def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=0.7, g_fac=1.0, r_fac=1.3,
seed_boost=1.0,
r_petro_kpc=None,
**kwargs):
fail_flag=True # looks for "bad" backgrounds, and tells us to try again
n_iter = 1
while(fail_flag and (n_iter < 2)):
fail_flag=False
try:
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])*(n_iter)*seed_boost
except:
try:
seed=int(filename[filename.index('.fits')-3:filename.index('.fits')])*(n_iter)*seed_boost
except:
seed=1234
n_iter+=1
b_image, rp, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=r_petro_kpc,
fix_seed=False,
**kwargs)
if(this_fail_flag):
fail_flag=True
g_image, dummy, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
r_image, dummy, the_used_seed, this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-6 ] = 1e100 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
def return_synthetic_jwst_img(filename,
lupton_alpha=0.5,
lupton_Q=0.5,
scale_min=1e-4,
b_fac=1.0,
g_fac=1.0,
r_fac=1.0,
max=1.0,
dynrng=1e3,
r_petro_kpc=None,
**kwargs):
fail_flag = True # looks for "bad" backgrounds, and tells us to try again
n_iter = 1
while (fail_flag and (n_iter < 2)):
fail_flag = False
try:
seed = int(filename[filename.index('broadband_') +
10:filename.index('.fits')])
except:
try:
seed = int(filename[filename.index('broadband_red_') +
14:filename.index('.fits')])
except:
seed = int(filename[filename.index('broadband_rest_') +
15:filename.index('.fits')])
n_iter += 1
b_image, rp, the_used_seed, this_fail_flag, _, _ = sunpy__synthetic_image.build_synthetic_image(
filename,
'NIRCAM_prelimfiltersonly_F277W', # 25,
seed=seed,
fix_seed=False,
r_petro_kpc=r_petro_kpc,
**kwargs)
if (this_fail_flag):
fail_flag = True
dummy, dummy, the_used_seed, this_fail_flag, _, _ = sunpy__synthetic_image.build_synthetic_image(
filename,
'NIRCAM_prelimfiltersonly_F277W',
seed=the_used_seed,
fix_seed=True,
r_petro_kpc=rp,
**kwargs)
if (this_fail_flag):
fail_flag = True
g_image, dummy, the_used_seed, this_fail_flag, _, _ = sunpy__synthetic_image.build_synthetic_image(
filename,
'NIRCAM_prelimfiltersonly_F356W',
seed=the_used_seed,
fix_seed=True,
r_petro_kpc=rp,
**kwargs)
if (this_fail_flag):
fail_flag = True
r_image, dummy, the_used_seed, this_fail_flag, _, _ = sunpy__synthetic_image.build_synthetic_image(
filename,
'NIRCAM_prelimfiltersonly_F444W',
seed=the_used_seed,
fix_seed=True,
r_petro_kpc=rp,
**kwargs)
if (this_fail_flag):
fail_flag = True
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image) / 3
val = np.arcsinh(lupton_alpha * lupton_Q * (I - scale_min)) / lupton_Q
I[I < 1e-6] = 1e100 # from below, this effectively sets the pixel to 0
img[:, :, 0] = r_image * val / I
img[:, :, 1] = g_image * val / I
img[:, :, 2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind, 0] = img[changeind, 0] / maxrgbval[changeind]
img[changeind, 1] = img[changeind, 1] / maxrgbval[changeind]
img[changeind, 2] = img[changeind, 2] / maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind, 0] = 0
img[changeind, 1] = 0
img[changeind, 2] = 0
changind = I < 0
img[changind, 0] = 0
img[changind, 1] = 0
img[changind, 2] = 0
img[img < 0] = 0
del b_image, g_image, r_image, I, val
gc.collect()
img[img < 0] = 0
return rp, img
def return_synthetic_hst_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=1.0, g_fac=1.0, r_fac=1.0, max=1.0, dynrng=1e3,
**kwargs):
try:
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])
except:
try:
seed=int(filename[filename.index('broadband_red_')+14:filename.index('.fits')])
except:
seed=int(filename[filename.index('broadband_rest_')+15:filename.index('.fits')])
b_image, rp, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 22, #25,
seed=seed, fix_seed=True,
#r_petro_kpc=None,
**kwargs)
dummy, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 25,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
g_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 26,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
r_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 27,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
if 0:
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-8 ] = 1e20 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
else:
img[:,:,0] = np.log10(r_image ) # / max=1.0, dynrng=1e3,
img[:,:,1] = np.log10(g_image )
img[:,:,2] = np.log10(b_image )
img -= np.log10( max/dynrng )
img /= np.log10( dynrng )
img[img>1] = 1
I = img
val = img
img[img<0] = 0
print "img min/max/mean "+str(img.min())+" "+str(img.max())+" "+str(img.mean())
print " "
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5,
lupton_Q=0.5,
scale_min=1e-4,
b_fac=0.7,
g_fac=1.0,
r_fac=1.3,
seed_boost=1.0,
r_petro_kpc=None,
**kwargs):
fail_flag = True # looks for "bad" backgrounds, and tells us to try again
n_iter = 1
while (fail_flag and (n_iter < 2)):
fail_flag = False
try:
seed = int(
filename[filename.index('broadband_') +
10:filename.index('.fits')]) * (n_iter) * seed_boost
except:
try:
seed = int(filename[filename.index('.fits') - 3:filename.
index('.fits')]) * (n_iter) * seed_boost
except:
seed = 1234
n_iter += 1
# The calls to build_synthetic_image had to be updated to account for changes in the number
# of return values (previously 4, now 6). We don't need the other 2 values here, but we
# have to catch them so the code won't complain about too many return values to unpack.
b_image, rp, the_used_seed, this_fail_flag, _, _ = \
sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=r_petro_kpc,
fix_seed=False,
**kwargs)
if (this_fail_flag):
fail_flag = True
g_image, dummy, the_used_seed, this_fail_flag, _, _ = \
sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if (this_fail_flag):
fail_flag = True
r_image, dummy, the_used_seed, this_fail_flag, _, _ = \
sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if (this_fail_flag):
fail_flag = True
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image) / 3
val = np.arcsinh(lupton_alpha * lupton_Q * (I - scale_min)) / lupton_Q
I[I < 1e-6] = 1e100 # from below, this effectively sets the pixel to 0
img[:, :, 0] = r_image * val / I
img[:, :, 1] = g_image * val / I
img[:, :, 2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind, 0] = img[changeind, 0] / maxrgbval[changeind]
img[changeind, 1] = img[changeind, 1] / maxrgbval[changeind]
img[changeind, 2] = img[changeind, 2] / maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind, 0] = 0
img[changeind, 1] = 0
img[changeind, 2] = 0
changind = I < 0
img[changind, 0] = 0
img[changind, 1] = 0
img[changind, 2] = 0
img[img < 0] = 0
del b_image, g_image, r_image, I, val
gc.collect()
img[img < 0] = 0
return rp, img
def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5,
lupton_Q=0.5,
scale_min=1e-4,
b_fac=0.7,
g_fac=1.0,
r_fac=1.3,
**kwargs):
seed = int(filename[filename.index('broadband_') +
10:filename.index('.fits')])
b_image, rp = sunpy__synthetic_image.build_synthetic_image(
filename, 'g_SDSS.res', seed=seed, r_petro_kpc=None, **kwargs)
g_image, dummy = sunpy__synthetic_image.build_synthetic_image(
filename, 'r_SDSS.res', seed=seed, r_petro_kpc=rp, **kwargs)
r_image, dummy = sunpy__synthetic_image.build_synthetic_image(
filename, 'i_SDSS.res', seed=seed, r_petro_kpc=rp, **kwargs)
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image) / 3
val = np.arcsinh(lupton_alpha * lupton_Q * (I - scale_min)) / lupton_Q
I[I < 1e-6] = 1e100 # from below, this effectively sets the pixel to 0
img[:, :, 0] = r_image * val / I
img[:, :, 1] = g_image * val / I
img[:, :, 2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind, 0] = img[changeind, 0] / maxrgbval[changeind]
img[changeind, 1] = img[changeind, 1] / maxrgbval[changeind]
img[changeind, 2] = img[changeind, 2] / maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind, 0] = 0
img[changeind, 1] = 0
img[changeind, 2] = 0
changind = I < 0
img[changind, 0] = 0
img[changind, 1] = 0
img[changind, 2] = 0
img[img < 0] = 0
print "img min/max/mean " + str(img.min()) + " " + str(
img.max()) + " " + str(img.mean())
print " "
del b_image, g_image, r_image, I, val
gc.collect()
img[img < 0] = 0
return rp, img
def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=0.7, g_fac=1.0, r_fac=1.3,
seed_boost=1.0,
r_petro_kpc=None,
**kwargs):
fail_flag=True # looks for "bad" backgrounds, and tells us to try again
n_iter = 1
while(fail_flag and (n_iter < 2)):
fail_flag=False
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])*(n_iter)*seed_boost
n_iter+=1
b_image, rp, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=r_petro_kpc,
fix_seed=False,
**kwargs)
if(this_fail_flag):
fail_flag=True
g_image, dummy, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
r_image, dummy, the_used_seed, this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-6 ] = 1e100 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
def return_synthetic_hst_img(filename,
lupton_alpha=0.5,
lupton_Q=0.5,
scale_min=1e-4,
b_fac=1.0,
g_fac=1.0,
r_fac=1.0,
max=1.0,
dynrng=1e3,
**kwargs):
try:
seed = int(filename[filename.index('broadband_') +
10:filename.index('.fits')])
except:
try:
seed = int(filename[filename.index('broadband_red_') +
14:filename.index('.fits')])
except:
seed = int(filename[filename.index('broadband_rest_') +
15:filename.index('.fits')])
b_image, rp, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(
filename,
22, #25,
seed=seed,
fix_seed=True,
#r_petro_kpc=None,
**kwargs)
dummy, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(
filename,
25,
seed=seed,
fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
g_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(
filename,
26,
seed=seed,
fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
r_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(
filename,
27,
seed=seed,
fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
if 0:
I = (r_image + g_image + b_image) / 3
val = np.arcsinh(lupton_alpha * lupton_Q * (I - scale_min)) / lupton_Q
I[I < 1e-8] = 1e20 # from below, this effectively sets the pixel to 0
img[:, :, 0] = r_image * val / I
img[:, :, 1] = g_image * val / I
img[:, :, 2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind, 0] = img[changeind, 0] / maxrgbval[changeind]
img[changeind, 1] = img[changeind, 1] / maxrgbval[changeind]
img[changeind, 2] = img[changeind, 2] / maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind, 0] = 0
img[changeind, 1] = 0
img[changeind, 2] = 0
changind = I < 0
img[changind, 0] = 0
img[changind, 1] = 0
img[changind, 2] = 0
img[img < 0] = 0
else:
img[:, :, 0] = np.log10(r_image) # / max=1.0, dynrng=1e3,
img[:, :, 1] = np.log10(g_image)
img[:, :, 2] = np.log10(b_image)
img -= np.log10(max / dynrng)
img /= np.log10(dynrng)
img[img > 1] = 1
I = img
val = img
img[img < 0] = 0
print "img min/max/mean " + str(img.min()) + " " + str(
img.max()) + " " + str(img.mean())
print " "
del b_image, g_image, r_image, I, val
gc.collect()
img[img < 0] = 0
return rp, img
def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=0.7, g_fac=1.0, r_fac=1.3,
**kwargs):
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])
b_image, rp = sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=None,
**kwargs)
g_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=seed,
r_petro_kpc=rp,
**kwargs)
r_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=seed,
r_petro_kpc=rp,
**kwargs)
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-6 ] = 1e100 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
print "img min/max/mean "+str(img.min())+" "+str(img.max())+" "+str(img.mean())
print " "
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
