def auto_cut_PSFs(img, radius=100, view=True):
PSFx, PSFy = find_loc_max(img)
if view == True:
for i in range(len(PSFx)):
cut_img = cut_image(img, [PSFx[i], PSFy[i]], radius=radius)
if True not in np.isnan(cut_img) and np.max(cut_img) == cut_img[
len(cut_img) / 2][len(cut_img) / 2]:
print("plot for position: [{0}, {1}]".format(PSFx[i], PSFy[i]),
"idx:", i)
cut_img[np.isnan(cut_img)] = 0
print("total flux:", cut_img.sum())
plt_fits(cut_img)
print("================")
return PSFx, PSFy
if dis < (obj[j][1] + obj_temp[i][1]) / 2:
count += 1
if count == 0:
obj.append(obj_temp[i])
print("the number of nearby objs:", len(obj))
ct = ct + 1
from mask_objects import find_loc_max, measure_FWHM
for k in run_list: #['G', 'R', 'I', 'Z', 'Y']
print("Fiting the: " + filename_list[k])
if_dual = False
psf, QSO_img, QSO_std = psf_l[k], QSO_img_l[k], QSO_std_l[k]
#for a quick glimsp of the number of image's local maximum
x, y = find_loc_max(QSO_img, neighborhood_size=3, threshold=1)
arr_x, arr_y = np.asarray(x, dtype=float), np.asarray(y, dtype=float)
center = len(QSO_img) / 2
bool_x, bool_y = (arr_x > (center - 18)) * (arr_x < (center + 18)), (
arr_y > (center - 18)) * (arr_y < (center + 18))
arr_x = arr_x[bool_x * bool_y]
arr_y = arr_y[bool_x * bool_y]
qsoid = filename_list[k].split('.fits')[0]
if len(arr_x) >= 2:
if_dual = True
claim = "This {0} is likely to be a {1} system!!!".format(
filename_list[k], 'BH' * len(arr_x))
elif len(arr_x) == 1:
FWHM_ver, FWHM_hor, FWHM_xy, FWHM_xy_ = measure_FWHM(
QSO_img) #Measure FWHM from ver, hor, up_right, up_left
if k in run_list:
psf_l.append(PSF_list[k])
QSO_img_l.append(QSO_im_list[k][ct:-ct, ct:-ct])
QSO_std_l.append(err_map_list[k][ct:-ct, ct:-ct])
else:
psf_l.append([])
QSO_img_l.append([])
QSO_std_l.append([])
#%%
for k in [band_run_list[0]]: #['G', 'R', 'I', 'Z', 'Y']
print("The two point position using I band: ")
psf, QSO_img, QSO_std = psf_l[k], QSO_img_l[k], QSO_std_l[k]
#for a quick glimsp of the number of image's local maximum
x, y = find_loc_max(QSO_img, neighborhood_size=3)
arr_x, arr_y = np.asarray(x, dtype=float), np.asarray(y, dtype=float)
center = len(QSO_img) / 2
bool_x, bool_y = (arr_x > (center - 18)) * (arr_x < (center + 18)), (
arr_y > (center - 18)) * (arr_y < (center + 18))
arr_x = arr_x[bool_x * bool_y]
arr_y = arr_y[bool_x * bool_y]
plt.imshow(QSO_img, origin='low', norm=LogNorm())
for i in range(len(arr_x)):
plt.text(arr_x[i], arr_y[i], 'BH{0}'.format(i))
plt.plot(arr_x[i], arr_y[i], 'ro')
if pltshow == 1:
plt.show()
else:
plt.close()
# result_dic[folder] = [None, None]
# continue
multi_band_list, kwargs_model, kwargs_result, chain_list, fix_setting, mcmc_new_list = pickle.load(
open(read_file, 'rb'))
fixed_lens, fixed_source, fixed_lens_light, fixed_ps, fixed_cosmo = fix_setting
mcmc_new_list = np.array(mcmc_new_list)
H0_list = mcmc_new_list[:, -1]
lens_data = pyfits.getdata(sim_folder + 'Drz_QSO_image.fits')
lens_mask = cr_mask(lens_data, 'normal_mask.reg')
framesize = len(multi_band_list[0][0]['image_data']) #81
ct = int((len(lens_data) - framesize) / 2)
lens_data = lens_data[ct:-ct, ct:-ct]
lens_mask = (1 - lens_mask)[ct:-ct, ct:-ct]
x, y = find_loc_max(lens_data)
x_s, y_s = [], []
center = (framesize - 1) / 2
deltaPix = 0.08
for i in range(len(x)):
x0, y0 = (float(x[i]) - center) * deltaPix, (float(y[i]) -
center) * deltaPix
# print(x0, y0)
ds = (x0 - kwargs_ps['ra_image'])**2 + (y0 - kwargs_ps['dec_image'])**2
if ds.min() < 0.01:
x_s.append(x[i])
y_s.append(y[i])
y_grid, x_grid = np.indices(
(framesize, framesize)) #with higher resolution 60*6
# for i in range(len(x_s)):
# lens_mask[np.sqrt((y_grid-y_s[i])**2 + (x_grid-x_s[i])**2) <4] = 0