def GiveSpectralIndexMap(self, CellSizeRad=1., GaussPars=[(1, 1, 0)], DoConv=True, MaxSpi=100, MaxDR=1e+6,
threshold=None):
dFreq = 1e6
# f0=self.DicoSMStacked["AllFreqs"].min()
# f1=self.DicoSMStacked["AllFreqs"].max()
RefFreq = self.DicoSMStacked["RefFreq"]
f0 = RefFreq / 1.5
f1 = RefFreq * 1.5
M0 = self.GiveModelImage(f0)
M1 = self.GiveModelImage(f1)
if DoConv:
# M0=ModFFTW.ConvolveGaussian(M0,CellSizeRad=CellSizeRad,GaussPars=GaussPars)
# M1=ModFFTW.ConvolveGaussian(M1,CellSizeRad=CellSizeRad,GaussPars=GaussPars)
# M0,_=ModFFTW.ConvolveGaussianWrapper(M0,Sig=GaussPars[0][0]/CellSizeRad)
# M1,_=ModFFTW.ConvolveGaussianWrapper(M1,Sig=GaussPars[0][0]/CellSizeRad)
M0, _ = ModFFTW.ConvolveGaussianScipy(M0, Sig=GaussPars[0][0] / CellSizeRad)
M1, _ = ModFFTW.ConvolveGaussianScipy(M1, Sig=GaussPars[0][0] / CellSizeRad)
# print M0.shape,M1.shape
# compute threshold for alpha computation by rounding DR threshold to .1 digits (i.e. 1.65e-6 rounds to 1.7e-6)
if threshold is not None:
minmod = threshold
elif not np.all(M0 == 0):
minmod = float("%.1e" % (np.max(np.abs(M0)) / MaxDR))
else:
minmod = 1e-6
# mask out pixels above threshold
mask = (M1 < minmod) | (M0 < minmod)
print("computing alpha map for model pixels above %.1e Jy (based on max DR setting of %g)" % (
minmod, MaxDR), file=log)
M0[mask] = minmod
M1[mask] = minmod
# with np.errstate(invalid='ignore'):
# alpha = (np.log(M0)-np.log(M1))/(np.log(f0/f1))
# print
# print np.min(M0),np.min(M1),minmod
# print
alpha = (np.log(M0) - np.log(M1)) / (np.log(f0 / f1))
alpha[mask] = 0
# mask out |alpha|>MaxSpi. These are not physically meaningful anyway
mask = alpha > MaxSpi
alpha[mask] = MaxSpi
masked = mask.any()
mask = alpha < -MaxSpi
alpha[mask] = -MaxSpi
if masked or mask.any():
print(ModColor.Str("WARNING: some alpha pixels outside +/-%g. Masking them." % MaxSpi, col="red"), file=log)
return alpha
def GiveSpectralIndexMap(self,
CellSizeRad=1.,
GaussPars=[(1, 1, 0)],
DoConv=True,
MaxSpi=100,
MaxDR=1e+6,
threshold=None):
dFreq = 1e6
RefFreq = self.DicoSMStacked["RefFreq"]
f0 = RefFreq / 1.5 #self.DicoSMStacked["AllFreqs"].min()
f1 = RefFreq * 1.5 #self.DicoSMStacked["AllFreqs"].max()
M0 = self.GiveModelImage(f0)
M1 = self.GiveModelImage(f1)
if DoConv:
#M0=ModFFTW.ConvolveGaussian(M0,CellSizeRad=CellSizeRad,GaussPars=GaussPars)
#M1=ModFFTW.ConvolveGaussian(M1,CellSizeRad=CellSizeRad,GaussPars=GaussPars)
#M0,_=ModFFTW.ConvolveGaussianWrapper(M0,Sig=GaussPars[0][0]/CellSizeRad)
#M1,_=ModFFTW.ConvolveGaussianWrapper(M1,Sig=GaussPars[0][0]/CellSizeRad)
M0, _ = ModFFTW.ConvolveGaussianScipy(M0,
Sig=GaussPars[0][0] /
CellSizeRad)
M1, _ = ModFFTW.ConvolveGaussianScipy(M1,
Sig=GaussPars[0][0] /
CellSizeRad)
# compute threshold for alpha computation by rounding DR threshold to .1 digits (i.e. 1.65e-6 rounds to 1.7e-6)
if threshold is not None:
minmod = threshold
elif not np.all(M0 == 0):
minmod = float("%.1e" % (np.max(np.abs(M0)) / MaxDR))
else:
minmod = 1e-6
# mask out pixels above threshold
mask = (M1 < minmod) | (M0 < minmod)
print >> log, "computing alpha map for model pixels above %.1e Jy (based on max DR setting of %g)" % (
minmod, MaxDR)
M0[mask] = minmod
M1[mask] = minmod
alpha = (np.log(M0) - np.log(M1)) / (np.log(f0 / f1))
alpha[mask] = 0
# Np=1000
# indx,indy=np.int64(np.random.rand(Np)*M0.shape[0]),np.int64(np.random.rand(Np)*M0.shape[1])
# med=np.median(np.abs(M0[:,:,indx,indy]))
# Mask=((M1>100*med)&(M0>100*med))
# alpha=np.zeros_like(M0)
# alpha[Mask]=(np.log(M0[Mask])-np.log(M1[Mask]))/(np.log(f0/f1))
return alpha
def restoreFittedBeam(rot):
imgSize = 256
cellSize = np.deg2rad(4./3600.)
params = (10, 5, rot) #maj, min, theta
#create input with code borrowed from Tigger:
xx,yy = np.meshgrid(np.arange(0,imgSize),np.arange(0,imgSize))
inp = gauss2d([1,imgSize/2,imgSize/2,params[1],params[0],params[2]],circle=0,rotate=1,vheight=0)(xx,yy)
inp = inp.reshape(1,1,imgSize,imgSize)
#fit
fittedParams = tuple((fitter.FitCleanBeam(inp[0, 0, :, :]) *
np.array([cellSize, cellSize, 1])).tolist())
#restore fitted clean beam with an FFT convolution:
delta = np.zeros([1, 1, imgSize, imgSize])
delta[0, 0, imgSize // 2, imgSize // 2] = 1
rest, _ = fftconvolve.ConvolveGaussianScipy(delta,
Sig=5,
GaussPar=fittedParams)
rest = fftconvolve.ConvolveGaussian(shareddict={"in":delta,
"out":delta},
field_in="in",
field_out="out",
ch=0,
CellSizeRad=cellSize,
GaussPars_ch=fittedParams,
Normalise=False)
assert np.allclose(inp, rest, rtol=1e-2, atol=1e-2)