ",self._bdp_in[1]._type
if self._bdp_in[1]._type != bt.CUBESTATS_BDP:
raise Exception, "bdp_in[1] not a CubeStats_BDP, should never happen"
# a table (cubestats)
b1s = self._bdp_in[1]
pos.append(b1s.maxpos[0])
pos.append(b1s.maxpos[1])
logging.info('CubeStats::maxpos,val=%s,%f' %
(str(b1s.maxpos), b1s.maxval))
cmean = b1s.mean
csigma = b1s.sigma
dt.tag("CubeStats-pos")
if self._bdp_in[
2] != None: # check if Moment_BDP (probably from CubeSum)
#print "BDP[2] type: ",self._bdp_in[2]._type
if self._bdp_in[2]._type != bt.MOMENT_BDP:
raise Exception, "bdp_in[2] not a Moment_BDP, should never happen"
b1m = self._bdp_in[2]
fim = b1m.getimagefile(bt.CASA)
pos1, maxval = self.maxpos_im(
self.dir(fim)) # compute maxpos, since it is not in bdp (yet)
logging.info('CubeSum::maxpos,val=%s,%f' % (str(pos1), maxval))
pos.append(pos1[0])
pos.append(pos1[1])
dt.tag("Moment-pos")
if self._bdp_in[3] != None: # check if SourceList
#print "BDP[3] type: ",self._bdp_in[3]._type
# a table (SourceList)
b1p = self._bdp_in[3]
ra = b1p.table.getFullColumnByName("RA")
dec = b1p.table.getFullColumnByName("DEC")
peak = b1p.table.getFullColumnByName("Peak")
if sources == []:
# use the whole SourceList
for (r, d, p) in zip(ra, dec, peak):
rdc = convert_sexa(r, d)
pos.append(rdc[0])
pos.append(rdc[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(rdc), p))
else:
# select specific ones from the source list
for ipos in sources:
if ipos < len(ra):
radec = convert_sexa(ra[ipos], dec[ipos])
pos.append(radec[0])
pos.append(radec[1])
logging.info('SourceList::maxpos,val=%s,%f' %
(str(radec), peak[ipos]))
else:
logging.warning('Skipping illegal source number %d' %
ipos)
dt.tag("SourceList-pos")
# if pos[] still blank, use the AT keyword.
if len(pos) == 0:
pos = self.getkey("pos")
# if still none, try the map center
if len(pos) == 0:
# @todo this could result in a masked pixel and cause further havoc
# @todo could also take the reference pixel, but that could be outside image
taskinit.ia.open(self.dir(fin))
s = taskinit.ia.summary()
pos = [int(s['shape'][0]) / 2, int(s['shape'][1]) / 2]
logging.warning(
"No input positions supplied, map center choosen: %s" %
str(pos))
dt.tag("map-center")
# exhausted all sources where pos[] can be set; if still zero, bail out
if len(pos) == 0:
raise Exception, "No positions found from input BDP's or pos="
# convert this regular list to a list of tuples with duplicates removed
# sadly the order is lost.
pos = list(set(zip(pos[0::2], pos[1::2])))
npos = len(pos)
dt.tag("open")
bdp_name = self.mkext(fin, "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
imval = range(npos) # spectra, one for each pos (placeholder)
planes = range(npos) # labels for the tables (placeholder)
images = {} # png's accumulated
for i in range(npos): # loop over pos, they can have mixed types now
sd = []
caption = "Spectrum"
xpos = pos[i][0]
ypos = pos[i][1]
if type(xpos) != type(ypos):
print "POS:", xpos, ypos
raise Exception, "position pair not of the same type"
if type(xpos) == int:
# for integers, boxes are allowed, even multiple
box = '%d,%d,%d,%d' % (xpos, ypos, xpos, ypos)
# convention for summary is (box)
cbox = '(%d,%d,%d,%d)' % (xpos, ypos, xpos, ypos)
# use extend here, not append, we want individual values in a list
sd.extend([xpos, ypos, cbox])
caption = "Average Spectrum at %s" % cbox
if False:
# this will fail on 3D cubes (see CAS-7648)
imval[i] = casa.imval(self.dir(fin), box=box)
else:
# work around that CAS-7648 bug
# another approach is the ia.getprofile(), see CubeStats, this will
# also integrate over regions, imval will not (!!!)
region = 'centerbox[[%dpix,%dpix],[1pix,1pix]]' % (xpos,
ypos)
caption = "Average Spectrum at %s" % region
imval[i] = casa.imval(self.dir(fin), region=region)
elif type(xpos) == str:
# this is tricky, to stay under 1 pixel , or you get a 2x2 back.
region = 'centerbox[[%s,%s],[1pix,1pix]]' % (xpos, ypos)
caption = "Average Spectrum at %s" % region
sd.extend([xpos, ypos, region])
imval[i] = casa.imval(self.dir(fin), region=region)
else:
print "Data type: ", type(xpos)
raise Exception, "Data type for region not handled"
dt.tag("imval")
flux = imval[i]['data']
if len(flux.shape
) > 1: # rare case if we step on a boundary between cells?
logging.warning(
"source %d has spectrum shape %s: averaging the spectra" %
(i, repr(flux.shape)))
flux = np.average(flux, axis=0)
logging.debug('minmax: %f %f %d' %
(flux.min(), flux.max(), len(flux)))
smax.append(flux.max())
if i == 0: # for first point record few extra things
if len(imval[i]['coords'].shape) == 2: # normal case: 1 pixel
freqs = imval[i]['coords'].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
elif len(imval[i]['coords'].shape
) == 3: # rare case if > 1 point in imval()
freqs = imval[i]['coords'][0].transpose(
)[2] / 1e9 # convert to GHz @todo: input units ok?
else:
logging.fatal(
"bad shape %s in freq return from imval - SHOULD NEVER HAPPEN"
% imval[i]['coords'].shape)
chans = np.arange(len(freqs)) # channels 0..nchans-1
unit = imval[i]['unit']
restfreq = casa.imhead(
self.dir(fin), mode="get",
hdkey="restfreq")['value'] / 1e9 # in GHz
dt.tag("imhead")
vel = (
1 - freqs / restfreq
) * utils.c # @todo : use a function (and what about relativistic?)
# construct the Table for CubeSpectrum_BDP
# @todo note data needs to be a tuple, later to be column_stack'd
labels = ["channel", "frequency", "flux"]
units = ["number", "GHz", unit]
data = (chans, freqs, flux)
if i == 0:
# plane 0 : we are allowing a multiplane table, so the first plane is special
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
# planes 1,2,3.... are stacked onto the previous one
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
if use_vel:
x = vel
xlab = 'VLSR (km/s)'
else:
x = chans
xlab = 'Channel'
y = [flux]
sd.append(xlab)
if type(xpos) == int:
# grab the RA/DEC... kludgy
h = casa.imstat(self.dir(fin), box=box)
ra = h['blcf'].split(',')[0]
dec = h['blcf'].split(',')[1]
title = '%s %d @ %d,%d = %s,%s' % (bdp_name, i, xpos, ypos, ra,
dec)
else:
title = '%s %d @ %s,%s' % (
bdp_name, i, xpos, ypos
) # or use box, once we allow non-points
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux (%s)' % unit
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x,
y,
title,
p1,
xlab=xlab,
ylab=ylab,
thumbnail=True)
# Why not use p1 as the key?
ii = images["pos%d" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
thumbname = myplot.getThumbnail(figno=myplot.figno, relative=True)
sd.extend([ii, thumbname, caption, fin])
self.spec_description.append(sd)
logging.regression("CSP: %s" % str(smax))
image = Image(images=images, description="CubeSpectrum")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", csigma) # TODO: not always available
b2.setkey("mean", cmean) # TODO: not always available
if True:
# @todo only first plane due to limitation in exportTable()
islash = bdp_name.find('/')
if islash < 0:
tabname = self.dir("testCubeSpectrum.tab")
else:
tabname = self.dir(bdp_name[:islash] + "/testCubeSpectrum.tab")
table.exportTable(tabname, cols=["frequency", "flux"])
dt.tag("done")
# For a single spectrum this is
# SummaryEntry([[data for spec1]], "CubeSpectrum_AT",taskid)
# For multiple spectra this is
# SummaryEntry([[data for spec1],[data for spec2],...], "CubeSpectrum_AT",taskid)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"CubeSpectrum_AT",
self.id(True))
taskargs = "pos=" + str(pos)
taskargs += ' <span style="background-color:white"> ' + fin.split(
'/')[0] + ' </span>'
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
# 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
"""
dt = utils.Dtime("CubeSum") # tagging time
self._summary = {} # an ADMIT summary will be created here
numsigma = self.getkey("numsigma") # get the input keys
sigma = self.getkey("sigma")
use_lines = self.getkey("linesum")
pad = self.getkey("pad")
b1 = self._bdp_in[0] # spw image cube
b1a = self._bdp_in[1] # cubestats (optional)
b1b = self._bdp_in[2] # linelist (optional)
f1 = b1.getimagefile(bt.CASA)
taskinit.ia.open(self.dir(f1))
s = taskinit.ia.summary()
nchan = s['shape'][2]
if b1b != None:
ch0 = b1b.table.getFullColumnByName("startchan")
ch1 = b1b.table.getFullColumnByName("endchan")
s = Segments(ch0, ch1, nchan=nchan)
# @todo something isn't merging here as i would have expected,
# e.g. test0.fits [(16, 32), (16, 30), (16, 29)]
if pad > 0:
for (c0, c1) in s.getsegmentsastuples():
s.append([c0 - pad, c0])
s.append([c1, c1 + pad])
s.merge()
s.recalcmask()
# print "PJT segments:",s.getsegmentsastuples()
ns = len(s.getsegmentsastuples())
chans = s.chans(not use_lines)
if use_lines:
msum = s.getmask()
else:
msum = 1 - s.getmask()
logging.info("Read %d segments" % ns)
# print "chans",chans
# print "msum",msum
# from a deprecated keyword, but kept here to pre-smooth the spectrum before clipping
# examples are: ['boxcar',3] ['gaussian',7] ['hanning',5]
smooth = []
sig_const = False # figure out if sigma is taken as constant in the cube
if b1a == None: # if no 2nd BDP was given, sigma needs to be specified
if sigma <= 0.0:
raise Exception, "Neither user-supplied sigma nor CubeStats_BDP input given. One is required."
else:
sig_const = True # and is constant
else:
if sigma > 0:
sigma = b1a.get("sigma")
sig_const = True
if sig_const:
logging.info("Using constant sigma = %f" % sigma)
else:
logging.info("Using varying sigma per plane")
infile = b1.getimagefile(bt.CASA) # ADMIT filename of the image (cube)
bdp_name = self.mkext(
infile, 'csm'
) # morph to the new output name with replaced extension 'csm'
image_out = self.dir(bdp_name) # absolute filename
args = {
"imagename": self.dir(infile)
} # assemble arguments for immoments()
args["moments"] = 0 # only need moments=0 (or [0] is ok as well)
args["outfile"] = image_out # note full pathname
dt.tag("start")
if sig_const:
args["excludepix"] = [-numsigma * sigma,
numsigma * sigma] # single global sigma
if b1b != None:
# print "PJT: ",chans
args["chans"] = chans
else:
# @todo in this section bad channels can cause a fully masked cubesum = bad
# cubestats input
sigma_array = b1a.table.getColumnByName(
"sigma") # channel dependent sigma
sigma_pos = sigma_array[np.where(sigma_array > 0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f" % (smin, smax))
maxval = b1a.get("maxval") # max in cube
nzeros = len(np.where(sigma_array <= 0.0)[0]) # check bad channels
if nzeros > 0:
logging.warning("There are %d NaN channels " % nzeros)
# raise Exception,"need to recode CubeSum or use constant sigma"
dt.tag("grab_sig")
if len(smooth) > 0:
# see also LineID and others
filter = Filter1D.Filter1D(
sigma_array, smooth[0],
**Filter1D.Filter1D.convertargs(smooth))
sigma_array = filter.run()
dt.tag("smooth_sig")
# create a CASA image copy for making the mirror sigma cube to mask against
file = self.dir(infile)
mask = file + "_mask"
taskinit.ia.fromimage(infile=file, outfile=mask)
nx = taskinit.ia.shape()[0]
ny = taskinit.ia.shape()[1]
nchan = taskinit.ia.shape()[2]
taskinit.ia.fromshape(shape=[nx, ny, 1])
plane = taskinit.ia.getchunk(
[0, 0, 0],
[-1, -1, 0]) # convenience plane for masking operation
dt.tag("mask_sig")
taskinit.ia.open(mask)
dt.tag("open_mask")
count = 0
for i in range(nchan):
if sigma_array[i] > 0:
if b1b != None:
if msum[i]:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval,
blc=[0, 0, i, -1])
else:
taskinit.ia.putchunk(plane * 0 + sigma_array[i],
blc=[0, 0, i, -1])
count = count + 1
else:
taskinit.ia.putchunk(plane * 0 + maxval, blc=[0, 0, i, -1])
taskinit.ia.close()
logging.info("%d/%d channels used for CubeSum" % (count, nchan))
dt.tag("close_mask")
names = [file, mask]
tmp = file + '.tmp'
if numsigma == 0.0:
# hopefully this will also make use of the mask
exp = "IM0[IM1<%f]" % (0.99 * maxval)
else:
exp = "IM0[abs(IM0/IM1)>%f]" % (numsigma)
# print "PJT: exp",exp
casa.immath(mode='evalexpr',
imagename=names,
expr=exp,
outfile=tmp)
args["imagename"] = tmp
dt.tag("immath")
casa.immoments(**args)
dt.tag("immoments")
if sig_const is False:
# get rid of temporary files
utils.remove(tmp)
utils.remove(mask)
# get the flux
taskinit.ia.open(image_out)
st = taskinit.ia.statistics()
taskinit.ia.close()
dt.tag("statistics")
# report that flux, but there's no way to get the units from casa it seems
# ia.summary()['unit'] is usually 'Jy/beam.km/s' for ALMA
# imstat() does seem to know it.
if st.has_key('flux'):
rdata = [st['flux'][0], st['sum'][0]]
logging.info("Total flux: %f (sum=%f)" % (st['flux'], st['sum']))
else:
rdata = [st['sum'][0]]
logging.info("Sum: %f (beam parameters missing)" % (st['sum']))
logging.regression("CSM: %s" % str(rdata))
# Create two output images for html and their thumbnails, too
implot = ImPlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
implot.plotter(rasterfile=bdp_name, figname=bdp_name, colorwedge=True)
figname = implot.getFigure(figno=implot.figno, relative=True)
thumbname = implot.getThumbnail(figno=implot.figno, relative=True)
dt.tag("implot")
thumbtype = bt.PNG # really should be correlated with self._plot_type!!
# 2. Create a histogram of the map data
# get the data for a histogram
data = casautil.getdata(image_out, zeromask=True).compressed()
dt.tag("getdata")
# get the label for the x axis
bunit = casa.imhead(imagename=image_out, mode="get", hdkey="bunit")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
auxname = bdp_name + "_histo"
auxtype = bt.PNG # really should be correlated with self._plot_type!!
myplot.histogram(columns=data,
figname=auxname,
xlab=bunit,
ylab="Count",
title="Histogram of CubeSum: %s" % (bdp_name),
thumbnail=True)
auxname = myplot.getFigure(figno=myplot.figno, relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno, relative=True)
images = {bt.CASA: bdp_name, bt.PNG: figname}
casaimage = Image(images=images,
auxiliary=auxname,
auxtype=auxtype,
thumbnail=thumbname,
thumbnailtype=thumbtype)
if hasattr(b1, "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(b1, "line"))
if type(line) != type(Line):
line = Line(name="Undetermined")
else:
line = Line(name="Undetermined") # fake a Line if there wasn't one
self.addoutput(
Moment_BDP(xmlFile=bdp_name,
moment=0,
image=deepcopy(casaimage),
line=line))
imcaption = "Integral (moment 0) of all emission in image cube"
auxcaption = "Histogram of cube sum for image cube"
taskargs = "numsigma=%.1f sigma=%g smooth=%s" % (numsigma, sigma,
str(smooth))
self._summary["cubesum"] = SummaryEntry([
figname, thumbname, imcaption, auxname, auxthumb, auxcaption,
bdp_name, infile
], "CubeSum_AT", self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
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 CMEform(comp, truthName, CMEDir, fMHz=-1.):
'''
Form the truth image, We assume that the CME moves outward in frequency.
comp is a list of doubles with structure = (MHz, delta, offangle, major, minor, PA, flux, resolution(arcmin), imSize)
if fMHz is defined, it takes the place of comp[0]
truthName (string) is the basename of the .truth created. CMEDir is the center of the image.
Puts a single gaussian source on a truth model to be fed through the simulation pipeline.
'''
##
if fMHz == -1.:
freq0 = comp[0]
else:
freq0 = fMHz
delta = comp[1]
delta1 = qa.toangle(str(delta) + 'deg')
offangle = comp[2]
# offangle1 = initoffangle #qa.toangle(str(offangle)+'deg') #for random
offangle1 = qa.toangle(str(offangle) + 'deg')
NewDir = me.shift(CMEDir, offset=delta1, pa=offangle1)
major = comp[3]
SizeMajor = qa.toangle(str(major) + 'deg')
minor = comp[4]
SizeMinor = qa.toangle(str(minor) + 'deg')
PA = comp[5] #(90 + initoffangle['value'])%360. #comp[5]
PA = qa.toangle(str(PA) + 'deg')
Flux = comp[6]
resolution = comp[7] #in arcmin
imSize = int(comp[8])
#Construct an empty casa image from a shape
ia.fromshape(truthName, shape=[imSize, imSize, 1, 1], overwrite=True)
#adding components to the empty image file
cl.addcomponent(dir=NewDir, \
flux=Flux, fluxunit='Jy', freq=str(freq0)+'MHz', \
shape="Gaussian", majoraxis=SizeMajor, minoraxis=SizeMinor, positionangle=PA)
# cl.addcomponent(dir=CMEDir, flux=1e10, fluxunit='Jy', freq=comp[0],
# shape='point')
#
# cl.addcomponent(dir=NewDir, flux=1e9, fluxunit='Jy', freq=comp[0],
# shape='point')
#
# cl.addcomponent(dir=NewDir2, flux=7e9, fluxunit='Jy', freq=comp[0],
# shape='point')
cs = ia.coordsys()
cs.setunits(['rad', 'rad', '', 'Hz'])
cell_rad = qa.convert(qa.quantity(str(resolution) + "arcmin"),
"rad")['value']
cs.setincrement([-cell_rad, cell_rad], 'direction')
cs.setreferencevalue([CMEDir['m0']['value'], CMEDir['m1']['value']],
type="direction")
cs.setrestfrequency(comp[0])
# add important header keywords
imhead(imagename=truthName,
mode="put",
hdkey="object",
hdvalue="Model CME")
imhead(imagename=truthName,
mode="put",
hdkey="imtype",
hdvalue='Intensity')
imhead(imagename=truthName,
mode="put",
hdkey="observer",
hdvalue="simulation")
imhead(imagename=truthName,
mode="put",
hdkey="date-obs",
hdvalue="2023/03/15/00:00:00")
imhead(imagename=truthName,
mode="put",
hdkey="reffreqtype",
hdvalue='TOPO')
imhead(imagename=truthName,
mode="put",
hdkey="restfreq",
hdvalue=str(comp[0]) + 'MHz')
imhead(imagename=truthName, mode='list')
cs.setreferencevalue(str(comp[0]) + 'MHz', 'spectral')
cs.settelescope('VLA')
ia.setcoordsys(cs.torecord())
ia.setbrightnessunit("Jy/pixel")
ia.modify(cl.torecord(), subtract=False)
# pix = ia.getchunk()
# pix = pix.reshape((imSize, imSize))
# p = np.flipud(pix.T)
ia.close()
cl.done()
qa.done()
me.done()
return #pix
def imageForm(filename, truthImage, freq, dataArr):
brightness = sum(dataArr)
print 'tot brightness is ' + str(brightness)
imSize = 400
zoomimg = spndint.zoom(dataArr, float(imSize) / 400)
zdims = np.shape(zoomimg)
for i in range(zdims[0]):
for j in range(zdims[1]):
if zoomimg[i][j] < 0.0:
zoomimg[i][j] = 0.0
newbrightness = sum(zoomimg)
zoomimg *= brightness / newbrightness
print 'tot brightness is ' + str(sum(zoomimg))
z = zoomimg.copy().T
z = np.fliplr(z) #these operations flip to CASA style of storing data
#which starts at lower left corner, and goes up by columns left to right
casaArr = z.reshape((imSize, imSize, 1, 1))
toutfile = os.path.join('.', truthImage)
#if the truth image already exists remove it
if os.path.exists(toutfile):
shutil.rmtree(toutfile)
ia.fromarray(truthImage, pixels=casaArr, overwrite=True)
cs = ia.coordsys()
cs.setunits(['rad', 'rad', '', 'Hz'])
res1 = 400
res = 400
width = res1 * .038 * np.pi / 180.
dres = width / res
cell_rad = dres #qa.convert(qa.quantity("1arcmin"),"rad")['value']
imSize = res
cs.setincrement([-cell_rad, cell_rad], 'direction')
cs.setreferencevalue([CMEDir['m0']['value'], CMEDir['m1']['value']],
type="direction")
cs.setrestfrequency(freq)
# add important header keywords
imhead(imagename=truthImage, mode="put", hdkey="object", hdvalue="DRAGN")
imhead(imagename=truthImage,
mode="put",
hdkey="imtype",
hdvalue='Intensity')
imhead(imagename=truthImage,
mode="put",
hdkey="observer",
hdvalue="simulation")
imhead(imagename=truthImage,
mode="put",
hdkey="date-obs",
hdvalue="2023/03/15/00:00:00")
imhead(imagename=truthImage,
mode="put",
hdkey="reffreqtype",
hdvalue='TOPO')
imhead(imagename=truthImage,
mode="put",
hdkey="restfreq",
hdvalue=str(freq))
imhead(imagename=truthImage, mode='list')
cs.setreferencevalue(str(freq) + 'Hz', 'spectral')
Telescope = 'VLA' #or else it breaks and whines
cs.settelescope(Telescope)
ia.setcoordsys(cs.torecord())
ia.setbrightnessunit("Jy/pixel")
pix = ia.getchunk()
pix = pix.reshape((imSize, imSize))
zoomimg = pix
ia.close()
return zoomimg
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 CMEform(comp):
'''
Form the truth image, We assume that the CME moves outward in frequency.
'''
##
delta = comp[1]
delta1 = qa.toangle(str(delta) + 'deg')
offangle = comp[2]
offangle1 = initoffangle #qa.toangle(str(offangle)+'deg')
NewDir = me.shift(CMEDir, offset=delta1, pa=offangle1)
major = comp[3]
SizeMajor = qa.toangle(str(major) + 'deg')
minor = comp[4]
SizeMinor = qa.toangle(str(minor) + 'deg')
PA = (90 + initoffangle['value']) % 360. #comp[5]
PA = qa.toangle(str(PA) + 'deg')
Flux = comp[6]
resolution = comp[7]
imSize = int(comp[8])
delta2 = qa.toangle(str(delta) + 'deg')
offangle2 = qa.toangle(str(45) + 'deg')
NewDir2 = me.shift(CMEDir,
offset=qa.toangle(str(2) + 'deg'),
pa=qa.toangle(str(280) + 'deg'))
#Construct an empty casa image from a shape
ia.fromshape(imageName, shape=[imSize, imSize, 1, 1], overwrite=True)
#adding components to the empty image file
cl.addcomponent(dir=NewDir,
flux=Flux / 10,
fluxunit='Jy',
freq=comp[0],
shape="Gaussian",
majoraxis=SizeMajor,
minoraxis=SizeMinor,
positionangle=PA)
# cl.addcomponent(dir=CMEDir, flux=1e10, fluxunit='Jy', freq=comp[0],
# shape='point')
#
# cl.addcomponent(dir=NewDir, flux=5e9, fluxunit='Jy', freq=comp[0],
# shape='point')
#
# cl.addcomponent(dir=NewDir2, flux=7e9, fluxunit='Jy', freq=comp[0],
# shape='point')
cs = ia.coordsys()
cs.setunits(['rad', 'rad', '', 'Hz'])
cell_rad = qa.convert(qa.quantity("1.0arcmin"), "rad")['value']
cs.setincrement([-cell_rad, cell_rad], 'direction')
cs.setreferencevalue([CMEDir['m0']['value'], CMEDir['m1']['value']],
type="direction")
cs.setrestfrequency(comp[0])
# add important header keywords
imhead(imagename=imageName,
mode="put",
hdkey="object",
hdvalue="Model CME")
imhead(imagename=imageName,
mode="put",
hdkey="imtype",
hdvalue='Intensity')
imhead(imagename=imageName,
mode="put",
hdkey="observer",
hdvalue="simulation")
imhead(imagename=imageName,
mode="put",
hdkey="date-obs",
hdvalue="2023/03/15/00:00:00")
imhead(imagename=imageName,
mode="put",
hdkey="reffreqtype",
hdvalue='TOPO')
imhead(imagename=imageName,
mode="put",
hdkey="restfreq",
hdvalue=str(comp[0]))
imhead(imagename=imageName, mode='list')
cs.setreferencevalue(str(comp[0]) + 'MHz', 'spectral')
cs.settelescope('VLA')
ia.setcoordsys(cs.torecord())
ia.setbrightnessunit("Jy/pixel")
ia.modify(cl.torecord(), subtract=False)
pix = ia.getchunk()
pix = pix.reshape((imSize, imSize))
ia.close()
return pix
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("Regrid")
dt.tag("start")
do_spatial_regrid = self.getkey("do_spatial_regrid")
pix_scale = self.getkey("pix_scale")
do_freq_regrid=self.getkey("do_freq_regrid")
chan_width = self.getkey("chan_width")
pix_size=[]
chan_size=[]
im_size =[]
pix_wc_x = []
pix_wc_y = []
pix_wc_nu=[]
src_dec = []
RADPERARCSEC = 4.848137E-6
for ibdp in self._bdp_in:
# Convert input CASA images to numpy arrays.
istem = ibdp.getimagefile(bt.CASA)
ifile = ibdp.baseDir() + istem
h = casa.imhead(ifile, mode='list')
pix_size.append(np.abs(h['cdelt1'])) # pix scale in asec @todo QA ?
chan_size.append(np.abs(h['cdelt3']))
# grab the pixels
pix_x = h['shape'][0]
pix_y = h['shape'][1]
pix_nu= h['shape'][2]
taskinit.ia.open(ifile)
mycs = taskinit.ia.coordsys(axes=[0,1,2])
# getting all four corners handles the case of images where
# x-y axis not aligned with RA-dec
for xpix in [0,pix_x]:
for ypix in [0,pix_y]:
x = mycs.toworld([xpix,ypix])['numeric'][0]
y = mycs.toworld([xpix,ypix])['numeric'][1]
pix_wc_x.append(x)
pix_wc_y.append(y)
nu= mycs.toworld([pix_x,pix_y,0])['numeric'][2]
pix_wc_nu.append(nu)
nu= mycs.toworld([pix_x,pix_y,pix_nu])['numeric'][2]
pix_wc_nu.append(nu)
taskinit.ia.close()
min_ra = np.min(pix_wc_x)
max_ra = np.max(pix_wc_x)
min_dec = np.min(pix_wc_y)
max_dec = np.max(pix_wc_y)
mean_ra = 0.5*(min_ra + max_ra)
mean_dec = 0.5*(min_dec + max_dec)
if(pix_scale < 0):
pix_scale = np.min(pix_size)
else:
pix_scale = pix_scale * RADPERARCSEC
npix_ra = int((max_ra - min_ra) / pix_scale * np.cos(mean_dec))
npix_dec = (max_dec - min_dec) / pix_scale
npix_dec = (int(npix_dec) if npix_dec == int(npix_dec) else int(npix_dec) + 1)
min_nu = np.min(pix_wc_nu)
max_nu = np.max(pix_wc_nu)
mean_nu = 0.5*(min_nu + max_nu)
if(chan_width < 0):
chan_width = np.min(chan_size)
npix_nu =int((max_nu - min_nu)/chan_width)+1
# now regrid everything
innames =[]
outnames = []
incdelt = []
outcdelt = []
#=========================================================
#@todo - check if bdp_ins refer to same input file.
# If so, the current code will fail because the output
# file name is fixed to $INPUT_regrid. A valid use case
# is "regrid the same input file different ways" -- which
# is not supported in the current code but should be
#=========================================================
for ibdp in self._bdp_in:
istem = ibdp.getimagefile(bt.CASA)
ifile = ibdp.baseDir() + istem
ostem = "%s_regrid/" % (istem)
ofile = self.baseDir() + ostem
# save the input/output file names
innames.append(istem)
outnames.append(ostem)
header=casa.imregrid(imagename=ifile,template='get')
# save the input cdelt1,2,3 for summary table
incdelt.append((header['csys']['direction0']['cdelt'][0]/RADPERARCSEC,header['csys']['direction0']['cdelt'][1]/RADPERARCSEC,utils.freqtovel(mean_nu,header['csys']['spectral2']['wcs']['cdelt'])))
if(do_spatial_regrid):
header['csys']['direction0']['cdelt'] = [ -1*pix_scale,pix_scale]
header['csys']['direction0']['crval'] = [ mean_ra,mean_dec]
header['shap'][0] = npix_ra
header['shap'][1] = npix_dec
header['csys']['direction0']['crpix'] = [npix_ra/2,npix_dec/2]
if(do_freq_regrid):
header['csys']['spectral2']['wcs']['crval']=min_nu
header['shap'][2] = npix_nu
chan_size = np.abs(header['csys']['spectral2']['wcs']['cdelt'])
header['csys']['spectral2']['wcs']['cdelt'] = chan_width
flux_correction = chan_width/chan_size
casa.imregrid(imagename=ifile,output=ofile,template=header)
# save the output cdelt1,2,3 for summary table
newhead = casa.imregrid(imagename=ofile,template='get')
outcdelt.append((newhead['csys']['direction0']['cdelt'][0]/RADPERARCSEC,newhead['csys']['direction0']['cdelt'][1]/RADPERARCSEC,utils.freqtovel(mean_nu,newhead['csys']['spectral2']['wcs']['cdelt'])))
if(do_freq_regrid):
taskinit.ia.open(ofile)
print flux_correction
taskinit.ia.calc(pixels=ofile.replace(r"/",r"\/")+'*'+str(flux_correction))
taskinit.ia.done()
obdp = admit.SpwCube_BDP(ostem)
self.addoutput(obdp)
# make a table for summary
atable = admit.util.Table()
atable.columns = ["Input Image","cdelt1", "cdelt2", "cdelt3", "Regridded Image","cdelt1","cdelt2","cdelt3"]
atable.units = ["","arcsec", "arcsec", "km/s", "","arcsec","arcsec","km/s"]
for i in range(len(innames)):
atable.addRow([ innames[i],incdelt[i][0],incdelt[i][1],incdelt[i][2], outnames[i],outcdelt[i][0],outcdelt[i][1],outcdelt[i][2] ])
#keys = "pixsize=%.4g naxis1=%d naxis2=%d mean_ra=%0.4f mean_dec=%0.4f reffreq=%g chan_width=%g" % (pix_scale/(4.848137E-6), npix_ra, npix_dec, mean_ra,mean_dec,min_nu,chan_width)
taskargs = "pix_scale = " + str(self.getkey("pix_scale")) + " chan_width = "+str(self.getkey("chan_width"))
self._summary["regrid"] = SummaryEntry(atable.serialize(),"Regrid_AT",self.id(True),taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method, creates the slices, regrids if requested, and
creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("LineCube")
self._summary = {}
# look for an input noise level, either through keyword or input
# CubeStats BDP or calculate it if needed
pad = self.getkey("pad")
equalize = self.getkey("equalize")
minchan = 0
linelist = self._bdp_in[1]
if linelist == None or len(linelist) == 0:
logging.info("No lines found in input LineList_BDP, exiting.")
return
spw = self._bdp_in[0]
# get the columns from the table
cols = linelist.table.getHeader()
# get the casa image
imagename = spw.getimagefile(bt.CASA)
imh = imhead(self.dir(imagename), mode='list')
# set the overall parameters for imsubimage
args = {"imagename" : self.dir(imagename),
"overwrite" : True}
dt.tag("start")
if pad != 0:
nchan = imh['shape'][2]
dt.tag("pad")
# if equal size cubes are requested, this will honor the requested pad
if equalize:
start = linelist.table.getColumnByName("startchan")
end = linelist.table.getColumnByName("endchan")
# look for the widest line
for i in range(len(start)):
diff = end[i] - start[i] + 1
minchan = max(minchan , diff + (pad * 2))
dt.tag("equalize")
# get all of the rows in the table
rows = linelist.getall()
delrow = set()
procblend = [0]
# search through looking for blended lines, leave only the strongest from each blend
# in the list
for i, row in enumerate(rows):
if row.blend in procblend:
continue
strongest = -100.
index = -1
indexes = []
blend = row.blend
for j in range(i, len(rows)):
if rows[j].blend != blend:
continue
indexes.append(j)
if rows[j].linestrength > strongest:
strongest = rows[j].linestrength
index = j
indexes.remove(index)
delrow = delrow | set(indexes)
procblend.append(blend)
dr = list(delrow)
dr.sort()
dr.reverse()
for row in dr:
del rows[row]
# check on duplicate UID's, since those are the directory names here
uid1 = []
for row in rows:
uid1.append(row.getkey("uid"))
uid2 = set(uid1)
if len(uid1) != len(uid2):
print "LineList:",uid1
logging.warning("There are duplicate names in the LineList")
#raise Exception,"There are duplicate names in the LineList"
# Create Summary table
lc_description = admit.util.Table()
lc_description.columns = ["Line Name","Start Channel","End Channel","Output Cube"]
lc_description.units = ["","int","int",""]
lc_description.description = "Parameters of Line Cubes"
# loop over all entries in the line list
rdata = []
for row in rows:
uid = row.getkey("uid")
cdir = self.mkext(imagename,uid)
self.mkdir(cdir)
basefl = uid
lcd = [basefl]
outfl = cdir + os.sep + "lc.im"
args["outfile"] = self.dir(outfl)
start = row.getkey("startchan")
end = row.getkey("endchan")
diff = end - start + 1
startch = 0
if diff < minchan:
add = int(math.ceil(float(minchan - diff) / 2.0))
start -= add
end += add
startch += add
if start < 0:
logging.info("%s is too close to the edge to encompass with the "
+ "requested channels, start=%d resetting to 0" %
(uid, start))
startch += abs(start)
start = 0
if end >= nchan:
logging.info("%s is too close to the edge to encompass with the "
+ "requested channels, end=%d resetting to %d" %
(uid, end, nchan - 1))
end = nchan - 1
#print "\n\nDIFF ",startch,"\n\n"
if pad > 0 and not equalize:
start -= pad
end += pad
if start < 0:
logging.warning("pad=%d too large, start=%d resetting to 0"
% (pad, start))
startch += abs(start)
start = 0
else:
startch += pad
if end >= nchan:
logging.warning("pad=%d too large, end=%d resetting to %d"
% (pad, end, nchan - 1))
end = nchan - 1
elif pad < 0 and not equalize:
mid = (start + end) / 2
start = mid + pad / 2
end = mid - pad / 2 - 1
if start < 0:
logging.warning("pad=%d too large, start=%d resetting to 0"
% (pad, start))
startch += abs(start)
start = 0
else:
startch += abs(start)
if end >= nchan:
logging.warning("pad=%d too large, end=%d resetting to %d"
% (pad, end, nchan - 1))
end = nchan - 1
endch = startch + diff
args["chans"] = "%i~%i" % (start, end)
rdata.append(start)
rdata.append(end)
# for the summmary, which will be a table of
# Line name, start channel, end channel, output image
lc_description.addRow([basefl, start, end, outfl])
# create the slices
imsubimage(**args)
line = row.converttoline()
# set the restfrequency ouf the output cube
imhead(imagename=args["outfile"], mode="put", hdkey="restfreq",
hdvalue="%fGHz" % (row.getkey("frequency")))
# set up the output BDP
images = {bt.CASA : outfl}
casaimage = Image(images=images)
# note that Summary.getLineFluxes() implicitly relies on the BDP out order
# being the same order as in the line list table. If this is ever not
# true, then Summary.getLineFluxes mismatch BDPs and flux values.
#self.addoutput(LineCube_BDP(xmlFile=cdir + os.sep + basefl + ".lc",
self.addoutput(LineCube_BDP(xmlFile=outfl,
image=casaimage, line=line, linechans="%i~%i" % (startch, endch)))
dt.tag("trans-%s" % cdir)
logging.regression("LC: %s" % str(rdata))
taskargs = "pad=%s equalize=%s" % (pad, equalize)
self._summary["linecube"] = SummaryEntry(lc_description.serialize(), "LineCube_AT",
self.id(True), taskargs)
dt.tag("done")
dt.end()
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
taskinit.ia.open(self.dir("mom0.masked"))
defmask = taskinit.ia.maskhandler('default')
taskinit.ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
taskinit.ia.open(self.dir(imagename))
taskinit.ia.maskhandler('set', defmask)
taskinit.ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,colorwedge=True)
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
