def buildGal(x0,
y0,
components=1,
gaussian=False,
spherical=False,
startpars=None,
fixamp=False):
from imageSim import SBModels
GX = {}
GY = {}
GR = {}
GQ = {}
GP = {}
GN = {}
GA = {}
gals = []
for i in range(components):
GX[i] = pymc.Uniform('GX%i' % i, x0 - 25., x0 + 25., value=x0)
GY[i] = pymc.Uniform('GY%i' % i, y0 - 25., y0 + 25., value=y0)
GP[i] = pymc.Uniform('GP%i' % i, -180., 180., value=-23)
if i == 0:
galpars = [GX[i], GY[i], GP[i]]
galcov = list(numpy.array([0.1, 0.1, 1.]) / 1e0)
else:
galpars += [GX[i], GY[i], GP[i]]
galcov += list(numpy.array([0.05, 0.05, 1.]) / 1e0)
GQ[i] = pymc.Uniform('GQ%i' % i, 0.2, 1, value=0.79)
GR[i] = pymc.Uniform('GR%i' % i, 1., 100., value=10)
GN[i] = pymc.Uniform('GN%i' % i, 0.25, 8., value=2.34)
galpars += [GQ[i], GR[i], GN[i]]
galcov += list(numpy.array([0.03, 0.03, 0.07]) / 1e0)
if fixamp != True:
GA[i] = pymc.Uniform('GA%i' % i, 0, 100.,
value=0.1) #no good in longrun
galpars += [GA[i]]
galcov += [0.01]
var = {
'x': GX[i],
'y': GY[i],
'q': GQ[i],
'pa': GP[i],
're': GR[i],
'n': GN[i],
'amp': GA[i]
}
gals.append(SBModels.Sersic('galaxy%i' % i, var))
else:
var = {
'x': GX[i],
'y': GY[i],
'q': GQ[i],
'pa': GP[i],
're': GR[i],
'n': GN[i],
'amp': 1
}
gals.append(SBModels.Sersic('galaxy%i' % i, var))
return gals, galpars, galcov
def buildSource(x0,
y0,
components=1,
gaussian=False,
spherical=False,
startpars=None,
fixamp=False):
from imageSim import SBModels
SX = {}
SY = {}
SR = {}
SQ = {}
SP = {}
SN = {}
SA = {}
gals = []
for i in range(components):
SX[i] = pymc.Uniform('SX%i' % i, x0 - 25., x0 + 25., value=x0)
SY[i] = pymc.Uniform('SY%i' % i, y0 - 25., y0 + 25., value=y0)
SP[i] = pymc.Uniform('SP%i' % i, -180., 180., value=-23)
if i == 0:
galpars = [SX[i], SY[i], SP[i]]
galcov = list(numpy.array([0.1, 0.1, 1.]) / 1e0)
else:
galpars += [SX[i], SY[i], SP[i]]
galcov += list(numpy.array([0.05, 0.05, 1.]) / 1e0)
SQ[i] = pymc.Uniform('SQ%i' % i, 0.2, 1, value=0.79)
SR[i] = pymc.Uniform('SR%i' % i, 0.1, 100., value=10)
SN[i] = pymc.Uniform('SN%i' % i, 0.25, 8., value=2.34)
galpars += [SQ[i], SR[i], SN[i]]
galcov += list(numpy.array([0.03, 0.03, 0.07]) / 1e0)
if fixamp != True:
SA[i] = pymc.Uniform('SA%i' % i, 0, 100.,
value=0.1) #no good in longrun
galpars += [SA[i]]
galcov += [0.01]
var = {
'x': SX[i],
'y': SY[i],
'q': SQ[i],
'pa': SP[i],
're': SR[i],
'n': SN[i],
'amp': SA[i]
}
gals.append(SBModels.Sersic('galaxy%i' % i, var))
else:
var = {
'x': SX[i],
'y': SY[i],
'q': SQ[i],
'pa': SP[i],
're': SR[i],
'n': SN[i],
'amp': 1
}
gals.append(SBModels.Sersic('galaxy%i' % i, var))
return gals, galpars, galcov
def setSourcePars(self,
b,
m,
x,
y,
q,
p,
r,
n=1,
pixelunits=False,
sourcenumber=1):
if pixelunits == False:
x = self.trytoconvert(x, self.pixelsize)
y = self.trytoconvert(y, self.pixelsize)
r = self.trytoconvert(r, self.pixelsize)
b = self.trytoconvert(b, self.pixelsize)
self.xs[sourcenumber] = x + self.xl + self.deltaxl + 0.000001
self.ys[sourcenumber] = y + self.yl + self.deltayl + 0.000001
self.ms[sourcenumber] = m
self.qs[sourcenumber] = q
self.ps[sourcenumber] = p
self.rs[sourcenumber] = r
self.ns[sourcenumber] = n
self.bl[sourcenumber] = b
self.src[sourcenumber]=SBModels.Sersic('src%i'%sourcenumber,
{'x':self.xs[sourcenumber],'y':self.ys[sourcenumber],\
'q':self.qs[sourcenumber],'pa':self.ps[sourcenumber],\
're':self.rs[sourcenumber],'n':self.ns[sourcenumber]})
self.sourcenumbers.append(sourcenumber)
self.sourcemodel[sourcenumber] = {}
self.totallensedsrcmag[sourcenumber] = {}
self.fakeResidual[sourcenumber] = {}
self.SN[sourcenumber] = {}
self.SNRF[sourcenumber] = {}
self.convolvedsrc[sourcenumber] = {}
def setSourcePars(self,
b,
m,
x,
y,
q,
p,
r,
n=1,
pixelunits=False,
sourcenumber=1):
if pixelunits == False: # if pixelunits doesnt exist
x = self.trytoconvert(
x, self.pixelsize
) # x is the result of trytoconvert function where par is x, and p is the pixelsize
y = self.trytoconvert(
y, self.pixelsize
) # y is the result of trytoconvert function where par is y, and p is the pixelsize
r = self.trytoconvert(
r, self.pixelsize
) # r is the result of trytoconvert function where par is r, and p is the pixelsize
b = self.trytoconvert(
b, self.pixelsize
) # b is the result of trytoconvert function where par is b, and p is the pixelsize
self.xSum[
sourcenumber] = x + self.xl + self.deltaxl + 0.000001 # xsum with a sourcenumber of 1 is the sum of all x
self.ySum[
sourcenumber] = y + self.yl + self.deltayl + 0.000001 # ysum with a sourcenumber of 1 is the sum of all y
self.ms[sourcenumber] = m
self.qs[sourcenumber] = q
self.ps[sourcenumber] = p
self.rs[sourcenumber] = r
self.ns[sourcenumber] = n
self.bl[sourcenumber] = b
# The identified sourcenumber in the source dictionary is stated.
# where x = xSum, y = ySum, q = qs, pa = ps, re =rs, n=ns where sourcenumber is the key
self.src[sourcenumber] = SBModels.Sersic(
'src%i' % sourcenumber, {
'x': self.xSum[sourcenumber],
'y': self.ySum[sourcenumber],
'q': self.qs[sourcenumber],
'pa': self.ps[sourcenumber],
're': self.rs[sourcenumber],
'n': self.ns[sourcenumber]
})
# sourcenumbers array increases with source number
self.sourcenumbers.append(sourcenumber)
# sourceModel, totallensedssrcmag, fakeResidual, SN, SNRF, convolvedsrc using the
# input of sourcenumber is now a dictonary
self.sourceModel[sourcenumber] = {}
self.totallensedsrcmag[sourcenumber] = {}
self.fakeResidual[sourcenumber] = {}
self.SN[sourcenumber] = {}
self.SNRF[sourcenumber] = {}
self.convolvedsrc[sourcenumber] = {}
def setLensPars(self,
m,
r,
q,
n=4,
pixelunits=False,
reset=True,
xb=0,
xp=0,
jiggle=0):
if reset: self.Reset() #if reset is true, reset all parameters
self.rl = {} #define rl variable as a dictionary
if pixelunits == False: #if there is no pixelunits
# loop to fill rl array with bands and pixelsizes
for band in r.keys(
): #if band is within the dictionary r keys, then...
self.rl[band] = self.trytoconvert(
r[band], self.pixelsize
) # the rl band array is set to be the rband captured by the trytoconvert method
self.ml = m # defining an array of band, pixelsize where ml is nodel?
self.ql = q
self.deltaxl = (
numpy.random.rand() -
0.5) * 2 * jiggle #delta xl coordinate = 0, when jiggle = 0
self.deltayl = (numpy.random.rand() - 0.5
) * 2 * jiggle #delta yl coordinate, when jiggle = 0
if jiggle != 0:
self.deltap = 0.0 + (
numpy.random.rand() - 0.5
) * 180 # if jiggle is not 0, then delta p is numpy.random.rand()-0.5)*180
n = (numpy.random.rand() + 1) * 4
else:
self.deltap = 0.0
self.nl = n
# defining gal
self.gal = SBModels.Sersic(
'gal', {
'x': self.xl + self.deltaxl,
'y': self.yl + self.deltayl,
'q': self.ql,
'pa': 90 + self.deltap,
're': self.rl[band],
'n': self.nl
})
#defining xb and xp as themselves in this class
self.xb = xb
self.xp = xp
def setLensPars(self,
m,
r,
q,
n=4,
pixelunits=False,
reset=True,
xb=0,
xp=0,
jiggle=0):
if reset: self.Reset()
self.rl = {}
if pixelunits == False:
for band in r.keys():
self.rl[band] = self.trytoconvert(r[band], self.pixelsize)
self.ml = m
self.ql = q
self.deltaxl = (numpy.random.rand() - 0.5) * 2 * jiggle
self.deltayl = (numpy.random.rand() - 0.5) * 2 * jiggle
if jiggle != 0:
self.deltap = 0. + (numpy.random.rand() - 0.5) * 180
n = (numpy.random.rand() + 1) * 4
else:
self.deltap = 0.
self.nl = n
self.gal = SBModels.Sersic(
'gal', {
'x': self.xl + self.deltaxl,
'y': self.yl + self.deltayl,
'q': self.ql,
'pa': 90 + self.deltap,
're': self.rl[band],
'n': self.nl
})
self.xb = xb
self.xp = xp
def __init__(self, config):
self.pars = []
self.par2index = {}
self.index2par = {}
self.sci = {}
self.err = {}
self.convol_matrix = {}
self.zp = {}
self.light_sb_models = []
self.source_sb_models = []
self.light_sed_models = []
self.source_sed_models = []
self.light_mags = []
self.source_mags = []
self.lens_models = []
self.logp = None
# defines bands
self.bands = []
self.reference_band = config['reference_band']
for band in config['filters']:
self.bands.append(band)
self.nbands = len(self.bands)
# reads in data
filtnames = {}
psfdic = {}
for i in range(self.nbands):
band = self.bands[i]
self.zp[band] = config['zeropoints'][i]
filtname = config['filter_prefix'] + band + config['filter_suffix']
filtnames[band] = filtname
hdu = pyfits.open(
os.path.expandvars(config['data_dir']) + '/' +
config['filename'] + '_%s' % band + config['science_tag'])[0]
img = hdu.data.copy()
subimg = img.copy()
self.sci[band] = subimg
suberr = pyfits.open(
os.path.expandvars(config['data_dir']) + '/' +
config['filename'] + '_%s' % band +
config['err_tag'])[0].data.copy()
if config['err_type'] == 'VAR':
self.err[band] = suberr**0.5
elif config['err_type'] == 'SIGMA':
self.err[band] = suberr.copy()
else:
df
psf_file = pyfits.open(
os.path.expandvars(config['data_dir']) + '/' +
config['filename'] + '_%s' % band + config['psf_tag'])
nhdu = len(psf_file)
found = False
n = 0
while not found and n < nhdu:
if psf_file[n].data is not None:
psf = psf_file[n].data.copy()
found = True
else:
n += 1
if not found:
df
m = (psf[:2].mean() + psf[-2:].mean() + psf[:, :2].mean() +
psf[:, -2:].mean()) / 4.
psf -= m
psf /= psf.sum()
psfdic[band] = psf
self.convol_matrix[band] = convolve.convolve(self.sci[band],
psf)[1]
# prepares mask and data array
ny, nx = self.sci[self.bands[0]].shape
X, Y = np.meshgrid(np.arange(1. * nx), np.arange(1. * ny))
self.ny = ny
self.nx = nx
self.X = X
self.Y = Y
self.R = ((X - nx / 2)**2 + (Y - ny / 2)**2)**0.5
if config['mask_dir'] is None:
mask_dir = './'
else:
mask_dir = os.path.expandvars(config['mask_dir'])
if config['maskname'] is not None:
MASK = pyfits.open(mask_dir + config['maskname'])[0].data.copy()
else:
MASK = np.ones(X.shape, dtype=int)
if config['rmax'] is not None:
MASK[self.R > config['rmax']] = 0
mask = MASK > 0
mask_r = mask.ravel()
self.modelstack = np.zeros((self.nbands * ny, nx))
self.scistack = 0. * self.modelstack
self.errstack = 0. * self.modelstack
self.maskstack = np.zeros((self.nbands * ny, nx), dtype=bool)
for i in range(self.nbands):
self.scistack[i * ny:(i + 1) * ny, :] = self.sci[self.bands[i]]
self.errstack[i * ny:(i + 1) * ny, :] = self.err[self.bands[i]]
self.maskstack[i * ny:(i + 1) * ny, :] = mask
self.maskstack_r = self.maskstack.ravel()
#defines model parameters
ncomp = 0
npar = 0
for comp in config['light_components']:
ncomp += 1
for par in comp['pars']:
parpar = comp['pars'][par]
if parpar['link'] is None and parpar['var'] == 1:
self.pars.append(
Par(par + str(ncomp),
lower=parpar['low'],
upper=parpar['up'],
value=parpar['value'],
step=parpar['step']))
self.par2index['light' + str(ncomp) + '.' + par] = npar
self.index2par[npar] = 'light' + str(ncomp) + '.' + par
npar += 1
ncomp = 0
for comp in config['source_components']:
ncomp += 1
for par in comp['pars']:
parpar = comp['pars'][par]
if parpar['link'] is None and parpar['var'] == 1:
self.pars.append(
Par(par + str(ncomp),
lower=parpar['low'],
upper=parpar['up'],
value=parpar['value'],
step=parpar['step']))
self.par2index['source' + str(ncomp) + '.' + par] = npar
self.index2par[npar] = 'source' + str(ncomp) + '.' + par
npar += 1
ncomp = 0
for comp in config['lens_components']:
ncomp += 1
for par in comp['pars']:
parpar = comp['pars'][par]
if parpar['link'] is None and parpar['var'] == 1:
self.pars.append(
Par(par + str(ncomp),
lower=parpar['low'],
upper=parpar['up'],
value=parpar['value'],
step=parpar['step']))
self.par2index['lens' + str(ncomp) + '.' + par] = npar
self.index2par[npar] = 'lens' + str(ncomp) + '.' + par
npar += 1
i = 0
filtnames = {}
for band in config['filters']:
filtname = config['filter_prefix'] + band + config['filter_suffix']
filtnames[band] = filtname
ncomp = 1
for comp in config['lens_components']:
name = 'lens%d' % ncomp
pars_here = {}
for par in comp['pars']:
if comp['pars'][par]['link'] is None:
if comp['pars'][par]['var'] == 1:
pars_here[par] = self.pars[self.par2index[name + '.' +
par]]
elif comp['pars'][par]['var'] == 0:
pars_here[par] = Par(par,
lower=comp['pars'][par]['value'],
upper=comp['pars'][par]['value'],
value=comp['pars'][par]['value'])
else:
df
else:
pars_here[par] = self.pars[self.par2index[comp['pars'][par]
['link']]]
ncomp += 1
lens = MassModels.PowerLaw(name, pars_here)
self.lens_models.append(lens)
self.nlens = len(self.lens_models)
ncomp = 1
for comp in config['light_components']:
name = 'light%d' % ncomp
pars_here = {}
sed_here = {}
for par in comp['pars']:
if par in SBModels.parlists[comp['class']]:
if comp['pars'][par]['link'] is None:
if comp['pars'][par]['var'] == 1:
pars_here[par] = self.pars[self.par2index[name +
'.' +
par]]
else:
pars_here[par] = Par(
par,
lower=comp['pars'][par]['value'],
upper=comp['pars'][par]['value'],
value=comp['pars'][par]['value'])
else:
pars_here[par] = self.pars[self.par2index[
comp['pars'][par]['link']]]
else:
if comp['pars'][par]['link'] is None:
if comp['pars'][par]['var'] == 1:
sed_here[par] = self.pars[self.par2index[name +
'.' +
par]]
else:
sed_here[par] = Par(
par,
lower=comp['pars'][par]['value'],
upper=comp['pars'][par]['value'],
value=comp['pars'][par]['value'])
else:
sed_here[par] = self.pars[self.par2index[
comp['pars'][par]['link']]]
if comp['class'] == 'Sersic':
light = SBModels.Sersic('light%d' % ncomp, pars_here)
elif comp['class'] == 'PointSource':
light = SBModels.PointSource('light%d' % ncomp, psfdic,
pars_here)
else:
df
self.light_sb_models.append(light)
if comp['sed'] == 'colorpars':
sed = SEDModels.Colors('light_sed%d' % ncomp, sed_here,
self.bands, self.reference_band,
self.zp)
elif comp['sed'] == 'template':
sed = SEDModels.Template('light_sed%d' % ncomp, sed_here,
filtnames, self.zp)
elif comp['sed'] == 'sps':
modelname = config['sps_model_dir'] + comp['sps_model']
sed = SEDModels.SPS('light_sed%d' % ncomp, sed_here,
self.bands, self.zp, modelname)
else:
df
self.light_sed_models.append(sed)
self.light_mags.append({})
ncomp += 1
self.nlight = len(self.light_sb_models)
ncomp = 1
for comp in config['source_components']:
name = 'source%d' % ncomp
pars_here = {}
sed_here = {}
for par in comp['pars']:
if par in SBModels.parlists['Sersic']:
if comp['pars'][par]['link'] is None:
if comp['pars'][par]['var'] == 1:
pars_here[par] = self.pars[self.par2index[name +
'.' +
par]]
else:
pars_here[par] = comp['pars'][par]['value']
else:
pars_here[par] = self.pars[self.par2index[
comp['pars'][par]['link']]]
else:
if comp['pars'][par]['link'] is None:
if comp['pars'][par]['var'] == 1:
sed_here[par] = self.pars[self.par2index[name +
'.' +
par]]
else:
sed_here[par] = Par(
par,
lower=comp['pars'][par]['value'],
upper=comp['pars'][par]['value'],
value=comp['pars'][par]['value'])
else:
sed_here[par] = self.pars[self.par2index[
comp['pars'][par]['link']]]
if comp['class'] == 'Sersic':
source = SBModels.Sersic('source%d' % ncomp, pars_here)
else:
df
self.source_sb_models.append(source)
if comp['sed'] == 'colorpars':
sed = SEDModels.Colors('light_sed%d' % ncomp,
sed_here,
self.bands,
self.reference_band,
zp=self.zp)
elif comp['sed'] == 'template':
sed = SEDModels.Template('light_sed%d' % ncomp,
sed_here,
filtnames,
zp=self.zp)
self.source_sed_models.append(sed)
self.source_mags.append({})
ncomp += 1
self.nsource = len(self.source_sb_models)