def HIIrecover_loop(ima, ima_err, name, plot=False, p=0, F_min=0):
if F_min == 0:
fmin = 0
F_min = flux_min(ima, name)
else:
fmin = F_min
IP, JP = max_coord(ima, name, 0.0)
IP2, JP2 = [], []
(n, m) = np.shape(ima)
l = 17 #default box size
cont = []
I, J = [], []
h = 0
H = []
K = []
gauss_param = []
counter = 0
map_data = ima.data
IPd = []
JPd = []
IPs = []
JPs = []
chi2_basicfit, chi2_dualfit, chi2_gfit, chi2, parameter = 0, 0, 0, 0, 0
alpha_list = []
sig = scale_based_on_redshift(redshift_input)
for i in range(0, len(IP)):
ip, jp = IP[i], JP[i]
k = 3 * sig
subima = ima.subimage(center=(jp, ip),
size=(k, k),
unit_center=None,
unit_size=None)
subima_err = ima_err.subimage(center=(jp, ip),
size=(k, k),
unit_center=None,
unit_size=None)
subima_err = error_ponderation(subima_err)
size = np.shape(subima)
center = center2sub(x=jp, y=ip, ima=ima, subima=subima)
subima.unmask()
subima.mask_region(center=[(size[0] // 2) - 1, (size[1] // 2) - 1],
radius=k // 2,
unit_center=None,
unit_radius=None,
inside=False)
try:
xy_mesh, coeff, fit_errors = gauss2D_fit(ima, subima, subima_err,
center)
try:
gfitim = gaussian_2dc(xy_mesh, coeff[0], coeff[1], coeff[2],
coeff[3], coeff[4])
except:
print('gaussian fit failed')
parameter = 4
chi2_basicfit = chi_square(subima, subima_err, gfitim, parameter)
alpha = 0
alpha_list.append(alpha)
# if (coeff[0] > F_min) and (fit_errors[3] < 0.08*coeff[3]) and (((size[0]/2-coeff[1])**2 + (size[1]/2-coeff[2])**2)**0.5 < 3) and (coeff[3] < redshift(name)) and (coeff[3]>0.5):
if (coeff[0] > F_min) and (fit_errors[3] < 0.08 * coeff[3]) and (
((size[0] / 2 - coeff[1])**2 +
(size[1] / 2 - coeff[2])**2)**0.5 < 3) and (
coeff[3] < scale_based_on_redshift(redshift_input)) and (
coeff[3] > 0.5):
gfitim = gaussian_2dc(xy_mesh, coeff[0], coeff[1], coeff[2],
coeff[3], coeff[4])
center = sub2center(coeff[1], coeff[2], ima, subima)
if plot == True:
fig, ax = plt.subplots(1,
4,
figsize=(16, 4),
tight_layout=True)
counter += 1
gfitim = gaussian_2dc(xy_mesh, coeff[0], coeff[1],
coeff[2], coeff[3], coeff[4])
gfitim = Image(data=gfitim, wcs=subima.wcs)
gfitim.plot(
ax=ax[1],
colorbar='v',
vmax=np.amax(subima.data),
title=r'Simple Gauss2D FIT - flux = %s [10$^{-16}$ cgs]'
% round(np.sum(gfitim.data), 2))
subima.plot(colorbar='v',
ax=ax[0],
vmin=0,
zscale=False,
title='HII number %s' % (counter))
res = Image(data=(subima.data - gfitim.data),
wcs=subima.wcs)
res.plot(ax=ax[2],
vmin=0,
vmax=np.amax(subima.data),
colorbar='v',
title=r'Residuals - flux = %s [10$^{-16}$ cgs]' %
round(np.sum(res.data), 2))
res.plot(ax=ax[3],
vmin=-F_min,
vmax=F_min,
colorbar='v',
title=r'Residuals - flux = %s [10$^{-16}$ cgs]' %
round(np.sum(res.data), 2))
plt.savefig(
OUTPUT_IMAGES_PATH +
'gaussfit{}/gfit_num_{}.png'.format(p, counter),
bbox_inches='tight',
transparent=True)
plt.close()
if (center[0] < n) and (center[1] < m) and (
center[0] > 0) and (center[1] > 0):
JP2.append(center[0])
IP2.append(center[1])
JPs.append(center[0])
IPs.append(center[1])
gauss_param.append([
center[0], center[1], coeff[3], coeff[3],
map_data[int(center[0])][int(center[1])], 0.0,
coeff[4], coeff[0], 0, chi2_basicfit, k, F_min
])
J.append(center[0] + l)
I.append(center[1] + l)
J.append(center[0] + l)
I.append(center[1] - l)
J.append(center[0] - l)
I.append(center[1] - l)
J.append(center[0] - l)
I.append(center[1] + l)
cont.append(coeff[4])
cont.append(coeff[4])
cont.append(coeff[4])
cont.append(coeff[4])
except:
chi2_basicfit = 10
# plt.figure()
# plt.hist(alpha_list, color='darkmagenta')
return IP2, JP2, gauss_param, I, J, cont, IPd, JPd, IPs, JPs
def HIIplot(ima, param, plot=False):
map_gfitim = np.zeros_like(ima)
(n, m) = np.shape(ima)
x = np.linspace(0, m, m)
y = np.linspace(0, n, n)
xy_mesh = np.meshgrid(x, y)
alpha, I1, xc1, yc1, sigma1, I2, xc2, yc2, sigma2, cont, chi2, size = param
if alpha[0] == 0:
gfitim = gaussian_2dc(xy_mesh, I1[0], xc1[0], yc1[0], sigma1[0], 0.0)
elif alpha[0] == 1:
gfitim = bimodal_gaussian_2d(xy_mesh, I1[0], xc1[0], yc1[0], sigma1[0],
I2[0], xc2[0], yc2[0], sigma2[0], 0.0)
elif alpha[0] == 2:
gfitim = gauss_image(shape=(n, m),
gauss=None,
center=(xc1[0], yc1[0]),
unit_center=None,
unit_fwhm=u.arcsec,
flux=I1[0],
peak=True,
rot=0.0,
fwhm=(sigma1[0], sigma1[0]),
cont=0.0,
wcs=ima.wcs,
unit=ima.unit)
gfitim = gfitim.data
for i in range(1, len(alpha)):
if alpha[i] == 0:
gfitim = gfitim + gaussian_2dc(xy_mesh, I1[i], xc1[i], yc1[i],
sigma1[i], 0.0)
elif alpha[i] == 1:
gfitim = gfitim + bimodal_gaussian_2d(
xy_mesh, I1[i], xc1[i], yc1[i], sigma1[i], I2[i], xc2[i],
yc2[i], sigma2[i], 0.0)
elif alpha[i] == 2:
gfitim2 = gauss_image(shape=(n, m),
gauss=None,
center=(xc1[i], yc1[i]),
unit_center=None,
unit_fwhm=u.arcsec,
flux=I1[i],
peak=True,
rot=0.0,
fwhm=(sigma1[i], sigma1[i]),
cont=0.0,
wcs=ima.wcs,
unit=ima.unit)
gfitim = gfitim + gfitim2.data
if plot == True:
plt.figure()
gfitim = Image(data=(gfitim), wcs=ima.wcs)
gfitim.plot(scale='log',
vmin=0,
vmax=np.amax(ima.data),
colorbar='v',
zscale=False)
plt.show()
return gfitim
def xav_explorer(ima, ima_err, name, plot=False):
F_min = flux_min(ima, name)
IP2, JP2, gauss_im, I2, J2, pcont2, IPd, JPd, IPs, JPs = HIIrecover_loop(
ima, ima_err, name=name, plot=False, F_min=0, p=0)
gfitim, cont2, param = HIIplot2(IPs, JPs, gauss_im, ima, param=False)
JP3, IP3, tcont = background_interpolation(ima, name)
grid = interpolate_continuum(ima=ima,
JP=JP2,
IP=IP2,
I=I2,
J=J2,
pcont=pcont2,
cont=cont2,
JP3=JP3,
IP3=IP3,
tcont=tcont,
plot=False)
gfitim2 = HIIplot_cont(gfitim, grid, ima)
#res = Image(data=(ima.data - gfitim.data), wcs=ima.wcs)
p = 0
print(p)
gfitim, gfitim2, IP2, JP2, IPd, JPd, grid, param = loop2(IP2,
JP2,
I2,
J2,
pcont2,
cont2,
gfitim,
gfitim2,
ima,
ima_err,
IPd,
JPd,
param,
JP3,
IP3,
tcont,
name,
plot=False)
res2 = Image(data=(ima.data - gfitim2.data), wcs=ima.wcs)
catal = cat(param, name)
I_list = chi_histogram(param, plot=True)
b = 0
for k in range(0, len(I_list)):
catal.remove_row(I_list[k] - b)
b += 1
#param2 = read(cat=catal)
#gftim = HIIplot(ima, param2)
gfitim2 = HIIplot_cont(gfitim, grid, ima)
#ima = Image(filename=DATA_PATH + '{}_flux_drz.fits'.format(name))
ima2 = ima[:, :]
#if name == 'ASASSN14jg':
# ima2=ima2[40:290, 20:260]
chi2_global = chi_square(subima=ima2,
subima_err=ima_err,
gfitim=gfitim2,
parameter=1)
alpha = catal['alpha']
#Table with important data for plotting HII regions
data = [len(alpha), chi2_global, F_min]
CreateTable(name, OUTPUT_TABLES_PATH, data)
print(IPd, JPd)
if plot == True:
# fig, ax = plt.subplots(2, 3, constrained_layout=True)
fig, ax = plt.subplots(2, 3, figsize=(12, 8))
fig.suptitle('# {} HII regions - Chi2 {}'.format(
len(alpha), np.round(chi2_global, 2)),
fontsize=16)
ima.plot(scale='log',
colorbar='v',
ax=ax[0, 0],
vmin=0,
vmax=0.9 * np.amax(ima.data),
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(ima.data), 2)))
ax[0, 1].scatter(IPd, JPd, color='red', marker='.', linewidth=0.01)
#res = Image(data=(ima.data - gfitim.data), wcs=ima.wcs)
gfitim.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[0, 1],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(gfitim.data), 2)))
gfitim2.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[0, 2],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(gfitim2.data), 2)))
grid.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[1, 0],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(grid.data), 2)))
res2 = Image(data=(ima.data - gfitim2.data), wcs=ima.wcs)
print('RESIDUALS: mean = {}, median = {}, std = {}'.format(
np.round(np.mean(np.ravel(res2.data)), 2),
np.round(np.median(np.ravel(res2.data)), 2),
np.round(np.std(np.ravel(res2.data)), 2))) #
#F_min
# res2.plot(vmin=np.amin(res2.data), vmax=np.amax(res2.data), colorbar='v', ax=ax[1,1], zscale=False, title=r'flux = %s [10$^{-20}$ cgs]'%( np.round(np.sum(res2.data),2)))
print("F_min" + str(F_min))
res2.plot(vmin=-3 * F_min,
vmax=3 * F_min,
colorbar='v',
ax=ax[1, 1],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(res2.data), 2)))
res2.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[1, 2],
zscale=False,
title='mean = {}, median = {}, std = {}'.format(
np.round(np.mean(np.ravel(res2.data)), 3),
np.round(np.median(np.ravel(res2.data)), 3),
np.round(np.std(np.ravel(res2.data)), 3)))
fig.tight_layout()
fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.pdf'.format(name),
bbox_inches='tight',
transparent=True)
fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.png'.format(name),
bbox_inches='tight',
transparent=True)
plt.close()
#res.write('res')
gfitim.write(name + '_gfitim_xav_fm.fits')
gfitim2.write(name + '_gfitim2_xav_fm.fits')
grid.write(name + '_grid_xav_fm.fits')
res2.write(name + '_res2_xav_fm.fits')
return catal, grid, res2, gfitim, gfitim2, chi2_global
for i in range(len(directories)):
#Opening FITS data for gaussian and diffuse regions
gfitim = Image(os.getcwd() + '/' + name + '/' + directories[i] + '/N3614p_gfitim_' + ID[i] + '.fits')
gfitim2 = Image(os.getcwd() + '/' + name + '/' + directories[i] + '/N3614p_gfitim2_' + ID[i] + '.fits')
grid = Image(os.getcwd() + '/' + name + '/' + directories[i] + '/N3614p_grid_' + ID[i] + '.fits')
res2 = Image(os.getcwd() + '/' + name + '/' + directories[i] + '/N3614p_res2_' + ID[i] + '.fits')
#Obtaining values such as gaussian regions quantity and min flux from table
table = np.genfromtxt(os.getcwd() + '/' + name + '/' + directories[i] + '/data_N3614p_' + ID[i] + '.csv', delimiter = ',', skip_header=0)
alpha, chi2_global, F_min = float(table[0]), float(table[1]), float(table[2])
#Plotting figure
fig, ax = plt.subplots(2, 3, figsize=(12,8))
fig.suptitle('# {} HII regions - Chi2 {}'.format(alpha,np.round(chi2_global,2)), fontsize=16)
ima.plot(scale='log', colorbar='v', ax=ax[0,0], vmin=0, vmax=0.9*np.amax(ima.data), zscale=False, title=r'flux = %s [10$^{-16}$ cgs]'%( np.round(np.sum(ima.data),2)))
#ax[0,1].scatter(IPd,JPd, color='red', marker='.', linewidth=0.01)
gfitim.plot(scale='log', vmin=0, vmax=0.9*np.amax(gfitim.data), colorbar='v', ax=ax[0,1], zscale=False, title=r'flux = %s [10$^{-16}$ cgs]'%( np.round(np.sum(gfitim.data),2)))
gfitim2.plot(scale='log', vmin=0, vmax=0.9*np.amax(gfitim2.data), colorbar='v', ax=ax[0,2], zscale=False, title=r'flux = %s [10$^{-16}$ cgs]'%( np.round(np.sum(gfitim2.data),2)))
grid.plot(scale='log', vmin=0, vmax=0.9*np.amax(grid.data), colorbar='v', ax=ax[1,0], zscale=False, title=r'flux = %s [10$^{-16}$ cgs]'%( np.round(np.sum(grid.data),2)))
res2.plot(vmin=-3*F_min, vmax=3*F_min, colorbar='v', ax=ax[1,1], zscale=False, title=r'flux = %s [10$^{-16}$ cgs]'%( np.round(np.sum(res2.data),2)))
res2.data[res2.data < 0] = 0
res2.plot(scale='log', vmin=0, vmax=0.9*np.amax(res2.data), colorbar='v', ax=ax[1,2], zscale=False, title='mean = {}, median = {}, std = {}'.format(np.round(np.mean(np.ravel(res2.data)),3), np.round(np.median(np.ravel(res2.data)),3), np.round(np.std(np.ravel(res2.data)),3)))
fig.tight_layout()
# Save plotting in pdf and png
#fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.pdf'.format(name), bbox_inches='tight', transparent=True)
#fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.png'.format(name), bbox_inches='tight', transparent=True)
