def transfer_to_result_ps(data,
ps_result,
image_ps,
image_host,
error_map,
filt,
fixcenter,
ID,
cut=0,
plot_compare=True,
QSO_msk_list="",
pix_sz='drz06',
QSO_msk=None,
tag=None):
#==============================================================================
# Translate the e1, e2 to phi_G and q
#==============================================================================
#==============================================================================
# Save the result
#==============================================================================
from roundme import roundme
result = {}
##==============================================================================
##Plot the images for adopting in the paper
##==============================================================================
from flux_profile import total_compare
data = data
QSO = image_ps
if QSO_msk is None:
QSO_mask = data * 0 + 1
else:
QSO_mask = QSO_msk
if len(image_host) == 0:
host = data * 0
label = ['data', 'QSO', 'host=0', 'model', 'normalized residual']
elif len(image_host) > 0:
host = np.zeros_like(image_host[0])
for i in range(len(image_host)):
host += image_host[i]
label = [
'data', 'QSO', '{0}objs'.format(i), 'model', 'normalized residual'
] #Print the numbers of objects
flux_list = [data, QSO, host, error_map]
import glob
mask_list = glob.glob(QSO_msk_list) # Read *.reg files in a list.
# print "mask_list,muhahah", mask_list
fig = total_compare(label_list=label,
flux_list=flux_list,
target_ID=ID,
pix_sz=pix_sz,
data_mask_list=mask_list,
data_cut=cut,
zp=zp,
plot_compare=plot_compare,
msk_image=QSO_mask)
if tag is not None:
fig.savefig("{0}_SB_profile.pdf".format(tag), bbox_inches='tight')
# =============================================================================
# Calculate reduced Chisq and save to result
# =============================================================================
chiq_map = ((data - image_ps - host) / (error_map))**2 * QSO_mask
pixels = len(error_map)**2 - (1 - QSO_mask).sum()
reduced_Chisq = chiq_map.sum() / pixels
result = roundme(result)
result['redu_Chisq'] = round(reduced_Chisq, 6)
return result
for i in range(len(chain_list)):
f, axes = chain_plot.plot_chain_list(chain_list,i)
plt.show()
agn_image = pyfits.getdata('./allscience/l{0}_{1}_cutout.fits'.format(ID,fr))
if len(image_host) == 1:
host = image_host[0]
label = ['data', 'QSO', 'host', 'model', 'normalized residual']
elif len(image_host) >1:
host = np.zeros_like(image_host[0])
for i in range(len(image_host)):
host += image_host[i]
label = ['data', 'QSO', 'host as {0} components'.format(i+1), 'model', 'normalized residual'] #Print the numbers
flux_list = [agn_image, image_ps[0], host, error_map]
fig = total_compare(label_list = label, flux_list = flux_list, target_ID = ID, pix_sz=pix_sz, zp = zp,
plot_compare = False, msk_image = QSO_msk)
from IPython.display import display
display(fig)
#%% 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)
modelPlot.decomposition_plot(ax=axes[1,0], text='Host galaxy', source_add=True, unconvolved=True)
modelPlot.decomposition_plot(ax=axes[1,1], text='Host galaxy convolved', source_add=True)
modelPlot.decomposition_plot(ax=axes[1,2], text='All components convolved', source_add=True, lens_light_add=True, point_source_add=True)
def transfer_to_result(data,
source_result,
ps_result,
image_ps,
image_host,
error_map,
zp,
fixcenter,
ID,
cut=0,
plot_compare=True,
QSO_msk_list="",
pix_sz=0.168,
QSO_msk=None,
tag=None):
#==============================================================================
# Translate the e1, e2 to phi_G and q
#==============================================================================
import lenstronomy.Util.param_util as param_util
source_result[0]['phi_G'], source_result[0][
'q'] = param_util.ellipticity2phi_q(source_result[0]['e1'],
source_result[0]['e2'])
#==============================================================================
# Save the result
#==============================================================================
from roundme import roundme
import copy
result = copy.deepcopy(source_result[0])
del result['e1']
del result['e2']
result['QSO_amp'] = ps_result[0]['point_amp'][0]
result['host_amp'] = image_host[0].sum()
result['host_flux_ratio_percent'] = result['host_amp'] / (
result['QSO_amp'] + result['host_amp']) * 100
zp = zp
result['host_mag'] = -2.5 * np.log10(result['host_amp']) + zp
#print "The host flux is ~:", image_host.sum()/(image_ps.sum() + image_host.sum())
# =============================================================================
# Save QSO position to result if not fix center
# =============================================================================
if fixcenter == False:
result['qso_x'] = ps_result[0]['ra_image'][0]
result['qso_y'] = ps_result[0]['dec_image'][0]
##==============================================================================
##Plot the images for adopting in the paper
##==============================================================================
from flux_profile import total_compare
data = data
QSO = image_ps
if QSO_msk is None:
QSO_mask = data * 0 + 1
else:
QSO_mask = QSO_msk
if len(image_host) == 1:
host = image_host[0]
label = ['data', 'QSO', 'host', 'model', 'normalized residual']
elif len(image_host) > 1:
host = np.zeros_like(image_host[0])
for i in range(len(image_host)):
host += image_host[i]
label = [
'data', 'QSO', 'host+{0}objs'.format(i), 'model',
'normalized residual'
] #Print the numbers of objects
flux_list = [data, QSO, host, error_map]
import glob
mask_list = glob.glob(QSO_msk_list) # Read *.reg files in a list.
# print "mask_list,muhahah", mask_list
fig = total_compare(label_list=label,
flux_list=flux_list,
target_ID=ID,
pix_sz=pix_sz,
data_mask_list=mask_list,
data_cut=cut,
zp=zp,
plot_compare=plot_compare,
msk_image=QSO_mask)
if tag is not None:
fig.savefig("{0}_SB_profile.pdf".format(tag), bbox_inches='tight')
fig1 = total_compare(label_list=label,
flux_list=flux_list,
target_ID=ID,
pix_sz=pix_sz,
data_mask_list=mask_list,
data_cut=cut,
zp=zp,
plot_compare=plot_compare,
msk_image=QSO_mask,
if_annuli=True)
if tag is not None:
fig1.savefig("{0}_SB_profile_annuli.pdf".format(tag),
bbox_inches='tight')
# =============================================================================
# Calculate reduced Chisq and save to result
# =============================================================================
chiq_map = ((data - image_ps - host) / (error_map))**2 * QSO_mask
pixels = len(error_map)**2 - (1 - QSO_mask).sum()
reduced_Chisq = chiq_map.sum() / pixels
result = roundme(result)
result['redu_Chisq'] = round(reduced_Chisq, 6)
return result
from flux_profile import total_compare
QSO_im, err_map, QSO_bkg, PSF, pix_scale, zp, qso_fr_center, fr_c_RA_DEC = gen_fit_id(
image_folder, QSO_RA, QSO_DEC, filename, cut_frame=120, subbkl=sub_bkg)
ct = (len(QSO_im) -
len(image_host[0])) / 2 # If want to cut to 61, QSO_im[ct:-ct,ct:-ct]
if len(image_host) == 1:
host = image_host[0]
label = ['data', 'QSO', 'host', 'model', 'normalized residual']
elif len(image_host) > 1:
host = np.zeros_like(image_host[0])
for i in range(len(image_host)):
host += image_host[i]
label = [
'data', 'QSO', 'host as {0} components'.format(i + 1), 'model',
'normalized residual'
] #Print the numbers of objects
agn_image = QSO_im[ct:-ct, ct:-ct]
error_map = err_map[ct:-ct, ct:-ct]
flux_list = [agn_image, image_ps, host, error_map]
total_compare(label_list=label,
flux_list=flux_list,
target_ID=QSO_id + '-' + band,
pix_sz=pix_scale,
zp=zp,
plot_compare=False,
msk_image=np.ones_like(agn_image))
#fig.savefig("{0}_SB_profile.pdf".format(name_save), bbox_inches = 'tight')