Iband_inf = source_result # in order to save the I band source_reslt to fix Reff and n for other band.
if pltshow == 0:
plot_compare = False
fits_plot = False
else:
plot_compare = True
fits_plot = True
# for the plot propose:
_ = 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_list[k],
fixcenter=fixcenter,
ID=image_ID + band_seq[k],
QSO_msk=None,
tag=tag,
plot_compare=plot_compare)
#
result = transfer_obj_to_result(source_result[0], image_host[0], zp)
result['redu_Chisq'] = reduced_chisq
fitsFile = pyfits.open(image_folder + filename_list[k])
file_header = copy.deepcopy(fitsFile[1].header)
file_header['CRPIX1'] = file_header['CRPIX1'] - qso_center_list[k][
0] + len(QSO_img) / 2
file_header['CRPIX2'] = file_header['CRPIX2'] - qso_center_list[k][
1] + len(QSO_img) / 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
'''
corner_plot=False,
flux_ratio_plot=True,
dump_result=run_MCMC,
pso_diag=True)
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[0],
image_host=image_host,
error_map=error_map,
zp=zp,
fixcenter=fixcenter,
ID='ID' + repr(ID),
tag=tag,
plot_compare=plot_compare)
print("Saving results:")
pickle.dump([result, framesize],
open(folder + '/{0}.pkl'.format(save_name), 'wb'))
filtname = folder + '/{0}.txt'.format(save_name)
result_file = open(filtname, 'w')
result_file.write(repr(result))
result_file.close()
# #%%
# print("The truth:")
# QSO_msk = QSO_msk
# else:
# QSO_msk = QSO_img*0 + 1
# label = ['data', 'QSO', '{0} Sersics'.format(len(image_host)), 'model', 'normalized residual']
# flux_list = [QSO_img, image_ps, host, multi_band_list[0][0]['noise_map']]
# fig = total_compare_6(label_list = label, flux_list = flux_list, target_ID = ID+'-'+band, pix_sz=pix_scale,
# data_mask_list = [], data_cut = 0, zp = 27.0,
# plot_compare = True, msk_image = QSO_msk)
# fig.savefig("{0}_SB_profile.pdf".format(ID+'-'+band), bbox_inches = 'tight')
_ = 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=multi_band_list[0][0]['noise_map'],
zp=27.0,
fixcenter=True,
ID=ID + '-' + band,
QSO_msk=QSO_msk,
tag='021930.51-055643.0/fit_image_021930.51-055643.0_HSC-' + band,
plot_compare=True)
# #%% Recover the plot
# from lenstronomy.Plots.model_plot import ModelPlot
# modelPlot = ModelPlot(multi_band_list, kwargs_model, kwargs_result,
# arrow_size=0.02, cmap_string="gist_heat", likelihood_mask_list=[QSO_msk])
# f, axes = plt.subplots(3, 3, figsize=(16, 16), sharex=False, sharey=False)
# modelPlot.data_plot(ax=axes[0,0], text="Data")
# modelPlot.model_plot(ax=axes[0,1])
# modelPlot.normalized_residual_plot(ax=axes[0,2], v_min=-6, v_max=6)
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
modelPlot.subtract_from_data_plot(ax=axes[2,0], text='Data - Point Source', point_source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,1], text='Data - host galaxy', source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,2], text='Data - host galaxy - Point Source', source_add=True, point_source_add=True)
f.tight_layout()
plt.show()
import sys
sys.path.insert(0, '../../../py_tools/')
from transfer_to_result import transfer_to_result
if len(image_ps) >1:
image_ps = np.sum(image_ps, axis=0)
else:
image_ps = image_ps[0]
_ = 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=multi_band_list[0][0]['noise_map'],
zp=27.0, fixcenter=True,ID='Data', QSO_msk = QSO_msk, tag=None, plot_compare = True)
#%%Recover the translated cornor plot
plt.imshow(QSO_img, origin='low', norm=LogNorm())
for i in range(len(ps_result)):
obj_x, obj_y = len(QSO_img)/2 - ps_result[i]['ra_image'][0]/pix_scale, len(QSO_img)/2+ps_result[i]['dec_image'][0]/pix_scale
# print(obj_x, obj_y)
plt.text(obj_x, obj_y, "QSO{0}".format(i), fontsize=10, color='k')
plt.show()
plt.imshow(QSO_img, origin='low', norm=LogNorm())
for i in range(len(source_result)):
obj_x, obj_y = len(QSO_img)/2 - source_result[i]['center_x']/pix_scale, len(QSO_img)/2+source_result[i]['center_y']/pix_scale
plt.text(obj_x, obj_y, "obj{0}".format(i), fontsize=15, color='k')
