def run(self):
""" Main program for OverlapIntegral
"""
dt = utils.Dtime("OverlapIntegral")
self._summary = {}
chans =self.getkey("chans")
cmap = self.getkey("cmap")
normalize = self.getkey("normalize")
doCross = True
doCross = False
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
dt.tag("start")
n = len(self._bdp_in)
if n==0:
raise Exception,"Need at least 1 input Image_BDP "
logging.debug("Processing %d input maps" % n)
data = range(n) # array in which each element is placeholder for the data
mdata = range(n) # array to hold the max in each array
summarytable = admit.util.Table()
summarytable.columns = ["File name","Spectral Line ID"]
summarytable.description = "Images used in Overlap Integral"
for i in range(n):
bdpfile = self._bdp_in[i].getimagefile(bt.CASA)
if hasattr(self._bdp_in[i],"line"):
line = getattr(self._bdp_in[i],"line")
logging.info("Map %d: %s" % (i,line.uid))
lineid = line.uid
else:
lineid="no line"
data[i] = casautil.getdata(self.dir(bdpfile),chans)
mdata[i] = data[i].max()
logging.info("shape[%d] = %s with %d good data" % (i,data[i].shape,data[i].count()))
if i==0:
shape = data[i].shape
outfile = self.mkext("testOI","oi")
else:
if shape != data[i].shape:
raise Exception,"Shapes not the same, cannot overlap them"
# collect the file names and line identifications for the summary
summarytable.addRow([bdpfile,lineid])
logging.regression("OI: %s" % str(mdata))
if len(shape)>2 and shape[2] > 1:
raise Exception,"Cannot handle 3D cubes yet"
if doCross:
# debug: produce all cross-corr's of the N input maps (expensive!)
crossn(data, myplot)
dt.tag("crossn")
b1 = Image_BDP(outfile)
self.addoutput(b1)
b1.setkey("image", Image(images={bt.CASA:outfile}))
dt.tag("open")
useClone = True
# to create an output dataset, clone the first input, but using the chans=ch0~ch1
# e.g. using imsubimage(infile,outfile=,chans=
if len(chans) > 0:
# ia.regrid() doesn't have the chans=
taskinit.ia.open(self.dir(self._bdp_in[0].getimagefile(bt.CASA)))
taskinit.ia.regrid(outfile=self.dir(outfile))
taskinit.ia.close()
else:
# 2D for now
if not useClone:
logging.info("OVERLAP out=%s" % outfile)
taskinit.ia.fromimage(infile=self.dir(self._bdp_in[0].getimagefile(bt.CASA)),
outfile=self.dir(outfile), overwrite=True)
taskinit.ia.close()
dt.tag("fromimage")
if n==3:
# RGB
logging.info("RGB mode")
out = rgb1(data[0],data[1],data[2], normalize)
else:
# simple sum
out = data[0]
for i in range(1,n):
out = out + data[i]
if useClone:
casautil.putdata_raw(self.dir(outfile),out,clone=self.dir(self._bdp_in[0].getimagefile(bt.CASA)))
else:
taskinit.ia.open(self.dir(outfile))
s1 = taskinit.ia.shape()
s0 = [0,0,0,0]
r1 = taskinit.rg.box(blc=s0,trc=s1)
pixeldata = out.data
pixelmask = ~out.mask
taskinit.ia.putregion(pixels=pixeldata, pixelmask=pixelmask, region=r1)
taskinit.ia.close()
title = "OverlapIntegral"
pdata = np.rot90(out.squeeze())
logging.info("PDATA: %s" % str(pdata.shape))
myplot.map1(pdata,title,"testOI",thumbnail=True,cmap=cmap)
#-----------------------------
# Populate summary information
#-----------------------------
taskargs = "chans=%s cmap=%s" % (chans, cmap)
imname = ""
thumbnailname = ""
# uncomment when ready.
imname = myplot.getFigure(figno=myplot.figno,relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,relative=True)
#@todo fill in caption with more info - line names, etc.
caption = "Need descriptive caption here"
summaryinfo = [summarytable.serialize(),imname,thumbnailname,caption]
self._summary["overlap"] = SummaryEntry(summaryinfo,
"OverlapIntegral_AT",
self.id(True),taskargs)
#-----------------------------
dt.tag("done")
dt.end()
def run(self):
""" The run method creates the BDP
Parameters
----------
None
Returns
-------
None
"""
dt = utils.Dtime("SFind2D") # tagging time
self._summary = {}
# get key words that user input
nsigma = self.getkey("numsigma")
sigma = self.getkey("sigma")
region = self.getkey("region")
robust = self.getkey("robust")
snmax = self.getkey("snmax")
ds9 = True # writes a "ds9.reg" file
mpl = True # aplot.map1() plot
dynlog = 20.0 # above this value of dyn range finder chart is log I-scaled
bpatch = True # patch units to Jy/beam for ia.findsources()
# get the input casa image from bdp[0]
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
if mpl:
data = np.flipud(np.rot90(casautil.getdata(self.dir(infile)).data))
# check if there is a 2nd image (which will be a PB)
for i in range(len(self._bdp_in)):
print 'BDP',i,type(self._bdp_in[i])
if self._bdp_in[2] != None:
bdpin_pb = self._bdp_in[1]
bdpin_cst = self._bdp_in[2]
print "Need to process PB"
else:
bdpin_pb = None
bdpin_cst = self._bdp_in[1]
print "No PB given"
# get the output bdp basename
slbase = self.mkext(infile,'sl')
# make sure it's a 2D map
if not casautil.mapdim(self.dir(infile),2):
raise Exception,"Input map dimension not 2: %s" % infile
# arguments for imstat call if required
args = {"imagename" : self.dir(infile)}
if region != "":
args["region"] = region
dt.tag("start")
# The following code sets the sigma level for searching for sources using
# the sigma and snmax keyword as appropriate
# if no CubeStats BDP was given and no sigma was specified:
# find a noise level via casa.imstat()
# if a CubeStat_BDP is given get it from there.
if bdpin_cst == None:
# get statistics from input image with imstat because no CubeStat_BDP
stat = casa.imstat(**args)
dmin = float(stat["min"][0]) # these would be wrong if robust were used already
dmax = float(stat["max"][0])
args.update(casautil.parse_robust(robust)) # only now add robust keywords for the sigma
stat = casa.imstat(**args)
if sigma <= 0.0 :
sigma = float(stat["sigma"][0])
dt.tag("imstat")
else:
# get statistics from CubeStat_BDP
sigma = bdpin_cst.get("sigma")
dmin = bdpin_cst.get("minval")
dmax = bdpin_cst.get("maxval")
self.setkey("sigma",sigma)
# calculate cutoff based either on RMS or dynamic range limitation
drange = dmax/(nsigma*sigma)
if snmax < 0.0 :
snmax = drange
if drange > snmax :
cutoff = 1.0/snmax
else:
cutoff = 1.0/drange
logging.info("sigma, dmin, dmax, snmax, cutoff %g %g %g %g %g" % (sigma, dmin, dmax, snmax, cutoff))
# define arguments for call to findsources
args2 = {"cutoff" : cutoff}
args2["nmax"] = 30
if region != "" :
args2["region"] = region
#args2["mask"] = ""
args2["point"] = False
args2["width"] = 5
args2["negfind"] = False
# set-up for SourceList_BDP
slbdp = SourceList_BDP(slbase)
# connect to casa image and call casa ia.findsources tool
taskinit.ia.open(self.dir(infile))
# findsources() cannot deal with 'Jy/beam.km/s' ???
# so for the duration of findsources() we patch it
bunit = taskinit.ia.brightnessunit()
if bpatch and bunit != 'Jy/beam':
logging.warning("Temporarely patching your %s units to Jy/beam for ia.findsources()" % bunit)
taskinit.ia.setbrightnessunit('Jy/beam')
else:
bpatch = False
atab = taskinit.ia.findsources(**args2)
if bpatch:
taskinit.ia.setbrightnessunit(bunit)
taskargs = "nsigma=%4.1f sigma=%g region=%s robust=%s snmax=%5.1f" % (nsigma,sigma,str(region),str(robust),snmax)
dt.tag("findsources")
nsources = atab["nelements"]
xtab = []
ytab = []
logscale = False
sumflux = 0.0
if nsources > 0:
# @TODO: Why are Xpix, YPix not stored in the table?
# -> PJT: I left them out since they are connected to an image which may not be available here
# but we should store the frequency of the observation here for later bandmerging
logging.debug("%s" % str(atab['component0']['shape']))
logging.info("Right Ascen. Declination X(pix) Y(pix) Peak Flux Major Minor PA SNR")
funits = atab['component0']['flux']['unit']
if atab['component0']['shape'].has_key('majoraxis'):
sunits = atab['component0']['shape']['majoraxis']['unit']
aunits = atab['component0']['shape']['positionangle']['unit']
else:
sunits = "n/a"
aunits = "n/a"
punits = taskinit.ia.summary()['unit']
logging.info(" %s %s %s %s %s" % (punits,funits,sunits,sunits,aunits))
#
# @todo future improvement is to look at image coordinates and control output appropriately
#
if ds9:
# @todo variable name
regname = self.mkext(infile,'ds9.reg')
fp9 = open(self.dir(regname),"w!")
for i in range(nsources):
c = "component%d" % i
name = "%d" % (i+1)
r = atab[c]['shape']['direction']['m0']['value']
d = atab[c]['shape']['direction']['m1']['value']
pixel = taskinit.ia.topixel([r,d])
xpos = pixel['numeric'][0]
ypos = pixel['numeric'][1]
rd = taskinit.ia.toworld([xpos,ypos],'s')
ra = rd['string'][0][:12]
dec = rd['string'][1][:12]
flux = atab[c]['flux']['value'][0]
sumflux = sumflux + flux
if atab[c]['shape'].has_key('majoraxis'):
smajor = atab[c]['shape']['majoraxis']['value']
sminor = atab[c]['shape']['minoraxis']['value']
sangle = atab[c]['shape']['positionangle']['value']
else:
smajor = 0.0
sminor = 0.0
sangle = 0.0
peakstr = taskinit.ia.pixelvalue([xpos,ypos,0,0])
if len(peakstr) == 0:
logging.warning("Problem with source %d @ %d,%d" % (i,xpos,ypos))
continue
peakf = peakstr['value']['value']
snr = peakf/sigma
if snr > dynlog:
logscale = True
logging.info("%s %s %8.2f %8.2f %10.3g %10.3g %7.3f %7.3f %6.1f %6.1f" % (ra,dec,xpos,ypos,peakf,flux,smajor,sminor,sangle,snr))
xtab.append(xpos)
ytab.append(ypos)
slbdp.addRow([name,ra,dec,flux,peakf,smajor,sminor,sangle])
if ds9:
ras = ra
des = dec.replace('.',':',2)
msg = 'ellipse(%s,%s,%g",%g",%g) # text={%s}' % (ras,des,smajor,sminor,sangle+90.0,i+1)
fp9.write("%s\n" % msg)
if ds9:
fp9.close()
logging.info("Wrote ds9.reg")
dt.tag("table")
logging.regression("CONTFLUX: %d %g" % (nsources,sumflux))
summary = taskinit.ia.summary()
beammaj = summary['restoringbeam']['major']['value']
beammin = summary['restoringbeam']['minor']['value']
beamunit = summary['restoringbeam']['minor']['unit']
beamang = summary['restoringbeam']['positionangle']['value']
angunit = summary['restoringbeam']['positionangle']['unit']
# @todo add to table comments?
logging.info(" Fitted Gaussian size; NOT deconvolved source size.")
logging.info(" Restoring Beam: Major axis: %10.3g %s , Minor axis: %10.3g %s , PA: %5.1f %s" % (beammaj, beamunit, beammin, beamunit, beamang, angunit))
# form into a xml table
# output is a table_bdp
self.addoutput(slbdp)
# instantiate a plotter for all plots made herein
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
# make output png with circles marking sources found
if mpl:
circles=[]
nx = data.shape[1] # data[] array was already flipud(rot90)'d
ny = data.shape[0] #
for (x,y) in zip(xtab,ytab):
circles.append([x,y,1])
# @todo variable name
if logscale:
logging.warning("LogScaling applied")
data = data/sigma
data = np.where(data<0,-np.log10(1-data),+np.log10(1+data))
title = "SFind2D: %d sources" % nsources
myplot.map1(data,title,slbase,thumbnail=True,circles=circles)
#---------------------------------------------------------
# Get the figure and thumbmail names and create a caption
#---------------------------------------------------------
imname = myplot.getFigure(figno=myplot.figno,relative=True)
thumbnailname = myplot.getThumbnail(figno=myplot.figno,relative=True)
caption = "Image of input map with sources found by SFind2D overlayed in green."
slbdp.table.description="Table of source locations and sizes (not deconvolved)"
#---------------------------------------------------------
# Add finder image to the BDP
#---------------------------------------------------------
image = Image(images={bt.PNG: imname},
thumbnail=thumbnailname,
thumbnailtype=bt.PNG, description=caption)
slbdp.image.addimage(image, "finderimage")
#-------------------------------------------------------------
# Create the summary entry for the table and image
#-------------------------------------------------------------
self._summary["sources"] = SummaryEntry([slbdp.table.serialize(),
slbdp.image.serialize()],
"SFind2D_AT",
self.id(True), taskargs)
dt.tag("done")
dt.end()
class PVCorr_AT(AT):
"""PV correllation in a PVSlice map.
PVCorr_AT computes a cross-correlation of a feature in a PVSlice with the
whole PVSlice, looking for repeated patterns to detect spectral lines. Much
like the output from CubeStats_AT and CubeSpectrum_AT, this table can then be
given to LineID_AT to attempt a line identification.
See also :ref:`PVCorr-AT-Design` for the design document.
**Keywords**
**numsigma**: float
Minimum intensity, in terms of sigma, above which a selected portion
of the spectrum will be used for cross-correlation.
Default: 3.0.
**range**: integer list
If given, it has to be a list with 2 channel numbers, the first and last channel
(0-based channels) of the range which to use for the cross-correlation.
Default: [].
**nchan**: integer
The number of channels (in case range= was not used) that defines the line.
The line is centers on the strongest point in the input PV-map.
If 0 is given, it will watershed down from the strongest line.
Default: 0.
**Input BDPs**
**PVSlice_BDP**: count: 1
Input PV Slice. As created with e.g. PVSlice_AT.
**CubeStats_BDP**: count: 1
Input cube statistics from which the RMS is taken.
**Output BDPs**
**PVCorr_BDP**: count: 1
Output table.
"""
def __init__(self,**keyval):
keys = {"numsigma" : 3.0, # N-sigma
"range" : [], # optional channel range
"nchan" : 0, # number of channels around the channel where the peak is
}
AT.__init__(self,keys,keyval)
self._version = "1.0.1"
self.set_bdp_in([(Image_BDP,1,bt.REQUIRED),
# @todo optional 2nd PVSlice can be used to draw the template from
(CubeStats_BDP,1,bt.REQUIRED)])
self.set_bdp_out([(PVCorr_BDP,1)])
def summary(self):
"""Returns the summary dictionary from the AT, for merging
into the ADMIT Summary object.
PVCorr_AT adds the following to ADMIT summary:
.. table::
:class: borderless
+----------+----------+-----------------------------------+
| Key | type | Description |
+==========+==========+===================================+
| pvcorr | list | correlation diagram |
+----------+----------+-----------------------------------+
Parameters
----------
None
Returns
-------
dict
Dictionary of SummaryEntry
"""
if hasattr(self,"_summary"):
return self._summary
else:
return {}
def run(self):
dt = utils.Dtime("PVCorr")
self._summary = {}
numsigma = self.getkey("numsigma")
mode = 1 # PV corr mode (1,2,3)
normalize = True
# normalize = False
b1 = self._bdp_in[0] # PVSlice_BDP
fin = b1.getimagefile(bt.CASA) # CASA image
data = casautil.getdata_raw(self.dir(fin)) # grab the data as a numpy array
self.myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
#print 'DATA[0,0]:',data[0,0]
#print 'pv shape: ',data.shape
npos = data.shape[0]
nvel = data.shape[1]
dt.tag("getdata")
b2 = self._bdp_in[1] # CubeStats_BDP
sigma = b2.sigma # global sigma in the cube
cutoff = numsigma * sigma
freq = b2.table.getColumnByName("frequency")
chans = self.getkey("range") # range of channels, if used
if len(chans) > 0:
if len(chans) != 2:
logging.fatal("range=%s" % chans)
raise Exception,"range= needs two values, left and right (inclusive) channel"
ch0 = chans[0]
ch1 = chans[1]
else:
nchan = self.getkey("nchan")
imstat0 = casa.imstat(self.dir(fin)) # @todo can use data[] now
xmaxpos = imstat0['maxpos'][0]
ymaxpos = imstat0['maxpos'][1]
logging.info("MAXPOS-VEL %s %g" % (str(imstat0['maxpos']),imstat0['max'][0]))
if nchan > 0:
# expand around it, later ch0,ch1 will be checked for running off the edge
ch0 = ymaxpos - nchan/2
ch1 = ymaxpos + nchan/2
else:
# watershed down to find ch0 and ch1 ?
# this doesn't work well in crowded areas
ch0 = ymaxpos
ch1 = ymaxpos
spmax = data.max(axis=0)
k = spmax.argmax()
n = len(spmax)
logging.debug('spmax %s %d %g' % (str(spmax.shape),k,spmax[k]))
# find lower cutoff
for i in range(n):
ch0 = ymaxpos - i
if ch0<0: break
if spmax[ch0] < cutoff: break
ch0 = ch0 + 1
# find higher cutoff
for i in range(n):
ch1 = ymaxpos + i
if ch1==n: break
if spmax[ch1] < cutoff: break
ch1 = ch1 - 1
dt.tag("imstat")
bdp_name = self.mkext(fin,"pvc") # output PVCorr_BDP
b3 = PVCorr_BDP(bdp_name)
self.addoutput(b3)
if ch0<0 or ch1>=nvel:
# this probably only happens to small cubes (problematic for PVCorr)
# or when the strongest line is really close to the edge of the band
# (which is probably ok)
if ch0<0 and ch1>=nvel:
logging.warning("Serious issues with the size of this cube")
if ch0<0:
logging.warning("Resetting ch0 edge to 0")
ch0=0
if ch1>=nvel:
ch1=nvel-1
logging.warning("Resetting ch1 edge to the maximum")
if ch0 > ch1:
logging.warning("Sanity swapping ch0,1 due to likely noisy data")
ch0,ch1 = ch1,ch0
if mode == 1:
out,rms = mode1(data, ch0, ch1, cutoff, normalize)
corr = out
elif mode == 2:
out,rms = mode2(data, ch0, ch1, cutoff) # slower 2D version
corr = out[npos/2,:] # center cut, but could also try feature detection
elif mode == 3:
out,rms = self.mode3(data, ch0, ch1, cutoff) # Doug's faster 2D version
# get the peak of each column
corr = np.amax(out,axis=0)
# print "PVCORR SHAPE ",corr.shape," mode", mode
if len(corr) > 0:
# print "SHAPE out:",out.shape,corr.shape,npos/2
ch = range(len(corr))
if len(corr) != len(freq):
logging.fatal("ch (%d) and freq (%d) do not have same size" % (len(corr),len(freq)))
raise Exception,"ch and freq do not have same dimension"
dt.tag("mode")
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
data = (ch, freq, corr)
table = Table(columns=labels,units=units,data=np.column_stack(data))
else:
# still construct a table, but with no rows
labels = ["channel", "frequency", "pvcorr"]
units = ["number", "GHz", "N/A"]
table = Table(columns=labels,units=units)
b3.setkey("table",table)
b3.setkey("sigma",float(rms))
dt.tag("table")
if len(corr) > 0:
table.exportTable(self.dir("testPVCorr.tab"),cols=['frequency','pvcorr'])
test_single(ch,freq,corr)
logging.regression("PVC: %f %f" % (corr.min(),corr.max()))
title = 'PVCorr mode=%d [%d,%d] %g' % (mode,ch0,ch1,cutoff)
x = ch
xlab = 'Channel'
y = [corr]
ylab = 'PV Correlation'
p1 = "%s_%d" % (bdp_name,0)
segp = []
segp.append( [0,len(ch),0.0,0.0] )
segp.append( [0,len(ch),3.0*rms, 3.0*rms] )
# @todo: in principle we know with given noise and size of box, what the sigma in pvcorr should be
self.myplot.plotter(x,y,title,figname=p1,xlab=xlab,ylab=ylab,segments=segp, thumbnail=True)
#out1 = np.rot90 (data.reshape((nvel,npos)) )
if mode > 1:
self.myplot.map1(data=out,title="testing PVCorr_AT: mode%d"%mode,figname='testPVCorr', thumbnail=True)
taskargs = "numsigma=%.1f range=[%d,%d]" % (numsigma,ch0,ch1)
caption = "Position-velocity correlation plot"
thumbname = self.myplot.getThumbnail(figno=self.myplot.figno,relative=True)
figname = self.myplot.getFigure(figno=self.myplot.figno,relative=True)
image = Image(images={bt.PNG: figname}, thumbnail=thumbname, thumbnailtype=bt.PNG,
description=caption)
b3.image.addimage(image, "pvcorr")
self._summary["pvcorr"] = SummaryEntry([figname,thumbname,caption,fin],"PVCorr_AT",self.id(True),taskargs)
else:
self._summary["pvcorr"] = None
logging.warning("No summary")
logging.regression("PVC: -1")
dt.tag("done")
dt.end()
def mode3(self, data, v0, v1, dmin=0.0):
""" v0..v1 (both inclusive) are channel selections
threshold on dmin
@todo the frequency axis is not properly calibrated here
@todo a full 2D is slow, we only need the 1D version
"""
print "PVCorr mode3: v0,1=",v0,v1
smin = data.min()
#s = data[v0:v1+1,:]
s = data[:,v0:v1+1]
if dmin==0.0:
logging.warning("Using all data in crosscorr")
f = s
else:
f = np.where(s>dmin,s,0)
# find out where the zeros are
temp = np.amax(f,axis=1)
nz = np.nonzero(temp)
# trim the kernel in the y direction, removing rows that are all 0.0
f = f[nz[0][0]:nz[0][-1],:]
f0 = np.where(s>smin,1,0)
f1 = np.where(s>dmin,1,0)
fmax = f.max()
print "PVCorr mode3:",f1.sum(),'/',f0.sum(),'min/max',smin,fmax
out = scipy.signal.correlate2d(data,f,mode='same')
self.myplot.map1(data=f,title="PVCorr 2D Kernel",figname='PVCorrKernel', thumbnail=True)
print 'PVCorr min/max:',out.min(),out.max()
n1,m1,s1,n2,m2,s2 = stats.mystats(out.flatten())
print "PVCorr stats", n1,m1,s1,n2,m2,s2
rms_est = s2/np.sqrt(f1.sum())
return out,rms_est
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
pvslicesummary = []
sumslicetype = 'slice'
sliceargs = []
dt = utils.Dtime("PVSlice")
# import here, otherwise sphinx cannot parse
from impv import impv
from imsmooth import imsmooth
pvslice = self.getkey(
'slice') # x_s,y_s,x_e,y_e (start and end of line)
pvslit = self.getkey(
'slit') # x_c,y_c,len,pa (center, length and PA of line)
# BDP's used :
# b10 = input BDP
# b11 = input BDP (moment)
# b12 = input BDP (new style cubestats w/ maxpos)
# b2 = output BDP
b10 = self._bdp_in[0] # input SpwCube
fin = b10.getimagefile(bt.CASA) # input name
b11 = self._bdp_in[1] #
b12 = self._bdp_in[2]
clip = self.getkey('clip') # clipping to data for Moment-of-Inertia
gamma = self.getkey(
'gamma') # gamma factor to data for Moment-of-Inertia
if b11 != None and len(pvslice) == 0 and len(pvslit) == 0:
# if a map (e.g. cubesum ) given, and no slice/slit, get a best pvslice from that
(pvslice, clip) = map_to_slit(self.dir(b11.getimagefile(bt.CASA)),
clip=clip,
gamma=gamma)
elif b12 != None and len(pvslice) == 0 and len(pvslit) == 0:
# PPP doesn't seem to work too well yet
logging.debug("testing new slice computation from a PPP")
max = b12.table.getColumnByName("max")
maxposx = b12.table.getColumnByName("maxposx")
maxposy = b12.table.getColumnByName("maxposy")
if maxposx == None:
raise Exception, "PPP was not enabled in your CubeStats"
(pvslice, clip) = tab_to_slit([maxposx, maxposy, max],
clip=clip,
gamma=gamma)
sliceargs = deepcopy(pvslice)
if len(sliceargs) == 0:
logging.warning("no slice for plot yet")
# ugh, this puts single quotes around the numbers
formattedslice = str(["%.2f" % a for a in sliceargs])
taskargs = "slice=" + formattedslice
dt.tag("slice")
pvname = self.mkext(fin, 'pv') # output image name
b2 = PVSlice_BDP(pvname)
self.addoutput(b2)
width = self.getkey(
'width'
) # @todo also: "4arcsec" (can't work since it's a single keyword)
if len(pvslice) == 4:
start = pvslice[:
2] # @todo also allow: ["14h20m20.5s","-30d45m25.4s"]
end = pvslice[2:]
impv(self.dir(fin),
self.dir(pvname),
"coords",
start=start,
end=end,
width=width,
overwrite=True)
elif len(pvslit) == 4:
sumslicetype = 'slit'
sliceargs = deepcopy(pvslit)
formattedslice = str(["%.2f" % a for a in sliceargs])
taskargs = "slit=" + formattedslice
# length="40arcsec" same as {"value": 40, "unit": "arcsec"})
center = pvslit[:
2] # @todo also: ["14h20m20.5s","-30d45m25.4s"].
length = pvslit[
2] # @todo also: "40arcsec", {"value": 40, "unit": "arcsec"})
if type(pvslit[3]) is float or type(pvslit[3]) is int:
pa = "%gdeg" % pvslit[3]
else:
pa = pvslit[3]
impv(self.dir(fin),
self.dir(pvname),
"length",
center=center,
length=length,
pa=pa,
width=width,
overwrite=True)
else:
raise Exception, "no valid input slit= or slice= or bad Moment_BDP input"
sliceargs.append(width)
taskargs = taskargs + " width=%d" % width
dt.tag("impv")
smooth = self.getkey('pvsmooth')
if len(smooth) > 0:
if len(smooth) == 1:
smooth.append(smooth[0])
major = '%dpix' % smooth[0]
minor = '%dpix' % smooth[1]
logging.info("imsmooth PV slice: %s %s" % (major, minor))
imsmooth(self.dir(pvname),
outfile=self.dir(pvname) + '.smooth',
kernel='boxcar',
major=major,
minor=minor)
dt.tag("imsmooth")
# utils.rename(self.dir(pvname)+'.smooth',self.dir(pvname))
# @todo we will keep the smooth PVslice for inspection, no further flow work
# get some statistics
data = casautil.getdata_raw(self.dir(pvname))
rpix = stats.robust(data.flatten())
r_mean = rpix.mean()
r_std = rpix.std()
r_max = rpix.max()
logging.info("PV stats: mean/std/max %f %f %f" %
(r_mean, r_std, r_max))
logging.regression("PVSLICE: %f %f %f" % (r_mean, r_std, r_max))
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
# hack to get a slice on a mom0map
# @todo if pmode is not png, can viewer handle this?
figname = pvname + ".png"
slicename = self.dir(figname)
overlay = pvname + "_overlay"
if b11 != None:
f11 = b11.getimagefile(bt.CASA)
tb = taskinit.tbtool()
tb.open(self.dir(f11))
data = tb.getcol('map')
nx = data.shape[0]
ny = data.shape[1]
tb.close()
d1 = np.flipud(np.rot90(data.reshape((nx, ny))))
if len(pvslice) == 4:
segm = [[pvslice[0], pvslice[2], pvslice[1], pvslice[3]]]
pa = np.arctan2(pvslice[2] - pvslice[0],
pvslice[1] - pvslice[3]) * 180.0 / np.pi
title = "PV Slice location : slice PA=%.1f" % pa
xcen = (pvslice[0] + pvslice[2]) / 2.0
ycen = (pvslice[1] + pvslice[3]) / 2.0
elif len(pvslit) == 4:
# can only do this now if using pixel coordinates
xcen = pvslit[0]
ycen = pvslit[1]
slen = pvslit[2]
pard = pvslit[3] * np.pi / 180.0
cosp = np.cos(pard)
sinp = np.sin(pard)
halflen = 0.5 * slen
segm = [[
xcen - halflen * sinp, xcen + halflen * sinp,
ycen + halflen * cosp, ycen - halflen * cosp
]]
pa = pvslit[3]
title = "PV Slice location : slit PA=%g" % pa
else:
# bogus, some error in pvslice
logging.warning("bogus segm since pvslice=%s" % str(pvslice))
segm = [[10, 20, 10, 20]]
pa = -999.999
title = "PV Slice location - bad PA"
logging.info("MAP1 segm %s %s" % (str(segm), str(pvslice)))
if d1.max() < clip:
logging.warning("datamax=%g, clip=%g" % (d1.max(), clip))
title = title + ' (no signal over %g?)' % clip
myplot.map1(d1,
title,
overlay,
segments=segm,
thumbnail=True,
zoom=self.getkey("zoom"),
star=[xcen, ycen])
else:
myplot.map1(d1,
title,
overlay,
segments=segm,
range=[clip],
thumbnail=True,
zoom=self.getkey("zoom"),
star=[xcen, ycen])
dt.tag("plot")
overlayname = myplot.getFigure(figno=myplot.figno, relative=True)
overlaythumbname = myplot.getThumbnail(figno=myplot.figno,
relative=True)
Qover = True
else:
Qover = False
implot = ImPlot(pmode=self._plot_mode,
ptype=self._plot_type,
abspath=self.dir())
implot.plotter(rasterfile=pvname, figname=pvname, colorwedge=True)
thumbname = implot.getThumbnail(figno=implot.figno, relative=True)
figname = implot.getFigure(figno=implot.figno, relative=True)
if False:
# debug:
#
# @todo tmp1 is ok, tmp2 is not displaying the whole thing
# use casa_imview, not casa.imview - if this is enabled.
# old style: viewer() seems to plot full image, but imview() wants square pixels?
casa.viewer(infile=self.dir(pvname),
outfile=self.dir('tmp1.pv.png'),
gui=False,
outformat="png")
casa.imview(raster={
'file': self.dir(pvname),
'colorwedge': True,
'scaling': -1
},
axes={'y': 'Declination'},
out=self.dir('tmp2.pv.png'))
#
# -> this one works, axes= should be correct
# imview(raster={'file':'x.pv', 'colorwedge' : True, 'scaling':-1},axes={'y':'Frequency'})
#
# @TODO big fixme, we're going to reuse 'tmp1.pv.png' because implot give a broken view
figname = 'tmp1.pv.png'
# @todo technically we don't know what map it was overlay'd on.... CubeSum/Moment0
overlaycaption = "Location of position-velocity slice overlaid on a CubeSum map"
pvcaption = "Position-velocity diagram through emission centroid"
pvimage = Image(images={
bt.CASA: pvname,
bt.PNG: figname
},
thumbnail=thumbname,
thumbnailtype=bt.PNG,
description=pvcaption)
b2.setkey("image", pvimage)
b2.setkey("mean", float(r_mean))
b2.setkey("sigma", float(r_std))
if Qover:
thispvsummary = [
sumslicetype, sliceargs, figname, thumbname, pvcaption,
overlayname, overlaythumbname, overlaycaption, pvname, fin
]
else:
thispvsummary = [
sumslicetype, sliceargs, figname, thumbname, pvcaption, pvname,
fin
]
# Yes, this is a nested list. Against the day when PVSLICE can
# compute multiple slices per map.
pvslicesummary.append(thispvsummary)
self._summary["pvslices"] = SummaryEntry(pvslicesummary, "PVSlice_AT",
self.id(True), taskargs)
dt.tag("done")
dt.end()