def interp_band_to_band(img, ref_map, map):
map_size = map['signal'].shape
ref_size = ref_map['signal'].shape
#create a grid of RA/DEC coordinates for image we want to interpolate
w = world(map['shead'])
#holder for the x, y pixel coordinates that we want.
x = np.arange(0, map_size[0])
y = np.arange(0, map_size[1])
xvec = np.repeat(x[:, np.newaxis], map_size[1], axis=1)
yvec = np.repeat(y[np.newaxis, :], map_size[0], axis=0)
c = pixel_to_skycoord(xvec, yvec, w, origin=0)
#this is converting the pixel coords to right ascension and declination in fk4
ra = np.asarray(c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(c.dec.to_string(decimal=True), dtype=float)
# reformat map data and coordinates
data = np.ravel(img)
points = np.column_stack((np.ravel(ra), np.ravel(dec)))
#create a agrid of RA/DEC coordinates that we want to interpolate over
ref_w = world(ref_map['shead'])
ref_grid_x, ref_grid_y = np.mgrid[0:ref_size[0], 0:ref_size[1]]
ref_grid_ra, ref_grid_dec = ref_w.wcs_pix2world(ref_grid_x, ref_grid_y, 0)
#do the interpolation
interp_map = griddata(points,
data, (ref_grid_ra, ref_grid_dec),
method='linear')
return interp_map
def interp_back_to_ref(img, ra, dec, ref_head, ref_shape):
'''
Purpose: Perform the inperolation of the completed Planck Map to a reference header
Inputs: img - the Planck flux map
ra - grid of the Right Ascension values used to create the Planck Map
dec - grid of the Decliation values used to create the Planck Map
ref_head - the reference header for the interpolatino grid
ref_shape - the shape of the interpolation grid
'''
map_size = img.shape
# reformat map data and coordinates
data = np.ravel(img)
points = np.column_stack((np.ravel(ra), np.ravel(dec)))
#create a agrid of RA/DEC coordinates that we want to interpolate over
ref_w = world(ref_head)
ref_grid_x, ref_grid_y = np.mgrid[0:ref_shape[0], 0:ref_shape[1]]
ref_grid_ra, ref_grid_dec = ref_w.wcs_pix2world(ref_grid_x, ref_grid_y, 0)
#do the interpolation
interp_map = griddata(points, data, (ref_grid_ra, ref_grid_dec))
final_map = np.swapaxes(interp_map, 0, 1)
return final_map, ref_grid_ra, ref_grid_dec
def one_map(name, header, band):
if 'CDELT1' in header.keys():
#calculating the pixsize based off of astrometry parameters.
pixsize = 3600 * np.mean(
[abs(header['CDELT1']),
abs(header['CDELT2'])])
#if the given astrometry has CDELT values and not a cd matrix create a cd matrix.
header['cd_1'] = header['CDELT1']
header['cd_2'] = 0
header['cd_1'] = 0
header['cd_2'] = header['CDELT2']
else:
#if the astrometry is given with a cd matrix calculate the pixsize this way.
pixsize = 3600 * \
np.mean([abs(header['CD1_1']+ header['CD2_1']), \
abs(header['CD2_1'] + header['CD2_2'])])
#this is to call IRIS code
ra_c = header['crval1'] * u.deg
dec_c = header['crval2'] * u.deg
cent = [ra_c.value, dec_c.value]
coord = SkyCoord(ra=ra_c, dec=dec_c)
b1950_coord = coord.transform_to(FK4(equinox='B1950'))
ra_c_b = float(b1950_coord.ra.to_string(decimal=True))
dec_c_b = float(b1950_coord.dec.to_string(decimal=True))
#square patch of sky, this can be changed if desired.
naxis = [header['naxis1'], header['naxis2']]
iris_head = make_header(pixsize, naxis, ra_c_b, dec_c_b)
iris_map = mosaic(iris_head, band=4)
x = np.arange(0, iris_head['NAXIS1'])
y = np.arange(0, iris_head['NAXIS2'])
yvec = np.repeat(x[:, np.newaxis], iris_head['NAXIS2'], axis=1)
xvec = np.repeat(y[np.newaxis, :], iris_head['NAXIS1'], axis=0)
w = world(iris_head)
c = pixel_to_skycoord(xvec, yvec, w, origin=0)
c = c.transform_to('icrs')
#this is converting the pixel coords to right ascension and declination in fk4
ra = np.asarray(c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(c.dec.to_string(decimal=True), dtype=float)
iris_interp, r, d = interp_back_to_ref(iris_map, ra.ravel(), dec.ravel(),
header)
c = 2.99792458e8 #m/s
nu = c / clus_get_lambdas(band, center=False)
planck, ra, dec, avg_T, avg_beta, avg_tau = create_map(header, nu=nu)
planck_head = save_header(header, avg_beta, avg_T, avg_tau,
'Planck_' + band, 'Jy/Beam', cent)
return planck, iris_interp, avg_T, avg_beta, avg_tau, planck_head
def mosaic(header, band=4, catname=config.IrisLookupFile, dir=config.IrisDir):
'''
purpose: create mosaic of images
Inputs : Header - header information -should contain some astrometry information
band - the band you wish to look at
- 1 : 12 micron
- 2 : 25 micron
- 3 : 60 micron
- 4 : 100 micron (default)
catname- filename of the catalog
dir - directory where iris maps are
Outputs:
'''
header.set('NAXIS', 2)
w = world(header)
try:
equinox = header['EQUINOX']
except KeyError:
equinox = header['EPOCH']
x_size = header['NAXIS1']
y_size = header['NAXIS2']
print('Handling %s elements' % (x_size * y_size))
x = np.arange(0, x_size)
y = np.arange(0, y_size)
xlist = np.tile(1, x_size)
ylist = np.tile(1, y_size)
xmap = np.outer(x, ylist)
ymap = np.outer(xlist, y)
result = np.zeros((x_size, y_size))
weight = np.zeros((x_size, y_size))
new_c = pixel_to_skycoord(xmap, ymap, w, origin=0)
#this is converting the pixel coords to right ascension and declination in fk4
ra = np.asarray(new_c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(new_c.dec.to_string(decimal=True), dtype=float)
ctype = get_cord_type(header)
if ctype == 2:
fk4 = 1
equinox = 1950 #force the equinox to be 1950 in the case of galactic coordinates
print('converting from Galactic to Celestial')
sk = SkyCoord(ra * u.deg, dec * u.deg, frame=Galactic)
new_c = sk.transform_to(FK4)
ra = np.asarray(new_c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(new_c.dec.to_string(decimal=True), dtype=float)
elif ctype == 3:
print('converting from Ecliptic to Celestial')
sk = SkyCoord(ra * u.deg, dec * u.deg, frame=BarycentricTrueEcliptic)
new_c = sk.transform_to(FK4)
ra = []
dec = []
coordinates = new_c.to_string('decimal')
ra = np.asarray(new_c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(new_c.dec.to_string(decimal=True), dtype=float)
if equinox == 2000.0:
print('precessing coordinates from J2000 to B1950')
new_c.transform_to(FK4(equinox='B1950'))
ra = np.asarray(new_c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(new_c.dec.to_string(decimal=True), dtype=float)
ra = nan2undef(ra)
dec = nan2undef(dec)
ra = np.asarray(ra)
dec = np.asarray(dec)
#converted these to arrays for easier data manipulation
inum, ramin, ramax, raavg, decmin, decmax, decavg, medianval, noise_key = np.loadtxt(catname, unpack=True)
numel = int(inum[-1]) #number of entries in the text file
print('Checking for ISSA maps that intersect with the given header')
ind1 = np.where(ra != -32768)[0]
ind2 = np.where(dec != -32768)[0]
ind = []
for i in range(ra.shape[0]):
for j in range(ra.shape[1]):
if ra[i,j] != -32768 and dec[i,j] != -32768:
ind.append([i,j])
ind = np.asarray(ind)
good_inds = np.zeros(numel)
c1min = np.min(ra[ind])
c1max = np.max(ra[ind])
c2min = np.min(dec[ind])
c2max = np.max(dec[ind])
for i in range(numel):
if c1min > ramin[i] and c1min < ramax[i] and c2min > decmin[i] and c2min < decmax[i]:
good_inds[i] = 1
elif c1min > ramin[i] and c1min < ramax[i] and c2max > decmin[i] and c2max < decmax[i]:
good_inds[i] = 1
elif c1max > ramin[i] and c1max < ramax[i] and c2max > decmin[i] and c2max < decmax[i]:
good_inds[i] = 1
elif c1max > ramin[i] and c1max < ramax[i] and c2min > decmin[i] and c2min < decmax[i]:
good_inds[i] = 1
elif ramin[i] > c1min and ramin[i] < c1max and decmin[i] > c2min and decmin[i] < c2min:
good_inds[i] = 1
elif ramax[i] > c1min and ramax[i] < c1max and decmin[i] > c2min and decmin[i] < c2min:
good_inds[i] = 1
elif ramin[i] > c1min and ramin[i] < c1max and decmax[i] > c2min and decmax[i] < c2min:
good_inds[i] = 1
elif ramax[i] > c1min and ramax[i] < c1max and decmax[i] > c2min and decmax[i] < c2min:
good_inds[i] = 1
good_inds = np.where(good_inds > 0)[0]
if good_inds.shape[0] == 0:
print('No ISSA map corresponds to the header given')
exit()
print('%s ISSA maps will be combined to produce the mosaic' %(good_inds.shape[0]))
for i in range(good_inds.shape[0]):
mapi = get_iris(inum[good_inds[i]], dir=dir, band=band)
mapi[0].header['EQUINOX'] = 2000.0
try:
del(mapi[0].header['NAXIS3'])
except KeyError:
pass
#do the transform back to pixel coords
w = world(mapi[0].header)
x, y = skycoord_to_pixel(new_c, w, origin=0)
tempo = mbilinear(x, y, mapi[0].data)
for j in range(tempo.shape[0]):
for k in range(tempo.shape[1]):
if tempo[j,k] != -32768:
weight[j,k] = weight[j,k] + 1
result[j,k] = result[j,k] + tempo[j,k]
indw = []
complement = []
for i in range(weight.shape[0]):
for j in range(weight.shape[1]):
if weight[i,j] > 0:
result[i,j] = result[i,j] / weight[i,j]
else:
result[i,j] = -32768
#because of the way the image gets made it is rotated and flipped along its x-axis
#this is a correction to get it to line up with the idl version and does not have any
#real effect on its astrometry
result = np.rot90(result, k=3)
result = np.fliplr(result)
#for testing
# plt.imshow(result, origin='lower')
# plt.show()
return result
def read_in_fits(filename, center, ref_head, ref_pixsize=8, ref_mapsize=260):
'''
Purpose : This function reads in the fits files for the components and parses them so that we are left with data only for our field.
Inputs: filename (str) - the singular filename for a component used in the MBB fit
center (float array) - center of the field of interest
ref_head (Astropy.header) - header for the reference field
ref_pixsize - pixel size of the reference map
ref_mapsize - mapsize of the reference map
Outputs: map (float array) - an array of flux values at the given field
pixsize (float) - pixel size of the uninterpolated component maps
x_side (int) - the length of the x-axis of the map
y_side (int) - the length of the y-axis of the map
RA_grid (float array) - grid of the Right Ascension values used to pull out components
DEC_grid (float array) - grid of the Declination values used to pull out components
'''
hdul = fits.open(filename)
head = hdul[1].header
if 'Temperature' in filename:
data = hdul[1].data.field('TEMP')
error = hdul[1].data.field('ERR_TEMP')
elif 'Spectral-Index' in filename:
data = hdul[1].data.field('BETA')
error = hdul[1].data.field('ERR_BETA')
elif 'Opacity' in filename:
data = hdul[1].data.field('TAU353')
error = hdul[1].data.field('ERR_TAU')
else:
data = hdul[1].data.field(0)
nside = head['NSIDE']
order = head['ORDERING']
hdul.close()
#Galactic Coordinate System
hp = HEALPix(nside=nside, order=order, frame='galactic')
#create a pixel grid in terms of the nu=353 grid for GNILC to create our intensity maps
pixsize = hp.pixel_resolution.to(u.arcsecond).value
#* 2 is for boosting the size of the map so to fix edge effects from interpolation
map_arc_x = ref_mapsize[0] * 2 * ref_pixsize #map size in arcseconds
map_arc_y = ref_mapsize[1] * 2 * ref_pixsize
npixxside = ceil(map_arc_x / pixsize) #convert to map size in pixels for nu = 353 map.
npixyside = ceil(map_arc_y / pixsize)
#* 2 is for boosting the size of the map so to fix edge effects from interpolation
x = np.linspace(0, ref_mapsize[0] * 2, npixxside)
y = np.linspace(0, ref_mapsize[1] * 2, npixyside)
X, Y = np.meshgrid(x, y)
w = world(ref_head)
skycoords = pixel_to_skycoord(X.ravel(), Y.ravel(), wcs=w, origin=0)
RA_grid = np.asarray(skycoords.ra.to_string(decimal=True), dtype='float') * u.deg
DEC_grid = np.asarray(skycoords.dec.to_string(decimal=True), dtype='float') * u.deg
# coords = SkyCoord(RA_grid.ravel(), DEC_grid.ravel(), frame='icrs')
coords = SkyCoord(ra=RA_grid.ravel(), dec=DEC_grid.ravel(), frame='icrs')
gal_coords = coords.galactic
map = hp.interpolate_bilinear_skycoord(gal_coords, data)
x_side = len(x)
y_side = len(y)
return map, pixsize, y_side, x_side, RA_grid, DEC_grid
def mosaic(header,
band=4,
catname=iris_config.IrisLookupFile,
dir=iris_config.IrisDir):
'''
purpose: create mosaic of images
Inputs : Header - header information -should contain some astrometry information
band - the band you wish to look at
- 1 : 12 micron
- 2 : 25 micron
- 3 : 60 micron
- 4 : 100 micron (default)
catname- filename of the catalog
dir - directory where iris maps are
Outputs:
'''
header.set('NAXIS', 2)
w = world(header)
try:
equinox = header['EQUINOX']
except KeyError:
equinox = header['EPOCH']
x_size = header['NAXIS2']
y_size = header['NAXIS1']
print('Handling %s elements' % (x_size * y_size))
x = np.arange(0, x_size)
y = np.arange(0, y_size)
xlist = np.tile(1, x_size)
ylist = np.tile(1, y_size)
xmap = np.outer(x, ylist)
ymap = np.outer(xlist, y)
result = np.zeros((x_size, y_size))
weight = np.zeros((x_size, y_size))
new_c = pixel_to_skycoord(xmap, ymap, w, origin=0)
#this is converting the pixel coords to right ascension and declination in fk4
ra = np.asarray(new_c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(new_c.dec.to_string(decimal=True), dtype=float)
ctype = get_cord_type(header)
ra = nan2undef(ra)
dec = nan2undef(dec)
ra = np.asarray(ra)
dec = np.asarray(dec)
#converted these to arrays for easier data manipulation
inum, ramin, ramax, raavg, decmin, decmax, decavg, medianval, noise_key = np.loadtxt(
catname, unpack=True)
numel = int(inum[-1]) #number of entries in the text file
print('Checking for ISSA maps that intersect with the given header')
ind1 = np.where(ra != -32768)[0]
ind2 = np.where(dec != -32768)[0]
ind = []
for i in range(ra.shape[0]):
for j in range(ra.shape[1]):
if ra[i, j] != -32768 and dec[i, j] != -32768:
ind.append([i, j])
ind = np.asarray(ind)
good_inds = np.zeros(numel)
c1min = np.min(ra[ind])
c1max = np.max(ra[ind])
c2min = np.min(dec[ind])
c2max = np.max(dec[ind])
for i in range(numel):
if c1min > ramin[i] and c1min < ramax[i] and c2min > decmin[
i] and c2min < decmax[i]:
good_inds[i] = 1
elif c1min > ramin[i] and c1min < ramax[i] and c2max > decmin[
i] and c2max < decmax[i]:
good_inds[i] = 1
elif c1max > ramin[i] and c1max < ramax[i] and c2max > decmin[
i] and c2max < decmax[i]:
good_inds[i] = 1
elif c1max > ramin[i] and c1max < ramax[i] and c2min > decmin[
i] and c2min < decmax[i]:
good_inds[i] = 1
elif ramin[i] > c1min and ramin[i] < c1max and decmin[
i] > c2min and decmin[i] < c2min:
good_inds[i] = 1
elif ramax[i] > c1min and ramax[i] < c1max and decmin[
i] > c2min and decmin[i] < c2min:
good_inds[i] = 1
elif ramin[i] > c1min and ramin[i] < c1max and decmax[
i] > c2min and decmax[i] < c2min:
good_inds[i] = 1
elif ramax[i] > c1min and ramax[i] < c1max and decmax[
i] > c2min and decmax[i] < c2min:
good_inds[i] = 1
good_inds = np.where(good_inds > 0)[0]
if good_inds.shape[0] == 0:
print('No ISSA map corresponds to the header given')
exit()
print('%s ISSA maps will be combined to produce the mosaic' %
(good_inds.shape[0]))
for i in range(good_inds.shape[0]):
mapi = get_iris(inum[good_inds[i]], dir=dir, band=band)
try:
del (mapi[0].header['NAXIS3'])
except KeyError:
pass
#do the transform back to pixel coords
w = world(mapi[0].header)
x, y = skycoord_to_pixel(new_c, w, origin=0)
tempo = mbilinear(x, y, mapi[0].data)
for j in range(tempo.shape[0]):
for k in range(tempo.shape[1]):
if tempo[j, k] != -32768:
weight[j, k] = weight[j, k] + 1
result[j, k] = result[j, k] + tempo[j, k]
indw = []
complement = []
for i in range(weight.shape[0]):
for j in range(weight.shape[1]):
if weight[i, j] > 0:
result[i, j] = result[i, j] / weight[i, j]
else:
result[i, j] = -32768
#in interpolating the image the x and y axes get flipped.
result = np.swapaxes(result, 0, 1)
if band == 4:
result -= 0.65 #subtract CIB to get just dgl / cirrus
return result
pixsize = 0.0250000000000 #pixels / arcsecond of observation to interpolate IRIS
# maps to.
naxis = 22
name = 'rxj1347'
#square patch of sky, this can be changed if desired.
naxis1 = naxis2 = naxis
y = np.arange(0, naxis1)
x = np.arange(0, naxis2)
yvec = np.repeat(x[:, np.newaxis], naxis2, axis=1)
xvec = np.repeat(y[np.newaxis, :], naxis1, axis=0)
head = make_header(pixsize, naxis, ra_c_b,
dec_c_b) #this function assumes a square.
w = world(head)
c = pixel_to_skycoord(xvec, yvec, w, origin=0)
c = c.transform_to('icrs')
#this is converting the pixel coords to right ascension and declination in fk4
ra = np.asarray(c.ra.to_string(decimal=True), dtype=float)
dec = np.asarray(c.dec.to_string(decimal=True), dtype=float)
#get information from the IRAS astrometry positions
map = mosaic(head, band=4)
hdu = fits.PrimaryHDU(map, head)
hdul = fits.HDUList([hdu])
hdul.writeto(config.DataDir + 'iris_0' + name + '_fx.fits')
#ra
hdu = fits.PrimaryHDU(ra, head)
