def test():
nn = 101.
x, y = np.mgrid[0:nn, 0:nn]
xx = sorted(list(set(x.flatten().tolist())))
GaussPars = 3., 15., 190. * np.pi / 180
#GaussPars=0.001,0.001,30.*np.pi/180
Sm, SM, PA = GaussPars
#########
psfPars = 1, 10., 120. * np.pi / 180
#psfPars=0.001,.001,30.*np.pi/180
PSm, PSM, PPA = psfPars
z1 = Gaussian.GaussianXY(x, y, 1., off=(50, 50), sig=(PSm, PSM), pa=PPA)
#########
CPSF = ClassConfPSF(psfPars)
GaussParsConv = CPSF.GiveConvGaussPars(GaussPars)
Sm2, SM2, PA2 = GaussParsConv
z0 = Gaussian.GaussianXY(x, y, 1., off=(50, 50), sig=(Sm, SM), pa=PA)
z2 = Gaussian.GaussianXY(x, y, 1., off=(50, 50), sig=(Sm2, SM2), pa=PA2)
pylab.clf()
pylab.subplot(1, 3, 1)
pylab.imshow(z0, interpolation="nearest")
pylab.subplot(1, 3, 2)
pylab.imshow(z1, interpolation="nearest")
pylab.subplot(1, 3, 3)
pylab.imshow(z2, interpolation="nearest")
pylab.draw()
pylab.show(False)
def GiveCovMat(self):
x = self.x
y = self.y
N = x.size
dN = 10
N = 2 * dN + 1
x, y = np.mgrid[-dN:dN:N * 1j, -dN:dN:N * 1j]
x = x.flatten()
y = y.flatten()
N = x.size
GG = np.zeros((N, N), np.float32)
for i in range(N):
GG[i, :] = Gaussian.GaussianXY(x[i] - x,
y[i] - y,
self.noise**2,
sig=(self.psf[0], self.psf[1]),
pa=self.psf[2])
Ginv = ModLinAlg.invSVD(GG)
pylab.clf()
pylab.subplot(1, 2, 1)
pylab.imshow(GG, interpolation="nearest")
pylab.subplot(1, 2, 2)
pylab.imshow(np.real(Ginv), interpolation="nearest")
pylab.draw()
pylab.show(False)
stop
def GiveGuess(self, Nsources):
x, y, z = self.data.copy()
S = []
#self.plotIter(self.data[2],z)
DicoGuess = {}
for i in range(Nsources):
DicoGuess[i] = {}
ind = np.argmax(z)
x0, y0, s0 = x[ind], y[ind], z[ind]
#print s0,np.max(z)
z -= Gaussian.GaussianXY(x0 - x,
y0 - y,
s0,
sig=(self.psf[0], self.psf[1]),
pa=self.psf[2])
S.append([x0, y0, s0])
DicoGuess[i]["l"] = x0
DicoGuess[i]["m"] = y0
DicoGuess[i]["s"] = s0
#self.plotIter2(x,y,self.data[2],z)
#self.plotIter(self.data[2],z)
self.AddMissingDefault(DicoGuess[i])
#S=np.array(S).T.flatten()
#S=np.array(S.tolist()+[.1])
Lout = self.DicoToListFreePars(DicoGuess)
return Lout
def func(self, pars, xyz):
x, y, z = xyz
l, m, s = self.GetPars(pars)
G = np.zeros_like(x)
for i in range(l.shape[0]):
G += Gaussian.GaussianXY(l[i] - x,
m[i] - y,
s[i],
sig=(self.psf[0], self.psf[1]),
pa=self.psf[2])
return G
def init():
nn = 101.
x, y = np.mgrid[0:nn, 0:nn]
xx = sorted(list(set(x.flatten().tolist())))
dx = xx[1] - xx[0]
dx = 1.5
z = Gaussian.GaussianXY(x,
y,
1.,
off=(50, 50),
sig=(1.2 * dx, 1.2 * dx),
pa=20. * np.pi / 180)
z += Gaussian.GaussianXY(x,
y,
1.,
off=(55, 50),
sig=(1.2 * dx, 1.2 * dx),
pa=20. * np.pi / 180)
z += Gaussian.GaussianXY(x,
y,
.5,
off=(25, 25),
sig=(1.2 * dx, 1.2 * dx),
pa=20. * np.pi / 180)
z += Gaussian.GaussianXY(x,
y,
.5,
off=(75, 25),
sig=(1.2 * dx, 1.2 * dx),
pa=20. * np.pi / 180)
noise = 0.01
#z+=np.random.randn(nn,nn)*noise
# z+=Gaussian.GaussianXY(x,y,1.,off=(50,50),sig=(1*dx,1*dx),pa=20.*np.pi/180)
#pylab.clf()
dx *= 1.5
pylab.imshow(z, interpolation="nearest")
pylab.show()
Fit = ClassPointFit(x, y, z, psf=(dx, dx, 0.), noise=noise)
Fit.DoAllFit()
def func(self, pars, xyz):
x, y, z = xyz
l, m, s, dp = self.GetPars(pars)
psf = self.givePsf(dp)
# print "psf0: %s"%str(self.psf)
# print "psf1: %s"%str(psf)
G = np.zeros_like(x)
for i in range(l.shape[0]):
G += Gaussian.GaussianXY(l[i] - x,
m[i] - y,
s[i],
sig=(psf[0], psf[1]),
pa=self.psf[2])
return G
def test():
nn = 101.
x, y = np.mgrid[0:nn, 0:nn]
xx = sorted(list(set(x.flatten().tolist())))
dx = xx[1] - xx[0]
dx = 1.
adp = 1.
psfPars = 1, 10., 120. * np.pi / 180
PSm, PSM, PPA = psfPars
CPSF = ClassConvPSF(psfPars)
GaussPars = 3, 10, 10. * np.pi / 180.
GaussParsConv = CPSF.GiveConvGaussPars(GaussPars)
Sm2, SM2, PA2 = GaussParsConv
# z=Gaussian.GaussianXY(x,y,1.,off=(50,50),sig=(adp*dx,adp*dx),pa=20.*np.pi/180)
# z+=Gaussian.GaussianXY(x,y,1.,off=(55,50),sig=(adp*dx,adp*dx),pa=20.*np.pi/180)
# z+=Gaussian.GaussianXY(x,y,.5,off=(25,25),sig=(adp*dx,adp*dx),pa=20.*np.pi/180)
# z+=Gaussian.GaussianXY(x,y,.5,off=(75,25),sig=(adp*dx,adp*dx),pa=20.*np.pi/180)
# #z+=Gaussian.GaussianXY(x,y,.5,off=(75,75),sig=(5*adp*dx,adp*dx),pa=20.*np.pi/180)
# z+=Gaussian.GaussianXY(x,y,.5,off=(50,50),sig=(5*adp*dx,adp*dx),pa=20.*np.pi/180)
z = Gaussian.GaussianXY(x, y, .5, off=(75, 75), sig=(Sm2, SM2), pa=PA2)
noise = 0.01
#z+=np.random.randn(nn,nn)*noise
# z+=Gaussian.GaussianXY(x,y,1.,off=(50,50),sig=(1*dx,1*dx),pa=20.*np.pi/180)
#pylab.clf()
#dx*=1.5
#pylab.ion()
pylab.clf()
pylab.imshow(z, interpolation="nearest")
pylab.draw()
pylab.show(False)
#Fit=ClassGaussFit(x,y,z,psf=(dx,dx,0.),noise=noise,FreePars=["l", "m","s","Sm","SM","PA"])
Fit = ClassGaussFit(x,
y,
z,
psf=psfPars,
noise=noise,
FreePars=["l", "m", "s", "Sm", "SM", "PA"])
Fit.DoAllFit()
def GiveGuess(self, Nsources):
x, y, z = self.data.copy()
S = []
#self.plotIter(self.data[2],z)
for i in range(Nsources):
ind = np.argmax(z)
x0, y0, s0 = x[ind], y[ind], z[ind]
#print s0,np.max(z)
z -= Gaussian.GaussianXY(x0 - x,
y0 - y,
s0,
sig=(self.psf[0], self.psf[1]),
pa=self.psf[2])
S.append([x0, y0, s0])
#self.plotIter2(x,y,self.data[2],z)
#self.plotIter(self.data[2],z)
return S
def funcNoPSF(self, pars, xyz):
x, y, z = xyz
#l,m,s,dp=self.GetPars(pars)
DicoPars = self.ListToDicoPars(pars)
l, m, s, Sm, SM, PA = self.DicoToListPars(DicoPars)
dp = 0.
psf = self.givePsf(dp)
G = np.zeros_like(x)
for i in range(l.shape[0]):
G += Gaussian.GaussianXY(l[i] - x,
m[i] - y,
s[i],
sig=(Sm[i], SM[i]),
pa=PA[i])
return G
def func(self, pars, xyz):
x, y, z = xyz
#l,m,s,dp=self.GetPars(pars)
DicoPars = self.ListToDicoPars(pars)
l, m, s, Sm, SM, PA = self.DicoToListPars(DicoPars)
#print Sm,SM,pars
dp = 0.
psf = self.givePsf(dp)
G = np.zeros_like(x)
for i in range(l.shape[0]):
#G+=Gaussian.GaussianXY(l[i]-x,m[i]-y,s[i],sig=(psf[0],psf[1]),pa=self.psf[2])
ThisSm, ThisSM, ThisPA = self.giveConvPsf((Sm[i], SM[i], PA[i]))
G += Gaussian.GaussianXY(l[i] - x,
m[i] - y,
s[i],
sig=(ThisSm, ThisSM),
pa=ThisPA)
return G