def gen_segmap_tresh_lum(img,
xc,
yc,
thresh=5.0,
Amin=5,
pixscale=None,
all_detection=False,
structure=None):
"""Generate a segmentation map by selecting pixels above sky+thresh*std
and then select the regions within 1arcsec to belong to the object. Objects
with a number of pixels less than Amin are non detections and are not present
in the final segmentation map.
The final segmentation map is dilated to smooth the detections"""
N, M = img.shape
MAP = np.zeros([N, M])
MAP[img > thresh] = 1
Regions, Nregions = mi.sci_nd.label(MAP)
for n in range(1, Nregions + 1):
npix = len(Regions[Regions == n])
if npix < Amin:
Regions[Regions == n] = 0
else:
continue
if structure == None:
SIZE = 6
structure = np.zeros([SIZE, SIZE])
dmat, d = distance_matrix(SIZE / 2. - 0.5, SIZE / 2. - 0.5, structure)
structure[dmat < SIZE / 2] = 1
if all_detection:
return Regions
else:
segmap = mi.select_object_map(xc, yc, Regions, pixscale=pixscale)
return mi.sci_nd.binary_dilation(segmap, structure=structure)
def compute_size(image,ra,dec,galmag,hsize,threshold,fractions,sblimit,size=6,safedist=1.0):
Sizes = np.zeros(len(fractions)+1)
Fluxes = np.zeros(len(fractions)+1)
SizesPSF = np.zeros(len(fractions)+1)
FluxesPSF = np.zeros(len(fractions)+1)
image_stamp = load_data(image,ra,dec,hsize)
if np.amax(image_stamp)==np.amin(image_stamp):
return Sizes - 99,-9
dilate = define_structure(size)
sky_med,sky_std = mi.sky_value(image_stamp,args.ksky)
psf_image = simulate_psf(galmag,hsize,sky_med)
segmap_psf = mi.gen_segmap_sbthresh(psf_image-sky_med,hsize,hsize,sblimit,args.pixscale,thresh=threshold,Amin=args.areamin,all_detection=True)
single_source_map_psf = mi.select_object_map(hsize,hsize,segmap_psf,pixscale=args.pixscale)
segmap = mi.gen_segmap_sbthresh(image_stamp-sky_med,hsize,hsize,sblimit,args.pixscale,thresh=threshold,Amin=args.areamin,all_detection=True)
single_source_map = mi.select_object_map(hsize,hsize,segmap,pixscale=args.pixscale)
image_stamp,single_source_map,imglag,segflag = mi.image_validation(image_stamp,single_source_map,args.pixscale,safedist)
if segflag==1:
return Sizes - 99,segflag
if args.nodilation:
dilated_map = single_source_map
dilated_map_psf = single_source_map_psf
else:
dilated_map = mi.sci_nd.binary_dilation(single_source_map,structure=dilate).astype(np.int32)
dilated_map_psf = mi.sci_nd.binary_dilation(single_source_map_psf,structure=dilate).astype(np.int32)
for i in range(len(fractions)):
Sizes[i],TotalArea,Fluxes[i],TotalFlux = compute_fraction_area(image_stamp,dilated_map,fractions[i])
SizesPSF[i],TotalAreaPSF,FluxesPSF[i],TotalFluxPSF = compute_fraction_area(psf_image,dilated_map_psf,fractions[i])
Sizes[i+1]=TotalArea
Fluxes[i+1]=TotalFlux
SizesPSF[i+1]=TotalAreaPSF
FluxesPSF[i+1]=TotalFluxPSF
Real_Sizes = Sizes - SizesPSF
if args.nosaving:
mpl.rcParams['image.cmap']='gist_stern_r'
segmap[segmap!=0]/=1
fig,ax=mpl.subplots(2,3,figsize=(20.6,12))
mpl.subplots_adjust(wspace=0.2,hspace=0.02)
ax=ax.reshape(np.size(ax))
ax[0].imshow(np.sqrt(np.abs(image_stamp)),cmap='hot')
mi.gen_circle(ax[1],hsize,hsize,hsize*(2*np.sqrt(2)*0.5/args.size),color='red')
ax[1].imshow(segmap)
ax[2].imshow(single_source_map)
flux_map = np.zeros(image_stamp.shape)
flux_map[dilated_map==1]=3
for j in range(len(Fluxes)-2,-1,-1):
flux_map[image_stamp>Fluxes[j]]=(len(Fluxes)-j)*4
flux_map*=dilated_map
imax,imin,jmax,jmin = mi.find_ij(dilated_map)
ax[3].imshow(flux_map[imin:imax,jmin:jmax],aspect='equal',cmap='gist_heat_r')
ax[3].text(12,9,'20',color='white',va='center',ha='center',weight='heavy')
ax[3].text(10,7.5,'50',color='white',va='center',ha='center',weight='heavy')
ax[3].text(8.5,5.5,'80',color='white',va='center',ha='center',weight='heavy')
ax[3].text(6,3,'100',color='white',va='center',ha='center',weight='heavy')
# masked_neighbours = mi.sci_nd.binary_dilation(segmap - single_source_map,structure=dilate)
compute_fraction_area(image_stamp,dilated_map,1.0,flux_sampling=500,draw_ax=ax[4])
ax[5].imshow(dilated_map)
for i in range(len(ax)):
if i!=4:
ax[i].set_xticks([])
ax[i].set_yticks([])
fig.text(0.95,0.50,r'$\curvearrowright$',va='center',ha='center',weight='heavy',rotation=-90,fontsize=150)
# fig.savefig('size_thresholding_example.png')
def exit_code(event):
if event.key=='escape':
sys.exit()
if event.key=='q':
mpl.close('all')
fig.canvas.mpl_connect('key_press_event',exit_code)
mpl.show()
return Real_Sizes,segflag
def compute_size(image_list,
pixscales,
ra,
dec,
hsize,
threshold,
fractions,
sblimit,
color,
size=6,
safedist=1.0):
nimages = len(image_list)
Sizes = np.zeros([len(fractions) + 1, nimages])
Fluxes = np.zeros([len(fractions) + 1, nimages])
segflags = np.zeros(nimages)
hsize_pix = [int(hsize / pixscales[k]) / 2 for k in range(nimages)]
image_stamps = [
load_data(image, ra, dec, hs)
for image, hs in zip(image_list, hsize_pix)
]
headers = [pyfits.getheader(image) for image in image_list]
# if np.amax(image_stamp)==np.amin(image_stamp):
# return Sizes - 99,-9
zp = np.zeros(nimages)
sblimits = np.zeros(nimages)
for k in range(nimages):
PHOTFLAM = float(headers[k]['PHOTFLAM'])
PHOTPLAM = float(headers[k]['PHOTPLAM'])
zp[k] = -2.5 * np.log10(PHOTFLAM) - 5 * np.log10(PHOTPLAM) - 2.408
sblimits[0] = sblimit
for k in range(nimages - 1):
ColorCorretion = 10**(0.4 * (color[k] - (zp[0] - zp[k + 1])))
sblimits[k + 1] = sblimit * ColorCorretion
if args.verbose:
print("Color Correction Term: %.5f" % ColorCorretion)
print("New Surface Brightness Limit: %.5f counts/s/arcsec**2" %
sblimits[k + 1])
dilate = define_structure(size)
for k in range(nimages):
if np.amax(image_stamps[k]) == np.amin(image_stamps[k]):
segflag = -9
else:
segmap = mi.gen_segmap_sbthresh(image_stamps[k],
hsize_pix[k],
hsize_pix[k],
sblimits[k],
pixscales[k],
thresh=threshold,
Amin=args.areamin,
all_detection=True)
single_source_map = mi.select_object_map(hsize_pix[k],
hsize_pix[k],
segmap,
pixscale=pixscales[k])
image_stamps[
k], single_source_map, imglag, segflag = mi.image_validation(
image_stamps[k], single_source_map, pixscales[k], safedist)
if np.abs(segflag) != 0:
Sizes[:, k] -= 99
segflags[k] = segflag
else:
if args.nodilation:
dilated_map = single_source_map
else:
dilated_map = mi.sci_nd.binary_dilation(
single_source_map, structure=dilate).astype(np.int32)
for i in range(len(fractions)):
Sizes[i, k], TotalArea, Fluxes[
i, k], TotalFlux = compute_fraction_area(
image_stamps[k], dilated_map, fractions[i])
Sizes[i + 1, k] = TotalArea
Fluxes[i + 1, k] = TotalFlux
if args.nosaving:
mpl.rcParams['image.cmap'] = 'gist_stern_r'
segmap[segmap != 0] /= 1
fig, ax = mpl.subplots(2, 3, figsize=(20.6, 12))
mpl.subplots_adjust(wspace=0.2, hspace=0.02)
ax = ax.reshape(np.size(ax))
ax[0].imshow(np.sqrt(np.abs(image_stamps[k])), cmap='hot')
mi.gen_circle(ax[1],
hsize_pix[k],
hsize_pix[k],
hsize_pix[k] *
(2 * np.sqrt(2) * 0.5 / args.size),
color='red')
ax[1].imshow(segmap)
ax[2].imshow(single_source_map)
flux_map = np.zeros(image_stamps[k].shape)
flux_map[dilated_map == 1] = 3
for j in range(len(Fluxes[:, k]) - 2, -1, -1):
flux_map[image_stamps[k] > Fluxes[j, k]] = (
len(Fluxes[:, k]) - j) * 4
flux_map *= dilated_map
imax, imin, jmax, jmin = mi.find_ij(dilated_map)
ax[3].imshow(flux_map[imin:imax, jmin:jmax],
aspect='equal',
cmap='gist_heat_r')
ax[3].text(12,
9,
'20',
color='white',
va='center',
ha='center',
weight='heavy')
ax[3].text(10,
7.5,
'50',
color='white',
va='center',
ha='center',
weight='heavy')
ax[3].text(8.5,
5.5,
'80',
color='white',
va='center',
ha='center',
weight='heavy')
ax[3].text(6,
3,
'100',
color='white',
va='center',
ha='center',
weight='heavy')
# masked_neighbours = mi.sci_nd.binary_dilation(segmap - single_source_map,structure=dilate)
compute_fraction_area(image_stamps[k],
dilated_map,
1.0,
flux_sampling=500,
draw_ax=ax[4])
ax[5].imshow(dilated_map)
for i in range(len(ax)):
if i != 4:
ax[i].set_xticks([])
ax[i].set_yticks([])
fig.text(0.95,
0.50,
r'$\curvearrowright$',
va='center',
ha='center',
weight='heavy',
rotation=-90,
fontsize=150)
# fig.savefig('size_thresholding_example.png')
fig.canvas.mpl_connect('key_press_event', exit_code)
if args.nosaving:
mpl.show()
return Sizes, segflags