psf_l, QSO_img_l, QSO_std_l = [], [], []
for k in range(len(band_seq)):
if k in run_list:
ct = int((len(QSO_im_list[k]) - fit_frame_size) /
2) # If want to cut to 61, QSO_im[ct:-ct,ct:-ct]
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([])
ct = 0
for k in run_list:
objs, Q_index = detect_obj(QSO_img_l[k], pltshow=pltshow)
qso_info = objs[Q_index]
obj_temp = [objs[i] for i in range(len(objs)) if i != Q_index]
if ct == 0:
obj = obj_temp
if k > 0 and len(obj_temp) > 0:
for i in range(len(obj_temp)):
count = 0
for j in range(len(obj)):
dis = np.sqrt(
np.sum((np.asarray(obj[j][0]) -
np.asarray(obj_temp[i][0]))**2))
if i == 1:
print(dis, (obj[j][1] + obj_temp[i][1]) / 2)
if dis < (obj[j][1] + obj_temp[i][1]) / 2:
count += 1
2)] # derive all the reg in the frame
selet_galaxy = ((abs(regs_pix_target[:, 0]) < 2) &
(abs(regs_pix_target[:, 1]) < 2))
galaxy_info = [
regs_pix_target[selet_galaxy][0], re_target[selet_galaxy][0]
] # The galaxy pixel position
selet_obj = ((abs(regs_pix_target[:, 0]) > 2) &
(abs(regs_pix_target[:, 1]) > 2))
obj_pos = regs_pix_target[selet_obj] # The objects pixel position
obj_re = re_target[selet_obj]
obj = [[obj_pos[i], obj_re[i]] for i in range(len(obj_pos))
] #Change the objects position list to right format
print "the number of nearby objs:", len(obj)
else:
for k in run_list:
objs, Q_index = detect_obj(galaxy_img_l[k], pltshow=1, snr=1.2)
galaxy_info = objs[Q_index]
obj_temp = [objs[i] for i in range(len(objs)) if i != Q_index]
if k == run_list[0]:
obj = obj_temp
if k != run_list[0] and len(obj_temp) > 0:
for i in range(len(obj_temp)):
count = 0
for j in range(len(obj)):
dis = np.sqrt(
np.sum((np.asarray(obj[j][0]) -
np.asarray(obj_temp[i][0]))**2))
if dis < (obj[j][1] + obj_temp[i][1]) / 2:
count += 1
print "the number of nearby objs:", len(obj)
obj = [obj[2]]
def decomp(fitsfile, psffile, deepseed, fix_center, runMCMC, name, iresults,
band):
#Setting the fitting condition:
deep_seed = deepseed #Set as True to put more seed and steps to fit.
# pltshow = 1 #Note that setting plt.ion() in line27, the plot won't show anymore if running in terminal.
pltshow = 0
pix_scale = 0.168
fixcenter = fix_center
run_MCMC = runMCMC
zp = 27.0
fitfile = fits.open(fitsfile)
psf1 = pyfits.getdata(psffile)
#was 3 before, changed it to 1
QSO_img1 = fitfile[1].data
#print(QSO_img1)
#print(QSO_img1.shape)
#I CHANGED IT TO 1 IT SHOUDL ABSOLUTELY BE 3
QSO_std1 = fitfile[3].data**0.5
frame_size = len(QSO_img1)
cut_to = 38
#cut_to = 30
ct = (frame_size - cut_to) / 2
'''
QSO_img = QSO_img1[ct:-ct, ct:-ct]
QSO_std = QSO_std1[ct:-ct, ct:-ct]
'''
#print('before for image: ', len(QSO_img1), len(QSO_img1[1]))
#print('before for std: ', len(QSO_std1), len(QSO_std1[1]))
#goes [y,x]
if (len(QSO_img1) - len(QSO_img1[1]) == 0):
cut_to = len(QSO_img1) - 2
ct = int((frame_size - cut_to) / 2)
#print(ct)
#print('square: ', len(QSO_img1),len(QSO_img1[1]))
QSO_img = QSO_img1[ct:-ct, ct:-ct]
if (len(QSO_img1) - len(QSO_img1[1]) > 0):
cut_to = len(QSO_img1[1]) - 1
ct = int((frame_size - cut_to) / 2)
#print('left is bigger', len(QSO_img1),len(QSO_img1[1]))
diff = len(QSO_img1) - len(QSO_img1[1])
QSO_img = QSO_img1[ct + diff:-ct, ct:-ct]
#QSO_img = QSO_img1[20:-ct, ct:-ct]
if (len(QSO_img1) - len(QSO_img1[1]) < 0):
cut_to = len(QSO_img1) - 2
ct = int((frame_size - cut_to) / 2)
diff = len(QSO_img1) - len(QSO_img1[1])
QSO_img = QSO_img1[ct + diff:-ct, ct:-ct]
#print('right is bigger', len(QSO_img1),len(QSO_img1[1]))
#print(len(QSO_img1), len(QSO_img1[1]))
#print('after for img: ', len(QSO_img), len(QSO_img[1]))
#print('ok now QSO_std time')
if (len(QSO_std1) == len(QSO_std1[1])):
ct = int((frame_size - cut_to) / 2)
QSO_std = QSO_std1[ct:-ct, ct:-ct]
if (len(QSO_std1) - len(QSO_std1[1]) > 0):
ct = int((frame_size - cut_to) / 2)
diff = len(QSO_std1) - len(QSO_std1[1])
QSO_std = QSO_std1[ct + diff:-ct, ct:-ct]
if (len(QSO_std1) - len(QSO_std1[1]) < 0):
ct = int((frame_size - cut_to) / 2)
diff = len(QSO_std1) - len(QSO_std1[1])
QSO_std = QSO_std1[ct + diff:-ct, ct:-ct]
#print('fix this!!!')
#print('after for std: ', len(QSO_std), len(QSO_std[1]))
#print('psf size before: ', psf1.shape)
#now do ct for psf
frame_size = len(psf1)
cut_to = 39
ct = int((frame_size - cut_to) / 2)
if (len(psf1) == len(psf1[1])):
cut_to = len(psf1) - 2
ct = int((frame_size - cut_to) / 2)
psf = psf1[ct:-ct, ct:-ct]
if (len(psf1) - len(psf1[1]) > 0):
cut_to = len(psf1[1]) - 1
ct = int((frame_size - cut_to) / 2)
#print('weird psf')
diff = len(psf1) - len(psf1[1])
psf = psf1[ct + diff:-ct, ct:-ct]
if (len(psf1) - len(psf1[1]) < 0):
cut_to = len(psf1) - 2
ct = int((frame_size - cut_to) / 2)
#print('weird psf')
diff = len(psf1) - len(psf1[1])
psf = psf1[ct:-ct, ct:-ct + diff]
#print('psf size after: ', psf.shape)
#if(len(psf1) - len(psf1[1]) == 1):
# psf = psf1[ct + 1:-ct, ct:-ct]
#if(len(psf1) - len(psf1[1]) == -1):
# psf = psf1[ct:-ct, ct+1:-ct]
#print('psf size after: ', psf.shape)
#print(QSO_img)
#print(QSO_img.shape)
#psf = psf1
#%%
#==============================================================================
# input the objects components and parameteres
#==============================================================================
ins = iresults[0]['n_sersic']
iRs = iresults[0]['R_sersic']
ie1 = iresults[0]['e1']
ie2 = iresults[0]['e2']
ixc = iresults[0]['center_x']
iyc = iresults[0]['center_y']
objs, Q_index = detect_obj(QSO_img, pltshow=pltshow)
qso_info = objs[Q_index]
obj = [objs[i] for i in range(len(objs)) if i != Q_index]
fixed_source = []
kwargs_source_init = []
kwargs_source_sigma = []
kwargs_lower_source = []
kwargs_upper_source = []
fixed_source.append({
'R_sersic': iRs,
'n_sersic': ins,
'e1': ie1,
'e2': ie2
})
kwargs_source_init.append({
'R_sersic': 0.3,
'n_sersic': 2.,
'e1': 0.,
'e2': 0.,
'center_x': 0.,
'center_y': 0.
})
kwargs_source_sigma.append({
'n_sersic': 0.5,
'R_sersic': 0.5,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
})
kwargs_lower_source.append({
'e1': -0.5,
'e2': -0.5,
'R_sersic': 0.1,
'n_sersic': 0.3,
'center_x': -0.5,
'center_y': -0.5
})
kwargs_upper_source.append({
'e1': 0.5,
'e2': 0.5,
'R_sersic': 3.,
'n_sersic': 7.,
'center_x': 0.5,
'center_y': 0.5
})
if len(obj) >= 1:
for i in range(len(obj)):
if len(iresults) <= i + 1:
fixed_source.append({})
kwargs_source_init.append({
'R_sersic': obj[i][1] * pix_scale,
'n_sersic': 2.,
'e1': 0.,
'e2': 0.,
'center_x': -obj[i][0][0] * pix_scale,
'center_y': obj[i][0][1] * pix_scale
})
kwargs_source_sigma.append({
'n_sersic': 0.5,
'R_sersic': 0.5,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
})
kwargs_lower_source.append({
'e1':
-0.5,
'e2':
-0.5,
'R_sersic':
obj[i][1] * pix_scale / 5,
'n_sersic':
0.3,
'center_x':
-obj[i][0][0] * pix_scale - 10,
'center_y':
obj[i][0][1] * pix_scale - 10
})
kwargs_upper_source.append({
'e1':
0.5,
'e2':
0.5,
'R_sersic':
3.,
'n_sersic':
7.,
'center_x':
-obj[i][0][0] * pix_scale + 10,
'center_y':
obj[i][0][1] * pix_scale + 10
})
else:
ins = iresults[i + 1]['n_sersic']
iRs = iresults[i + 1]['R_sersic']
ie1 = iresults[i + 1]['e1']
ie2 = iresults[i + 1]['e2']
ixc = iresults[i + 1]['center_x']
iyc = iresults[i + 1]['center_y']
fixed_source.append({
'R_sersic': iRs,
'n_sersic': ins,
'e1': ie1,
'e2': ie2
})
kwargs_source_init.append({
'R_sersic': obj[i][1] * pix_scale,
'n_sersic': 2.,
'e1': 0.,
'e2': 0.,
'center_x': -obj[i][0][0] * pix_scale,
'center_y': obj[i][0][1] * pix_scale
})
kwargs_source_sigma.append({
'n_sersic': 0.5,
'R_sersic': 0.5,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
})
kwargs_lower_source.append({
'e1':
-0.5,
'e2':
-0.5,
'R_sersic':
obj[i][1] * pix_scale / 5,
'n_sersic':
0.3,
'center_x':
-obj[i][0][0] * pix_scale - 10,
'center_y':
obj[i][0][1] * pix_scale - 10
})
kwargs_upper_source.append({
'e1':
0.5,
'e2':
0.5,
'R_sersic':
3.,
'n_sersic':
7.,
'center_x':
-obj[i][0][0] * pix_scale + 10,
'center_y':
obj[i][0][1] * pix_scale + 10
})
source_params = [
kwargs_source_init, kwargs_source_sigma, fixed_source,
kwargs_lower_source, kwargs_upper_source
]
#%%
#%%
# =============================================================================
# Creat the QSO mask
# =============================================================================
from mask_objects import mask_obj
_, _, deblend_sources = mask_obj(QSO_img,
snr=1.2,
npixels=50,
return_deblend=True)
print("deblend image to find the ID for the Objects for the mask:")
plt.imshow(deblend_sources,
origin='lower',
cmap=deblend_sources.cmap(random_state=12345))
plt.colorbar()
if pltshow == 0:
plt.close()
#else:
#plt.show()
QSO_msk = None
import os
if not os.path.exists(
'/Users/jasminwashington/Google Drive File Stream/My Drive/usrp/git_repo/hostgal/py_tools/targets/shortlist/'
+ name + '/'):
os.makedirs(
'/Users/jasminwashington/Google Drive File Stream/My Drive/usrp/git_repo/hostgal/py_tools/targets/shortlist/'
+ name + '/')
tag = '/Users/jasminwashington/Google Drive File Stream/My Drive/usrp/git_repo/hostgal/py_tools/targets/shortlist/' + name + '/' + name
#tag = 'home/michelle/Desktop/' + name
# for if you want to save the image to your computer
source_result, ps_result, image_ps, image_host, error_map = fit_qso(
QSO_img,
psf_ave=psf,
psf_std=None,
source_params=source_params,
QSO_msk=QSO_msk,
fixcenter=fixcenter,
pix_sz=pix_scale,
no_MCMC=(run_MCMC == False),
QSO_std=QSO_std,
tag=tag,
deep_seed=deep_seed,
pltshow=pltshow,
corner_plot=False,
flux_ratio_plot=True,
dump_result=run_MCMC,
band=band)
if pltshow == 0:
plot_compare = False
fits_plot = False
else:
plot_compare = True
fits_plot = True
# print("imps:")
# print(image_ps)
worm = np.array(image_ps[0])
# print(np.unique(image_ps))
image_ps = worm
print("Source")
print(source_result)
result, flux_list = transfer_to_result(data=QSO_img,
pix_sz=pix_scale,
source_result=source_result,
ps_result=ps_result,
image_ps=image_ps,
image_host=image_host,
error_map=error_map,
zp=zp,
fixcenter=fixcenter,
ID='Example',
QSO_msk=QSO_msk,
tag=tag,
plot_compare=plot_compare,
band=band)
if QSO_msk is None:
QSO_mask = QSO_img * 0 + 1
dmps = (flux_list[0] - flux_list[1]) * QSO_mask
hdu = fits.PrimaryHDU(dmps)
hdu.writeto(
'/Users/jasminwashington/Google Drive File Stream/My Drive/usrp/git_repo/hostgal/py_tools/targets/shortlist/'
+ name + '/' + name + '_' + band + '.fits',
overwrite=True)
hdu1 = fits.PrimaryHDU(flux_list[0])
hdu1.writeto(
'/Users/jasminwashington/Google Drive File Stream/My Drive/usrp/git_repo/hostgal/py_tools/targets/shortlist/'
+ name + '/' + name + '_' + band + '_all.fits',
overwrite=True)
return result
'''
def itsfits(fitsfile, psffile, deepseed, fix_center, runMCMC):
#Setting the fitting condition:
deep_seed = deepseed #Set as True to put more seed and steps to fit.
pltshow = 1 #Note that setting plt.ion() in line27, the plot won't show anymore if running in terminal.
pix_scale = 0.168
fixcenter = fix_center
run_MCMC = runMCMC
zp = 27.0
fitfile = fits.open(fitsfile)
psf = pyfits.getdata(psffile)
QSO_img1 = fitfile[1].data
#print(QSO_img1)
#print(QSO_img1.shape)
QSO_std1 = fitfile[3].data**0.5
frame_size = len(QSO_img1)
cut_to = 70
ct = (frame_size - cut_to) / 2
#print(frame_size, ct)
#QSO_img = QSO_img.data[ct:-ct, ct:-ct]
#QSO_std = QSO_std.data[ct:-ct, ct:-ct]
QSO_img = QSO_img1[ct:-ct, ct:-ct]
QSO_std = QSO_std1[ct:-ct, ct:-ct]
#print(QSO_img)
#print(QSO_img.shape)
#%%
#==============================================================================
# input the objects components and parameteres
#==============================================================================
objs, Q_index = detect_obj(QSO_img, pltshow=pltshow)
qso_info = objs[Q_index]
obj = [objs[i] for i in range(len(objs)) if i != Q_index]
fixed_source = []
kwargs_source_init = []
kwargs_source_sigma = []
kwargs_lower_source = []
kwargs_upper_source = []
fixed_source.append({})
kwargs_source_init.append({
'R_sersic': 0.3,
'n_sersic': 2.,
'e1': 0.,
'e2': 0.,
'center_x': 0.,
'center_y': 0.
})
kwargs_source_sigma.append({
'n_sersic': 0.5,
'R_sersic': 0.5,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
})
kwargs_lower_source.append({
'e1': -0.5,
'e2': -0.5,
'R_sersic': 0.1,
'n_sersic': 0.3,
'center_x': -0.5,
'center_y': -0.5
})
kwargs_upper_source.append({
'e1': 0.5,
'e2': 0.5,
'R_sersic': 3.,
'n_sersic': 7.,
'center_x': 0.5,
'center_y': 0.5
})
if len(obj) >= 1:
for i in range(len(obj)):
fixed_source.append({})
kwargs_source_init.append({
'R_sersic': obj[i][1] * pix_scale,
'n_sersic': 2.,
'e1': 0.,
'e2': 0.,
'center_x': -obj[i][0][0] * pix_scale,
'center_y': obj[i][0][1] * pix_scale
})
kwargs_source_sigma.append({
'n_sersic': 0.5,
'R_sersic': 0.5,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
})
kwargs_lower_source.append({
'e1':
-0.5,
'e2':
-0.5,
'R_sersic':
obj[i][1] * pix_scale / 5,
'n_sersic':
0.3,
'center_x':
-obj[i][0][0] * pix_scale - 10,
'center_y':
obj[i][0][1] * pix_scale - 10
})
kwargs_upper_source.append({
'e1':
0.5,
'e2':
0.5,
'R_sersic':
3.,
'n_sersic':
7.,
'center_x':
-obj[i][0][0] * pix_scale + 10,
'center_y':
obj[i][0][1] * pix_scale + 10
})
source_params = [
kwargs_source_init, kwargs_source_sigma, fixed_source,
kwargs_lower_source, kwargs_upper_source
]
#%%
# =============================================================================
# Creat the QSO mask
# =============================================================================
from mask_objects import mask_obj
_, _, deblend_sources = mask_obj(QSO_img,
snr=1.2,
npixels=50,
return_deblend=True)
print "deblend image to find the ID for the Objects for the mask:"
plt.imshow(deblend_sources,
origin='lower',
cmap=deblend_sources.cmap(random_state=12345))
plt.colorbar()
if pltshow == 0:
plt.close()
else:
plt.show()
QSO_msk = None
tag = 'example'
source_result, ps_result, image_ps, image_host, error_map = fit_qso(
QSO_img,
psf_ave=psf,
psf_std=None,
source_params=source_params,
QSO_msk=QSO_msk,
fixcenter=fixcenter,
pix_sz=pix_scale,
no_MCMC=(run_MCMC == False),
QSO_std=QSO_std,
tag=tag,
deep_seed=deep_seed,
pltshow=pltshow,
corner_plot=False,
flux_ratio_plot=True,
dump_result=run_MCMC)
if pltshow == 0:
plot_compare = False
fits_plot = False
else:
plot_compare = True
fits_plot = True
result = transfer_to_result(data=QSO_img,
pix_sz=pix_scale,
source_result=source_result,
ps_result=ps_result,
image_ps=image_ps,
image_host=image_host,
error_map=error_map,
zp=zp,
fixcenter=fixcenter,
ID='Example',
QSO_msk=QSO_msk,
tag=tag,
plot_compare=plot_compare)
return result