def regrid_images(image, template):
import mirpy
# Avoiding the miriad errors avoids errors from existing files
try:
mirpy.fits(image+'.fits',
out=image+'.mir',
op='xyin')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.fits(template+'.fits',
out=template+'.mir',
op='xyin')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.regrid(image+'.mir', tin=template+'.mir',
out=image+'_regrid.mir')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.fits(image+'_regrid.mir', out=image+'_regrid.fits',
op='xyout')
except mirpy.wrapper.MiriadError:
pass
def regrid_images(image, template):
import mirpy
# Avoiding the miriad errors avoids errors from existing files
try:
mirpy.fits(image + '.fits', out=image + '.mir', op='xyin')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.fits(template + '.fits', out=template + '.mir', op='xyin')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.regrid(image + '.mir',
tin=template + '.mir',
out=image + '_regrid.mir')
except mirpy.wrapper.MiriadError:
pass
try:
mirpy.fits(image + '_regrid.mir',
out=image + '_regrid.fits',
op='xyout')
except mirpy.wrapper.MiriadError:
pass
def main():
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/perseus/data')
# If true, deletes files to be written
clobber = 1
clobber_hi = 1
# First, change zeros in lee image to nans
data, header = pyfits.getdata('av/perseus_av_lee12_2mass.fits',
header=True)
data[data == 0] = np.nan
pyfits.writeto('av/perseus_av_lee12_2mass_nan.fits',
data,
header,
clobber=True)
in_images = (
'hi/perseus_hi_galfa_cube',
'hi/perseus_hi_galfa_dr1_cube',
'av/perseus_av_planck_tau353',
'av/perseus_av_error_planck_tau353',
'dust_temp/perseus_dust_temp',
'dust_temp/perseus_dust_temp_error',
'av/perseus_av_planck_radiance',
'av/perseus_av_error_planck_radiance',
'co/perseus_co_cfa_cube',
#'av/perseus_av_k09_nan',
'/d/bip3/ezbc/perseus/data/av/perseus_av_k09_nan',
'av/perseus_av_lee12_2mass_nan',
'av/perseus_av_lee12_iris_masked')
im_hi = 'hi/perseus_hi_galfa_cube'
im_hi_dr1 = 'hi/perseus_hi_galfa_dr1_cube'
im_pl = 'av/perseus_av_planck_tau353'
im_pl_err = 'av/perseus_av_error_planck_tau353'
im_Td = 'dust_temp/perseus_dust_temp'
im_Td_err = 'dust_temp/perseus_dust_temp_error'
im_pl2 = 'av/perseus_av_planck_radiance'
im_pl2_err = 'av/perseus_av_error_planck_radiance'
im_co = 'co/perseus_co_cfa_cube'
im_k09 = 'av/perseus_av_k09'
im_lee_2mass = 'av/perseus_av_lee12_2mass'
im_lee_iris = 'av/perseus_av_lee12_iris'
# Load the images into miriad
print('\nLoading images into miriad...')
out_images = (im_hi, im_hi_dr1, im_pl, im_pl_err, im_Td, im_Td_err, im_pl2,
im_pl2_err, im_co, im_k09, im_lee_2mass, im_lee_iris)
for i in xrange(len(in_images)):
if out_images[i] == im_hi:
clobber_temp = clobber_hi
else:
clobber_temp = clobber
exists = check_file(out_images[i] + '.mir', clobber=clobber_temp)
if not exists:
print('\tLoading {:s}.fits\n'.format(in_images[i]))
fits(in_images[i] + '.fits', out=out_images[i] + '.mir', op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (im_pl, im_pl_err, im_Td, im_Td_err, im_pl2, im_pl2_err, im_hi,
im_hi_dr1)
desc = (59.75, 0, -0.08333, 180, 26.05, 0, 0.08333, 132)
delta_ra = -0.083333333
delta_dec = 0.083333333
ref_pix, npix = calc_image_origin(x_limits=(70, 40),
y_limits=(21, 38),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_5arcmin.mir\n'.format(image))
regrid(image + '.mir', out=image + '_5arcmin.mir', desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcmin
im_beams = np.array([3.7, 3.7, 2.5, 2.5, 4.3]) # arcmin
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_hi_dr1, im_k09, im_lee_2mass, im_lee_iris]
for i in xrange(len(images)):
exists = check_file(images[i] + '_smooth_planckres.mir',
clobber=clobber)
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
print('\tSmoothing {:s}.mir\n'.format(image))
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_smooth_planckres.mir\n'.format(image))
if image == im_co or image == im_lee_2mass or image == im_lee_iris:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_Td + '_5arcmin',
im_Td_err + '_5arcmin',
im_pl2 + '_5arcmin',
im_pl2_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_lee_2mass + '_regrid_planckres',
im_lee_iris + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
fits(image + '.mir', out=image + '.fits', op='xyout')
# write masked Planck image as same as Lee+12
data_pl, header = pyfits.getdata(im_pl + '_5arcmin.fits', header=True)
data_l12 = pyfits.getdata(im_lee_iris + '_regrid_planckres.fits')
data_pl[np.isnan(data_l12)] = np.nan
pyfits.writeto(im_pl + '_5arcmin_lee12mask.fits',
data_pl,
header,
clobber=True)
def main():
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/perseus/data')
# If true, deletes files to be written
clobber = 1
clobber_hi = 1
in_images = ('/d/bip3/ezbc/perseus/data/hi/perseus_hi_galfa_cube', )
im_hi = 'hi/perseus_hi_galfa_cube_sub'
# Load the images into miriad
print('\nLoading images into miriad...')
out_images = (im_hi, )
for i in xrange(len(in_images)):
if out_images[i] == im_hi:
clobber_temp = clobber_hi
else:
clobber_temp = clobber
exists = check_file(out_images[i] + '.mir', clobber=clobber_temp)
if not exists:
print('\tLoading {:s}.fits\n'.format(in_images[i]))
fits(in_images[i] + '.fits', out=out_images[i] + '.mir', op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding images')
images = (im_hi, )
# The center coordinate of the region is RA: 4h13m26s, Dec =33d22.
# Will it be too much of asking if we want to get a coverage of around 20deg
# X20 deg?
# In terms of rectangular region it will be
# DEC ---> 20deg to 40 deg
# RA-------> 4h40m to 3h20m
#desc = (59.75,0,-0.08333,180,26.05,0,0.08333,132)
delta_ra = -0.083333333
delta_dec = 0.083333333
ref_pix, npix = calc_image_origin(x_limits=(15 * (3 + 50. / 60.),
15 * (3 + 10. / 60.)),
y_limits=(26, 35),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -50.0
high_vel = 30.0
vel_res = 1
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, ):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_regrid.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_regrid.mir\n'.format(image))
regrid(image + '.mir', out=image + '_regrid.mir', desc=desc)
images = [
im_hi,
]
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_hi + '_regrid',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
fits(image + '.mir', out=image + '.fits', op='xyout')
def main():
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/perseus/data')
# If true, deletes files to be written
clobber = 1
clobber_hi = 1
in_images = ('/d/bip3/ezbc/multicloud/data/hi/multicloud_hi_galfa_cube',
)
im_hi = 'hi/perseus_hi_galfa_cube_roy'
# Load the images into miriad
print('\nLoading images into miriad...')
out_images = (im_hi,
)
for i in xrange(len(in_images)):
if out_images[i] == im_hi:
clobber_temp = clobber_hi
else:
clobber_temp = clobber
exists = check_file(out_images[i] + '.mir', clobber=clobber_temp)
if not exists:
print('\tLoading {:s}.fits\n'.format(in_images[i]))
fits(in_images[i] + '.fits',
out=out_images[i] + '.mir',
op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding images')
images = (im_hi,)
# The center coordinate of the region is RA: 4h13m26s, Dec =33d22.
# Will it be too much of asking if we want to get a coverage of around 20deg
# X20 deg?
# In terms of rectangular region it will be
# DEC ---> 20deg to 40 deg
# RA-------> 4h40m to 3h20m
#desc = (59.75,0,-0.08333,180,26.05,0,0.08333,132)
delta_ra = -0.083333333 / 5.0
delta_dec = 0.083333333 / 5.0
ref_pix, npix = calc_image_origin(x_limits=(15 * (4 + 40./60.),
15 * (3 + 20./60.)),
y_limits=(20, 38),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi,):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_regrid.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_regrid.mir\n'.format(image))
regrid(image + '.mir',
out=image + '_regrid.mir',
desc=desc)
images = [im_hi,]
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_hi + '_regrid',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
fits(image + '.mir',
out=image + '.fits',
op='xyout')
def main():
''' Planck resolution is 5'. We will smooth and regrid all maps to the
Planck map.
Map Resolution Convol size
GALFA HI 3.7' 3.363
Kainulainen et al. (2009) Av 2.4' 4.386
Pineda et al. (2010) Av 3.3' 3.75
CfA CO Dame et al. (2001) 8' No smoothing
'''
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
os.chdir('/d/bip3/ezbc/taurus/data')
# If true, deletes files to be written
clobber = 1
in_images = (
#'av/taurus_av_kainulainen2009_nan',
'/d/bip3/ezbc/multicloud/data/av/multicloud_av_k09_nan',
'av/taurus_av_pineda2010',
'hi/taurus_hi_galfa_cube',
'hi/taurus_hi_galfa_dr1_cube',
'av/taurus_av_planck_tau353',
'av/taurus_av_error_planck_tau353',
'av/taurus_av_planck_radiance',
'av/taurus_av_error_planck_radiance',
'co/taurus_co_cfa_cube',
'co/taurus_co_planck',
'co/taurus_co_error_planck',
'co/taurus_co_1-0_planck',
'co/taurus_co_1-0_error_planck',
'co/taurus_co_2-1_planck',
'co/taurus_co_2-1_error_planck',
'co/taurus_co_3-2_planck',
'co/taurus_co_3-2_error_planck',
)
im_pl = 'av/taurus_av_planck_tau353'
im_pl_err = 'av/taurus_av_error_planck_tau353'
im_pl2 = 'av/taurus_av_planck_radiance'
im_pl2_err = 'av/taurus_av_error_planck_radiance'
im_hi = 'hi/taurus_hi_galfa_cube'
im_hi_dr1 = 'hi/taurus_hi_galfa_dr1_cube'
im_co = 'co/taurus_co_cfa_cube'
im_p10 = 'av/taurus_av_p10'
im_k09 = 'av/taurus_av_k09'
im_pl_co = 'co/taurus_co_planck'
im_pl_co_err = 'co/taurus_co_error_planck'
im_pl_co_10 = 'co/taurus_co_1-0_planck'
im_pl_co_10_err = 'co/taurus_co_1-0_error_planck'
im_pl_co_21 = 'co/taurus_co_2-1_planck'
im_pl_co_21_err = 'co/taurus_co_2-1_error_planck'
im_pl_co_32 = 'co/taurus_co_3-2_planck'
im_pl_co_32_err = 'co/taurus_co_3-2_error_planck'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (im_k09,
im_p10,
im_hi,
im_hi_dr1,
im_pl,
im_pl_err,
im_pl2,
im_pl2_err,
im_co,
im_pl_co,
im_pl_co_err,
im_pl_co_10,
im_pl_co_10_err,
im_pl_co_21,
im_pl_co_21_err,
im_pl_co_32,
im_pl_co_32_err,
)
for i in xrange(len(in_images)):
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\tReading {:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits',
out=out_images[i] + '.mir',
op='xyin')
print('\tout = ' + out_images[i] + '.mir')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (
im_pl,
im_pl_err,
im_pl2,
im_pl2_err,
im_hi,
im_hi_dr1,
#im_pl_co,
#im_pl_co_err,
#im_pl_co_10,
#im_pl_co_10_err,
#im_pl_co_21,
#im_pl_co_21_err,
#im_pl_co_32,
#im_pl_co_32_err,
)
delta_ra = -0.083333333
delta_dec = 0.083333333
lim_ra_wcs, lim_dec_wcs = 77.5, 18.0
ref_ra_wcs, ref_dec_wcs = 0.0, 0.0
ref_pix, npix = calc_image_origin(x_limits=(83, 53.0),
y_limits=(15.0, 35.0),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
print('\tReading image {:s}.mir'.format(image))
print('\tWriting image {:s}_5arcmin.mir\n'.format(image))
if not exists:
regrid(image + '.mir',
out=image + '_5arcmin.mir',
desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcsec
im_beams = np.array([2.4, 3.3, 3.7, 3.7]) # arcsec
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_k09, im_p10, im_hi, im_hi_dr1]
for i in xrange(len(images)):
exists = check_file(images[i] + '_smooth_planckres.mir',
clobber=clobber)
print('\t{:s}.mir\n'.format(image))
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
print('\t{:s}_smooth_planckres.mir\n'.format(image))
if not exists:
if image == im_co:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_pl2 + '_5arcmin',
im_pl2_err + '_5arcmin',
im_pl_co + '_5arcmin',
im_pl_co_err + '_5arcmin',
im_pl_co_10 + '_5arcmin',
im_pl_co_10_err + '_5arcmin',
im_pl_co_21+ '_5arcmin',
im_pl_co_21_err+ '_5arcmin',
im_pl_co_32+ '_5arcmin',
im_pl_co_32_err+ '_5arcmin',
im_k09 + '_regrid_planckres',
im_p10 + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
check_file(image + '.fits', clobber=clobber)
print('\t{:s}.mir\n'.format(image))
fits(image + '.mir',
out=image + '.fits',
op='xyout')
def main():
from mirpy import fits, regrid, smooth, imbin
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/multicloud/data')
# If true, deletes files to be written
clobber = False
clobber_hi = False
# First, change zeros in lee image to nans
data, header = pyfits.getdata('av/multicloud_av_lee12_2mass.fits',
header=True)
data[data == 0] = np.nan
pyfits.writeto('av/multicloud_av_lee12_2mass_nan.fits',
data,
header,
clobber=True)
data, header = pyfits.getdata('av/multicloud_av_k09.fits', header=True)
data[data == -1] = np.nan
pyfits.writeto('av/multicloud_av_k09_nan.fits', data, header, clobber=True)
in_images = ('hi/multicloud_hi_galfa_cube', 'av/multicloud_av_planck',
'av/multicloud_av_error_planck',
'dust_temp/multicloud_dust_temp',
'dust_temp/multicloud_dust_temp_error',
'dust_temp/multicloud_dust_beta',
'dust_temp/multicloud_dust_beta_error',
'av/multicloud_av_planck_radiance',
'av/multicloud_av_error_planck_radiance',
'co/multicloud_co_cfa_cube', 'av/multicloud_av_k09_nan',
'av/multicloud_av_lee12_2mass_nan',
'av/multicloud_av_lee12_iris_masked')
im_hi = 'hi/multicloud_hi_galfa_cube'
im_pl = 'av/multicloud_av_planck'
im_pl_err = 'av/multicloud_av_error_planck'
im_Td = 'dust_temp/multicloud_dust_temp'
im_Td_err = 'dust_temp/multicloud_dust_temp_error'
im_beta = 'dust_temp/multicloud_dust_beta'
im_beta_err = 'dust_temp/multicloud_dust_beta_error'
im_pl2 = 'av/multicloud_av_planck_radiance'
im_pl2_err = 'av/multicloud_av_error_planck_radiance'
im_co = 'co/multicloud_co_cfa_cube'
im_k09 = 'av/multicloud_av_k09_nan'
im_lee_2mass = 'av/multicloud_av_lee12_2mass'
im_lee_iris = 'av/multicloud_av_lee12_iris'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (im_hi, im_pl, im_pl_err, im_Td, im_Td_err, im_beta,
im_beta_err, im_pl2, im_pl2_err, im_co, im_k09, im_lee_2mass,
im_lee_iris)
for i in xrange(len(in_images)):
if in_images[i] == im_hi:
exists = check_file(out_images[i] + '.mir', clobber=clobber_hi)
else:
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\tLoading {:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits', out=out_images[i] + '.mir', op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (im_pl, im_pl_err, im_Td, im_Td_err, im_beta, im_beta_err, im_pl2,
im_pl2_err, im_hi)
desc = (59.75, 0, -0.08333, 180, 26.05, 0, 0.08333, 132)
delta_ra = -0.083333333
delta_dec = 0.083333333
ref_pix, npix = calc_image_origin(x_limits=(85, 32),
y_limits=(15, 48),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image == im_hi:
desc = desc_hi
exists = check_file(image + '_5arcmin.mir', clobber=clobber_hi)
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_5arcmin.mir\n'.format(image))
regrid(image + '.mir', out=image + '_5arcmin.mir', desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcmin
im_beams = np.array([3.7, 2.5, 2.5, 4.3]) # arcmin
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_k09, im_lee_2mass, im_lee_iris]
for i, image in enumerate(images):
if image == im_hi:
desc = desc_hi
exists = check_file(image + '_smooth_planckres.mir',
clobber=clobber_hi)
else:
desc = desc_av
exists = check_file(image + '_smooth_planckres.mir',
clobber=clobber)
print image
if not exists:
if images[i] == im_hi:
image = images[i] + '_5arcmin'
else:
image = images[i]
print('\tSmoothing {:s}.mir\n'.format(image))
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_smooth_planckres.mir\n'.format(image))
if image == im_co or image == im_lee_2mass or image == im_lee_iris:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
# Bin the images
print('\nBin the images')
images = [
im_pl + '_5arcmin', im_pl_err + '_5arcmin', im_Td + '_5arcmin',
im_Td_err + '_5arcmin', im_beta + '_5arcmin', im_beta_err + '_5arcmin',
im_pl2 + '_5arcmin', im_pl2_err + '_5arcmin',
im_k09 + '_regrid_planckres', im_lee_2mass + '_regrid_planckres',
im_lee_iris + '_regrid_planckres', im_co + '_regrid_planckres',
im_hi + '_regrid_planckres'
]
for image in images:
exists = check_file(image + '_bin.mir', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
bin_size = 10
if image == im_hi + '_regrid_planckres' or \
image == im_co + '_regrid_planckres':
bins = bin_size, bin_size, bin_size, bin_size, 1, 1
else:
bins = bin_size, bin_size, bin_size, bin_size,
imbin(
image + '.mir',
out=image + '_bin.mir',
bin=bins,
)
# Write the images out to fits images
print('\nWriting images to fits format')
for image in images:
if image == im_hi + '_regrid_planckres':
exists = check_file(image + '.fits', clobber=clobber_hi)
else:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.fits\n'.format(image))
fits(image + '.mir', out=image + '.fits', op='xyout')
exists = check_file(image + '_bin.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}_bin.fits\n'.format(image))
fits(image + '_bin.mir', out=image + '_bin.fits', op='xyout')
def main():
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/perseus/data')
# If true, deletes files to be written
clobber = 1
clobber_hi = 1
# First, change zeros in lee image to nans
data, header = pyfits.getdata('av/perseus_av_lee12_2mass.fits', header=True)
data[data == 0] = np.nan
pyfits.writeto('av/perseus_av_lee12_2mass_nan.fits',
data,
header,
clobber=True)
in_images = ('hi/perseus_hi_galfa_cube',
'hi/perseus_hi_galfa_dr1_cube',
'av/perseus_av_planck_tau353',
'av/perseus_av_error_planck_tau353',
'dust_temp/perseus_dust_temp',
'dust_temp/perseus_dust_temp_error',
'av/perseus_av_planck_radiance',
'av/perseus_av_error_planck_radiance',
'co/perseus_co_cfa_cube',
#'av/perseus_av_k09_nan',
'/d/bip3/ezbc/perseus/data/av/perseus_av_k09_nan',
'av/perseus_av_lee12_2mass_nan',
'av/perseus_av_lee12_iris_masked')
im_hi = 'hi/perseus_hi_galfa_cube'
im_hi_dr1 = 'hi/perseus_hi_galfa_dr1_cube'
im_pl = 'av/perseus_av_planck_tau353'
im_pl_err = 'av/perseus_av_error_planck_tau353'
im_Td = 'dust_temp/perseus_dust_temp'
im_Td_err = 'dust_temp/perseus_dust_temp_error'
im_pl2 = 'av/perseus_av_planck_radiance'
im_pl2_err = 'av/perseus_av_error_planck_radiance'
im_co = 'co/perseus_co_cfa_cube'
im_k09 = 'av/perseus_av_k09'
im_lee_2mass = 'av/perseus_av_lee12_2mass'
im_lee_iris = 'av/perseus_av_lee12_iris'
# Load the images into miriad
print('\nLoading images into miriad...')
out_images = (im_hi,
im_hi_dr1,
im_pl,
im_pl_err,
im_Td,
im_Td_err,
im_pl2,
im_pl2_err,
im_co,
im_k09,
im_lee_2mass,
im_lee_iris)
for i in xrange(len(in_images)):
if out_images[i] == im_hi:
clobber_temp = clobber_hi
else:
clobber_temp = clobber
exists = check_file(out_images[i] + '.mir', clobber=clobber_temp)
if not exists:
print('\tLoading {:s}.fits\n'.format(in_images[i]))
fits(in_images[i] + '.fits',
out=out_images[i] + '.mir',
op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (im_pl, im_pl_err, im_Td, im_Td_err, im_pl2, im_pl2_err, im_hi,
im_hi_dr1)
desc = (59.75,0,-0.08333,180,26.05,0,0.08333,132)
delta_ra = -0.083333333
delta_dec = 0.083333333
ref_pix, npix = calc_image_origin(x_limits=(70, 40),
y_limits=(21, 38),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_5arcmin.mir\n'.format(image))
regrid(image + '.mir',
out=image + '_5arcmin.mir',
desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcmin
im_beams = np.array([3.7, 3.7, 2.5, 2.5, 4.3]) # arcmin
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_hi_dr1, im_k09, im_lee_2mass, im_lee_iris]
for i in xrange(len(images)):
exists = check_file(images[i] + '_smooth_planckres.mir',
clobber=clobber)
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
print('\tSmoothing {:s}.mir\n'.format(image))
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_smooth_planckres.mir\n'.format(image))
if image == im_co or image == im_lee_2mass or image == im_lee_iris:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_Td + '_5arcmin',
im_Td_err + '_5arcmin',
im_pl2 + '_5arcmin',
im_pl2_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_lee_2mass + '_regrid_planckres',
im_lee_iris + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
fits(image + '.mir',
out=image + '.fits',
op='xyout')
# write masked Planck image as same as Lee+12
data_pl, header = pyfits.getdata(im_pl + '_5arcmin.fits', header=True)
data_l12 = pyfits.getdata(im_lee_iris + '_regrid_planckres.fits')
data_pl[np.isnan(data_l12)] = np.nan
pyfits.writeto(im_pl + '_5arcmin_lee12mask.fits',
data_pl,
header,
clobber=True)
def main():
''' Planck resolution is 5'. We will smooth and regrid all maps to the
Planck map.
Map Resolution Convol size
GALFA HI 3.7' 3.363
Kainulainen et al. (2009) Av 2.4' 4.386
Pineda et al. (2010) Av 3.3' 3.75
CfA CO Dame et al. (2001) 8' No smoothing
'''
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
os.chdir('/d/bip3/ezbc/california/data')
# If true, deletes files to be written
clobber = 1
in_images = (
'hi/california_hi_galfa_cube',
'hi/california_hi_galfa_dr1_cube',
'av/california_av_planck_tau353',
'av/california_av_error_planck_tau353',
'dust_temp/california_dust_temp',
'dust_temp/california_dust_temp_error',
'/d/bip3/ezbc/multicloud/data/av/multicloud_av_k09_nan',
'co/california_co_cfa_cube')
im_pl = 'av/california_av_planck_tau353'
im_pl_err = 'av/california_av_error_planck_tau353'
im_Td = 'dust_temp/california_dust_temp'
im_Td_err = 'dust_temp/california_dust_temp_error'
im_k09 = 'av/california_av_k09'
im_hi = 'hi/california_hi_galfa_cube'
im_hi_dr1 = 'hi/california_hi_galfa_dr1_cube'
im_co = 'co/california_co_cfa_cube'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (im_hi, im_hi_dr1, im_pl, im_pl_err,
im_Td,
im_Td_err,
im_k09, im_co)
for i in xrange(len(in_images)):
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\t{:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits',
out=out_images[i] + '.mir',
op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (im_pl, im_pl_err,
im_Td,
im_Td_err,
im_hi, im_hi_dr1,
)
delta_ra = -0.083333333
delta_dec = 0.083333333
# Greater RA value comes first
ref_pix, npix = calc_image_origin(x_limits=(81, 60),
y_limits=(25, 39),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
print('\t{:s}_5arcmin.mir\n'.format(image))
if not exists:
regrid(image + '.mir',
out=image + '_5arcmin.mir',
desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcsec
im_beams = np.array([3.7, 3.7, 2.7]) # arcsec
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_hi_dr1, im_k09]
for i in xrange(len(images)):
exists = check_file(images[i] + \
'_smooth_planckres.mir', clobber=clobber)
print('\t{:s}.mir\n'.format(images[i]))
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
print('\t{:s}_smooth_planckres.mir\n'.format(image))
if not exists:
if image == im_co:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_Td + '_5arcmin',
im_Td_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
print('\t{:s}.mir\n'.format(image))
fits(image + '.mir',
out=image + '.fits',
op='xyout')
def main():
''' Planck resolution is 5'. We will smooth and regrid all maps to the
Planck map.
Map Resolution Convol size
GALFA HI 3.7' 3.363
Kainulainen et al. (2009) Av 2.4' 4.386
Pineda et al. (2010) Av 3.3' 3.75
CfA CO Dame et al. (2001) 8' No smoothing
'''
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
os.chdir('/d/bip3/ezbc/california/data')
# If true, deletes files to be written
clobber = 1
in_images = ('hi/california_hi_galfa_cube',
'hi/california_hi_galfa_dr1_cube',
'av/california_av_planck_tau353',
'av/california_av_error_planck_tau353',
'dust_temp/california_dust_temp',
'dust_temp/california_dust_temp_error',
'/d/bip3/ezbc/multicloud/data/av/multicloud_av_k09_nan',
'co/california_co_cfa_cube')
im_pl = 'av/california_av_planck_tau353'
im_pl_err = 'av/california_av_error_planck_tau353'
im_Td = 'dust_temp/california_dust_temp'
im_Td_err = 'dust_temp/california_dust_temp_error'
im_k09 = 'av/california_av_k09'
im_hi = 'hi/california_hi_galfa_cube'
im_hi_dr1 = 'hi/california_hi_galfa_dr1_cube'
im_co = 'co/california_co_cfa_cube'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (im_hi, im_hi_dr1, im_pl, im_pl_err, im_Td, im_Td_err, im_k09,
im_co)
for i in xrange(len(in_images)):
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\t{:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits', out=out_images[i] + '.mir', op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (
im_pl,
im_pl_err,
im_Td,
im_Td_err,
im_hi,
im_hi_dr1,
)
delta_ra = -0.083333333
delta_dec = 0.083333333
# Greater RA value comes first
ref_pix, npix = calc_image_origin(x_limits=(81, 60),
y_limits=(25, 39),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
print('\t{:s}_5arcmin.mir\n'.format(image))
if not exists:
regrid(image + '.mir', out=image + '_5arcmin.mir', desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcsec
im_beams = np.array([3.7, 3.7, 2.7]) # arcsec
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_hi_dr1, im_k09]
for i in xrange(len(images)):
exists = check_file(images[i] + \
'_smooth_planckres.mir', clobber=clobber)
print('\t{:s}.mir\n'.format(images[i]))
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
print('\t{:s}_smooth_planckres.mir\n'.format(image))
if not exists:
if image == im_co:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_Td + '_5arcmin',
im_Td_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
exists = check_file(image + '.fits', clobber=clobber)
print('\t{:s}.mir\n'.format(image))
fits(image + '.mir', out=image + '.fits', op='xyout')
def main():
''' Planck resolution is 5'. We will smooth and regrid all maps to the
Planck map.
Map Resolution Convol size
GALFA HI 3.7' 3.363
Kainulainen et al. (2009) Av 2.4' 4.386
Pineda et al. (2010) Av 3.3' 3.75
CfA CO Dame et al. (2001) 8' No smoothing
'''
from mirpy import fits, regrid, smooth
from mycoords import calc_image_origin
os.chdir('/d/bip3/ezbc/taurus/data')
# If true, deletes files to be written
clobber = 1
in_images = (
#'av/taurus_av_kainulainen2009_nan',
'/d/bip3/ezbc/multicloud/data/av/multicloud_av_k09_nan',
'av/taurus_av_pineda2010',
'hi/taurus_hi_galfa_cube',
'hi/taurus_hi_galfa_dr1_cube',
'av/taurus_av_planck_tau353',
'av/taurus_av_error_planck_tau353',
'av/taurus_av_planck_radiance',
'av/taurus_av_error_planck_radiance',
'co/taurus_co_cfa_cube',
'co/taurus_co_planck',
'co/taurus_co_error_planck',
'co/taurus_co_1-0_planck',
'co/taurus_co_1-0_error_planck',
'co/taurus_co_2-1_planck',
'co/taurus_co_2-1_error_planck',
'co/taurus_co_3-2_planck',
'co/taurus_co_3-2_error_planck',
)
im_pl = 'av/taurus_av_planck_tau353'
im_pl_err = 'av/taurus_av_error_planck_tau353'
im_pl2 = 'av/taurus_av_planck_radiance'
im_pl2_err = 'av/taurus_av_error_planck_radiance'
im_hi = 'hi/taurus_hi_galfa_cube'
im_hi_dr1 = 'hi/taurus_hi_galfa_dr1_cube'
im_co = 'co/taurus_co_cfa_cube'
im_p10 = 'av/taurus_av_p10'
im_k09 = 'av/taurus_av_k09'
im_pl_co = 'co/taurus_co_planck'
im_pl_co_err = 'co/taurus_co_error_planck'
im_pl_co_10 = 'co/taurus_co_1-0_planck'
im_pl_co_10_err = 'co/taurus_co_1-0_error_planck'
im_pl_co_21 = 'co/taurus_co_2-1_planck'
im_pl_co_21_err = 'co/taurus_co_2-1_error_planck'
im_pl_co_32 = 'co/taurus_co_3-2_planck'
im_pl_co_32_err = 'co/taurus_co_3-2_error_planck'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (
im_k09,
im_p10,
im_hi,
im_hi_dr1,
im_pl,
im_pl_err,
im_pl2,
im_pl2_err,
im_co,
im_pl_co,
im_pl_co_err,
im_pl_co_10,
im_pl_co_10_err,
im_pl_co_21,
im_pl_co_21_err,
im_pl_co_32,
im_pl_co_32_err,
)
for i in xrange(len(in_images)):
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\tReading {:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits', out=out_images[i] + '.mir', op='xyin')
print('\tout = ' + out_images[i] + '.mir')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (
im_pl,
im_pl_err,
im_pl2,
im_pl2_err,
im_hi,
im_hi_dr1,
#im_pl_co,
#im_pl_co_err,
#im_pl_co_10,
#im_pl_co_10_err,
#im_pl_co_21,
#im_pl_co_21_err,
#im_pl_co_32,
#im_pl_co_32_err,
)
delta_ra = -0.083333333
delta_dec = 0.083333333
lim_ra_wcs, lim_dec_wcs = 77.5, 18.0
ref_ra_wcs, ref_dec_wcs = 0.0, 0.0
ref_pix, npix = calc_image_origin(x_limits=(83, 53.0),
y_limits=(15.0, 35.0),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image in (im_hi, im_hi_dr1):
desc = desc_hi
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
print('\tReading image {:s}.mir'.format(image))
print('\tWriting image {:s}_5arcmin.mir\n'.format(image))
if not exists:
regrid(image + '.mir', out=image + '_5arcmin.mir', desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcsec
im_beams = np.array([2.4, 3.3, 3.7, 3.7]) # arcsec
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_k09, im_p10, im_hi, im_hi_dr1]
for i in xrange(len(images)):
exists = check_file(images[i] + '_smooth_planckres.mir',
clobber=clobber)
print('\t{:s}.mir\n'.format(image))
if not exists:
if images[i] in (im_hi, im_hi_dr1):
image = images[i] + '_5arcmin'
else:
image = images[i]
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
print('\t{:s}_smooth_planckres.mir\n'.format(image))
if not exists:
if image == im_co:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1, 2))
# Write the images out to fits images
print('\nWriting images to fits format')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_pl2 + '_5arcmin',
im_pl2_err + '_5arcmin',
im_pl_co + '_5arcmin',
im_pl_co_err + '_5arcmin',
im_pl_co_10 + '_5arcmin',
im_pl_co_10_err + '_5arcmin',
im_pl_co_21 + '_5arcmin',
im_pl_co_21_err + '_5arcmin',
im_pl_co_32 + '_5arcmin',
im_pl_co_32_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_p10 + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres',
im_hi_dr1 + '_regrid_planckres',
]
for image in images:
check_file(image + '.fits', clobber=clobber)
print('\t{:s}.mir\n'.format(image))
fits(image + '.mir', out=image + '.fits', op='xyout')
def main():
from mirpy import fits, regrid, smooth, imbin
from mycoords import calc_image_origin
from astropy.io import fits as pyfits
import numpy as np
os.chdir('/d/bip3/ezbc/multicloud/data')
# If true, deletes files to be written
clobber = False
clobber_hi = False
# First, change zeros in lee image to nans
data, header = pyfits.getdata('av/multicloud_av_lee12_2mass.fits', header=True)
data[data == 0] = np.nan
pyfits.writeto('av/multicloud_av_lee12_2mass_nan.fits',
data,
header,
clobber=True)
data, header = pyfits.getdata('av/multicloud_av_k09.fits',
header=True)
data[data == -1] = np.nan
pyfits.writeto('av/multicloud_av_k09_nan.fits',
data,
header,
clobber=True)
in_images = ('hi/multicloud_hi_galfa_cube',
'av/multicloud_av_planck',
'av/multicloud_av_error_planck',
'dust_temp/multicloud_dust_temp',
'dust_temp/multicloud_dust_temp_error',
'dust_temp/multicloud_dust_beta',
'dust_temp/multicloud_dust_beta_error',
'av/multicloud_av_planck_radiance',
'av/multicloud_av_error_planck_radiance',
'co/multicloud_co_cfa_cube',
'av/multicloud_av_k09_nan',
'av/multicloud_av_lee12_2mass_nan',
'av/multicloud_av_lee12_iris_masked')
im_hi = 'hi/multicloud_hi_galfa_cube'
im_pl = 'av/multicloud_av_planck'
im_pl_err = 'av/multicloud_av_error_planck'
im_Td = 'dust_temp/multicloud_dust_temp'
im_Td_err = 'dust_temp/multicloud_dust_temp_error'
im_beta = 'dust_temp/multicloud_dust_beta'
im_beta_err = 'dust_temp/multicloud_dust_beta_error'
im_pl2 = 'av/multicloud_av_planck_radiance'
im_pl2_err = 'av/multicloud_av_error_planck_radiance'
im_co = 'co/multicloud_co_cfa_cube'
im_k09 = 'av/multicloud_av_k09_nan'
im_lee_2mass = 'av/multicloud_av_lee12_2mass'
im_lee_iris = 'av/multicloud_av_lee12_iris'
# Load the images into miriad
print('\nLoading images into miriad')
out_images = (im_hi,
im_pl,
im_pl_err,
im_Td,
im_Td_err,
im_beta,
im_beta_err,
im_pl2,
im_pl2_err,
im_co,
im_k09,
im_lee_2mass,
im_lee_iris)
for i in xrange(len(in_images)):
if in_images[i] == im_hi:
exists = check_file(out_images[i] + '.mir', clobber=clobber_hi)
else:
exists = check_file(out_images[i] + '.mir', clobber=clobber)
print('\tLoading {:s}.fits\n'.format(in_images[i]))
if not exists:
fits(in_images[i] + '.fits',
out=out_images[i] + '.mir',
op='xyin')
# Regrid Planck images and HI image to have one beam/pixel
print('\nRegridding Planck images')
images = (im_pl, im_pl_err, im_Td, im_Td_err, im_beta, im_beta_err, im_pl2, im_pl2_err, im_hi)
desc = (59.75,0,-0.08333,180,26.05,0,0.08333,132)
delta_ra = -0.083333333
delta_dec = 0.083333333
ref_pix, npix = calc_image_origin(x_limits=(85, 32),
y_limits=(15, 48),
delta_x=delta_ra,
delta_y=delta_dec)
desc_av = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1]]
low_vel = -100.0
high_vel = 100.0
vel_res = 0.16667
vel_npix = int((high_vel - low_vel) / vel_res)
ref_pix_vel = int(vel_npix / 2.0) * vel_res
ref_pix_vel = vel_npix / 2.0
desc_hi = [0, ref_pix[0], delta_ra, npix[0], \
0, ref_pix[1], delta_dec, npix[1], \
0, ref_pix_vel, vel_res, vel_npix]
for image in images:
# If HI, regrid the velocity axis as well
if image == im_hi:
desc = desc_hi
exists = check_file(image + '_5arcmin.mir', clobber=clobber_hi)
else:
desc = desc_av
exists = check_file(image + '_5arcmin.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_5arcmin.mir\n'.format(image))
regrid(image + '.mir',
out=image + '_5arcmin.mir',
desc=desc)
# Determine convolution beam sizes to smooth to Planck res
print('\nSmoothing images to Planck resolution')
planck_beam = 5.0 # arcmin
im_beams = np.array([3.7, 2.5, 2.5, 4.3]) # arcmin
conv_beams = (planck_beam**2 - im_beams**2)**0.5
images = [im_hi, im_k09, im_lee_2mass, im_lee_iris]
for i, image in enumerate(images):
if image == im_hi:
desc = desc_hi
exists = check_file(image + '_smooth_planckres.mir',
clobber=clobber_hi)
else:
desc = desc_av
exists = check_file(image + '_smooth_planckres.mir',
clobber=clobber)
print image
if not exists:
if images[i] == im_hi:
image = images[i] + '_5arcmin'
else:
image = images[i]
print('\tSmoothing {:s}.mir\n'.format(image))
smooth(image + '.mir',
out=images[i] + '_smooth_planckres.mir',
fwhm=conv_beams[i],
pa=0,
scale=0.0)
# Regrid all images to Planck image
print('\nRegridding images to Planck grid')
images.append(im_co)
for image in images:
exists = check_file(image + '_regrid_planckres.mir', clobber=clobber)
if not exists:
print('\tRegridding {:s}_smooth_planckres.mir\n'.format(image))
if image == im_co or image == im_lee_2mass or image == im_lee_iris:
regrid(image + '.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
else:
regrid(image + '_smooth_planckres.mir',
out=image + '_regrid_planckres.mir',
tin=im_pl + '_5arcmin.mir',
axes=(1,2))
# Bin the images
print('\nBin the images')
images = [
im_pl + '_5arcmin',
im_pl_err + '_5arcmin',
im_Td + '_5arcmin',
im_Td_err + '_5arcmin',
im_beta + '_5arcmin',
im_beta_err + '_5arcmin',
im_pl2 + '_5arcmin',
im_pl2_err + '_5arcmin',
im_k09 + '_regrid_planckres',
im_lee_2mass + '_regrid_planckres',
im_lee_iris + '_regrid_planckres',
im_co + '_regrid_planckres',
im_hi + '_regrid_planckres']
for image in images:
exists = check_file(image + '_bin.mir', clobber=clobber)
if not exists:
print('\tWriting {:s}.mir\n'.format(image))
bin_size = 10
if image == im_hi + '_regrid_planckres' or \
image == im_co + '_regrid_planckres':
bins = bin_size, bin_size, bin_size, bin_size,1,1
else:
bins = bin_size, bin_size, bin_size, bin_size,
imbin(image + '.mir',
out=image + '_bin.mir',
bin=bins,
)
# Write the images out to fits images
print('\nWriting images to fits format')
for image in images:
if image == im_hi + '_regrid_planckres':
exists = check_file(image + '.fits', clobber=clobber_hi)
else:
exists = check_file(image + '.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}.fits\n'.format(image))
fits(image + '.mir',
out=image + '.fits',
op='xyout')
exists = check_file(image + '_bin.fits', clobber=clobber)
if not exists:
print('\tWriting {:s}_bin.fits\n'.format(image))
fits(image + '_bin.mir',
out=image + '_bin.fits',
op='xyout')
