import rebin #From my share_tools
factor = 4
pattern_x = [0, 2, 0, 2, 1, 3, 1, 3]
pattern_y = [0, 0, 2, 2, 3, 3, 1, 1] #from the info. given by observation
################Bin the PSF and save it################
#exp_psf=rebin.expend_grid(psf_pixel_high_res)
cut_fd = int(
(len(psf_data) - ((int(len(psf_data) / 8 * 2) - 1) * 4 + 3)) / 2)
exp_psf_o = psf_data[1 + cut_fd:-cut_fd, 1 +
cut_fd:-cut_fd] + 0 # To change it from 251 to 247.
exp_psf = rebin.expend_grid(exp_psf_o)
cut_len = int(round(len(exp_psf_o) / factor) * factor)
cut_out_psf = np.zeros([len(pattern_x), cut_len, cut_len])
image_bin_psf = np.zeros(
[len(pattern_x),
int(cut_len / factor),
int(cut_len / factor)])
for i in range(len(pattern_x)):
cut_out_psf[i] = exp_psf[pattern_x[i]:cut_len + pattern_x[i],
pattern_y[i]:cut_len +
pattern_y[i]] #the size before bin
image_bin_psf[i] = rebin.block(
cut_out_psf[i], (int(cut_len / factor), int(cut_len / factor)),
factor=factor)
image_bin_psf[i] /= np.sum(image_bin_psf[i]) #unify the psf value
pyfits.PrimaryHDU(image_bin_psf[i]).writeto(
rebin_folder_name +
'/non_drizzled_psf_id{0}-{1}.fits'.format(psf_id, i + 1),
overwrite=False)
#
##==============================================================================
## #Bin the image res. from high to low.
##==============================================================================
sys.path.insert(0, '../share_tools')
import rebin
factor=4
pattern_x=[0,2,0,2,1,3,1,3]
pattern_y=[0,0,2,2,3,3,1,1] #from the info. given by observation
################Bin the lensed image################
exp_grid=rebin.expend_grid(image_highres)
cut_out=np.zeros([len(pattern_x),image_highres.shape[0]-5,image_highres.shape[1]-5])
image_bin =np.zeros([len(pattern_x),int(image_highres.shape[0]/factor)-1,int(image_highres.shape[1]/factor)-1])
for i in range(len(pattern_x)):
cut_out[i]=exp_grid[pattern_x[i]:(numPix-5)+pattern_x[i],pattern_y[i]:(numPix-5)+pattern_y[i]] #the size before bin
image_bin[i]=rebin.block(cut_out[i],(int(numPix/factor)-1,int(numPix/factor)-1),factor=factor)
plt.imshow(image_bin[0], origin='lower',cmap='gist_heat', norm=LogNorm())
plt.colorbar()
plt.show()
################Bin the PSF and save it################
#exp_psf=rebin.expend_grid(psf_pixel_high_res)
cut_fd=int((len(psf_data)-((int(len(psf_data)/8*2)-1)*4+3))/2)
exp_psf_o=psf_data[1+cut_fd:-cut_fd,1+cut_fd:-cut_fd]+ 0 # To change it from 251 to 247.
exp_psf=rebin.expend_grid(exp_psf_o)
cut_len=int(round(len(exp_psf_o)/factor)*factor)
cut_out_psf=np.zeros([len(pattern_x),cut_len,cut_len])
image_bin_psf=np.zeros([len(pattern_x),int(cut_len/factor),int(cut_len/factor)])
for i in range(len(pattern_x)):
cut_out_psf[i]=exp_psf[pattern_x[i]:cut_len+pattern_x[i],pattern_y[i]:cut_len+pattern_y[i]] #the size before bin
image_bin_psf[i]=rebin.block(cut_out_psf[i],(int(cut_len/factor),int(cut_len/factor)),factor=factor)
image_bin_psf[i] /= np.sum(image_bin_psf[i]) #unify the psf value
len(pattern_x), point_highres.shape[0] - 5,
point_highres.shape[1] - 5
])
point_image_bin = np.zeros([
len(pattern_x),
int(point_highres.shape[0] / factor) - 1,
int(point_highres.shape[1] / factor) - 1
])
for i in range(len(pattern_x)):
total_cut_out[i] = total_exp_grid[
pattern_x[i]:(numPix - 5) + pattern_x[i],
pattern_y[i]:(numPix - 5) +
pattern_y[i]] #the size before bin
total_image_bin[i] = rebin.block(
total_cut_out[i],
(int(numPix / factor) - 1, int(numPix / factor) - 1),
factor=factor)
pyfits.PrimaryHDU(total_image_bin[i]).writeto(
sim_folder_name +
'/non_drizzled-AGNclean-{0}.fits'.format(i + 1),
overwrite=False)
host_cut_out[i] = host_exp_grid[
pattern_x[i]:(numPix - 5) + pattern_x[i],
pattern_y[i]:(numPix - 5) +
pattern_y[i]] #the size before bin
host_image_bin[i] = rebin.block(
host_cut_out[i],
(int(numPix / factor) - 1, int(numPix / factor) - 1),
factor=factor)
pyfits.PrimaryHDU(host_image_bin[i]).writeto(