def emask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s, galflag):
imagefile = c.imagefile
sex_cata = c.sex_cata
clus_cata = c.out_cata
threshold = c.threshold
thresh_area = c.thresh_area
mask_reg = c.mask_reg
values = line_s.split()
mask_file = 'EM_' + c.fstring + '.fits'
xcntr_o = xcntr * 1.0 #x center of the object
ycntr_o = ycntr * 1.0 #y center of the object
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
mag_zero = c.mag_zero #magnitude zero point
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0 / float(values[12]) #axis ration b/a
area_o = float(values[13]) # object area
major_axis = float(values[14]) #major axis of the object
eg = 1.0 - axis_rat
f_tmp_mask = pyfits.open('seg.fits')
tmp_mask = f_tmp_mask[0].data
f_tmp_mask.close()
for line_j in open('SegCat.cat', 'r'):
try:
values = line_j.split()
id_n = float(values[0])
xcntr_n = float(values[1]) #x center of the neighbour
ycntr_n = float(values[2]) #y center of the neighbour
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0 / float(values[12]) #axis ration b/a
si = np.sin(pos_ang * np.pi / 180.0)
co = np.cos(pos_ang * np.pi / 180.0)
area = float(values[13])
maj_axis = float(values[14]) #major axis of neighbour
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0 - eg)**2.0
if np.abs(xcntr_n - xcntr_o) < 5.0 and np.abs(
ycntr_n - ycntr_o) < 5.0 and galflag == 1:
tmp_mask[np.where(tmp_mask == id_n)] = 0
except:
pass
boxcar = np.reshape(np.ones(3 * 3), (3, 3))
tmp_mask = pymconvolve.Convolve(tmp_mask, boxcar)
tmp_mask[tmp_mask > 1e-5] = 1
tmp_mask[tmp_mask != 1] = 0
if (galflag):
hdu = pyfits.PrimaryHDU(tmp_mask.astype(np.float32))
hdu.writeto(mask_file)
else:
try:
os.remove("BMask.fits")
except:
pass
hdu = pyfits.PrimaryHDU(tmp_mask.astype(np.float32))
hdu.writeto("BMask.fits")
def CLUMPNESS(z, xcntr, ycntr, pa, eg, sky, ext_rad, sigma, flag_image):
CutImDa, cut_xcntr, cut_ycntr, SizeY, SizeX, ymin, ymax, xmin, \
xmax, flag_out = ImSec(z, xcntr, ycntr, ext_rad)
co = np.cos(pa * np.pi / 180.0)
si = np.sin(pa * np.pi / 180.0)
one_minus_eg_sq = (1.0 - eg)**2.0
x = np.reshape(np.arange(SizeX * SizeY), (SizeY, SizeX)) % SizeX
x = x.astype(np.float32)
y = np.reshape(np.arange(SizeX * SizeY), (SizeY, SizeX)) / SizeX
y = y.astype(np.float32)
tx = (x - cut_xcntr) * co + (y - cut_ycntr) * si
ty = (cut_xcntr - x) * si + (y - cut_ycntr) * co
R = np.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
boxcar = np.reshape(np.ones(sigma * sigma), (sigma, sigma))
I_sigma = pymconvolve.Convolve(CutImDa, boxcar)
res = CutImDa - I_sigma #the residual image
if (flag_image):
# the below will find the image portion which is an anulus of inner
# radius 0.3 * eta(.2) and outer radius 1.5 * eta(.2)
# making the residual value equal to zero inside the
# ext_rad/20)
res[R <= ext_rad * (1 / 20.0)] = 0
res[R >= ext_rad] = 0
# sum of positive values of residue
res_inside_anulus_sum = res[res > 0].sum()
# Average inside ext_rad. res_inside_anulus_sum will be divided by
# z_inside_R_sum in casgm module
z_inside_R_sum = CutImDa[R <= ext_rad].sum() / (3.14 * \
ext_rad * ext_rad * np.sqrt(1 - eg**2.0))
# FIX I dont know why 1/6 instead of 1/20.
area = 3.14 * (ext_rad * ext_rad * np.sqrt(1 - eg**2.0)) - \
3.14 * (ext_rad * ext_rad * (1 / 6.0) * (1 / 6.0) * \
np.sqrt(1 - eg**2.0))
# END
S = res_inside_anulus_sum / area
e1sq = CutImDa[res > 0].sum() + I_sigma[res > 0].sum() + \
4. * CutImDa[res > 0].size * sky
else:
res[R >= ext_rad] = 0
res_inside_anulus_sum = res[np.where(res > 0)].sum()
area = 3.14 * ext_rad**2.0
S = res_inside_anulus_sum / area
z_inside_R_sum = 0 # just to return the value in the end
e1sq = CutImDa[res > 0].sum() + I_sigma[res > 0].sum() + \
2. * CutImDa[res > 0].size * sky
e2sq = res_inside_anulus_sum**2.0
e3sq = CutImDa[R <= ext_rad].sum() + 2 * CutImDa[R <= ext_rad].size * sky
e4sq = (CutImDa[R <= ext_rad].sum())**2.0
if (e2sq != 0):
error = e1sq / e2sq
else:
print "Could not find error"
error = 0.0
return S, error, z_inside_R_sum, e3sq, e4sq
def FindYetSky(gimg, X0, Y0):
RunSegSex(gimg)
f = pyfits.open(gimg)
z = f[0].data
f.close()
f = pyfits.open('seg.fits')
zm = f[0].data
f.close()
SexSky, SkyYet, SkyMed, SkyMin, SkyQua, SkySig = 9999, 9999, 9999, \
9999, 9999, 9999
for l_s in open('SegCat.cat'):
v_s = l_s.split()
try:
SexId = n.float(v_s[0])
xcntr = n.float(v_s[1])
ycntr = n.float(v_s[2])
pa = n.float(v_s[11])
bbya = 1.0 / n.float(v_s[12])
a = n.float(v_s[14]) * 3.0
b = a * bbya
hr = n.float(v_s[9])
sky = n.float(v_s[10])
if n.abs(X0 - xcntr) < 5.0 and n.abs(Y0 - ycntr) < 5.0:
boxcar = np.reshape(np.ones(3 * 3), (3, 3))
zm = pymconvolve.Convolve(zm, boxcar)
zm[n.where(zm > 0)] = 1
SkyQua = []
for ii in n.arange(1, 5):
SkyQua.append(QuarterMask(z, zm, xcntr-1.0, ycntr-1.0, \
bbya, pa, ii))
SkyQua = n.array(SkyQua)
SexSky = sky
tmpstd = n.std(ma.masked_array(z, zm).compressed())
tmpmed = n.median(ma.masked_array(z, zm).compressed())
zm[n.where((z - tmpmed) > 1.3 * tmpstd)] = 1
SkyYet = n.median(ma.masked_array(z, zm).compressed())
SkyMed = n.median(SkyQua)
SkyMin = n.min(SkyQua)
SkySig = n.std(ma.masked_array(z, zm).compressed())
# os.system('rm -f SegCat.cat default_seg.sex seg.fits')
break
except:
pass
return SexSky, SkyYet, SkyMed, SkyMin, SkyQua, SkySig
def gmask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s):
imagefile = c.imagefile
sex_cata = c.sex_cata
threshold = c.threshold
thresh_area = c.thresh_area
mask_reg = c.mask_reg
avoidme = c.avoidme
target = SEx_obj(NXPTS, NYPTS, line_s)
mask_file = 'M_' + c.fstring + '.fits'
mag_zero = c.mag_zero #magnitude zero point
f_tmp_mask = pyfits.open('seg.fits')
tmp_mask = f_tmp_mask[0].data
f_tmp_mask.close()
for line_j in open('SegCat.cat', 'r'):
if line_j[0] != '#': #line is not a comment
#try:
neighbor = SEx_obj(NXPTS, NYPTS, line_j)
if target.mask_or_fit(neighbor, threshold, thresh_area,
avoidme) != 1:
tmp_mask[np.where(tmp_mask == neighbor.sex_num)] = 0
#except:
# pass
if c.NoMask:
tmp_mask[np.where(tmp_mask > 0)] = 0
elif c.NormMask:
pass
boxcar = np.reshape(np.ones(3 * 3), (3, 3))
tmp_mask = pymconvolve.Convolve(tmp_mask, boxcar)
tmp_mask[tmp_mask > 1e-5] = 1
tmp_mask[tmp_mask != 1] = 0
hdu = pyfits.PrimaryHDU(tmp_mask.astype(np.float32))
hdu.writeto(mask_file)
def segmentation(zextract, xcntr, ycntr, pa, eg, sky, r20, r50, r80, \
ext_rad, sigma, skysig, lower):
"""This function find the segmentation map"""
def gini_coef(I):
"""This will calculate Gini Coefficient"""
oneD_I = np.reshape(I, (1,-1))
sorted_I = np.sort(oneD_I)
sorted_I = sorted_I[sorted_I > 0]
nn = sorted_I.size
average = np.abs(sorted_I).mean()
sumI = 0.0
for i in range(nn):
sumI += (2 * (i + 1) - nn - 1) * abs(sorted_I[i])
G = sumI / (average * nn * (nn - 1))
return G
sigma = int(sigma)
co= np.cos(pa*np.pi/180.0)
si= np.sin(pa*np.pi/180.0)
one_minus_eg_sq = (1.0 - eg)**2.0
SizeY = zextract.shape[0] # Y size
SizeX = zextract.shape[1] # X size
x = np.reshape(np.arange(SizeX * SizeY), (SizeY, SizeX)) % SizeX
x = x.astype(np.float32)
y = np.reshape(np.arange(SizeX * SizeY), (SizeY, SizeX)) / SizeX
y = y.astype(np.float32)
rx = (x - xcntr)* co + (y - ycntr) * si
ry = (xcntr - x) * si + (y - ycntr) * co
R = np.sqrt(rx**2.0 + ry**2.0 / one_minus_eg_sq)
boxcar = np.reshape(np.ones(sigma * sigma), (sigma, sigma))
I_sigma = pymconvolve.Convolve(zextract, boxcar)
lower_thre = (I_sigma[R <= ext_rad / 1.5].sum() - \
I_sigma[R <= ext_rad / 1.5 - 1].sum()) / \
(3.14 * ((ext_rad / 1.5)**2. - (ext_rad / 1.5 - 1)**2.))
upper_thre = 10.0 * skysig # the upper threshold
#The below will finds the segmentation image
I0 = np.where(R > (2.5 / 1.5) * ext_rad, 0, zextract)
I1 = np.where(I_sigma < lower_thre, 0, I0)
I2 = np.where(R > (ext_rad / 1.5 - (0.02 * ext_rad)), 0, I1)
I3 = np.where(R <= (ext_rad / 1.5 - (0.02 * ext_rad)), 0, I1)
I4 = np.where(I_sigma > upper_thre, 0, I3)
I = I2 + I4
G = gini_coef(I)
mo = moment(I, xcntr, ycntr)
M = mo.moment_of_light[0]
# FIX the following will calculate the Gini coef. & M20 of the residual
# image of asymmetry. Now it is turning off. In the future it can be
# replaced with the Gini coef. of residual galfit image
# f = pyfits.open(c.outdir +'AResidual.fits')
# res = f[0].data
# f.close()
# if os.access(c.outdir +'AResidual.fits', os.F_OK):
# os.remove(c.outdir +'AResidual.fits')
# res1=np.where(I == 0, 0, res)
# G_res = gini_coef(res1)
# mo1=moment(res, xcntr, ycntr)
# M_res = mo1.moment_of_light[0]
G_res = 0.0
M_res = 0.0
# END
I80 = np.where(R > r80, 0, zextract)
G80 = gini_coef(I80)
mo2 = moment(I80, xcntr, ycntr)
M80 = mo2.moment_of_light[0]
I50 = np.where(R > r50, 0, zextract)
G50 = gini_coef(I50)
mo3 = moment(I50, xcntr, ycntr)
M_50 = mo3.moment_of_light[0]
I20 = np.where(R > r20, 0, zextract)
G20 = gini_coef(I20)
mo4 = moment(I20, xcntr, ycntr)
M_20 = mo4.moment_of_light[0]
return G, G_res, G80, G50, G20, M, M_res, M80, M_50, M_20