def get_wcs(self, warnings_filter='ignore'):
'''
return wcs of input image
'''
import warnings
from astropy.wcs import WCS as wcs
fhead=self.get_input_head()
with warnings.catch_warnings():
warnings.simplefilter(warnings_filter)
return wcs(fhead)
def get_pixscale(self):
'''
get pixel scale of initial fits
in unit of arcsec/pixel
'''
fhead = self.get_fitsHead()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
w = wcs(fhead)
return np.average(pixel_scales(w)) * 3600 # arcsec/pixel
# scale of the datacube when plotting, e.g., [min, max], default is None
colormap=None
# colormap when plotting, e.g., 'rainbow', default is None
row_col=[3, 3]
# rows and columns of the panels in the figure, default is [3, 3]
outfig=fitsfile
# output eps file without suffix, default is fitsfile.pdf
dat, hdr=ft.getdata(fitsfile+'.fits', header=True)
hdr['NAXIS']=2
del hdr['NAXIS3'], hdr['PC3_1'], hdr['PC3_2'], hdr['PC1_3'], hdr['PC2_3'], hdr['PC3_3']
del hdr['CTYPE3'], hdr['CRVAL3'], hdr['CDELT3'], hdr['CRPIX3'], hdr['CUNIT3']
distance=1*1e3*u.pc
# distance to the source, unit is 1e3*u.pc
pixsz=np.mean(pixsc(wcs(hdr)))*u.deg
# pixel size of the datacube, unit is u.deg
""" import figure and subplot """
fig=plt.figure(figsize=figsize)
for pltid in range(dat.shape[0]):
ax=plt.subplot(row_col[0], row_col[1], pltid+1, projection=wcs(hdr))
plt.subplots_adjust(wspace=0, hspace=0)
xaxis, yaxis, xyaxis=ax.coords[0], ax.coords[1], ax.coords
""" axis labels and tick labels """
show=True if pltid==(row_col[0]-1)*row_col[1] else False
xax_vis, yax_vis=show, show
# whether to display the axis labels or not, defaut is True
xtk_vis, ytk_vis=show, show
# whether to display the tick labels or not, defaut is True
hdr = ft.getheader(fitsfile + '.fits')
unit = [hdr['cunit1'], hdr['cunit2'], hdr['cunit3']]
for i in range(len(unit)):
if unit[i] == 'km/s' or unit[i] == 'm/s':
inax = i
fac = 1e3 if unit[i] == 'km/s' else 1
else:
inax = False
if inax: dim[inax] = [i * 1e3 for i in dim[inax]]
for i in range(len(dim)):
if dim[i] != []:
world = [hdr['crval1'], hdr['crval2'], hdr['crval3']]
if inax: world[inax] = world[inax] * fac
world[i] = dim[i]
pixel = wcs(hdr).wcs_world2pix(world[0], world[1], world[2], 0)
dim[i] = [int(round(pixel[i][0])), int(round(pixel[i][1]))]
dim[i].sort()
else:
dim[i] = [0, hdr['naxis' + str(i + 1)] - 1]
""" clip the new datacube """
if not olshow:
dim.reverse()
shape = [i[1] - i[0] + 1 for i in dim]
dlpix = [i[0] for i in dim]
subdat = np.zeros(tuple(shape))
dat = ft.getdata(fitsfile + '.fits')
for i in range(shape[0]):
for j in range(shape[1]):
for k in range(shape[2]):
subdat[i, j, k] = dat[i + dlpix[0], j + dlpix[1], k + dlpix[2]]
import numpy as np
from astropy.io import fits as ft
from spectral_cube import SpectralCube as sc
from astropy.wcs import WCS as wcs
fitsfile='' # without suffix
spec=[] # in km/s
nchan= # in channel
rms=0 # in Jy/beam, default is 0
nexc=3 # pixels with value < -next*rms are excluded when doing moment0, default is 3/1.5
ncut=3 # pixel with value < ncut*integrated rms are set to be zero after doing moment0, default is 3/5
""" input normalization """
spec=[i*1e3 for i in spec]
dat, hdr=ft.getdata(fitsfile+'.fits', header=True)
pixel=wcs(hdr).wcs_world2pix(hdr['crval1'], hdr['crval2'], spec, 0)
chans=[round(pixel[2][0]), round(pixel[2][1])]
dchan=round(abs(chans[1]-chans[0])/nchan)
for i in range(nchan):
chans.insert(i+1, chans[i]+dchan)
chans.pop()
""" print information """
world=wcs(hdr).wcs_pix2world(hdr['crpix1'], hdr['crpix2'], chans, 0)
spec=[i/1e3 for i in world[2]]
dvelo=dchan*hdr['cdelt3']
print('Extraction of %i channels from %f km/s to %f km/s.' %(nchan, spec[0], spec[-1]))
print('The width of each channel is %f km/s (%i times of spectral resolution).' %(dvelo, dchan))
print('The ranges of each channel are:')
for i in range(len(spec)-1):
print('%f km/s to %f km/s' %(spec[i], spec[i+1]))
fitsfile = '' # fitsfile without suffix figsize = None # size of the figure, e.g., [6.4, 4.8]/[8.0, 6.0], default is None scale = None # scale of the datacube when plotting, e.g., [min, max], default is None colormap = None # colormap when plotting, e.g., 'rainbow', default is None outfig = fitsfile # output figure file without suffix, default is fitsfile.pdf dat, hdr = ft.getdata(fitsfile + '.fits', header=True) distance = 1 * 1e3 * u.pc # distance to the source, unit is 1e3*u.pc pixsz = np.mean(pixsc(wcs(hdr))) * u.deg # pixel size of the datacube, unit is u.deg """ import figure and subplot """ fig = plt.figure(figsize=figsize) ax = plt.subplot(1, 1, 1, projection=wcs(hdr)) xaxis, yaxis, xyaxis = ax.coords[0], ax.coords[1], ax.coords """ axis labels and tick labels """ xax_vis, yax_vis = True, True # whether to display the axis labels or not, defaut is True xtk_vis, ytk_vis = True, True # whether to display the tick labels or not, defaut is True xpad, ypad = 1.0, 1.0 # distance between axis labels and tick labels, defaut is 1.0 xsize, ysize = 'medium', 'medium' # size of the labels, e.g., 'x-small'/'small'/'medium'/'large'/'x-large', defaut is 'medium' xweit, yweit = 'normal', 'normal'
fitfunc = None # fitting function, 'Plummer', 'Gaussian' or 'Both', default is None
bgdegree = 0 # polynomial order used in background subtraction, 0 or 1, default is 0
bgdist = None # radial range used in background subtraction, like (0.3,0.5), unit is pc, default is None
fitdist_pm = 0.1 # radial range in which to fit the profile using 'Plummer', unit is pc, default is 0.1
fitdist_gs = 0.1 # radial range in which to fit the profile using 'Gaussian', unit is pc, default is 0.1
""" inputs """
fil_image, fil_header = ft.getdata(fitsfile + '.fits', header=True)
if fits_err is not None:
err_image, err_header = ft.getdata(fits_err + '.fits', header=True)
fil_path = ascii.read(pathfile + '.path',
format='fixed_width',
names=['x', 'y'])
fil_path_x, fil_path_y = [i for i in fil_path['x']], [j for j in fil_path['y']]
path_pixel = wcs(fil_header).wcs_world2pix(fil_path_x, fil_path_y, 0)
fil_path = np.zeros_like(fil_image)
for i in range(path_pixel[0].shape[0]):
fil_path[int(round(path_pixel[1][i])), int(round(path_pixel[0][i]))] = 1.0
fil_path = fil_path.astype(bool)
major_fwhm = fil_header['bmaj'] * 3600
minor_fwhm = fil_header['bmin'] * 3600
beam_fwhm = np.sqrt(major_fwhm * minor_fwhm)
""" mask and sample """
radobjmk = radfil_class.radfil(fil_image,
filspine=fil_path,
header=fil_header,
beamwidth=beam_fwhm,
distance=distance * 1e3)
radobjmk.build_profile(samp_int=step, cutdist=width * radobjmk.imgscale.value)
def clus_get_xid(maps,
cats,
savemap=0,
simmap=0,
verbose=1,
confusionerrors=1):
err = False
thresh = 3.0
mf = 1.0
mJy2Jy = 1000.0 / mf
catfile = config.CLUSDATA + 'placeholder' #this doesn't actually seem to do anything in the xid code,
# but you need something for this for some reason.
#Old code not used anymore
# print('Retrieving data from cats')
# inra = []
# indec = []
# for i in range(len(cats)):
# for j in range(len(cats[i]['ra'])):
# if cats[i]['ra'][j] not in inra and cats[i]['dec'][j] not in indec:
# inra.append(cats[i]['ra'][j])
# indec.append(cats[i]['dec'][j])
# print('Done retrieving data from cats')
inra = np.array(cats['ra'])
indec = np.array(cats['dec'])
ra = inra * u.deg
dec = indec * u.deg
c = SkyCoord(ra, dec, unit='deg')
plt.scatter(ra, dec, c=cats['flux'], alpha=0.5)
plt.show()
print(inra)
#initializing data containers.
pinds = []
files = []
primary_hdus = []
noise_maps = []
data_maps = []
headers = []
pixsizes = []
prf_sizes = []
priors = []
prfs = []
for i in range(len(maps)):
bands = [18, 25, 36] #units of arcseconds
fwhm = bands[i] / maps[i]['pixsize'] #converts to arcseconds/pixel
pixs = maps[i]['pixsize']
size = pixs * 5
moc = pymoc.util.catalog.catalog_to_moc(c, size, 15)
#getting data from the fits files
files.append(maps[i]['file'])
hdul = fits.open(files[i])
headers.append(hdul[1].header)
primary_hdus.append(hdul[0].header)
img = hdul[1].data
data_maps.append(img)
noise_maps.append(hdul[2].data)
pixsizes.append(maps[i]['pixsize'])
prf_sizes.append(get_spire_beam_fwhm(maps[i]['band']))
pinds.append(np.arange(0, 101, 1) * 1.0 / pixsizes[i])
# print(maps[i]['file'])
# print(pixsizes[i])
#setting up priors
prior = xidplus.prior(data_maps[i],
noise_maps[i],
primary_hdus[i],
headers[i],
moc=moc)
prior.prior_cat(inra, indec, catfile, moc=moc)
prior.prior_bkg(-5.0, 5)
#setting up prfs.
# This prf doesnt quite look correct
# In previous set up we needed to rebin to accuratly describe our beam sizes
prf = Gaussian2DKernel(
bands[i] / 2.355, x_size=101,
y_size=101) #maybe x_size and y_size need to change.
prf.normalize(mode='peak')
prfs.append(prf.array)
# print(prfs)
exit()
#appending prf to prior and setting point matrix
prior.set_prf(prfs[i], pinds[i], pinds[i]) #prfs, xpinds, ypinds
prior.get_pointing_matrix()
prior.upper_lim_map()
#appending prior to priors list.
priors.append(prior)
print('fitting %s sources' % (priors[0].nsrc))
print('using %s %s %s pixels' %
(priors[0].snpix, priors[1].snpix, priors[2].snpix))
fit = SPIRE.all_bands(
priors[0], priors[1], priors[2], iter=1000
) #number of iterations should be at least 100 just set lower for testing.
posterior = xidplus.posterior_stan(fit, [priors[0], priors[1], priors[2]])
# figs, fig = xidplus.plots.plot_Bayes_pval_map(priors, posterior)
# print(type(figs)) #figs is list.
# print(figs) #fig is matplotlib.figure.figure object.
# print(type(fig))
# cols = ['PSW', 'PMW', 'PLW']
# counter = 0
# for figure in figs:
# figure.save('xid_%s.png' %(cols[counter]))
# counter += 1
# plt.imshow(figs)
spire_cat = cat.create_SPIRE_cat(posterior, priors[0], priors[1],
priors[2])
# spire_cat.writeto('xid_model_2_%s.fits' % (maps[0]['name']))
xid_data = spire_cat[1].data
xid = []
#in units of mJy for fluxes and degrees for RA/DEC
xid1 = {
'band': 'PSW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_250'),
'serr': xid_data.field(
'FErr_SPIRE_250_u'
), #there was also FErr_SPIRE_250_l don't know which to use.
'pflux': xid_data.field('F_SPIRE_250'),
'perr': xid_data.field('FErr_SPIRE_250_u'),
'model': None,
'mf': mf
} #idk if perr and pflux is right there may be a conversion needed for pflux.
#in mikes code it has pflux = output from xid / mJy2Jy.
xid2 = {
'band': 'PMW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_350'),
'serr': xid_data.field('FErr_SPIRE_350_u'),
'pflux': xid_data.field('F_SPIRE_350'),
'perr': xid_data.field('FErr_SPIRE_350_u'),
'model': None,
'mf': mf
}
xid3 = {
'band': 'PLW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_500'),
'serr': xid_data.field('FErr_SPIRE_500_u'),
'pflux': xid_data.field('F_SPIRE_500'),
'perr': xid_data.field('FErr_SPIRE_500_u'),
'model': None,
'mf': mf
}
#there was another term in the dictionary sflux, pflux and sflux looked like maybe the same thing, but I'm not sure.
#I left it out so if there are issues with that then it is because that is gone.
xid.append(xid1)
xid.append(xid2)
xid.append(xid3)
# models = create_model(maps, xid)
# for i in range(len(xid)):
# xid[i]['model'] = models[i]
# # only look at data with a flux lower than 0.0
# for i in range(len(xid)):
# whpl = []
# for j in range(xid[i]['model'].shape[0]):
# for k in range(xid[i]['model'].shape[1]):
# if xid[i]['pflux'][j] >= 0.0:
# whpl.append(j)
# whpl = np.array(whpl)
#
# xid[i]['sra'] = xid[i]['sra'][whpl]
# xid[i]['sdec'] = xid[i]['sdec'][whpl]
# xid[i]['x'] = xid[i]['x'][whpl]
# xid[i]['y'] = xid[i]['y'][whpl]
# xid[i]['sflux'] = xid[i]['sflux'][whpl]
# xid[i]['serr'] = xid[i]['serr'][whpl]
for i in range(len(xid)):
ra = xid[i]['sra'] * u.deg
dec = xid[i]['sdec'] * u.deg
c = SkyCoord(ra, dec)
#initializing w class.
hdul = fits.open(maps[i]['file'])
w = wcs(hdul[1].header)
#converting ra/dec to pixel coords.
px, py = skycoord_to_pixel(c, w)
xid[i]['x'] = px
xid[i]['y'] = py
xid[i]['sra'] = xid[i]['sra'].tolist()
xid[i]['sdec'] = xid[i]['sdec'].tolist()
xid[i]['sflux'] = xid[i]['sflux'].tolist()
xid[i]['serr'] = xid[i]['serr'].tolist()
xid[i]['pflux'] = xid[i]['pflux'].tolist()
xid[i]['perr'] = xid[i]['perr'].tolist()
xid[i]['x'] = xid[i]['x'].tolist()
xid[i]['y'] = xid[i]['y'].tolist()
#saving to json file for further analysis.
with open('xid_a0370_take_9_%s.json' % (xid[i]['band']),
'w') as f: #code for saving output to a file.
json.dump(xid[i], f)
#model = image_model(x,y, sflux, maps[i]['astr']['NAXIS'][0], maps[i]['astr']['NAXIS'][1],
#maps[i]['psf'])
#need to finish converting model over to python.
#
# if savemap:
# outfile = config.CLUSSBOX + 'clus_get_xid_model_' + maps[i]['band'] + '.fit'
# writefits(outfile, data=model, header_dict=maps[i]['shead'])
return xid, err
box=','.join(box)
else:
ra=[0, ohdr['NAXIS1']-1]
dec=[0, ohdr['NAXIS2']-1]
box=''
""" integrated range retrieval """
if axis=='ra': inax=1
elif axis=='dec': inax=2
elif axis=='spec': inax=3
dim=[ra,dec,spec]
pixel=[ohdr['CRPIX1'], ohdr['CRPIX2'], ohdr['CRPIX3']]
pixel[inax-1]=dim[inax-1]
rlpix=pixel[inax-1]
rlval=wcs(ohdr).wcs_pix2world(pixel[0], pixel[1], pixel[2], 0)
rlval[2]=rlval[2]/1e3
rlval=list(rlval[inax-1])
""" moment """
importfits(fitsfile+'.fits', fitsfile+'.im', zeroblanks=True, overwrite=True)
if os.path.isdir('./'+fitsfile+'_mom.im'):
os.system('rm -f '+fitsfile+'_mom.im')
immoments(fitsfile+'.im', moments=moment, axis=axis, chans=chans, box=box, includepix=inpix, excludepix=expix, outfile=fitsfile+'_mom.im')
""" export and header fix """
exportfits(fitsfile+'_mom.im', fitsfile+'_mom.fits', velocity=True, dropstokes=True, stokeslast=False, overwrite=True)
tdat, thdr=ft.getdata(fitsfile+'_mom.fits', header=True)
if thdr['CUNIT3']=='m/s':