def reduce_all_cubes_for_map(mapname, lowhigh='high', **kwargs):
for dataset,maps in make_apex_cubes.datasets_2014.items():
if mapname in maps:
date = dataset[-10:]
both_directions = True
try:
make_apex_cubes.build_cube_2014(mapname,
lowhigh=lowhigh,
posang=[50,70],
datasets=[dataset],
extra_suffix='_cal{0}_lscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
try:
make_apex_cubes.build_cube_2014(mapname,
lowhigh=lowhigh,
posang=[140,160],
datasets=[dataset],
extra_suffix='_cal{0}_bscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
if both_directions:
fileb = os.path.join(outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_bscans.fits'.format(date, mapname, lowhigh))
filel = os.path.join(outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_lscans.fits'.format(date, mapname, lowhigh))
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
header = FITS_tools.fits_overlap(fileb, filel)
hdb = fits.getheader(fileb)
# Add back 3rd dimension... HACK
for key in hdb:
if key[0] == 'C' and key.strip()[-1] == '3':
header[key] = hdb[key]
FITS_tools.regrid_fits_cube(fileb, outheader=header, outfilename=fileb, clobber=True)
FITS_tools.regrid_fits_cube(filel, outheader=header, outfilename=filel, clobber=True)
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
log.fatal("Cube shapes don't match: {0}, {1}".format(cubeb.shape,cubel.shape))
raise ValueError
cube_comb = plait.plait_cube([cubeb,cubel], angles=[0, 90], scale=5)
cube_comb_naive = (cubeb+cubel)/2.
header = fits.getheader(fileb)
fits.PrimaryHDU(data=cube_comb,
header=header).writeto(os.path.join(outdir, '{1}_{2}_cal{0}_plait.fits'.format(date, mapname, lowhigh)),
clobber=True)
def reduce_all_cubes_for_map(mapname, lowhigh='high', **kwargs):
for dataset, maps in make_apex_cubes.datasets_2014.items():
if mapname in maps:
date = dataset[-10:]
both_directions = True
try:
make_apex_cubes.build_cube_2014(
mapname,
lowhigh=lowhigh,
posang=[50, 70],
datasets=[dataset],
extra_suffix='_cal{0}_lscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
try:
make_apex_cubes.build_cube_2014(
mapname,
lowhigh=lowhigh,
posang=[140, 160],
datasets=[dataset],
extra_suffix='_cal{0}_bscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
if both_directions:
fileb = os.path.join(
outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_bscans.fits'.format(
date, mapname, lowhigh))
filel = os.path.join(
outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_lscans.fits'.format(
date, mapname, lowhigh))
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
header = FITS_tools.fits_overlap(fileb, filel)
hdb = fits.getheader(fileb)
# Add back 3rd dimension... HACK
for key in hdb:
if key[0] == 'C' and key.strip()[-1] == '3':
header[key] = hdb[key]
FITS_tools.regrid_fits_cube(fileb,
outheader=header,
outfilename=fileb,
clobber=True)
FITS_tools.regrid_fits_cube(filel,
outheader=header,
outfilename=filel,
clobber=True)
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
log.fatal("Cube shapes don't match: {0}, {1}".format(
cubeb.shape, cubel.shape))
raise ValueError
cube_comb = plait.plait_cube([cubeb, cubel],
angles=[0, 90],
scale=5)
cube_comb_naive = (cubeb + cubel) / 2.
header = fits.getheader(fileb)
fits.PrimaryHDU(data=cube_comb,
header=header).writeto(os.path.join(
outdir, '{1}_{2}_cal{0}_plait.fits'.format(
date, mapname, lowhigh)),
clobber=True)
import FITS_tools
from astropy.io import fits
import paths
neiir = FITS_tools.regrid_cube_hdu(fits.open(paths.dpath('w51.neii.fits'))[0], outheader=fits.Header.fromtextfile(paths.dpath('w51.neii.square.hdr')))
neiir.writeto(paths.dpath('w51.neii.square.fits'))
sivr = FITS_tools.regrid_cube_hdu(fits.open(paths.dpath('w51.siv.fits'))[0], outheader=fits.Header.fromtextfile(paths.dpath('w51.siv.square.hdr')))
sivr.writeto(paths.dpath('w51.siv.square.fits'))
def match_regrid(filename1,
filename2,
return_type='hdu',
reappend_dim=True,
remove_hist=True,
save_output=False,
save_name='new_img'):
'''
Input two fits filenames. The output will be the projection of file 1
onto file 2
'''
fits1 = fits.open(filename1)
fits2 = fits.open(filename2)
hdr1 = fits1[0].header.copy()
hdr2 = fits2[0].header.copy()
# new_wcs = WCS(hdr2)
# new_wcs = drop_axis(new_wcs, 3)
# hdr2 = new_wcs.to_header()
hdr2["CUNIT4"] = 'km/s '
hdr2["CRVAL4"] = -48.1391
hdr2["CTYPE4"] = 'VELO-LSR'
hdr2["CDELT4"] = -1.288141
# hdr2["CDELT1"] = hdr2["CDELT1"] * (4096/256)
# hdr2["CDELT2"] = hdr2["CDELT2"] * (4096/256)
# hdr2["CDELT4"] = hdr2["CDELT4"] * (205/40)
# hdr2["NAXIS1"] = 4096
# hdr2["NAXIS2"] = 4096
# hdr2["NAXIS4"] = 40
# hdr2 = "SIMPLE = T /Standard FITS BITPIX = -32 /Floating point (32 bit) NAXIS = 3 NAXIS1 = 256 NAXIS2 = 256 NAXIS3 = 205 EXTEND = T BSCALE = 1.000000000000E+00 BZERO = 0.000000000000E+00 BMAJ = 1.755010949241E-03 BMIN = 1.572879685296E-03 BPA = 8.867265319824E+01 BTYPE = 'Intensity' OBJECT = 'M33P1 ' BUNIT = 'JY/BEAM ' /Brightness (pixel) unit EQUINOX = 2.000000000000E+03 RADESYS = 'FK5 ' LONPOLE = 1.800000000000E+02 LATPOLE = 3.065994166667E+01 PC01_01 = 1.000000000000E+00 PC02_01 = 0.000000000000E+00 PC03_01 = 0.000000000000E+00 PC01_02 = 0.000000000000E+00 PC02_02 = 1.000000000000E+00 PC03_02 = 0.000000000000E+00 PC01_03 = 0.000000000000E+00 PC02_03 = 0.000000000000E+00 PC03_03 = 1.000000000000E+00 CTYPE1 = 'RA---SIN' CRVAL1 = 2.346210000000E+01 CDELT1 = -4.166666666667E-04 CRPIX1 = 2.049000000000E+03 CUNIT1 = 'deg ' CTYPE2 = 'DEC--SIN' CRVAL2 = 3.065994166667E+01 CDELT2 = 4.166666666667E-04 CRPIX2 = 2.049000000000E+03 CUNIT2 = 'deg ' CTYPE3 = 'VELO-LSR' CRVAL3 = -48.1391 CDELT3 = 6.103165421963E+03 CRPIX3 = 1.000000000000E+00 CUNIT3 = 'km/s ' PV2_1 = 0.000000000000E+00 PV2_2 = 0.000000000000E+00 RESTFRQ = 1.420405751770E+09 /Rest Frequency (Hz) SPECSYS = 'BARYCENT' /Spectral reference frame ALTRVAL = -4.942706867471E+04 /Alternate frequency reference value ALTRPIX = 1.000000000000E+00 /Alternate frequency reference pixel VELREF = 258 /1 LSR, 2 HEL, 3 OBS, +256 Radio COMMENT casacore non-standard usage: 4 LSD, 5 GEO, 6 SOU, 7 GAL TELESCOP= 'VLA ' OBSERVER= 'unavailable' DATE-OBS= '1997-09-19T04:59:50.000002' TIMESYS = 'TAI ' OBSRA = 2.346210000000E+01 OBSDEC = 3.065994166667E+01 OBSGEO-X= -1.601185365000E+06 OBSGEO-Y= -5.041977547000E+06 OBSGEO-Z= 3.554875870000E+06 DATE = '2014-12-17T20:03:37.733514' /Date FITS file was written ORIGIN = 'CASA 4.2.2 (prerelease r30986)' END "
# hdr2 = fits.header.Header.fromstring(hdr2)
if remove_hist:
# Remove the huge CASA history
del hdr2["HISTORY"]
shape1 = fits1[0].data.shape
shape2 = fits2[0].data.shape[:-1]
fits2.close()
# We need to alter the header to make them compatible
# if len(shape1) < len(shape2):
# hdr2["NAXIS"] = len(shape1)
# del_keys = ["NAXIS", "CTYPE", "CDELT", "CRPIX", "CUNIT", "CRVAL"]
# extra_axes = \
# [posn + 1 for posn, val in enumerate(shape2[::-1]) if val == 1]
# if reappend_dim:
# deleted_keys = {}
# for ax in extra_axes:
# for del_key in del_keys:
# if reappend_dim:
# deleted_keys[del_key+str(ax)] = hdr2[del_key+str(ax)]
# del hdr2[del_key+str(ax)]
# Do the matching
if len(shape1) == 2:
regrid_img = ft.hcongrid.hcongrid(fits1[0].data, fits1[0].header, hdr2)
else:
regrid_img = ft.regrid_cube(fits1[0].data,
fits1[0].header,
hdr2,
specaxes=(3, 3))
# regrid_img = reproject(fits1[0], hdr2, shape_out=(205, 256, 256))[0]
# Now hack the header back together!
# if reappend_dim and len(shape1) < len(shape2):
# for key in deleted_keys:
# hdr2[key] = deleted_keys[key]
# hdr2["NAXIS"] = len(shape2)
# for _ in range(len(extra_axes)):
# regrid_img = regrid_img[np.newaxis]
# Finally, we want to take out the important portions of fits1 header
# hdr2["TELESCOPE"] = hdr1["TELESCOPE"]
# hdr2["DATE-OBS"] = hdr1["DATE-OBS"]
# hdr2["DATAMAX"] = hdr1["DATAMAX"]
# hdr2["DATAMIN"] = hdr1["DATAMIN"]
# hdr2["OBSERVER"] = hdr1["OBSERVER"]
# hdr2["OBJECT"] = hdr1["OBJECT"]
# hdr2["ORIGIN"] = hdr1["ORIGIN"]
# hdr2["BMAJ"] = hdr1["BMAJ"]
# hdr2["BMIN"] = hdr1["BMIN"]
# hdr2["BPA"] = hdr1["BPA"]
if save_output:
hdu = fits.PrimaryHDU(regrid_img, header=hdr2)
hdu.writeto(save_name + ".fits")
else:
return fits.PrimaryHDU(regrid_img, header=hdr2)
if not os.path.isdir('../data/spectra/'):
os.mkdir('../data/spectra/')
if not os.path.isdir('../data/spectra/apex/'):
os.mkdir('../data/spectra/apex/')
for cubefilename in cubefilenames:
cube31 = SpectralCube.read(cubefilename)
cube32filename = cubefilename.replace("31-30","32-31")
if os.path.isfile(cube32filename):
cube32 = SpectralCube.read(cube32filename)
else:
print("{0} does not exist, skipping".format(cube32filename))
if cube31.shape != cube32.shape:
cube32data = FITS_tools.regrid_cube_hdu(cube32.hdu, cube31.header).data
if np.all(np.isnan(cube32data)):
print("{0} and {1} do not overlap".format(cubefilename, cube32filename))
continue
else:
cube32data = cube32.filled_data[:]
meancubedata = (cube31.filled_data[:] + cube32data)/2.
hdu = cube31.hdu
hdu.data = meancubedata.value
outcubename = cubefilename.replace("31-30", "averaged").replace(".lmv",".fits")
hdu.writeto(outcubename, clobber=True)
meancubedata = pyspeckit.cubes.spectral_smooth(meancubedata.value, 5, downsample=True)
hdu.data = meancubedata
hdu.header['CDELT3'] = hdu.header.get('CDELT3') * float(5)
import FITS_tools
import scipy.stats
from sdpy import plait
from reduce_map_all import reduce_all_cubes_for_map
from astropy import log
from collections import defaultdict
for lowhigh in ('low', 'high'):
reduce_all_cubes_for_map('MAP_006',
lowhigh=lowhigh,
calibration_factors=defaultdict(lambda: 1))
files = glob.glob(os.path.join(paths.april2014path, '*MAP_006*scans.fits'))
for fn1, fn2 in itertools.combinations(files, 2):
header = FITS_tools.fits_overlap(fn1, fn2)
hd = fits.getheader(fn1)
# Add back 3rd dimension... HACK
for key in hd:
if key[0] == 'C' and key.strip()[-1] == '3':
header[key] = hd[key]
for fn in files:
FITS_tools.regrid_fits_cube(fn,
outheader=header,
outfilename=fn,
clobber=True)
cube530b = fits.getdata(
os.path.join(
#'LimaBean_H213CO22_cube_sub_smoothtoCband.fits',
'LimaBean_H2C18O22_cube_sub.fits',
#'LimaBean_H2C18O22_cube_sub_smoothtoCband.fits',
]
# TODO: Where can these be got?
ccontfile = datapath+'GCCBand_lb.2.fits'
# First do with Casey Law's images as the background
if os.path.exists(ccontfile):
for fn in fivecubes:
reproj_contfile = datapath+"GCCBand_reproj.fits"
header = FITS_tools.strip_headers.flatten_header(fits.getheader(datapath+fivecubes[0]))
ccont = FITS_tools.project_to_header(ccontfile, header) * u.Jy
cbfreq = 4.829 * u.GHz
gbbeam_5ghz = 1.22 * ((cbfreq.to(u.m, u.spectral()) / (100*u.m)) * u.rad).decompose()
fwhm = np.sqrt(8*np.log(2))
bmaj=4.2413E-02*u.deg
bmin=4.2413E-02*u.deg
ktojy5ghz = (1*u.K).to(u.Jy,u.brightness_temperature((2*np.pi*(bmaj*bmin/fwhm**2)), cbfreq))
cont_K = (ccont/ktojy5ghz).value
outfile = fits.PrimaryHDU(data=cont_K, header=header)
outfile.writeto(reproj_contfile,clobber=True)
continuum = cont_K
suffix = '_claw.fits'
def match_regrid(filename1, filename2, return_type='hdu', reappend_dim=True,
remove_hist=True, save_output=False, save_name='new_img'):
'''
Input two fits filenames. The output will be the projection of file 1
onto file 2
'''
fits1 = fits.open(filename1)
fits2 = fits.open(filename2)
hdr1 = fits1[0].header.copy()
hdr2 = fits2[0].header.copy()
# new_wcs = WCS(hdr2)
# new_wcs = drop_axis(new_wcs, 3)
# hdr2 = new_wcs.to_header()
hdr2["CUNIT4"] = 'km/s '
hdr2["CRVAL4"] = -48.1391
hdr2["CTYPE4"] = 'VELO-LSR'
hdr2["CDELT4"] = -1.288141
# hdr2["CDELT1"] = hdr2["CDELT1"] * (4096/256)
# hdr2["CDELT2"] = hdr2["CDELT2"] * (4096/256)
# hdr2["CDELT4"] = hdr2["CDELT4"] * (205/40)
# hdr2["NAXIS1"] = 4096
# hdr2["NAXIS2"] = 4096
# hdr2["NAXIS4"] = 40
# hdr2 = "SIMPLE = T /Standard FITS BITPIX = -32 /Floating point (32 bit) NAXIS = 3 NAXIS1 = 256 NAXIS2 = 256 NAXIS3 = 205 EXTEND = T BSCALE = 1.000000000000E+00 BZERO = 0.000000000000E+00 BMAJ = 1.755010949241E-03 BMIN = 1.572879685296E-03 BPA = 8.867265319824E+01 BTYPE = 'Intensity' OBJECT = 'M33P1 ' BUNIT = 'JY/BEAM ' /Brightness (pixel) unit EQUINOX = 2.000000000000E+03 RADESYS = 'FK5 ' LONPOLE = 1.800000000000E+02 LATPOLE = 3.065994166667E+01 PC01_01 = 1.000000000000E+00 PC02_01 = 0.000000000000E+00 PC03_01 = 0.000000000000E+00 PC01_02 = 0.000000000000E+00 PC02_02 = 1.000000000000E+00 PC03_02 = 0.000000000000E+00 PC01_03 = 0.000000000000E+00 PC02_03 = 0.000000000000E+00 PC03_03 = 1.000000000000E+00 CTYPE1 = 'RA---SIN' CRVAL1 = 2.346210000000E+01 CDELT1 = -4.166666666667E-04 CRPIX1 = 2.049000000000E+03 CUNIT1 = 'deg ' CTYPE2 = 'DEC--SIN' CRVAL2 = 3.065994166667E+01 CDELT2 = 4.166666666667E-04 CRPIX2 = 2.049000000000E+03 CUNIT2 = 'deg ' CTYPE3 = 'VELO-LSR' CRVAL3 = -48.1391 CDELT3 = 6.103165421963E+03 CRPIX3 = 1.000000000000E+00 CUNIT3 = 'km/s ' PV2_1 = 0.000000000000E+00 PV2_2 = 0.000000000000E+00 RESTFRQ = 1.420405751770E+09 /Rest Frequency (Hz) SPECSYS = 'BARYCENT' /Spectral reference frame ALTRVAL = -4.942706867471E+04 /Alternate frequency reference value ALTRPIX = 1.000000000000E+00 /Alternate frequency reference pixel VELREF = 258 /1 LSR, 2 HEL, 3 OBS, +256 Radio COMMENT casacore non-standard usage: 4 LSD, 5 GEO, 6 SOU, 7 GAL TELESCOP= 'VLA ' OBSERVER= 'unavailable' DATE-OBS= '1997-09-19T04:59:50.000002' TIMESYS = 'TAI ' OBSRA = 2.346210000000E+01 OBSDEC = 3.065994166667E+01 OBSGEO-X= -1.601185365000E+06 OBSGEO-Y= -5.041977547000E+06 OBSGEO-Z= 3.554875870000E+06 DATE = '2014-12-17T20:03:37.733514' /Date FITS file was written ORIGIN = 'CASA 4.2.2 (prerelease r30986)' END "
# hdr2 = fits.header.Header.fromstring(hdr2)
if remove_hist:
# Remove the huge CASA history
del hdr2["HISTORY"]
shape1 = fits1[0].data.shape
shape2 = fits2[0].data.shape[:-1]
fits2.close()
# We need to alter the header to make them compatible
# if len(shape1) < len(shape2):
# hdr2["NAXIS"] = len(shape1)
# del_keys = ["NAXIS", "CTYPE", "CDELT", "CRPIX", "CUNIT", "CRVAL"]
# extra_axes = \
# [posn + 1 for posn, val in enumerate(shape2[::-1]) if val == 1]
# if reappend_dim:
# deleted_keys = {}
# for ax in extra_axes:
# for del_key in del_keys:
# if reappend_dim:
# deleted_keys[del_key+str(ax)] = hdr2[del_key+str(ax)]
# del hdr2[del_key+str(ax)]
# Do the matching
if len(shape1) == 2:
regrid_img = ft.hcongrid.hcongrid(fits1[0].data, fits1[0].header, hdr2)
else:
regrid_img = ft.regrid_cube(fits1[0].data, fits1[0].header, hdr2, specaxes=(3, 3))
# regrid_img = reproject(fits1[0], hdr2, shape_out=(205, 256, 256))[0]
# Now hack the header back together!
# if reappend_dim and len(shape1) < len(shape2):
# for key in deleted_keys:
# hdr2[key] = deleted_keys[key]
# hdr2["NAXIS"] = len(shape2)
# for _ in range(len(extra_axes)):
# regrid_img = regrid_img[np.newaxis]
# Finally, we want to take out the important portions of fits1 header
# hdr2["TELESCOPE"] = hdr1["TELESCOPE"]
# hdr2["DATE-OBS"] = hdr1["DATE-OBS"]
# hdr2["DATAMAX"] = hdr1["DATAMAX"]
# hdr2["DATAMIN"] = hdr1["DATAMIN"]
# hdr2["OBSERVER"] = hdr1["OBSERVER"]
# hdr2["OBJECT"] = hdr1["OBJECT"]
# hdr2["ORIGIN"] = hdr1["ORIGIN"]
# hdr2["BMAJ"] = hdr1["BMAJ"]
# hdr2["BMIN"] = hdr1["BMIN"]
# hdr2["BPA"] = hdr1["BPA"]
if save_output:
hdu = fits.PrimaryHDU(regrid_img, header=hdr2)
hdu.writeto(save_name+".fits")
else:
return fits.PrimaryHDU(regrid_img, header=hdr2)
import itertools
from astropy.io import fits
import FITS_tools
import scipy.stats
from sdpy import plait
from reduce_map_all import reduce_all_cubes_for_map
from astropy import log
from collections import defaultdict
for lowhigh in ('low','high'):
reduce_all_cubes_for_map('MAP_006', lowhigh=lowhigh, calibration_factors=defaultdict(lambda: 1))
files = glob.glob(os.path.join(paths.april2014path, '*MAP_006*scans.fits'))
for fn1,fn2 in itertools.combinations(files, 2):
header = FITS_tools.fits_overlap(fn1, fn2)
hd = fits.getheader(fn1)
# Add back 3rd dimension... HACK
for key in hd:
if key[0] == 'C' and key.strip()[-1] == '3':
header[key] = hd[key]
for fn in files:
FITS_tools.regrid_fits_cube(fn, outheader=header, outfilename=fn, clobber=True)
cube530b = fits.getdata(os.path.join(paths.april2014path, 'APEX_H2CO_2014_MAP_006_{0}_cal2014-05-30_bscans.fits'.format(lowhigh)))
cube530l = fits.getdata(os.path.join(paths.april2014path, 'APEX_H2CO_2014_MAP_006_{0}_cal2014-05-30_lscans.fits'.format(lowhigh)))
cube730b = fits.getdata(os.path.join(paths.april2014path, 'APEX_H2CO_2014_MAP_006_{0}_cal2014-07-31_bscans.fits'.format(lowhigh)))
cube730l = fits.getdata(os.path.join(paths.april2014path, 'APEX_H2CO_2014_MAP_006_{0}_cal2014-07-30_lscans.fits'.format(lowhigh)))
import FITS_tools
from astropy.io import fits
import paths
neiir = FITS_tools.regrid_cube_hdu(fits.open(paths.dpath('w51.neii.fits'))[0],
outheader=fits.Header.fromtextfile(
paths.dpath('w51.neii.square.hdr')))
neiir.writeto(paths.dpath('w51.neii.square.fits'))
sivr = FITS_tools.regrid_cube_hdu(fits.open(paths.dpath('w51.siv.fits'))[0],
outheader=fits.Header.fromtextfile(
paths.dpath('w51.siv.square.hdr')))
sivr.writeto(paths.dpath('w51.siv.square.fits'))