def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeStats")
#maxvrms = 2.0 # maximum variation in rms allowed (hardcoded for now)
#maxvrms = -1.0 # turn maximum variation in rms allowed off
maxvrms = self.getkey("maxvrms")
psample = -1
psample = self.getkey("psample")
# BDP's used :
# b1 = input BDP
# b2 = output BDP
b1 = self._bdp_in[0]
fin = b1.getimagefile(bt.CASA)
bdp_name = self.mkext(fin,'cst')
b2 = CubeStats_BDP(bdp_name)
self.addoutput(b2)
# PeakPointPlot
use_ppp = self.getkey("ppp")
# peakstats: not enabled for mortal users yet
# peakstats = (psample=1, numsigma=4, minchan=3, maxgap=2, peakfit=False)
pnumsigma = 4
minchan = 3
maxgap = 2
peakfit = False # True will enable a true gaussian fit
# numsigma: adding all signal > numsigma ; not user enabled; for peaksum.
numsigma = -1.0
numsigma = 3.0
# grab the new robust statistics. If this is used, 'rms' will be the RMS,
# else we will use RMS = 1.4826*MAD (MAD does a decent job on outliers as well)
# and was the only method available before CASA 4.4 when robust was implemented
robust = self.getkey("robust")
rargs = casautil.parse_robust(robust)
nrargs = len(rargs)
if nrargs == 0:
sumrargs = "medabsdevmed" # for the summary, indicate the default robust
else:
sumrargs = str(rargs)
self._summary["rmsmethd"] = SummaryEntry([sumrargs,fin],"CubeStats_AT",self.id(True))
#@todo think about using this instead of putting 'fin' in all the SummaryEntry
#self._summary["casaimage"] = SummaryEntry(fin,"CubeStats_AT",self.id(True))
# extra CASA call to get the freq's in GHz, as these are not in imstat1{}
# @todo what if the coordinates are not in FREQ ?
# Note: CAS-7648 bug on 3D cubes
if False:
# csys method
ia.open(self.dir(fin))
csys = ia.coordsys()
spec_axis = csys.findaxisbyname("spectral")
# ieck, we need a valid position, or else it will come back and "Exception: All selected pixels are masked"
#freqs = ia.getprofile(spec_axis, region=rg.box([0,0],[0,0]))['coords']/1e9
#freqs = ia.getprofile(spec_axis)['coords']/1e9
freqs = ia.getprofile(spec_axis,unit="GHz")['coords']
dt.tag("getprofile")
else:
# old imval method
#imval0 = casa.imval(self.dir(fin),box='0,0,0,0') # this fails on 3D
imval0 = casa.imval(self.dir(fin))
freqs = imval0['coords'].transpose()[2]/1e9
dt.tag("imval")
nchan = len(freqs)
chans = np.arange(nchan)
# call CASA to get what we want
# imstat0 is the whole cube, imstat1 the plane based statistics
# warning: certain robust stats (**rargs) on the whole cube are going to be very slow
dt.tag("start")
imstat0 = casa.imstat(self.dir(fin), logfile=self.dir('imstat0.logfile'),append=False,**rargs)
dt.tag("imstat0")
imstat1 = casa.imstat(self.dir(fin),axes=[0,1],logfile=self.dir('imstat1.logfile'),append=False,**rargs)
dt.tag("imstat1")
# imm = casa.immoments(self.dir(fin),axis='spec', moments=8, outfile=self.dir('ppp.im'))
if nrargs > 0:
# need to get the peaks without rubust
imstat10 = casa.imstat(self.dir(fin), logfile=self.dir('imstat0.logfile'),append=True)
dt.tag("imstat10")
imstat11 = casa.imstat(self.dir(fin),axes=[0,1],logfile=self.dir('imstat1.logfile'),append=True)
dt.tag("imstat11")
# grab the relevant plane-based things from imstat1
if nrargs == 0:
mean = imstat1["mean"]
sigma = imstat1["medabsdevmed"]*1.4826 # see also: astropy.stats.median_absolute_deviation()
peakval = imstat1["max"]
minval = imstat1["min"]
else:
mean = imstat1["mean"]
sigma = imstat1["rms"]
peakval = imstat11["max"]
minval = imstat11["min"]
if True:
# work around a bug in imstat(axes=[0,1]) for last channel [CAS-7697]
for i in range(len(sigma)):
if sigma[i] == 0.0:
minval[i] = peakval[i] = 0.0
# too many variations in the RMS ?
sigma_pos = sigma[np.where(sigma>0)]
smin = sigma_pos.min()
smax = sigma_pos.max()
logging.info("sigma varies from %f to %f; %d/%d channels ok" % (smin,smax,len(sigma_pos),len(sigma)))
if maxvrms > 0:
if smax/smin > maxvrms:
cliprms = smin * maxvrms
logging.warning("sigma varies too much, going to clip to %g (%g > %g)" % (cliprms, smax/smin, maxvrms))
sigma = np.where(sigma < cliprms, sigma, cliprms)
# @todo (and check again) for foobar.fits all sigma's became 0 when robust was selected
# was this with mask=True/False?
# PeakPointPlot (can be expensive, hence the option)
if use_ppp:
logging.info("Computing MaxPos for PeakPointPlot")
xpos = np.zeros(nchan)
ypos = np.zeros(nchan)
peaksum = np.zeros(nchan)
ia.open(self.dir(fin))
for i in range(nchan):
if sigma[i] > 0.0:
plane = ia.getchunk(blc=[0,0,i,-1],trc=[-1,-1,i,-1],dropdeg=True)
v = ma.masked_invalid(plane)
v_abs = np.absolute(v)
max = np.unravel_index(v_abs.argmax(), v_abs.shape)
xpos[i] = max[0]
ypos[i] = max[1]
if numsigma > 0.0:
peaksum[i] = ma.masked_less(v,numsigma * sigma[i]).sum()
peaksum = np.nan_to_num(peaksum) # put 0's where nan's are found
ia.close()
dt.tag("ppp")
nzeros = len(np.where(sigma<=0.0))
if nzeros > 0:
zeroch = np.where(sigma<=0.0)
logging.warning("There are %d fully masked channels (%s)" % (nzeros,str(zeroch)))
# construct the admit Table for CubeStats_BDP
# note data needs to be a tuple, later to be column_stack'd
if use_ppp:
labels = ["channel" ,"frequency" ,"mean" ,"sigma" ,"max" ,"maxposx" ,"maxposy" ,"min", "peaksum"]
units = ["number" ,"GHz" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam" ,"number" ,"number" ,"Jy/beam", "Jy"]
data = (chans ,freqs ,mean ,sigma ,peakval ,xpos ,ypos ,minval, peaksum)
else:
labels = ["channel" ,"frequency" ,"mean" ,"sigma" ,"max" ,"min"]
units = ["number" ,"GHz" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam" ,"Jy/beam"]
data = (chans ,freqs ,mean ,sigma ,peakval ,minval)
table = Table(columns=labels,units=units,data=np.column_stack(data))
b2.setkey("table",table)
# get the full cube statistics, it depends if robust was pre-selected
if nrargs == 0:
mean0 = imstat0["mean"][0]
sigma0 = imstat0["medabsdevmed"][0]*1.4826
peak0 = imstat0["max"][0]
b2.setkey("mean" , float(mean0))
b2.setkey("sigma", float(sigma0))
b2.setkey("minval",float(imstat0["min"][0]))
b2.setkey("maxval",float(imstat0["max"][0]))
b2.setkey("minpos",imstat0["minpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
b2.setkey("maxpos",imstat0["maxpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
logging.info("CubeMax: %f @ %s" % (imstat0["max"][0],str(imstat0["maxpos"])))
logging.info("CubeMin: %f @ %s" % (imstat0["min"][0],str(imstat0["minpos"])))
logging.info("CubeRMS: %f" % sigma0)
else:
mean0 = imstat0["mean"][0]
sigma0 = imstat0["rms"][0]
peak0 = imstat10["max"][0]
b2.setkey("mean" , float(mean0))
b2.setkey("sigma", float(sigma0))
b2.setkey("minval",float(imstat10["min"][0]))
b2.setkey("maxval",float(imstat10["max"][0]))
b2.setkey("minpos",imstat10["minpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
b2.setkey("maxpos",imstat10["maxpos"][:3].tolist()) #? [] or array(..dtype=int32) ??
logging.info("CubeMax: %f @ %s" % (imstat10["max"][0],str(imstat10["maxpos"])))
logging.info("CubeMin: %f @ %s" % (imstat10["min"][0],str(imstat10["minpos"])))
logging.info("CubeRMS: %f" % sigma0)
b2.setkey("robust",robust)
rms_ratio = imstat0["rms"][0]/sigma0
logging.info("RMS Sanity check %f" % rms_ratio)
if rms_ratio > 1.5:
logging.warning("RMS sanity check = %f. Either bad sidelobes, lotsa signal, or both" % rms_ratio)
logging.regression("CST: %f %f" % (sigma0, rms_ratio))
# plots: no plots need to be made when nchan=1 for continuum
# however we could make a histogram, overlaying the "best" gauss so
# signal deviations are clear?
logging.info('mean,rms,S/N=%f %f %f' % (mean0,sigma0,peak0/sigma0))
if nchan == 1:
# for a continuum/1-channel we only need to stuff some numbers into the _summary
self._summary["chanrms"] = SummaryEntry([float(sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["dynrange"] = SummaryEntry([float(peak0)/float(sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["datamean"] = SummaryEntry([float(mean0), fin], "CubeStats_AT", self.id(True))
else:
y1 = np.log10(ma.masked_invalid(peakval))
y2 = np.log10(ma.masked_invalid(sigma))
y3 = y1-y2
y4 = np.log10(ma.masked_invalid(-minval))
y5 = y1-y4
y = [y1,y2,y3,y4]
title = 'CubeStats: ' + bdp_name+'_0'
xlab = 'Channel'
ylab = 'log(Peak,Noise,Peak/Noise)'
labels = ['log(peak)','log(rms noise)','log(peak/noise)','log(|minval|)']
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode,abspath=self.dir())
segp = [[chans[0],chans[nchan-1],math.log10(sigma0),math.log10(sigma0)]]
myplot.plotter(chans,y,title,bdp_name+"_0",xlab=xlab,ylab=ylab,segments=segp,labels=labels,thumbnail=True)
imfile = myplot.getFigure(figno=myplot.figno,relative=True)
thumbfile = myplot.getThumbnail(figno=myplot.figno,relative=True)
image0 = Image(images={bt.PNG:imfile},thumbnail=thumbfile,thumbnailtype=bt.PNG,description="CubeStats_0")
b2.addimage(image0,"im0")
if use_ppp:
# new trial for Lee
title = 'PeakSum: (numsigma=%.1f)' % (numsigma)
ylab = 'Jy*N_ppb'
myplot.plotter(chans,[peaksum],title,bdp_name+"_00",xlab=xlab,ylab=ylab,thumbnail=False)
if True:
# hack ascii table
y30 = np.where(sigma > 0, np.log10(peakval/sigma), 0.0)
table2 = Table(columns=["freq","log(P/N)"],data=np.column_stack((freqs,y30)))
table2.exportTable(self.dir("testCubeStats.tab"))
del table2
# the "box" for the "spectrum" is all pixels. Don't know how to
# get this except via shape.
ia.open(self.dir(fin))
s = ia.summary()
ia.close()
if 'shape' in s:
specbox = (0,0,s['shape'][0],s['shape'][1])
else:
specbox = ()
caption = "Emission characteristics as a function of channel, as derived by CubeStats_AT "
caption += "(cyan: global rms,"
caption += " green: noise per channel,"
caption += " blue: peak value per channel,"
caption += " red: peak/noise per channel)."
self._summary["spectra"] = SummaryEntry([0, 0, str(specbox), 'Channel', imfile, thumbfile , caption, fin], "CubeStats_AT", self.id(True))
self._summary["chanrms"] = SummaryEntry([float(sigma0), fin], "CubeStats_AT", self.id(True))
# @todo Will imstat["max"][0] always be equal to s['datamax']? If not, why not?
if 'datamax' in s:
self._summary["dynrange"] = SummaryEntry([float(s['datamax']/sigma0), fin], "CubeStats_AT", self.id(True))
else:
self._summary["dynrange"] = SummaryEntry([float(imstat0["max"][0]/sigma0), fin], "CubeStats_AT", self.id(True))
self._summary["datamean"] = SummaryEntry([imstat0["mean"][0], fin], "CubeStats_AT", self.id(True))
title = bdp_name + "_1"
xlab = 'log(Peak,Noise,P/N)'
myplot.histogram([y1,y2,y3],title,bdp_name+"_1",xlab=xlab,thumbnail=True)
imfile = myplot.getFigure(figno=myplot.figno,relative=True)
thumbfile = myplot.getThumbnail(figno=myplot.figno,relative=True)
image1 = Image(images={bt.PNG:imfile},thumbnail=thumbfile,thumbnailtype=bt.PNG,description="CubeStats_1")
b2.addimage(image1,"im1")
# note that the 'y2' can have been clipped, which can throw off stats.robust()
# @todo should set a mask for those.
title = bdp_name + "_2"
xlab = 'log(Noise))'
n = len(y2)
ry2 = stats.robust(y2)
y2_mean = ry2.mean()
y2_std = ry2.std()
if n>9: logging.debug("NORMALTEST2: %s" % str(scipy.stats.normaltest(ry2)))
myplot.hisplot(y2,title,bdp_name+"_2",xlab=xlab,gauss=[y2_mean,y2_std],thumbnail=True)
title = bdp_name + "_3"
xlab = 'log(diff[Noise])'
n = len(y2)
# dy2 = y2[0:-2] - y2[1:-1]
dy2 = ma.masked_equal(y2[0:-2] - y2[1:-1],0.0).compressed()
rdy2 = stats.robust(dy2)
dy2_mean = rdy2.mean()
dy2_std = rdy2.std()
if n>9: logging.debug("NORMALTEST3: %s" % str(scipy.stats.normaltest(rdy2)))
myplot.hisplot(dy2,title,bdp_name+"_3",xlab=xlab,gauss=[dy2_mean,dy2_std],thumbnail=True)
title = bdp_name + "_4"
xlab = 'log(Signal/Noise))'
n = len(y3)
ry3 = stats.robust(y3)
y3_mean = ry3.mean()
y3_std = ry3.std()
if n>9: logging.debug("NORMALTEST4: %s" % str(scipy.stats.normaltest(ry3)))
myplot.hisplot(y3,title,bdp_name+"_4",xlab=xlab,gauss=[y3_mean,y3_std],thumbnail=True)
title = bdp_name + "_5"
xlab = 'log(diff[Signal/Noise)])'
n = len(y3)
dy3 = y3[0:-2] - y3[1:-1]
rdy3 = stats.robust(dy3)
dy3_mean = rdy3.mean()
dy3_std = rdy3.std()
if n>9: logging.debug("NORMALTEST5: %s" % str(scipy.stats.normaltest(rdy3)))
myplot.hisplot(dy3,title,bdp_name+"_5",xlab=xlab,gauss=[dy3_mean,dy3_std],thumbnail=True)
title = bdp_name + "_6"
xlab = 'log(Peak+Min)'
n = len(y1)
ry5 = stats.robust(y5)
y5_mean = ry5.mean()
y5_std = ry5.std()
if n>9: logging.debug("NORMALTEST6: %s" % str(scipy.stats.normaltest(ry5)))
myplot.hisplot(y5,title,bdp_name+"_6",xlab=xlab,gauss=[y5_mean,y5_std],thumbnail=True)
logging.debug("LogPeak: m,s= %f %f min/max %f %f" % (y1.mean(),y1.std(),y1.min(),y1.max()))
logging.debug("LogNoise: m,s= %f %f %f %f min/max %f %f" % (y2.mean(),y2.std(),y2_mean,y2_std,y2.min(),y2.max()))
logging.debug("LogDeltaNoise: RMS/sqrt(2)= %f %f " % (dy2.std()/math.sqrt(2),dy2_std/math.sqrt(2)))
logging.debug("LogDeltaP/N: RMS/sqrt(2)= %f %f" % (dy3.std()/math.sqrt(2),dy3_std/math.sqrt(2)))
logging.debug("LogPeak+Min: robust m,s= %f %f" % (y5_mean,y5_std))
# compute two ratios that should both be near 1.0 if noise is 'normal'
ratio = y2.std()/(dy2.std()/math.sqrt(2))
ratio2 = y2_std/(dy2_std/math.sqrt(2))
logging.info("RMS BAD VARIATION RATIO: %f %f" % (ratio,ratio2))
# making PPP plot
if nchan > 1 and use_ppp:
smax = 10
gamma = 0.75
z0 = peakval/peakval.max()
# point sizes
s = np.pi * ( smax * (z0**gamma) )**2
cmds = ["grid", "axis equal"]
title = "Peak Points per channel"
pppimage = bdp_name + '_ppp'
myplot.scatter(xpos,ypos,title=title,figname=pppimage,size=s,color=chans,cmds=cmds,thumbnail=True)
pppimage = myplot.getFigure(figno=myplot.figno,relative=True)
pppthumbnail = myplot.getThumbnail(figno=myplot.figno,relative=True)
caption = "Peak point plot: Locations of per-channel peaks in the image cube " + fin
self._summary["peakpnt"] = SummaryEntry([pppimage, pppthumbnail, caption, fin], "CubeStats_AT", self.id(True))
dt.tag("plotting")
# making PeakStats plot
if nchan > 1 and psample > 0:
logging.info("Computing peakstats")
# grab peak,mean and width values for all peaks
(pval,mval,wval) = peakstats(self.dir(fin),freqs,sigma0,pnumsigma,minchan,maxgap,psample,peakfit)
title = "PeakStats: cutoff = %g" % (sigma0*pnumsigma)
xlab = 'Peak value'
ylab = 'FWHM (channels)'
pppimage = bdp_name + '_peakstats'
cval = mval
myplot.scatter(pval,wval,title=title,xlab=xlab,ylab=ylab,color=cval,figname=pppimage,thumbnail=False)
dt.tag("peakstats")
# myplot.final() # pjt debug
# all done!
dt.tag("done")
taskargs = "robust=" + sumrargs
if use_ppp:
taskargs = taskargs + " ppp=True"
else:
taskargs = taskargs + " ppp=False"
for v in self._summary:
self._summary[v].setTaskArgs(taskargs)
dt.tag("summary")
dt.end()
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 = {}
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 convert(self, chan=None, freq=None, velocity=None, spec=None, file=None, separator=None,
restfreq=None, vlsr=None):
""" Method to convert input data (either files or arrays) into a CubeSpectrum_BDP. If files
are used then then the columns containing the frequency and the intensity must be given
(channel numbers are optional). Any number of files can be given, but all spectra must
have the same length as they are assumed to come from the same data source. Blank lines
and lines starting with a comment '#' will be skipped, additionally any line with too
few columns will be skipped. If arrays are used an input then both the frequency and
intensity must be specified (the channel numbers are optional). Both lists and numpy
arrays are accepted as inputs. Multidimmensional arrays are supported with the following
parameters:
+ A single frequency list can be given to cover all input spectra, otherwise the shape
of the frequency array must match that of the spectra
+ A single channel list can be given to cover all input spectra, otherwise the shape
of the channel array must match that of the spectra
+ All spectra must have the same length
If a channel array is not specified then one will be constructed with the following
parameters:
+ The channel numbers will start at 0 (casa convention)
+ The first entry in the spectrum will be considered the first channel, regardless of
whether the frequency array increases or decreases.
Additionally, if there is velocity axis, but no frequency axis, a frequency axis can
be constructed by specifying a rest frequency (restfreq), and vlsr.
The convert method will return a single CubeSpectrum_BDP instance holding all input spectra
along with an image of each.
Parameters
----------
chan : array or int
An array holding the channel numbers for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the channel numbers, column numbers are 1 based.
Default: None
freq : array
An array holding the frequencies for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the frequencies, column numbers are 1 based.
Default: None
velocity : array
An array holding the velocity for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the velcoties, column numbers are 1 based. If this
parameter is specified then restfreq and vlsr must also be specified.
Default: None
spec : array
An array holding the intesities of the data, multidimmensional arrays are supported.
If an integer is specified then it is the number of the column in the file which
contains the intensities, column numbers are 1 based.
Default: None
file : list or str
A single file name or a list of file names to be read in for spectra.
Default: None
separator : str
The column separator for reading in the data files.
Default: None (any whitespace)
restfreq : float
The rest frequency to use to convert the spectra from velocity to frequency units.
The rest frequency is in GHz.
Default: None (no conversion done)
vlsr : float
The reference velocity for converting a velocity axis to frequency. The units are
km/s. If this is not set then it is assumed that the vlsr is 0.0.
Default: None
Returns
-------
CubeSpectrum_BDP instance containing all of the inpur spectra.
"""
self.restfreq = restfreq
self.vlsr = vlsr
# if a string was given as the file name then turn it into a list so it can be iterated over
if isinstance(file, str):
self.file = [file]
else:
self.file = file
# do some error checking
if isinstance(chan, np.ndarray) or isinstance(chan, list):
if isinstance(chan, list):
self.chan = np.array(chan)
else:
self.chan = copy.deepcopy(chan)
self.chancol = -1
elif isinstance(chan, int):
self.chancol = chan
self.chan = None
else:
self.chancol = -1
self.chan = None
if isinstance(freq, np.ndarray) or isinstance(freq, list):
if isinstance(freq, list):
self.freq = np.array(freq)
else:
self.freq = copy.deepcopy(freq)
self.freqcol = -1
elif isinstance(freq, int):
self.freqcol = freq
self.freq = None
else:
self.freqcol = -1
self.freq = None
if isinstance(velocity, np.ndarray) or isinstance(velocity, list):
if isinstance(velocity, list):
self.freq = np.array(velocity, dtype=np.float)
else:
self.freq = velocity.astype(np.float)
for i, frq in enumerate(self.freq):
self.freq[i] = self.restfreq + utils.veltofreq(frq - self.vlsr, self.restfreq)
self.freqcol = -1
elif isinstance(velocity, int):
self.velcol = velocity
self.velocity = None
else:
self.velcol = -1
self.velocity = None
if isinstance(spec, np.ndarray) or isinstance(spec, list):
if isinstance(spec, list):
self.spec = np.array(spec)
else:
self.spec = copy.deepcopy(spec)
self.speccol = -1
elif isinstance(spec, int):
self.speccol = spec
self.spec = None
else:
self.speccol = -1
self.spec = None
if isinstance(separator, str):
self.separator = separator
spectra = []
# read in the data from any files
if self.file:
for fl in self.file:
spectra.append(self.getfile(fl))
else:
# convert the input arrays
singlefreq = False
singlechan = False
havechan = False
# make sure they have the same shape or that the frequency array is 1D
if self.spec.shape != self.freq.shape:
if len(self.spec.shape) == 1 and len(self.freq.shape) != 1:
raise Exception("Frequency axis and spectral axis do not have the same shape.")
else:
singlefreq = True
# make sure they have the same shape or that the channel array is 1D
if self.chan:
havechan = True
if self.spec.shape != self.chan.shape:
if len(spec.shape) == 1 and len(self.chan.shape) != 1:
raise Exception("Channel axis and spectral axis do not have the same shape.")
else:
singlechan = True
# if the arrays are more than 1D, then go through each
if len(self.spec.shape) > 1:
for i in range(self.spec.shape[0]):
spec = self.spec[i]
if not havechan:
chan = np.arange(len(spec))
elif singlechan:
chan = self.chan
else:
chan = self.chan[i]
if singlefreq:
freq = self.freq
else:
freq = self.freq[i]
spectra.append(Spectrum(spec=spec, freq=freq, chans=chan))
else:
# construct the channel array if needed
if not havechan:
self.chan = np.arange(len(self.spec))
spectra.append(Spectrum(spec=self.spec, freq=self.freq, chans=self.chan))
first = True
images = {}
# make images from the spectra
for i, spec in enumerate(spectra):
data = (spec.chans(masked=False), spec.freq(masked=False),
spec.spec(csub=False, masked=False))
if first:
table = Table(columns=["channel", "frequency", "flux"],
units=["number", "GHz", "Unknown"], data=np.column_stack(data),
planes=["0"])
first = False
else:
table.addPlane(np.column_stack(data), "%i" % i)
myplot = APlot(ptype=admit.PlotControl.PNG, pmode=admit.PlotControl.BATCH,
abspath=os.getcwd())
myplot.plotter(spec.freq(masked=False), [spec.spec(csub=False, masked=False)],
title="Spectrum %i" % i, figname="fig_%i" % i, xlab="Frequency",
ylab="Intensity", thumbnail=True)
# Why not use p1 as the key?
images["fig%i" % i] = myplot.getFigure(figno=myplot.figno, relative=True)
image = Image(images=images, description="Spectra")
# construct the BDP
bdp = CubeSpectrum_BDP(image=image, table=table)
return bdp
def run(self):
"""Runs the task.
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
dt = utils.Dtime("CubeSpectrum")
seed = self.getkey("seed")
if seed <= 0:
np.random.seed()
else:
np.random.seed(seed)
#print "RANDOM.GET_STATE:",np.random.get_state()
contin = self.getkey("contin")
rms = 1.0 # not a user parameter, we do all spectra in S/N space
f0 = self.getkey("freq") # central frequency in band
df = self.getkey("delta") / 1000.0 # channel width (in GHz)
nspectra = self.getkey("nspectra")
taskargs = " contin=%f freq=%f delta=%f nspectra=%f " % (contin, f0,
df, nspectra)
spec = range(nspectra)
dt.tag("start")
if self.getkey("file") != "":
print "READING spectrum from", self.getkey("file")
(freq, spec[0]) = getspec(self.getkey("file"))
nchan = len(freq)
print "Spectrum %d chans from %f to %f: min/max = %f %f" % (
nchan, freq.min(), freq.max(), spec[0].min(), spec[0].max())
# @todo nspectra>1 not tested
for i in range(1, nspectra):
spec[i] = deepcopy(spec[0])
dt.tag("getspec")
else:
nchan = self.getkey("nchan")
freq = np.arange(nchan, dtype=np.float64)
center = int(nchan / 2)
for i in range(nchan):
freq[i] = f0 + (float((i - center)) * df)
for i in range(nspectra):
spec[i] = np.zeros(nchan)
chans = np.arange(nchan)
taskargs += " nchan = %d" % nchan
for i in range(nspectra):
if seed >= 0:
spec[i] += np.random.normal(contin, rms, nchan)
# print "MEAN/STD",spec[i].mean(),spec[i].std()
lines = self.getkey("lines")
sls = SpectralLineSearch(False)
for item in self.getkey("transitions"):
kw = {
"include_only_nrao": True,
"line_strengths": ["ls1", "ls2"],
"energy_levels": ["el2", "el4"],
"fel": True,
"species": item[0]
}
results = sls.search(item[1][0], item[1][1], "off", **kw)
# look at line strengths
if len(results) > 0:
mx = 0.0
indx = -1
for i in range(len(results)):
if results[i].getkey("linestrength") > mx:
indx = i
mx = results[i].getkey("linestrength")
for res in results:
if mx > 0.0:
lines.append([
item[2] * res.getkey("linestrength") / mx,
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
else:
lines.append([
item[2],
res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")),
item[3]
])
for item in lines:
for i in range(nspectra):
spec[i] += utils.gaussian1D(freq, item[0], item[1],
utils.veltofreq(item[2], item[1]))
if self.getkey("hanning"):
for i in range(nspectra):
filter = Filter1D.Filter1D(spec[i], "hanning", **{"width": 3})
spec[i] = filter.run()
dt.tag("hanning")
center = int(nchan / 2)
dt.tag("open")
bdp_name = self.mkext("Genspec", "csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
images = {} # png's accumulated
for i in range(nspectra):
sd = []
caption = "Generated Spectrum %d" % i
# 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", ""]
data = (chans, freq, spec[i])
# plane 0 : we are allowing a multiplane table, so the first plane is special
if i == 0:
table = Table(columns=labels,
units=units,
data=np.column_stack(data),
planes=["0"])
else:
table.addPlane(np.column_stack(data), "%d" % i)
# example plot , one per position for now
x = chans
xlab = 'Channel'
y = [spec[i]]
sd.append(xlab)
myplot = APlot(ptype=self._plot_type,
pmode=self._plot_mode,
abspath=self.dir())
ylab = 'Flux'
p1 = "%s_%d" % (bdp_name, i)
myplot.plotter(x, y, "", 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)
image = Image(images=images, description="CubeSpectrum")
sd.extend([ii, thumbname, caption])
self.spec_description.append(sd)
self._summary["spectra"] = SummaryEntry(self.spec_description,
"GenerateSpectrum_AT",
self.id(True), taskargs)
dt.tag("table")
b2.setkey("image", image)
b2.setkey("table", table)
b2.setkey("sigma", rms)
b2.setkey("mean", contin)
dt.tag("done")
dt.end()
def convert(self,
chan=None,
freq=None,
velocity=None,
spec=None,
file=None,
separator=None,
restfreq=None,
vlsr=None):
""" Method to convert input data (either files or arrays) into a CubeSpectrum_BDP. If files
are used then then the columns containing the frequency and the intensity must be given
(channel numbers are optional). Any number of files can be given, but all spectra must
have the same length as they are assumed to come from the same data source. Blank lines
and lines starting with a comment '#' will be skipped, additionally any line with too
few columns will be skipped. If arrays are used an input then both the frequency and
intensity must be specified (the channel numbers are optional). Both lists and numpy
arrays are accepted as inputs. Multidimmensional arrays are supported with the following
parameters:
+ A single frequency list can be given to cover all input spectra, otherwise the shape
of the frequency array must match that of the spectra
+ A single channel list can be given to cover all input spectra, otherwise the shape
of the channel array must match that of the spectra
+ All spectra must have the same length
If a channel array is not specified then one will be constructed with the following
parameters:
+ The channel numbers will start at 0 (casa convention)
+ The first entry in the spectrum will be considered the first channel, regardless of
whether the frequency array increases or decreases.
Additionally, if there is velocity axis, but no frequency axis, a frequency axis can
be constructed by specifying a rest frequency (restfreq), and vlsr.
The convert method will return a single CubeSpectrum_BDP instance holding all input spectra
along with an image of each.
Parameters
----------
chan : array or int
An array holding the channel numbers for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the channel numbers, column numbers are 1 based.
Default: None
freq : array
An array holding the frequencies for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the frequencies, column numbers are 1 based.
Default: None
velocity : array
An array holding the velocity for the data, multidimmensional arrays are
supported. If an integer is specified then it is the number of the column
in the file which contains the velcoties, column numbers are 1 based. If this
parameter is specified then restfreq and vlsr must also be specified.
Default: None
spec : array
An array holding the intesities of the data, multidimmensional arrays are supported.
If an integer is specified then it is the number of the column in the file which
contains the intensities, column numbers are 1 based.
Default: None
file : list or str
A single file name or a list of file names to be read in for spectra.
Default: None
separator : str
The column separator for reading in the data files.
Default: None (any whitespace)
restfreq : float
The rest frequency to use to convert the spectra from velocity to frequency units.
The rest frequency is in GHz.
Default: None (no conversion done)
vlsr : float
The reference velocity for converting a velocity axis to frequency. The units are
km/s. If this is not set then it is assumed that the vlsr is 0.0.
Default: None
Returns
-------
CubeSpectrum_BDP instance containing all of the inpur spectra.
"""
self.restfreq = restfreq
self.vlsr = vlsr
# if a string was given as the file name then turn it into a list so it can be iterated over
if isinstance(file, str):
self.file = [file]
else:
self.file = file
# do some error checking
if isinstance(chan, np.ndarray) or isinstance(chan, list):
if isinstance(chan, list):
self.chan = np.array(chan)
else:
self.chan = copy.deepcopy(chan)
self.chancol = -1
elif isinstance(chan, int):
self.chancol = chan
self.chan = None
else:
self.chancol = -1
self.chan = None
if isinstance(freq, np.ndarray) or isinstance(freq, list):
if isinstance(freq, list):
self.freq = np.array(freq)
else:
self.freq = copy.deepcopy(freq)
self.freqcol = -1
elif isinstance(freq, int):
self.freqcol = freq
self.freq = None
else:
self.freqcol = -1
self.freq = None
if isinstance(velocity, np.ndarray) or isinstance(velocity, list):
if isinstance(velocity, list):
self.freq = np.array(velocity, dtype=np.float)
else:
self.freq = velocity.astype(np.float)
for i, frq in enumerate(self.freq):
self.freq[i] = self.restfreq + utils.veltofreq(
frq - self.vlsr, self.restfreq)
self.freqcol = -1
elif isinstance(velocity, int):
self.velcol = velocity
self.velocity = None
else:
self.velcol = -1
self.velocity = None
if isinstance(spec, np.ndarray) or isinstance(spec, list):
if isinstance(spec, list):
self.spec = np.array(spec)
else:
self.spec = copy.deepcopy(spec)
self.speccol = -1
elif isinstance(spec, int):
self.speccol = spec
self.spec = None
else:
self.speccol = -1
self.spec = None
if isinstance(separator, str):
self.separator = separator
spectra = []
# read in the data from any files
if self.file:
for fl in self.file:
spectra.append(self.getfile(fl))
else:
# convert the input arrays
singlefreq = False
singlechan = False
havechan = False
# make sure they have the same shape or that the frequency array is 1D
if self.spec.shape != self.freq.shape:
if len(self.spec.shape) == 1 and len(self.freq.shape) != 1:
raise Exception(
"Frequency axis and spectral axis do not have the same shape."
)
else:
singlefreq = True
# make sure they have the same shape or that the channel array is 1D
if self.chan:
havechan = True
if self.spec.shape != self.chan.shape:
if len(spec.shape) == 1 and len(self.chan.shape) != 1:
raise Exception(
"Channel axis and spectral axis do not have the same shape."
)
else:
singlechan = True
# if the arrays are more than 1D, then go through each
if len(self.spec.shape) > 1:
for i in range(self.spec.shape[0]):
spec = self.spec[i]
if not havechan:
chan = np.arange(len(spec))
elif singlechan:
chan = self.chan
else:
chan = self.chan[i]
if singlefreq:
freq = self.freq
else:
freq = self.freq[i]
spectra.append(Spectrum(spec=spec, freq=freq, chans=chan))
else:
# construct the channel array if needed
if not havechan:
self.chan = np.arange(len(self.spec))
spectra.append(
Spectrum(spec=self.spec, freq=self.freq, chans=self.chan))
first = True
images = {}
# make images from the spectra
for i, spec in enumerate(spectra):
data = (spec.chans(masked=False), spec.freq(masked=False),
spec.spec(csub=False, masked=False))
if first:
table = Table(columns=["channel", "frequency", "flux"],
units=["number", "GHz", "Unknown"],
data=np.column_stack(data),
planes=["0"])
first = False
else:
table.addPlane(np.column_stack(data), "%i" % i)
myplot = APlot(ptype=admit.PlotControl.PNG,
pmode=admit.PlotControl.BATCH,
abspath=os.getcwd())
myplot.plotter(spec.freq(masked=False),
[spec.spec(csub=False, masked=False)],
title="Spectrum %i" % i,
figname="fig_%i" % i,
xlab="Frequency",
ylab="Intensity",
thumbnail=True)
# Why not use p1 as the key?
images["fig%i" % i] = myplot.getFigure(figno=myplot.figno,
relative=True)
image = Image(images=images, description="Spectra")
# construct the BDP
bdp = CubeSpectrum_BDP(image=image, table=table)
return bdp
def run(self):
""" The run method, calculates the moments and creates the BDP(s)
Parameters
----------
None
Returns
-------
None
"""
self._summary = {}
momentsummary = []
dt = utils.Dtime("Moment")
# variable to track if we are using a single cutoff for all moment maps
allsame = False
moments = self.getkey("moments")
numsigma = self.getkey("numsigma")
mom0clip = self.getkey("mom0clip")
# determine if there is only 1 cutoff or if there is a cutoff for each moment
if len(moments) != len(numsigma):
if len(numsigma) != 1:
raise Exception("Length of numsigma and moment lists do not match. They must be the same length or the length of the cutoff list must be 1.")
allsame = True
# default moment file extensions, this is information copied from casa.immoments()
momentFileExtensions = {-1: ".average",
0: ".integrated",
1: ".weighted_coord",
2: ".weighted_dispersion_coord",
3: ".median",
4: "",
5: ".standard_deviation",
6: ".rms",
7: ".abs_mean_dev",
8: ".maximum",
9: ".maximum_coord",
10: ".minimum",
11: ".minimum_coord",
}
logging.debug("MOMENT: %s %s %s" % (str(moments), str(numsigma), str(allsame)))
# get the input casa image from bdp[0]
# also get the channels the line actually covers (if any)
bdpin = self._bdp_in[0]
infile = bdpin.getimagefile(bt.CASA)
chans = self.getkey("chans")
# the basename of the moments, we will append _0, _1, etc.
basename = self.mkext(infile, "mom")
fluxname = self.mkext(infile, "flux")
# beamarea = nppb(self.dir(infile))
beamarea = 1.0 # until we have it from the MOM0 map
sigma0 = self.getkey("sigma")
sigma = sigma0
ia = taskinit.iatool()
dt.tag("open")
# if no CubseStats BDP was given and no sigma was specified, find a
# noise level via casa.imstat()
if self._bdp_in[1] is None and sigma <= 0.0:
raise Exception("A sigma or a CubeStats_BDP must be input to calculate the cutoff")
elif self._bdp_in[1] is not None:
sigma = self._bdp_in[1].get("sigma")
# immoments is a bit peculiar. If you give one moment, it will use
# exactly the outfile you picked for multiple moments, it will pick
# extensions such as .integrated [0], .weighted_coord [1] etc.
# we loop over the moments and will use the numeric extension instead.
# Might be laborious loop for big input cubes
#
# arguments for immoments
args = {"imagename" : self.dir(infile),
"moments" : moments,
"outfile" : self.dir(basename)}
# set the channels if given
if chans != "":
args["chans"] = chans
# error check the mom0clip input
if mom0clip > 0.0 and not 0 in moments:
logging.warning("mom0clip given, but no moment0 map was requested. One will be generated anyway.")
# add moment0 to the list of computed moments, but it has to be first
moments.insert(0,0)
if not allsame:
numsigma.insert(0, 2.0*sigma)
if allsame:
# this is only executed now if len(moments) > 1 and len(cutoff)==1
args["excludepix"] = [-numsigma[0] * sigma, numsigma[0] * sigma]
casa.immoments(**args)
dt.tag("immoments-all")
else:
# this is execute if len(moments)==len(cutoff) , even when len=1
for i in range(len(moments)):
args["excludepix"] = [-numsigma[i] * sigma, numsigma[i] * sigma]
args["moments"] = moments[i]
args["outfile"] = self.dir(basename + momentFileExtensions[moments[i]])
casa.immoments(**args)
dt.tag("immoments-%d" % moments[i])
taskargs = "moments=%s numsigma=%s" % (str(moments), str(numsigma))
if sigma0 > 0:
taskargs = taskargs + " sigma=%.2f" % sigma0
if mom0clip > 0:
taskargs = taskargs + " mom0clip=%g" % mom0clip
if chans == "":
taskargs = taskargs + " chans=all"
else:
taskargs = taskargs + " chans=%s" % str(chans)
taskargs += ' <span style="background-color:white"> ' + basename.split('/')[0] + ' </span>'
# generate the mask to be applied to all but moment 0
if mom0clip > 0.0:
# get the statistics from mom0 map
# this is usually a very biased map, so unclear if mom0sigma is all that reliable
args = {"imagename": self.dir(infile)}
stat = casa.imstat(imagename=self.dir(basename + momentFileExtensions[0]))
mom0sigma = float(stat["sigma"][0])
# generate a temporary masked file, mask will be copied to other moments
args = {"imagename" : self.dir(basename + momentFileExtensions[0]),
"expr" : 'IM0[IM0>%f]' % (mom0clip * mom0sigma),
"outfile" : self.dir("mom0.masked")
}
casa.immath(**args)
# get the default mask name
ia.open(self.dir("mom0.masked"))
defmask = ia.maskhandler('default')
ia.close()
dt.tag("mom0clip")
# loop over moments to rename them to _0, _1, _2 etc.
# apply a mask as well for proper histogram creation
map = {}
myplot = APlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
implot = ImPlot(pmode=self._plot_mode,ptype=self._plot_type,abspath=self.dir())
for mom in moments:
figname = imagename = "%s_%i" % (basename, mom)
tempname = basename + momentFileExtensions[mom]
# rename and remove the old one if there is one
utils.rename(self.dir(tempname), self.dir(imagename))
# copy the moment0 mask if requested; this depends on that mom0 was done before
if mom0clip > 0.0 and mom != 0:
#print "PJT: output=%s:%s" % (self.dir(imagename), defmask[0])
#print "PJT: inpmask=%s:%s" % (self.dir("mom0.masked"),defmask[0])
makemask(mode="copy", inpimage=self.dir("mom0.masked"),
output="%s:%s" % (self.dir(imagename), defmask[0]),
overwrite=True, inpmask="%s:%s" % (self.dir("mom0.masked"),
defmask[0]))
ia.open(self.dir(imagename))
ia.maskhandler('set', defmask)
ia.close()
dt.tag("makemask")
if mom == 0:
beamarea = nppb(self.dir(imagename))
implot.plotter(rasterfile=imagename,figname=figname,
colorwedge=True,zoom=self.getkey("zoom"))
imagepng = implot.getFigure(figno=implot.figno,relative=True)
thumbname = implot.getThumbnail(figno=implot.figno,relative=True)
images = {bt.CASA : imagename, bt.PNG : imagepng}
thumbtype=bt.PNG
dt.tag("implot")
# get the data for a histogram (ia access is about 1000-2000 faster than imval())
map[mom] = casautil.getdata(self.dir(imagename))
data = map[mom].compressed()
dt.tag("getdata")
# make the histogram plot
# get the label for the x axis
bunit = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="bunit")
# object for the caption
objectname = casa.imhead(imagename=self.dir(imagename), mode="get", hdkey="object")
# Make the histogram plot
# Since we give abspath in the constructor, figname should be relative
auxname = imagename + '_histo'
auxtype = bt.PNG
myplot.histogram(columns = data,
figname = auxname,
xlab = bunit,
ylab = "Count",
title = "Histogram of Moment %d: %s" % (mom, imagename), thumbnail=True)
casaimage = Image(images = images,
auxiliary = auxname,
auxtype = auxtype,
thumbnail = thumbname,
thumbnailtype = thumbtype)
auxname = myplot.getFigure(figno=myplot.figno,relative=True)
auxthumb = myplot.getThumbnail(figno=myplot.figno,relative=True)
if hasattr(self._bdp_in[0], "line"): # SpwCube doesn't have Line
line = deepcopy(getattr(self._bdp_in[0], "line"))
if not isinstance(line, Line):
line = Line(name="Unidentified")
else:
# fake a Line if there wasn't one
line = Line(name="Unidentified")
# add the BDP to the output array
self.addoutput(Moment_BDP(xmlFile=imagename, moment=mom,
image=deepcopy(casaimage), line=line))
dt.tag("ren+mask_%d" % mom)
imcaption = "%s Moment %d map of Source %s" % (line.name, mom, objectname)
auxcaption = "Histogram of %s Moment %d of Source %s" % (line.name, mom, objectname)
thismomentsummary = [line.name, mom, imagepng, thumbname, imcaption,
auxname, auxthumb, auxcaption, infile]
momentsummary.append(thismomentsummary)
if map.has_key(0) and map.has_key(1) and map.has_key(2):
logging.debug("MAPs present: %s" % (map.keys()))
# m0 needs a new mask, inherited from the more restricted m1 (and m2)
m0 = ma.masked_where(map[1].mask,map[0])
m1 = map[1]
m2 = map[2]
m01 = m0*m1
m02 = m0*m1*m1
m22 = m0*m2*m2
sum0 = m0.sum()
vmean = m01.sum()/sum0
# lacking the full 3D cube, get two estimates and take the max
sig1 = math.sqrt(m02.sum()/sum0 - vmean*vmean)
sig2 = m2.max()
#vsig = max(sig1,sig2)
vsig = sig1
# consider clipping in the masked array (mom0clip)
# @todo i can't use info from line, so just borrow basename for now for grepping
# this also isn't really the flux, the points per beam is still in there
loc = basename.rfind('/')
sum1 = ma.masked_less(map[0],0.0).sum() # mom0clip
# print out: LINE,FLUX1,FLUX0,BEAMAREA,VMEAN,VSIGMA for regression
# the linechans parameter in bdpin is not useful to print out here, it's local to the LineCube
s_vlsr = admit.Project.summaryData.get('vlsr')[0].getValue()[0]
s_rest = admit.Project.summaryData.get('restfreq')[0].getValue()[0]/1e9
s_line = line.frequency
if loc>0:
if basename[:loc][0:2] == 'U_':
# for U_ lines we'll reference the VLSR w.r.t. RESTFREQ in that band
if abs(vmean) > vsig:
vwarn = '*'
else:
vwarn = ''
vlsr = vmean + (1.0-s_line/s_rest)*utils.c
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vlsr,vsig)
else:
# for identified lines we'll assume the ID was correct and not bother with RESTFREQ
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % (basename[:loc],map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
else:
msg = "MOM0FLUX: %s %g %g %g %g %g %g" % ("SPW_FULL" ,map[0].sum(),sum0,beamarea,vmean,vmean,vsig)
logging.regression(msg)
dt.tag("mom0flux")
# create a histogram of flux per channel
# grab the X coordinates for the histogram, we want them in km/s
# restfreq should also be in summary
restfreq = casa.imhead(self.dir(infile),mode="get",hdkey="restfreq")['value']/1e9 # in GHz
# print "PJT %.10f %.10f" % (restfreq,s_rest)
imval0 = casa.imval(self.dir(infile))
freqs = imval0['coords'].transpose()[2]/1e9
x = (1-freqs/restfreq)*utils.c
#
h = casa.imstat(self.dir(infile), axes=[0,1])
if h.has_key('flux'):
flux0 = h['flux']
else:
flux0 = h['sum']/beamarea
flux0sum = flux0.sum() * abs(x[1]-x[0])
# @todo make a flux1 with fluxes derived from a good mask
flux1 = flux0
# construct histogram
title = 'Flux Spectrum (%g)' % flux0sum
xlab = 'VLSR (km/s)'
ylab = 'Flux (Jy)'
myplot.plotter(x,[flux0,flux1],title=title,figname=fluxname,xlab=xlab,ylab=ylab,histo=True)
dt.tag("flux-spectrum")
self._summary["moments"] = SummaryEntry(momentsummary, "Moment_AT",
self.id(True), taskargs)
# get rid of the temporary mask
if mom0clip > 0.0:
utils.rmdir(self.dir("mom0.masked"))
dt.tag("done")
dt.end()
def run(self):
"""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")
seed = self.getkey("seed")
if seed <= 0:
np.random.seed()
else:
np.random.seed(seed)
#print "RANDOM.GET_STATE:",np.random.get_state()
contin = self.getkey("contin")
rms = 1.0 # not a user parameter, we do all spectra in S/N space
f0 = self.getkey("freq") # central frequency in band
df = self.getkey("delta") / 1000.0 # channel width (in GHz)
nspectra = self.getkey("nspectra")
taskargs = " contin=%f freq=%f delta=%f nspectra=%f " % (contin,f0,df,nspectra)
spec = range(nspectra)
dt.tag("start")
if self.getkey("file") != "":
print "READING spectrum from",self.getkey("file")
(freq, spec[0]) = getspec(self.getkey("file"))
nchan = len(freq)
print "Spectrum %d chans from %f to %f: min/max = %f %f" % (nchan, freq.min(), freq.max(), spec[0].min(), spec[0].max())
# @todo nspectra>1 not tested
for i in range(1,nspectra):
spec[i] = deepcopy(spec[0])
dt.tag("getspec")
else:
nchan = self.getkey("nchan")
freq = np.arange(nchan, dtype=np.float64)
center = int(nchan/2)
for i in range(nchan):
freq[i] = f0 + (float((i - center)) * df)
for i in range(nspectra):
spec[i] = np.zeros(nchan)
chans = np.arange(nchan)
taskargs += " nchan = %d" % nchan
for i in range(nspectra):
if seed >= 0:
spec[i] += np.random.normal(contin, rms, nchan)
# print "MEAN/STD",spec[i].mean(),spec[i].std()
lines = self.getkey("lines")
sls = SpectralLineSearch(False)
for item in self.getkey("transitions"):
kw = {"include_only_nrao" : True,
"line_strengths": ["ls1", "ls2"],
"energy_levels" : ["el2", "el4"],
"fel" : True,
"species" : item[0]
}
results = sls.search(item[1][0], item[1][1], "off", **kw)
# look at line strengths
if len(results) > 0:
mx = 0.0
indx = -1
for i in range(len(results)):
if results[i].getkey("linestrength") > mx:
indx = i
mx = results[i].getkey("linestrength")
for res in results:
if mx > 0.0:
lines.append([item[2] * res.getkey("linestrength") / mx, res.getkey("frequency") +
utils.veltofreq(item[4], res.getkey("frequency")), item[3]])
else:
lines.append([item[2], res.getkey("frequency") + utils.veltofreq(item[4],
res.getkey("frequency")), item[3]])
for item in lines:
for i in range(nspectra):
spec[i] += utils.gaussian1D(freq, item[0], item[1], utils.veltofreq(item[2], item[1]))
if self.getkey("hanning"):
for i in range(nspectra):
filter = Filter1D.Filter1D(spec[i], "hanning", **{"width" : 3})
spec[i] = filter.run()
dt.tag("hanning")
center = int(nchan/2)
dt.tag("open")
bdp_name = self.mkext("Genspec","csp")
b2 = CubeSpectrum_BDP(bdp_name)
self.addoutput(b2)
images = {} # png's accumulated
for i in range(nspectra):
sd = []
caption = "Generated Spectrum %d" % i
# 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" ,"" ]
data = (chans ,freq ,spec[i] )
# plane 0 : we are allowing a multiplane table, so the first plane is special
if i==0:
table = Table(columns=labels,units=units,data=np.column_stack(data),planes=["0"])
else:
table.addPlane(np.column_stack(data),"%d" % i)
# example plot , one per position for now
x = chans
xlab = 'Channel'
y = [spec[i]]
sd.append(xlab)
myplot = APlot(ptype=self._plot_type,pmode=self._plot_mode, abspath=self.dir())
ylab = 'Flux'
p1 = "%s_%d" % (bdp_name,i)
myplot.plotter(x,y,"",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)
image = Image(images=images, description="CubeSpectrum")
sd.extend([ii, thumbname, caption])
self.spec_description.append(sd)
self._summary["spectra"] = SummaryEntry(self.spec_description,"GenerateSpectrum_AT",self.id(True), taskargs)
dt.tag("table")
b2.setkey("image",image)
b2.setkey("table",table)
b2.setkey("sigma",rms)
b2.setkey("mean",contin)
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):
""" 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()
