def search_local_max(image, radius=120, view=False, **kwargs):
"""
Use 'find_loc_max()' to search all the maxs. The edges position with a lot of zeros would be excluded.
Parameter
--------
image:
2D array type image.
radius:
A radius used to test if any empty pixels around.
Return
--------
A list of positions of 'PSF'
"""
from galight.tools.cutout_tools import cutout
PSFx, PSFy = find_loc_max(image, **kwargs)
PSF_locs = []
ct = 0
for i in range(len(PSFx)):
cut_img = cutout(image, [PSFx[i], PSFy[i]], radius=radius)
cut_img[np.isnan(cut_img)] = 0
if np.sum(cut_img == 0) > len(cut_img)**2 / 20:
continue
PSF_locs.append([PSFx[i], PSFy[i]])
if view == True:
print("plot for position: [{0}, {1}]".format(PSFx[i], PSFy[i]),
"idx:", ct)
print("total flux:", cut_img.sum())
print("measure FWHM:", np.round(measure_FWHM(cut_img), 3))
plt_fits(cut_img)
print("================")
ct += 1
return PSF_locs
def abs_res(self, rotate_pix, if_plot=False):
cal_areas, _, punish = self.segm_to_mask(rotate_pix)
res_ = self.residual(rotate_pix)
if if_plot == True:
plt_fits(abs(res_ * cal_areas), norm='log')
if punish == False:
return np.sum(abs(res_ * cal_areas))
else:
return 10**6
data_sb = data_sb[:2000, :2000]
data = data_sb / header['PHOTMJSR']
value_unit = header['BUNIT']
print('For flux value in unit of MJy/sr, the zp is 31.4.'
) #https://en.wikipedia.org/wiki/Jansky#AB_magnitude
print(
"Conversion factor from {units} to DN/S for filter {f}:".format(
units=header['BUNIT'], f=filt), header['PHOTMJSR'])
header0 = im[0].header
img_filter = header0['FILTER']
img_cam = header0['APERNAME'] #In JDAT'simulation it is 'DETECTOR'
exptime = header0['TEXPTIME'] #The assumed exp time.
from galight.tools.astro_tools import plt_fits
plt_fits(data)
#%% Model using galight
from galight.data_process import DataProcess
zp = 31.4 - 2.5 * np.log10(
header['PHOTMJSR']
) #Calculate the correspondingly zp as DN/S #This is wrong! See 3_...
data_process = DataProcess(fov_image=data,
target_pos=[1170., 940.],
pos_type='pixel',
header=header,
rm_bkglight=False,
exptime=np.ones_like(data) * exptime,
if_plot=False,
zp=zp) #Gain value assuming as 1
data_process.generate_target_materials(radius=65,
# fit_run.plot_final_qso_fit()
# fit_run.dump_result()
# print(fit_run.final_result_galaxy[0])
#%% Co-add residual
import glob
import pickle
from galight.tools.astro_tools import plt_fits
tot_residual = None
for i in range(len(info)-1):
ID, filename, _ = info[i]
picklename = 'first_pkl_files/'+ID+'_'+str(i)+'.pkl'
picklename = glob.glob(picklename)
if picklename != []:
fitting_run_class = pickle.load(open(picklename[0],'rb'))
data = fitting_run_class.fitting_specify_class.kwargs_data['image_data']
psf = fitting_run_class.image_ps_list[0]
nearby = fitting_run_class.image_host_list[1:]
host_resi = data - psf
# if i == 10:
# fitting_run_class.plot_final_qso_fit()
for j in range(len(nearby)):
host_resi = host_resi - nearby[j]
# plt_fits(host_resi)
if tot_residual is None:
tot_residual = host_resi
else:
tot_residual += host_resi
plt_fits(tot_residual, colorbar = True)
header = im[1].header
# print('For flux value in unit of MJy/sr.') #https://en.wikipedia.org/wiki/AB_magnitude
value_unit = header['BUNIT']
# flux(Mjy/sr) * 2.350443 * 10**(-5) *0.03**2 #Flux to Jy https://irsa.ipac.caltech.edu/data/SPITZER/docs/spitzermission/missionoverview/spitzertelescopehandbook/18/
print("Data unit:", value_unit)
# data_sb = data_sb[2000:4000,2000:4000]
data_sb = data_sb[100:2100, 100:2100]
zp = -2.5 * np.log10(2.350443 * 10**(-5) * 0.03**2 / 3631)
header0 = im[0].header
img_filter = header0['FILTER']
img_cam = header0['APERNAME'] #In JDAT'simulation it is 'DETECTOR'
exptime = header0['TEXPTIME'] #The assumed exp time.
from galight.tools.astro_tools import plt_fits
plt_fits(data_sb)
#%%Obtain PSF stars:
from galight.data_process import DataProcess
# zp = 31.4 - 2.5*np.log10(header['PHOTMJSR']) #Calculate the correspondingly zp as DN/S #This is wrong! See 3_...
data_process_ = DataProcess(fov_image=data_sb,
target_pos=[1170., 940.],
pos_type='pixel',
header=header,
rm_bkglight=True,
exptime=np.ones_like(data_sb) * exptime,
if_plot=False,
zp=zp) #Gain value assuming as 1
# data_process_.generate_target_materials(radius=65, create_mask = False, nsigma=2.8, if_select_obj=False,
# exp_sz= 1.2, npixels = 15, if_plot=True)
data_process_.find_PSF(radius=60, user_option=True, psf_edge=10)
data_process_.plot_overview(label='Example', target_label=None)
#%%
import pickle
from galight.tools.asymmetry_tools import Measure_asy
from galight.tools.astro_tools import plt_fits
fit_run_pkl = fit_run
obj_id = 0
asy_class = Measure_asy(
fit_run_pkl,
seg_cal_reg='or',
obj_id=obj_id, #consider_petrosian=True,
extend=1.5,
eta=0.2)
plt_fits(asy_class.img, colorbar=True)
asy_class.asy_segm(mask_type='segm')
# asy_class.asy_segm(mask_type='aper') #!!!
pos = asy_class.find_pos()
print(pos["x"])
asy = asy_class.cal_asymmetry(
rotate_pix=pos["x"],
if_remeasure_bkg=True, #!!!
if_plot=True,
if_plot_bkg=True)
from galight.tools.asymmetry_tools import cal_r_petrosian
r_p = cal_r_petrosian(asy_class.img, center=pos['x'], if_plot=True)
# plt.imshow(asy_class._segm, origin='lower')
# data_sb1 = copy.deepcopy(data[pos[0]:pos[0]+4400,pos[0]:pos[0]+4400])
# data_sb2 = copy.deepcopy(data[:,5800:])
# plt_fits(data_sb1)
#%%
from galight.tools.measure_tools import detect_obj
from photutils.segmentation import SourceCatalog
from galight.data_process import DataProcess
from galight.tools.cutout_tools import psf_clean
psfs, FWHMs = [], []
for data_sb in data_sbs:
zp = -2.5 * np.log10(2.350443 * 10**(-5) * 0.01499**2 / 3631)
header0 = im[0].header
img_filter = header0['FILTER']
img_cam = header0['APERNAME'] #In JDAT'simulation it is 'DETECTOR'
exptime = header0['TEXPTIME'] #The assumed exp time.
plt_fits(data_sb)
# Obtain PSF stars:
# zp = 31.4 - 2.5*np.log10(header['PHOTMJSR']) #Calculate the correspondingly zp as DN/S #This is wrong! See 3_...
data_process_ = DataProcess(fov_image=data_sb,
target_pos=[1170., 940.],
pos_type='pixel',
header=header,
rm_bkglight=True,
exptime=np.ones_like(data_sb) * exptime,
if_plot=False,
zp=zp) #Gain value assuming as 1
# data_process_.generate_target_materials(radius=65, create_mask = False, nsigma=2.8, if_select_obj=False,
# exp_sz= 1.2, npixels = 15, if_plot=True)
data_process_.find_PSF(radius=60,
user_option=True,
psf_edge=150,
plt.show()
# return asy, obj_flux, bkg_asy
print(np.sum(obj_masks), np.sum(cal_areas))
return asy / obj_flux - bkg_asy / np.sum(obj_masks) * np.sum(
cal_areas) / obj_flux
return asy, obj_flux, bkg_asy, np.sum(obj_masks), np.sum(cal_areas)
# Q:
# Which mask to use as total flux of object? The obj_mask + obj_mask_?
# When calculate residual image, which object total flux should be used?
# How to norm the bkg asy?
# %%
image = pyfits.open('./test_img.fits')[0].data.copy()
plt_fits(image)
pos = [0, 5.2]
image_shift = shift_img(image, pos, order=2)
# plt_fits(image_shift)
image_rotate = rotate_image(image, pos, order=2)
# plt_fits(image_rotate)
from galight.tools.measure_tools import detect_obj
apertures, segm_deblend = detect_obj(image,
if_plot=True,
segm_map=True,
nsigma=1,
auto_sort_center=True)
# plt.imshow(segm_mask_rot, origin='lower')
# plt.show()
img_class = Test_asy(img=image,
segm=segm_deblend,
# apertures = [copy.deepcopy(apertures[0])] + apertures
# apertures[0].a, apertures[0].b = apertures[0].a*0.2, apertures[0].b*0.2
# data_process.apertures = apertures
from galight.fitting_specify import FittingSpecify
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=0) #, fix_n_list= [[0,4],[1,1]])
fit_sepc.build_fitting_seq()
fit_sepc.plot_fitting_sets(
) #The apertures shows how the images will be modelled.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename=savename, fitting_level='deep')
fit_run.run(algorithm_list=['PSO'], setting_list=[None])
fit_run.dump_result()
else:
fit_run = pickle.load(open(glob.glob(savename + '*.pkl')[0], 'rb'))
fit_run.fitting_specify_class.plot_fitting_sets()
file_header = copy.deepcopy(fitsFile[1].header)
file_header['CRPIX1'] = file_header['CRPIX1'] - data_process.target_pos[
0] + len(data_process.target_stamp) / 2
file_header['CRPIX2'] = file_header['CRPIX2'] - data_process.target_pos[
1] + len(data_process.target_stamp) / 2
fit_run.plot_all()
# fit_run.plot_final_galaxy_fit()
res = (fit_run.flux_2d_out['data'] - fit_run.flux_2d_out['model']) #* masks
plt_fits(res)
pyfits.PrimaryHDU(res).writeto(rebin_name + '_data-model' + '.fits',
overwrite=True)
PSF_loc = [35.29689501, -4.685896878]
data_process.find_PSF(
radius=20,
PSF_pos_list=[PSF_loc],
pos_type='wcs',
)
#Generate the fitting materials
data_process.generate_target_materials(radius=30,
create_mask=False,
nsigma=2.8,
exp_sz=1.5,
npixels=40,
if_plot=False)
plt_fits(data_process.PSF_list[0])
# data_process.apertures = [] #Assuming there is no host (i.e., as constant.) #!!!
# Manually input the PSF:
# data_process.PSF_list = [PSF]
# Check if all the materials is given, if so to pass to the next step.
data_process.checkout() #Check if all the materials is known.
fit_sepc = FittingSpecify(data_process)
# fit_sepc.prepare_fitting_seq(point_source_num = 2)
fit_sepc.prepare_fitting_seq(point_source_num=2,
ps_pix_center_list=[[-2.0, -1.0],
[6.0, -1.0]])
# if fit_sepc.kwargs_params['point_source_model'][0][0] == fit_sepc.kwargs_params['point_source_model'][0][1]:
# fit_sepc.prepare_fitting_seq(point_source_num = 2, ps_pix_center_list= [[-2.0, -1.0], [6.0, -1.0]])
# print(file)