def pull_values(self, id_coords):
#use id_coordinates as center values of new map.
ra_cent = float(id_coords.ra.to_string(decimal=True))
dec_cent = float(id_coords.dec.to_string(decimal=True))
ref_head = self.astrometry #make a copy of the reference header, need to change center values.
ref_head['CRVAL1'] = ra_cent
ref_head['CRVAL2'] = dec_cent
#Galactic Coordinate System
hp = HEALPix(
nside=2048, order='ring', frame='galactic'
) #this is with nside for the Planck Maps for the Planck Maps.
#create a grid representing the potential map
planck_pix = hp.pixel_resolution.to(
u.arcsecond).value #pixel size of the planck parameter maps
#the 1.5 factor is to oversize the maps
map_deg_x = self.x_size * self.r_pix / (1.5 * 3600
) #mapsize in degrees.
map_deg_y = self.y_size * self.r_pix / (1.5 * 3600)
npixxside = ceil(
map_deg_x * 3600 /
planck_pix) #number of pixels along each axis in reference map
npixyside = ceil(map_deg_y * 3600 / planck_pix)
#oversized to account for interpolation later
ra = np.linspace(ra_cent - map_deg_x, ra_cent + map_deg_x,
npixxside) * u.deg
dec = np.linspace(dec_cent - map_deg_y, dec_cent + map_deg_y,
npixyside) * u.deg
ra_grid, dec_grid = np.meshgrid(ra, dec)
#commit coordinate grid to a skycoord class
coords = SkyCoord(ra=ra_grid.ravel(),
dec=dec_grid.ravel(),
frame='icrs')
#pull in the parameters at these specified coordiantes
beta = hp.interpolate_bilinear_skycoord(coords, self.full_params[2])
T = hp.interpolate_bilinear_skycoord(coords, self.full_params[1])
Tau = hp.interpolate_bilinear_skycoord(coords, self.full_params[0])
I = calc_intensity(beta, Tau, T,
nu=self.nu) * 1e20 #1e20 for conversion to Mjy
#reshape the map to a 2D array as opposed to a 1D array.
I_map = np.reshape(I, (npixxside, npixyside))
#interpolate to match the reference astrometry.
interped_map, ragrd, decgrd = interp_back_to_ref(
I_map, ra_grid, dec_grid, ref_head)
#calculate the rms and surface brightness for this realization.
rms = np.sqrt(np.mean(interped_map))
return rms
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
