def make_mask(self, isl, subn, subm, nsrc, src_id, g_list, delc):
import functions as func
# define stuff for calculating gaussian
boxx, boxy = isl.bbox
subn = boxx.stop - boxx.start
subm = boxy.stop - boxy.start
x, y = N.indices((subn, subm))
# construct image of each source in the island
src_image = N.zeros((subn, subm, nsrc), dtype=N.float32)
nn = 1
for isrc in range(nsrc):
if nsrc == 1:
g_sublist = g_list
else:
posn = N.where(src_id == isrc)[0]
g_sublist = []
for i in posn:
g_sublist.append(g_list[i])
for g in g_sublist:
params = func.g2param(g)
params[1] -= delc[0]
params[2] -= delc[1]
gau = func.gaus_2d(params, x, y)
src_image[:, :, isrc] = src_image[:, :, isrc] + gau
# mark each pixel as belonging to one source
# just compare value, should compare with sigma later
mask = N.argmax(src_image, axis=2) + src_id
orig_mask = isl.mask_active
mask[N.where(orig_mask)] = -1
return mask
def make_mask(self, isl, subn, subm, nsrc, src_id, g_list, delc):
import functions as func
# define stuff for calculating gaussian
boxx, boxy = isl.bbox
subn = boxx.stop-boxx.start; subm = boxy.stop-boxy.start
x, y = N.indices((subn, subm))
# construct image of each source in the island
src_image = N.zeros((subn, subm, nsrc), dtype=N.float32)
nn = 1
for isrc in range(nsrc):
if nsrc == 1:
g_sublist = g_list
else:
posn = N.where(src_id == isrc)[0]
g_sublist=[]
for i in posn:
g_sublist.append(g_list[i])
for g in g_sublist:
params = func.g2param(g)
params[1] -= delc[0]; params[2] -= delc[1]
gau = func.gaus_2d(params, x, y)
src_image[:,:,isrc] = src_image[:,:,isrc] + gau
# mark each pixel as belonging to one source
# just compare value, should compare with sigma later
mask = N.argmax(src_image, axis=2) + src_id
orig_mask = isl.mask_active
mask[N.where(orig_mask)] = -1
return mask
def make_subim(self, subn, subm, g_list, delc, mc=False):
import functions as func
subim = N.zeros((subn, subm), dtype=N.float32)
x, y = N.indices((subn, subm))
for g in g_list:
params = func.g2param(g)
params[1] -= delc[0]; params[2] -= delc[1]
if mc:
# draw random variables from distributions given by errors
params_err = func.g2param_err(g)
for i in range(len(params)):
mc_param = N.random.normal(loc=params[i], scale=params_err[i])
params[i] = mc_param
gau = func.gaus_2d(params, x, y)
subim = subim + gau
return subim
def make_subim(self, subn, subm, g_list, delc, mc=False):
import functions as func
subim = N.zeros((subn, subm), dtype=N.float32)
x, y = N.indices((subn, subm))
for g in g_list:
params = func.g2param(g)
params[1] -= delc[0]; params[2] -= delc[1]
if mc:
# draw random variables from distributions given by errors
params_err = func.g2param_err(g)
for i in range(len(params)):
mc_param = N.random.normal(loc=params[i], scale=params_err[i])
params[i] = mc_param
gau = func.gaus_2d(params, x, y)
subim = subim + gau
return subim