def __init__(self,xs,ys,vs,cellsize,dv,UVFITSfile,cube_template_file): """ This function initializes the modeling object. Parameters ---------- xs: float X-axis size of the model image (in arcsec) ys: float Y-axis size of the model image (in arcsec) vs: float Velocity-axis size of the model image (in km/s) cellsize: float Arcseconds in a pixel dv: float Km/s in a channel UVFITSfile: string Filename of the UVFITS visibility file cube_template_file: string Filename of the image cube to be used as a model template (the dimensions and coordinates are the only thing that's used). It's best obtained by imaging the visibilities themselves, using mode='channel'. This ensures that the cell size is sufficient to suitable for the UV coverage. Returns ------- """ self.xs=xs self.ys=ys #y-axis size of the model image (in arcsec) self.vs=vs self.cellsize=cellsize self.dv=dv self.UVFITSfile=UVFITSfile #'.uvfits' self.cube_template_file=cube_template_file #'.fits' self.sbrad=np.arange(0,2,.01) #radius vector defining the light profile, sbprof is the light intensity evaluated at these radial values self.velrad=self.sbrad #use the same radius vector for the velocity definition, used by velprof to define the velocity profile self.nsamps=1e5 #number of clouds, should be sufficient self.vis_complex_data, self.wgt_data = uvutil.visload(self.UVFITSfile) #load visibility and weights self.vis_complex_model_template=np.copy(self.vis_complex_data) self.wgt_data=self.wgt_data.flatten() self.good_vis=self.wgt_data>0 self.wgt_data=self.wgt_data[self.good_vis] #get rid of flagged data self.vis_complex_data=self.vis_complex_data.flatten()[self.good_vis] self.modelheader = fits.getheader(self.cube_template_file) self.uu_cube, self.vv_cube, self.ww_cube = uvutil.uvload(self.UVFITSfile) self.pcd = uvutil.pcdload(self.UVFITSfile) self.Nxpix=self.modelheader['NAXIS1'] self.Nypix=self.modelheader['NAXIS2'] self.Nchan=self.modelheader['NAXIS3'] self.Nxpix_small=int(self.xs/self.cellsize) self.Nypix_small=int(self.ys/self.cellsize) self.Nchan_small=int(self.vs/self.dv) #In this code we assume the continuum emission can be modelled as a 2D Gaussian, with known parameters. This is often the case and the continuum parameters are often precisely known. If they are not, include the continuum parameters as fitting parameters and make sure the visibilities cover a good range of continuum-only channels. self.y,self.x,self.zz=np.mgrid[:self.Nypix_small,:self.Nxpix_small,:self.Nchan_small] #define a coordinate cube of the same size as the KinMS model, to make the continuum model, to be added in. self.offset_lnlike=0 self.xpos_center_padded,self.ypos_center_padded= int(self.Nxpix/2),int(self.Nypix/2)
def replace_visibilities_galario(visdataloc,sbmodelloc,modelvisloc):
#this needs to be open, channel by channel
modelimage_cube = fits.getdata(sbmodelloc)
#this can be the same throughout
modelheader = fits.getheader(sbmodelloc)
#these need to be peeled, channel by channel for the following
uu_cube, vv_cube, ww_cube = uvutil.uvload(visdataloc)
vis_complex_cube, wgt_cube = uvutil.visload(visdataloc)
#this can be the same throughout
pcd = uvutil.pcdload(visdataloc)
#all the following is done channel by channel
for chan in range(modelimage_cube.shape[0]):
uu=np.ones((uu_cube.shape[0],uu_cube.shape[1],1,uu_cube.shape[3]))
vv=np.ones((vv_cube.shape[0],vv_cube.shape[1],1,vv_cube.shape[3]))
uu[:,:,0,:]=uu_cube[:,:,chan,:]
vv[:,:,0,:]=vv_cube[:,:,chan,:]
modelimage=modelimage_cube[chan]
uushape = uu.shape
if len(uushape) == 2:
npol = uushape[0]
nrow = uushape[1]
uushape = (npol, 1, nrow)
uu = uu.flatten()
vv = vv.flatten()
uu=uu.copy(order='C')
vv=vv.copy(order='C')
modelimage=np.roll(np.flip(modelimage,axis=0),1,axis=0).copy(order='C').byteswap().newbyteorder()
model_complex = sampleImage(modelimage, np.absolute(modelheader['CDELT1'])/180*np.pi, uu, vv) # sample_vis.uvmodel(modelimage, modelheader, uu, vv, pcd)
vis_complex = model_complex.reshape(uushape)
vis_complex_cube[:,:,chan,:]=vis_complex[:,:,0,:]
if chan%10==0:
print chan
#modelvisloc='../replaced_visib.uvfits'
os.system('rm -rf ' + modelvisloc[:-7]+'*')
cmd = 'cp ' + visdataloc + ' ' + modelvisloc
os.system(cmd)
real = np.real(vis_complex_cube)
imag = np.imag(vis_complex_cube)
visfile = fits.open(modelvisloc, mode='update')
visibilities = visfile[0].data
visheader = visfile[0].header
if visheader['NAXIS'] == 7:
visibilities['DATA'][:, 0, 0, :, :, :, 0] = real
visibilities['DATA'][:, 0, 0, :, :, :, 1] = imag
elif visheader['NAXIS'] == 6:
visibilities['DATA'][:, 0, 0, :, :, 0] = real
visibilities['DATA'][:, 0, 0, :, :, 1] = imag
else:
print("Visibility dataset has >7 or <6 axes. I can't read this.")
#visibilities['DATA'][:, 0, 0, :, :, 2] = wgt
visfile[0].data = visibilities
visfile.flush()
visfile.close()
def subtract(sbmodelloc, visdataloc, modelvisloc, miriad=False):
vis_model = getVis(sbmodelloc, visdataloc)
# load the visibilities
vis_data, vis_weight = uvutil.visload(visdataloc)
vis_data -= vis_model
#print(visdataloc, modelvisloc, miriad)
writeVis(vis_data, visdataloc, modelvisloc, miriad=miriad)
return
def subtract(sbmodelloc, visdataloc, modelvisloc, miriad=False):
vis_model = getVis(sbmodelloc, visdataloc)
# load the visibilities
vis_data, vis_weight = uvutil.visload(visdataloc)
vis_data -= vis_model
#print(visdataloc, modelvisloc, miriad)
writeVis(vis_data, visdataloc, modelvisloc, miriad=miriad)
return
def plot(cleanup=True, configloc='sandbox.yaml', interactive=True, threshold=1.2, plot=True, tag='sandbox'):
'''
Parameters
----------
threshold: float
in mJy, cleaning threshold
'''
# read the input parameters
configfile = open(configloc, 'r')
config = yaml.load(configfile)
paramSetup = setuputil.loadParams(config)
fixindx = setuputil.fixParams(paramSetup)
testfit = paramSetup['p_l']
visfile = config['UVData']
uuu, vvv, www = uvutil.uvload(visfile)
pcd = uvutil.pcdload(visfile)
vis_complex, wgt = uvutil.visload(visfile)
# remove the data points with zero or negative weight
positive_definite = wgt > 0
vis_complex = vis_complex[positive_definite]
wgt = wgt[positive_definite]
uuu = uuu[positive_definite]
vvv = vvv[positive_definite]
testlnprob, testmu = lnprob(testfit, vis_complex, wgt, uuu, vvv, pcd,
fixindx, paramSetup, computeamp=True)
# prepend 1 dummy value to represent lnprob
testfit = numpy.append(testlnprob, testfit)
# append nmu dummy values that represent the magnification factors
nlensedsource = paramSetup['nlensedsource']
nlensedregions = paramSetup['nlensedregions']
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
for i in range(nmu):
testfit = numpy.append(testfit, 0)
print("lnprob: %f" %testlnprob)
print("Using the following model parameters:")
for k, v in zip(paramSetup['pname'], testfit[1:-4]):
print("%s : %.4f" %(k,v))
if plot:
visualutil.plotFit(config, testfit, threshold,
tag=tag, cleanup=cleanup,
interactive=interactive)
return testlnprob
def rollUp(visfile):
u, v, w = uvutil.uvload(visfile)
data, weight = uvutil.visload(visfile)
real = np.real(data)
imag = np.imag(data)
nvis = u.size
rolled = np.zeros((6, nvis))
rolled[0, :] = u.flatten()
rolled[1, :] = v.flatten()
rolled[2, :] = w.flatten()
rolled[3, :] = real.flatten()
rolled[4, :] = imag.flatten()
rolled[5, :] = weight.flatten()
return rolled
def rollUp(visfile):
u, v, w = uvutil.uvload(visfile)
data, weight = uvutil.visload(visfile)
real = np.real(data)
imag = np.imag(data)
nvis = u.size
rolled = np.zeros((6, nvis))
rolled[0, :] = u.flatten()
rolled[1, :] = v.flatten()
rolled[2, :] = w.flatten()
rolled[3, :] = real.flatten()
rolled[4, :] = imag.flatten()
rolled[5, :] = weight.flatten()
return rolled
def plot(cleanup=True, configloc='sandbox.yaml', interactive=True):
# read the input parameters
configfile = open(configloc, 'r')
config = yaml.load(configfile)
paramSetup = setuputil.loadParams(config)
fixindx = setuputil.fixParams(paramSetup)
testfit = paramSetup['p_l']
visfile = config['UVData']
uuu, vvv, www = uvutil.uvload(visfile)
pcd = uvutil.pcdload(visfile)
vis_complex, wgt = uvutil.visload(visfile)
# remove the data points with zero or negative weight
positive_definite = wgt > 0
vis_complex = vis_complex[positive_definite]
wgt = wgt[positive_definite]
uuu = uuu[positive_definite]
vvv = vvv[positive_definite]
testlnprob, testmu = lnprob(testfit,
vis_complex,
wgt,
uuu,
vvv,
pcd,
fixindx,
paramSetup,
computeamp=True)
# prepend 1 dummy value to represent lnprob
testfit = numpy.append(testlnprob, testfit)
# append nmu dummy values that represent the magnification factors
nlensedsource = paramSetup['nlensedsource']
nlensedregions = paramSetup['nlensedregions']
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
for i in range(nmu):
testfit = numpy.append(testfit, 0)
tag = 'sandbox'
print("lnprob: %f" % testlnprob)
print("Using the following model parameters:")
for k, v in zip(paramSetup['pname'], testfit[1:-4]):
print("%s : %.4f" % (k, v))
visualutil.plotFit(config,
testfit,
tag=tag,
cleanup=cleanup,
interactive=interactive)
nfiles = len(fitsfiles)
nvis = []
# read in the observed visibilities
uuu = []
vvv = []
real = []
imag = []
wgt = []
for file in fitsfiles:
print file
vis_data = fits.open(file)
uu, vv = uvutil.uvload(vis_data)
pcd = uvutil.pcdload(vis_data)
real_raw, imag_raw, wgt_raw = uvutil.visload(vis_data)
uuu.extend(uu)
vvv.extend(vv)
real.extend(real_raw)
imag.extend(imag_raw)
wgt.extend(wgt_raw)
# convert the list to an array
real = numpy.array(real)
imag = numpy.array(imag)
wgt = numpy.array(wgt)
uuu = numpy.array(uuu)
vvv = numpy.array(vvv)
#www = numpy.array(www)
# remove the data points with zero or negative weight
#--------------------------------------------------------------------------
# read in Python visibilities
fitsfiles = config.FitsFiles
#print fitsfiles
nfiles = len(fitsfiles)
# read in the observed visibilities
for file in fitsfiles:
# read in the observed visibilities
obsdata = fits.open(file)
# get the real and imaginary components
data_real, data_imag, data_wgt = uvutil.visload(obsdata)
#----------------------------------------------------------------------
# Python version of UVMODEL
# "Observe" the lensed emission with the SMA and write to a new file
#----------------------------------------------------------------------
# Python replace:
nameindx = file.find('uvfits')
name = file[0:nameindx-1]
py_replace = uvmodel.replace(outfits, file)
#py_real, py_imag, py_wgt = uvmodel.components(py_replace)
#dreal = py_real - data_real
#dimag = py_imag - data_imag
#py_chi2 = py_wgt * (dreal ** 2 + dimag ** 2)
#print name, 'py-replace ', py_chi2.sum(), py_wgt.sum()
visfile = newvisfile
else:
miriad = False
else:
miriad = False
# attempt to process multiple visibility files. This won't work if miriad=True
try:
filetype = visfile[-6:]
if filetype == 'uvfits':
uvfits = True
else:
uvfits = False
uuu, vvv, www = uvutil.uvload(visfile)
pcd = uvutil.pcdload(visfile)
vis_complex, wgt = uvutil.visload(visfile)
except:
try:
for i, ivisfile in enumerate(visfile):
filetype = ivisfile[-6:]
if filetype == 'uvfits':
uvfits = True
else:
uvfits = False
iuuu, ivvv, iwww = uvutil.uvload(ivisfile)
ipcd = uvutil.pcdload(ivisfile)
ivis_complex, iwgt = uvutil.visload(ivisfile)
if i == 0:
uuu = iuuu
vvv = ivvv
pcd = ipcd
