def sb_profile_only(image_stamp,galmag,color,sblimit,threshold,redshift,title='',plot_profile=True):
radius = np.arange(0,3/args.pixscale,1)
fluxes = np.zeros(np.size(radius))-99
sky_med,sky_std = mi.sky_value(image_stamp,args.ksky)
if np.abs(color)<10:
corrected_thresh = color_correction(threshold,color)
new_sblimit = corrected_thresh*sblimit
threshold=corrected_thresh
else:
if args.verbose:
print("Invalid color value: %.4f"%color)
return radius,fluxes,-99,-99,-99,-99
if args.nodilation:
image_stamp = mi.sci_nd.gaussian_filter(image_stamp,sigma=1.0)
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_connected(hsize,hsize,image_stamp,segmap,pixscale=args.pixscale)
image_stamp,single_source_map,imglag,segflag = mi.image_validation(image_stamp,single_source_map,args.pixscale,1.0)
if segflag==1:
return radius,fluxes,-99,-99,-99,-99
radius,fluxes,barx,bary,q,theta = compute_sbprofile(image_stamp-sky_med,single_source_map,args.pixscale)
if plot_profile:
segmap[segmap!=0]/=1
fig,ax=mpl.subplots(1,2,figsize=(20.6,16))
ax=ax.reshape(np.size(ax))
fig.suptitle(title)
ax[0].set_title(r'$K-I=%.4f\ F_\mathrm{correction} = %.5f$'%(color,10**(0.4*(color))))
ax[1].set_title(r'$k=%.4f\ k\sigma=%.5f\ \mathrm{[e^{-}s^{-1}arcsec^{-2}]}\ \ k_\mathrm{uncorr}=%.4f$'%(threshold,new_sblimit,args.sigma0*((1+redshift)/(1+2.0))**(-3)))
mpl.subplots_adjust(wspace=0.2,hspace=0.02)
ax[0].imshow(np.sqrt(np.abs(image_stamp)),cmap='hot')
mi.gen_ellipse(ax[0],barx,bary,3*(2*hsize/args.size),q,-theta)
ax[1].plot(radius,fluxes,'o-',color='CornflowerBlue')
ax[1].hlines(sblimit,min(radius),max(radius),linestyle='--',color='Crimson')
ax[1].hlines(sblimit*threshold,min(radius),max(radius))
ax[1].set_ylim(1.1*min(fluxes),1.1*max(fluxes))
# ax[3].hlines(sblimit*1.5*((1+redshift)/(1+4.0))**(-3),min(rad),max(rad),linestyle='-',color='LimeGreen')
ax[1].set_xlabel(r"$r\ [\mathrm{pix}]$")
ax[1].set_ylabel(r"$f(r)\ [\mathrm{e^{-}s^{-1}arcsec^{-2}}]$")
fig.canvas.mpl_connect('key_press_event',exit_code)
mpl.show()
return radius,fluxes,barx,bary,q,theta
def montecarlo_sky(image,segmap,sky_med,sblimit,hsize,ntries=100):
# high_segmap = mi.gen_segmap_sbthresh(image-sky_med,hsize,hsize,sblimit,args.pixscale,thresh=3,Amin=args.areamin,all_detection=True)
N,M=image.shape
Npixs=[]
for n in range(ntries):
xr = npr.randint(0,N)
yr = npr.randint(0,M)
single_source_map = mi.select_object_map_connected(yr,xr,image,segmap,pixscale=args.pixscale)
# fig,ax=mpl.subplots()
# ax.imshow(single_source_map,cmap='YlGnBu_r')
# fig.canvas.mpl_connect('key_press_event',exit_code)
# mpl.show()
Npixs.append(np.size(single_source_map[single_source_map==1]))
Npixs=np.array(Npixs)
mpl.hist(Npixs,bins=50)
return np.mean(Npixs),np.std(Npixs),np.median(Npixs)
def find_pairs_and_clumps(image_stamp,redshift,galmag,color,hsize,threshold,fractions,sblimit,pixelscale,zeropoint,ksky=3.0,Areamin=10,Aperture=0.5,no_dilation=True,degrade=None,size=5,safedist=1.0,title=None,plot_results=False,segmap_output=False,erosion=[3],verbose=False,ident=None,zphot_sel=None):
if np.amax(image_stamp)==np.amin(image_stamp):
if verbose:
print("Invalid data values: %.4f,%.4f"%(np.amax(image_stamp),np.amin(image_stamp)))
return {}
dilate = define_structure(size)
sky_med,sky_std = mi.sky_value(image_stamp,ksky)
if degrade is not None:
N,M=image_stamp.shape
image_stamp = mi.rebin2d(image_stamp,int(N/degrade),int(M/degrade),flux_scale=True)
pixelscale*=degrade
if no_dilation:
image_smooth = mi.sci_nd.gaussian_filter(image_stamp,sigma=1.0)
if args.error:
factor=-1.0
else:
factor=1.0
if np.abs(color)<10:
corrected_thresh = color_correction(threshold,color)
new_sblimit = corrected_thresh*sblimit
# if verbose:
# print("K-I=%.4f\t old sb limit = %.5f\t new sb limit = %.5f counts/s/arcsec**2"%(color,threshold*sblimit,new_sblimit))
threshold=corrected_thresh
elif np.abs(color)>10:
if verbose:
print("Invalid color value: %.4f"%color)
return {}
segmap = mi.gen_segmap_sbthresh(factor*(image_smooth-sky_med),hsize,hsize,sblimit,pixelscale,thresh=threshold,Amin=Areamin,all_detection=True)
single_source_map = mi.select_object_map_connected(hsize,hsize,factor*image_smooth,segmap,pixscale=pixelscale,radius=Aperture)
image_smooth,single_source_map,imglag,segflag = mi.image_validation(image_smooth,single_source_map,pixelscale,safedist)
# fig,ax=mpl.subplots(1,3,figsize=(25,10))
# ax[0].imshow(image_smooth)
# ax[1].imshow(segmap)
# ax[2].imshow(single_source_map)
# fig.canvas.mpl_connect('key_press_event',exit_code)
# mpl.show()
if no_dilation:
dilated_map = single_source_map
else:
dilated_map = mi.sci_nd.binary_dilation(single_source_map,structure=dilate).astype(np.int32)
gal_selection,gal_magnitudes = galaxy_map(image_stamp,segmap,zeropoint,sky_med,factor)
ngals=np.amax(gal_selection)
#
FullSet={}
if verbose:
print('Ngals=%i'%ngals)
for i in range(ngals):
GalPositionsBar={}
GalPositionsMax={}
GalMags={}
GalDistances={}
GalSizes={}
single_gal_map=gal_selection.copy()
single_gal_map[gal_selection!=(i+1)]=0
single_gal_map[gal_selection==(i+1)]=1
# fig,ax=mpl.subplots(1,4,figsize=(25,12))
# ax[0].imshow(factor*image_smooth,vmin=0)
# ax[1].imshow(segmap)
# ax[2].imshow(gal_selection)
# ax[3].imshow(single_gal_map)
# fig.canvas.mpl_connect('key_press_event',exit_code)
# mpl.show()
nregs=[]
nregs2=[]
# fractions = [0.2,1.0]#np.linspace(0,1,101)
Xcen,Ycen=mi.barycenter(factor*image_stamp,single_gal_map)
if zphot_sel is None:
prob_dist = 1.0
prob_dist_area = 1.0
else:
GalDists = mi.dist(Ycen,Xcen,zphot_sel[:,0],zphot_sel[:,1])
kmin = np.argmin(GalDists)
dmin = GalDists[kmin]
vpair=500. #km/s
redshift_error = np.sqrt( vpair*vpair + 200*200. + 20000*20000.)/2.9979e5*(1+redshift)
if dmin < 1.0/pixelscale:
z,zl,zu = zphot_sel[kmin,2],zphot_sel[kmin,3],zphot_sel[kmin,4]
prob_dist = probability_zphot(z,zl,zu,redshift-redshift_error,redshift+redshift_error)
else:
prob_dist = -99
if gal_magnitudes[i]<MagCapak[0]:
prob_dist_area=0.0
else:
NCounts = simps(NumCapak[MagCapak<gal_magnitudes[i]],MagCapak[MagCapak<gal_magnitudes[i]])
radius = mi.dist(Ycen,Xcen,hsize,hsize)*pixelscale
prob_dist_area = max(1-NCounts/(3600.*3600.)*radius*radius*np.pi,0)
for f in fractions:
S = get_segmap_level(factor*image_smooth,single_gal_map,f)
clump_map_full,nr= mi.sci_nd.label(S)
clump_map_clean,nr2 = clean_map(clump_map_full,minarea=Areamin)
# fig,ax=mpl.subplots(1,2)
# ax[0].imshow(clump_map_full)
# ax[1].imshow(clump_map_clean)
# mpl.show()
M,Pb,Pm,Sc=get_clump_stats(factor*image_stamp,clump_map_clean,zeropoint)
GalMags[str(f)]=M
GalPositionsBar[str(f)]=Pb
GalPositionsMax[str(f)]=Pm
GalSizes[str(f)]=Sc
nregs.append(nr)
nregs2.append(nr2)
for f in fractions:
FP = GalPositionsBar[str(f)]
nclumps=np.shape(FP)[0]
DistsSingle=np.zeros(nclumps)
for n in range(nclumps):
DistsSingle[n] = pixelscale*np.sqrt((FP[n,0]-Xcen)*(FP[n,0]-Xcen)+(FP[n,1]-Ycen)*(FP[n,1]-Ycen))
GalDistances[f]=DistsSingle
if verbose:
print('\t %i ----> f=%.2f \t nclumps=%i'%(i,f,nclumps))
if verbose:
print(50*'=')
FullSet[i+1]={}
FullSet[i+1]['weight']=[prob_dist,prob_dist_area]
FullSet[i+1]['mags']=GalMags
FullSet[i+1]['posibar']=GalPositionsBar
FullSet[i+1]['posimax']=GalPositionsMax
FullSet[i+1]['dist']=GalDistances
FullSet[i+1]['size']=GalSizes
## Real_Sizes = Sizes - SizesPSF
if plot_results:
print("mag_cat=",galmag)
print('sky median = %.5f +- %.6f'%(sky_med,sky_std))
print("sky_threshold = %.8f (sigma = %.8f)"%(sblimit*threshold,threshold))
print("Redshift = %.4f"%redshift)
# mpl.rcParams['image.cmap']='gist_stern_r'
# mpl.rcParams['axes.labelsize']=12
# mpl.rcParams['xtick.labelsize']=10
# mpl.rcParams['ytick.labelsize']=10
# rad,flux,xc,yc,q,theta = compute_sbprofile(image_smooth-sky_med,single_source_map,pixelscale)
# radPSF,fluxPSF,xcPSF,ycPSF,qPSF,thetaPSF = compute_sbprofile(psf_image-sky_med,single_source_map_psf,pixelscale)
#==============================================================================
# PAPER FIGURE
#==============================================================================
sidecut=40
import matplotlib.colors as mpc
import matplotlib.cm as cm
# from sklearn.cluster import MeanShift, estimate_bandwidth
# from sklearn.datasets.samples_generator import make_blobs
#
# centers = [[1, 1], [-1, -1], [1, -1]]
# X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)
figS,axS=mpl.subplots()
dmax = 20/(cosmos.angular_distance(redshift)/(180/np.pi*3600)*1000)
new_image = (factor*image_stamp)
hsize_new = new_image.shape[0]/2
if 'Hband' in args.image:
CMAP='Reds_r'
elif 'Iband' in args.image:
CMAP='YlGnBu_r'
else:
CMAP='viridis'
VMAX = np.amax(new_image[hsize_new-15:hsize_new+15,hsize_new-15:hsize_new+15])
axS.imshow(np.abs(new_image),cmap=CMAP,extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale),vmax=VMAX)
mi.gen_circle(axS,0,0,dmax,color='white',lw=3)
colors = ['gold','lime','red','cyan','cyan',\
'orange','blue','black','yellow','magenta',\
'brown','gray','Olive','OrangeRed','Coral','Yellow','Magenta','Thistle',\
'SpringGreen','Turquoise','RosyBrown','Silver','SlateGray','Black',\
'Aquamarine','LimeGreen','PeachPuff','Lavender','MediumOrchid']
P=np.array([])
for k in range(ngals):
single_gal_map=gal_selection.copy()
single_gal_map[gal_selection!=(k+1)]=0
single_gal_map[gal_selection==(k+1)]=1
gps,nc = detect_all_clumps(FullSet[k+1],np.arange(0.1,0.8,0.1))
P=np.append(P,gps)
Xcen,Ycen=mi.barycenter(new_image,single_gal_map)
GalDists = mi.dist(Ycen,Xcen,zphot_sel[:,0],zphot_sel[:,1])
kmin = np.argmin(GalDists)
dmin = GalDists[kmin]
# vpair=500. #km/s
# z_search = vpair/2.9979e5*(1+redshift)
# z_spec_err = 0.0017*(1+redshift)
# z_phot_err = 0.1*(1+redshift)
# redshift_error = np.sqrt( z_search*z_search + z_spec_err*z_spec_err + z_phot_err*z_phot_err)
vpair=500. #km/s
redshift_error = np.sqrt( vpair*vpair + 200*200. + 20000*20000.)/2.9979e5*(1+redshift)
if dmin < 1.0/pixelscale:
z,zl,zu = zphot_sel[kmin,2],zphot_sel[kmin,3],zphot_sel[kmin,4]
prob_dist = probability_zphot(z,zl,zu,redshift-redshift_error,redshift+redshift_error,plot_results=True)
else:
prob_dist = -99
print('p(z), on this run',prob_dist)
X = pixelscale*(gps[:,1]-hsize_new+0.5)
Y = pixelscale*(gps[:,0]-hsize_new+0.5)
axS.plot(X,Y,'o',mfc='none',mec=colors[k],ms=20,mew=3)
S = get_segmap_level(factor*image_smooth,single_gal_map,1.0)
# mi.draw_border(axS,S,colors[k],extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
NDS = define_structure(3)
axS.contour(mi.sci_nd.binary_dilation(S,structure=NDS).astype(np.int),levels=[0.5],colors=colors[k],linewidths=3.0,extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
for eixo in [axS]:
for t in eixo.xaxis.get_ticklines():
t.set_color('white')
for t in eixo.yaxis.get_ticklines():
t.set_color('white')
for side in ['left','top','bottom','right']:
eixo.spines[side].set_color('white')
P = P.reshape([len(P)/2,2])
ZF=1.30
axS.set_xlim(-hsize_new*pixelscale/ZF,hsize_new*pixelscale/ZF)
axS.set_ylim(-hsize_new*pixelscale/ZF,hsize_new*pixelscale/ZF)
axS.set_xlabel(r'$\Delta \alpha\ [\mathrm{arcsec}]$')
axS.set_ylabel(r'$\Delta \delta\ [\mathrm{arcsec}]$')
# figS.savefig('clumps_first_pass_SingleGalaxy.png')
if args.zphot:
fig2,ax2=mpl.subplots()
for element in zphot_sel:
xc,yc=element[:2]
ax2.plot(xc,yc,'s',mfc='none',mec='k',markersize=12)
for k in range(ngals):
gps,nc = detect_all_clumps(FullSet[k+1],np.arange(0.1,0.8,0.1))
Weights = FullSet[k+1]['weight']
X = gps[:,1]
Y = gps[:,0]
print(X,Y,Weights)
ax2.plot(X,Y,'o',mfc='none',mec=colors[k],ms=20,mew=3)
# figS.savefig('../ClumpyGalaxies/images/clumps_example_SingleGalaxy.pdf')
# figS.savefig('../ClumpyGalaxies/images/clumps_example_SingleGalaxy.png')
fig,ax=mpl.subplots(ngals,3,figsize=(20,18))
ax=ax.reshape(np.size(ax))
mpl.subplots_adjust(wspace=0)
jetcmap = cm.get_cmap('YlGnBu_r', 10) #generate a jet map with 10 values
jet_vals = jetcmap(np.arange(10)) #extract those values as an array
jet_vals[0] = [0., 0, 0., 0.] #change the first value
# new_jet = mpc.LinearSegmentedColormap.from_list("newjet", jet_vals)
for i in range(ngals):
axnum=i*3
single_gal_map=gal_selection.copy()
single_gal_map[gal_selection!=(i+1)]=0
single_gal_map[gal_selection==(i+1)]=1
ax[axnum+1].imshow(new_image,cmap=CMAP,extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
ax[axnum+2].imshow(gal_selection,cmap='viridis',extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
nregs=[]
nregs2=[]
# fractions = [0.2,0.51.0]#np.linspace(0,1,101)
for f in fractions:
S = get_segmap_level(image_smooth,single_gal_map,f)
clump_map_full,nr= mi.sci_nd.label(S)
clump_map_clean,nr2 = clean_map(clump_map_full,minarea=5)
M,P,Pm,Sc=get_clump_stats(factor*image_stamp,clump_map_clean,zeropoint)
nregs.append(nr)
nregs2.append(nr2)
Xcen,Ycen=mi.barycenter(factor*image_stamp,single_gal_map)
ax[axnum+1].set_ylim((Xcen-hsize_new-50)*pixelscale,(Xcen-hsize_new+50)*pixelscale)
ax[axnum+1].set_xlim((Ycen-hsize_new-50)*pixelscale,(Ycen-hsize_new+50)*pixelscale)
mi.gen_circle(ax[axnum+2],(Ycen-hsize_new)*pixelscale,(Xcen-hsize_new)*pixelscale,0.75,color='cyan',lw=2)
# ax[axnum+2].set_ylim((Xcen-hsize_new-50)*pixelscale,(Xcen-hsize_new+50)*pixelscale)
# ax[axnum+2].set_xlim((Ycen-hsize_new-50)*pixelscale,(Ycen-hsize_new+50)*pixelscale)
ax[axnum].plot(np.array(fractions),nregs,'s-',color='DarkRed',label='All Disconnected')
ax[axnum].plot(np.array(fractions),nregs2,'o-',color='Navy',label='A>10pix Disconnected')
for f,c in zip([0.2,0.5,1.0],['red','lime','cyan','gold']):
S = get_segmap_level(image_smooth,single_gal_map,f)
labelmap,nr= mi.sci_nd.label(S)
# ax[axnum+1].hlines(pixelscale*f*((new_image.shape[0]-2*sidecut)/2),-2,-1,color=c,lw=3)
# ax[axnum+1].text(-1.5,pixelscale*f*((new_image.shape[0]-2*sidecut)/2),r'$f=%.2f$'%f,color=c,fontsize=12,va='bottom',ha='center')
ax[axnum].vlines(f,0,nr,color=c,lw=3)
mi.draw_border(ax[axnum+1],S,c,extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
# NDS = define_structure(3)
# ax[axnum].contour(mi.sci_nd.binary_dilation(S,structure=NDS).astype(np.int),levels=[0.5],colors=c,linewidths=3.0,extent=(-hsize_new*pixelscale,hsize_new*pixelscale,-hsize_new*pixelscale,hsize_new*pixelscale))
# for eixo in ax[1:]:
# mi.gen_circle(eixo,hsize_new,hsize_new,(2*hsize/args.size)*args.aperture,color='red')
# eixo.tick_params(labelleft='off',labelbottom='off')
ax[axnum].hlines(1,0,1.1,'k',':')
ax[axnum].set_xlim(0,1.1)
ax[axnum].set_ylim(0,1.4*max(nregs))
ax[axnum].set_xlabel(r'$f$')
ax[axnum].set_ylabel(r'$N_c$')
ax[0].legend(loc='best')
# fig.savefig('clumps_first_pass.png')
fig.canvas.mpl_connect('key_press_event',exit_code)
mpl.show()
sys.exit()
#==============================================================================
# END PAPER FIGURE
#==============================================================================
return FullSet
def find_pairs_and_clumps(image_stamp,
redshift,
galmag,
color,
hsize,
threshold,
fractions,
sblimit,
pixelscale,
zeropoint,
ksky=3.0,
Areamin=10,
Aperture=0.5,
no_dilation=True,
degrade=None,
size=5,
safedist=1.0,
title=None,
plot_results=False,
segmap_output=False,
erosion=[3],
verbose=False,
ident=None):
if np.amax(image_stamp) == np.amin(image_stamp):
if verbose:
print("Invalid data values: %.4f,%.4f" %
(np.amax(image_stamp), np.amin(image_stamp)))
return {}
dilate = define_structure(size)
sky_med, sky_std = mi.sky_value(image_stamp, ksky)
if args.error:
factor = -1.0
else:
factor = 1.0
if degrade is not None:
N, M = image_stamp.shape
image_stamp = mi.rebin2d(image_stamp,
int(N / degrade),
int(M / degrade),
flux_scale=True)
pixelscale *= degrade
if no_dilation:
image_smooth = mi.sci_nd.gaussian_filter(image_stamp, sigma=1.0)
if np.abs(color) < 10:
corrected_thresh = color_correction(threshold, color)
new_sblimit = corrected_thresh * sblimit
if verbose:
print(
"Color=%.4f\t old sb limit = %.5f\t new sb limit = %.5f counts/s/arcsec**2"
% (color, threshold * sblimit, new_sblimit))
threshold = corrected_thresh
elif np.abs(color) > 10:
if verbose:
print("Invalid color value: %.4f" % color)
return {}
segmap = mi.gen_segmap_sbthresh(factor * (image_smooth - sky_med),
hsize,
hsize,
sblimit,
pixelscale,
thresh=threshold,
Amin=Areamin,
all_detection=True)
single_source_map = mi.select_object_map_connected(hsize,
hsize,
factor * image_smooth,
segmap,
pixscale=pixelscale,
radius=Aperture)
image_smooth, single_source_map, imglag, segflag = mi.image_validation(
image_smooth, single_source_map, pixelscale, safedist)
if no_dilation:
dilated_map = single_source_map
else:
dilated_map = mi.sci_nd.binary_dilation(single_source_map,
structure=dilate).astype(
np.int32)
gal_selection = galaxy_map(image_stamp, segmap, zeropoint, sky_med, factor)
ngals = np.amax(gal_selection)
FullSet = {}
if verbose:
print('Ngals=%i' % ngals)
for i in range(ngals):
single_gal_map = gal_selection.copy()
single_gal_map[gal_selection != (i + 1)] = 0
single_gal_map[gal_selection == (i + 1)] = 1
Imap, LM = MID.local_maxims(factor * image_smooth, single_gal_map)
Mimap, PMimap, PBimap, Simap = get_clump_stats_imap(
factor * image_stamp, Imap, zeropoint)
nclumps = len(Mimap)
nclumpsbright = np.size(Mimap[Mimap < 28])
Xcen, Ycen = mi.barycenter(factor * image_smooth, single_gal_map)
DistsSingle = np.zeros(nclumps)
for n in range(nclumps):
DistsSingle[n] = pixelscale * np.sqrt((PBimap[n, 0] - Xcen) *
(PBimap[n, 0] - Xcen) +
(PBimap[n, 1] - Ycen) *
(PBimap[n, 1] - Ycen))
if verbose:
print('\t %i ----> \t nclumps=%i (m<28: %i)' %
(i, nclumps, nclumpsbright))
# nregs=[]
# nregs2=[]
## fractions = [0.2,1.0]#np.linspace(0,1,101)
# for f in fractions:
# S = get_segmap_level(image_smooth,single_gal_map,f)
#
# clump_map_full,nr= mi.sci_nd.label(S)
# clump_map_clean,nr2 = clean_map(clump_map_full,minarea=Areamin)
# M,Pb,Pm,Sc=get_clump_stats(image_stamp,clump_map_clean,zeropoint)
# GalMags[str(f)]=M
# GalPositionsBar[str(f)]=Pb
# GalPositionsMax[str(f)]=Pm
# GalSizes[str(f)]=Sc
#
# nregs.append(nr)
# nregs2.append(nr2)
#
#
# for f in fractions:
# FP = GalPositionsBar[str(f)]
# Xcen,Ycen=GalPositionsBar['1.0'][0]
# nclumps=np.shape(FP)[0]
#
# DistsSingle=np.zeros(nclumps)
# for n in range(nclumps):
# DistsSingle[n] = pixelscale*np.sqrt((FP[n,0]-Xcen)*(FP[n,0]-Xcen)+(FP[n,1]-Ycen)*(FP[n,1]-Ycen))
#
# GalDistances[f]=DistsSingle
#
# if verbose:
# print '\t %i ----> f=%.2f \t nclumps=%i'%(i,f,nclumps)
if verbose:
print(50 * '=')
FullSet[i + 1] = {}
FullSet[i + 1]['galpos'] = (Xcen, Ycen)
FullSet[i + 1]['mags'] = Mimap
FullSet[i + 1]['posibar'] = PBimap
FullSet[i + 1]['posimax'] = PMimap
FullSet[i + 1]['dist'] = DistsSingle
FullSet[i + 1]['size'] = Simap
## Real_Sizes = Sizes - SizesPSF
if plot_results:
print("mag_cat=", galmag)
print('sky median = %.5f +- %.6f' % (sky_med, sky_std))
print("sky_threshold = %.8f (sigma = %.8f)" %
(sblimit * threshold, threshold))
print("Redshift = %.4f" % redshift)
mpl.rcParams['image.cmap'] = 'gist_stern_r'
mpl.rcParams['axes.labelsize'] = 12
mpl.rcParams['xtick.labelsize'] = 10
mpl.rcParams['ytick.labelsize'] = 10
# rad,flux,xc,yc,q,theta = compute_sbprofile(image_smooth-sky_med,single_source_map,pixelscale)
# radPSF,fluxPSF,xcPSF,ycPSF,qPSF,thetaPSF = compute_sbprofile(psf_image-sky_med,single_source_map_psf,pixelscale)
#==============================================================================
# PAPER FIGURE
#==============================================================================
sidecut = 40
import matplotlib.colors as mpc
import matplotlib.cm as cm
fig, ax = mpl.subplots(2, ngals, figsize=(25, 15))
ax = ax.reshape(np.size(ax))
mpl.subplots_adjust(wspace=0)
jetcmap = cm.get_cmap('YlGnBu_r',
10) #generate a jet map with 10 values
jet_vals = jetcmap(np.arange(10)) #extract those values as an array
jet_vals[0] = [0., 0, 0., 0.] #change the first value
new_jet = mpc.LinearSegmentedColormap.from_list("newjet", jet_vals)
new_image = (factor * (image_stamp))
hsize_new = new_image.shape[0] / 2
for i in range(ngals):
axnum = i
single_gal_map = gal_selection.copy()
single_gal_map[gal_selection != (i + 1)] = 0
single_gal_map[gal_selection == (i + 1)] = 1
ax[axnum].imshow(
new_image,
cmap='YlGnBu_r',
extent=(-hsize_new * pixelscale, hsize_new * pixelscale,
-hsize_new * pixelscale, hsize_new * pixelscale),
vmin=0)
Imap, LM = MID.local_maxims(factor * image_smooth, single_gal_map)
Mimap, PMimap, PBimap, Simap = get_clump_stats_imap(
factor * image_stamp, Imap, zeropoint)
ax[axnum + ngals].imshow(
Imap,
cmap='viridis',
extent=(-hsize_new * pixelscale, hsize_new * pixelscale,
-hsize_new * pixelscale, hsize_new * pixelscale))
for k in range(len(Mimap)):
pos = PMimap[k][0]
if Mimap[k] < 28:
ax[axnum + ngals].plot((pos[1] - hsize_new) * pixelscale,
(pos[0] - hsize_new) * pixelscale,
'x',
color='white')
ax[axnum + ngals].text((pos[1] - hsize_new) * pixelscale,
(pos[0] - hsize_new) * pixelscale,
'%.3f' % Mimap[k],
color='white',
fontsize=8,
ha='left',
va='bottom')
Xcen, Ycen = FullSet[i + 1]['galpos']
ax[axnum + ngals].set_ylim((Xcen - hsize_new - 50) * pixelscale,
(Xcen - hsize_new + 50) * pixelscale)
ax[axnum + ngals].set_xlim((Ycen - hsize_new - 50) * pixelscale,
(Ycen - hsize_new + 50) * pixelscale)
mi.gen_circle(ax[axnum], (Ycen - hsize_new) * pixelscale,
(Xcen - hsize_new) * pixelscale,
0.75,
color='white',
lw=2)
for eixo in ax:
eixo.set_xticks([])
eixo.set_yticks([])
# nregs=[]
# nregs2=[]
## fractions = [0.2,0.51.0]#np.linspace(0,1,101)
# for f in fractions:
# S = get_segmap_level(image_smooth,single_gal_map,f)
#
# clump_map_full,nr= mi.sci_nd.label(S)
# clump_map_clean,nr2 = clean_map(clump_map_full,minarea=5)
# M,P,Pm,Sc=get_clump_stats(image_stamp,clump_map_clean,zeropoint)
# nregs.append(nr)
# nregs2.append(nr2)
#
# for f in fractions:
# FP = GalPositionsBar[str(f)]
# FPm = GalPositionsMax[str(f)]
#
# Xcen,Ycen=GalPositionsBar['1.0'][0]
# nclumps=np.shape(FP)[0]
#
# DistsSingle=np.zeros(nclumps)
# for n in range(nclumps):
# DistsSingle[n] = pixelscale*np.sqrt((FP[n,0]-Xcen)*(FP[n,0]-Xcen)+(FP[n,1]-Ycen)*(FP[n,1]-Ycen))
#
# GalDistances[f]=DistsSingle
#
# ax[axnum].plot(np.array(fractions),nregs,'s-',color='DarkRed',label='All Disconnected')
# ax[axnum].plot(np.array(fractions),nregs2,'o-',color='Navy',label='A>10pix Disconnected')
#
# for f,c in zip([0.2,0.5,1.0],['red','lime','cyan','gold']):
# S = get_segmap_level(image_smooth,single_gal_map,f)
# labelmap,nr= mi.sci_nd.label(S)
# ax[axnum+1].hlines(f*((new_image.shape[0]-2*sidecut)/2),5,15,color=c,lw=3)
# ax[axnum+1].text(10,f*((new_image.shape[0]-2*sidecut)/2),r'$f=%.2f$'%f,color=c,fontsize=12,va='bottom',ha='center')
# ax[axnum].vlines(f,0,nr,color=c,lw=3)
# mi.draw_border(ax[axnum+1],S,c)
#
## for eixo in ax[1:]:
## mi.gen_circle(eixo,hsize_new,hsize_new,(2*hsize/args.size)*args.aperture,color='red')
## eixo.tick_params(labelleft='off',labelbottom='off')
#
# ax[axnum].hlines(1,0,1.1,'k',':')
# ax[axnum].set_xlim(0,1.1)
# ax[axnum].set_ylim(0,1.4*max(nregs))
# ax[axnum].set_xlabel(r'$f$')
# ax[axnum].set_ylabel(r'$N_c$')
# ax[0].legend(loc='best')
# fig.savefig('clumps_first_pass_INTESNITY.png')
fig.canvas.mpl_connect('key_press_event', exit_code)
mpl.show()
sys.exit()
#==============================================================================
# END PAPER FIGURE
#==============================================================================
return FullSet