dist_psfs = (psfs_pos-center_QSO)[:,0]**2+(psfs_pos-center_QSO)[:,1]**2 #The distance of the PSF close to the QSO.
psfs_pos = psfs_pos[dist_psfs.argsort()] # Sort the PSF order based on their distance.
count = 0
#The following lines are to check if the PSF's Gaussian center is consistent or not with the brighest pixel
PSF_gauss_centers, PSF_bright_centers=[],[]
PSFs_ = []
for i in range(len(psfs_pos)):
print 'PSF',count
PSF, PSF_center = cut_center_bright(image=img_sub, center=psfs_pos[i], radius=75, return_center=True, plot=False, center_float=True)
PSF_gauss_centers.append(PSF_center)
_, PSF_br_center = cut_center_bright(image=img_sub, center=psfs_pos[i], radius=75, kernel = 'center_bright', return_center=True, plot=False)
PSF_bright_centers.append(PSF_br_center)
#Creat the PSFs mask that block the objects nearby
_, PSF_mask = mask_obj(img=PSF, exp_sz=1.4, pltshow = 1)
if len(PSF_mask) > 1:
PSF_mask = np.sum(np.asarray(PSF_mask),axis=0)
elif len(PSF_mask) == 1:
PSF_mask = PSF_mask[0]
PSF_mask = (1 - (PSF_mask != 0)*1.)
PSFs_.append([PSF,PSF_center,PSF_mask])
count += 1
center_match = (np.sum(abs(np.asarray(PSF_gauss_centers)-np.asarray(PSF_bright_centers)<0.7),axis = 1) == 2)
psf_pos_list = [PSF_gauss_centers[i] for i in range(len(PSFs_)) if center_match[i]==True]
PSFs = [PSFs_[i] for i in range(len(PSFs_)) if center_match[i]==True]
save_loc_png(img_sub,center_QSO,psf_pos_list, ID=ID, label='PSF' ,reg_ty = None)
# Compare the Kron slope of the PSFs:
PSF_list = [PSFs[i][0] for i in range(len(PSFs))]
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
'''
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
if count == 0:
obj.append(obj_temp[i])
print("the number of nearby objs:", len(obj))
data_host_list = []
Iband_inf = []
# from regions import PixCoord, EllipsePixelRegion
# from photutils import EllipticalAperture
# aperture = EllipticalAperture(qso_info[0], a, b, theta=theta)
# reg = EllipsePixelRegion(center=center, width=aperture.a*2, height=aperture.b*2, angle=theta)
masks_0 = mask_obj(QSO_img_l[1], snr=1., exp_sz= 1.4, pltshow = 0, npixels=20, return_deblend = False)
masks_1 = mask_obj(QSO_img_l[1], snr=1.4, exp_sz= 1.6, pltshow = 0, npixels=20, return_deblend = False)
QSO_msk = masks_0[0] * (1-masks_1[1][0])
# obj.append(((8, 20),
# 1.0812992677234345,
# 0.8441567009970872))
plt.imshow(QSO_img*QSO_msk, origin='low', norm=LogNorm())
# for i in range(len(obj)):
# obj_x, obj_y = len(QSO_img)/2 + obj[i][0][0] , len(QSO_img)/2+ obj[i][0][1]
# plt.text(obj_x, obj_y, "obj{0}".format(i), fontsize=15, color='k')
plt.show()
obj = []
#%%
for k in run_list:#len(band_seq)):
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 7 14:39:05 2018
@author: dartoon
Test create mask auto
"""
import numpy as np
import astropy.io.fits as pyfits
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import sys
from mask_objects import mask_obj
import os
path = os.getcwd()
ID = path.split('/')[-2]
QSO_img = pyfits.getdata('test.fits')
masks = mask_obj(img=QSO_img, exp_sz=1.2)
obj_mask = masks[0] + masks[1] + masks[2]
obj_mask = [obj_mask == 0][0] + 0
plt.imshow(obj_mask)
print "the yellow area(==1) is the area to fit"
#kwargs_numerics['mask'] = obj_mask
plt.show()
print("The adopted PSF:")
psf = kernel_util.cut_psf(psf, psf_size=kernel_size)
plt.imshow(psf, norm=LogNorm(), origin='lower')
plt.savefig('./zoutput/{0}_psf.png'.format(ID))
if pltshow == 0:
plt.close()
else:
plt.show()
from mask_objects import mask_obj
_, _, deblend_sources = mask_obj(agn_image,
snr=snratio,
npixels=numberpixels,
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()
plt.savefig('./zoutput/{0}_mask.png'.format(ID))
if pltshow == 0:
plt.close()
else:
plt.show()
if block_id == []:
QSO_msk = np.ones_like(agn_image)
psf_size) / 2 # If want to cut to 81, agn_image[ct:-ct,ct:-ct]
psf = psf[pct:-pct, pct:-pct]
psf /= psf.sum()
print "The adopted PSF:"
plt.imshow(psf, norm=LogNorm(), origin='lower')
if pltshow == 0:
plt.close()
else:
plt.show()
# =============================================================================
# Creat the QSO mask
# =============================================================================
from mask_objects import mask_obj
_, _, deblend_sources = mask_obj(agn_image,
snr=1.,
npixels=200,
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()
# Based on the deblend_sources, it is the ID 1 and 2 to block:
block_id = []
if block_id == []:
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