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Commit 1482483d authored by Marco Selig's avatar Marco Selig
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auto-smoothing with sigma=-1.

parent 0e58088a
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nifty_core.py
View file @ 1482483d
......@@ -2948,21 +2948,23 @@ class rg_space(space):
x = self.enforce_shape(np.array(x,dtype=self.datatype))
## check sigma
if(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
elif(sigma==0):
if(sigma==0):
return x
else:
## smooth
naxes = (np.size(self.para)-1)//2
Gx = gs.smooth_field(x,self.fourier,self.para[-naxes:].astype(np.bool).tolist(),bool(self.para[naxes]==1),self.vol,smooth_length=sigma)
## check complexity
if(not self.para[naxes]): ## purely real
## check imaginary part
if(np.any(Gx.imag!=0))and(np.dot(Gx.imag.flatten(order='C'),Gx.imag.flatten(order='C'),out=None)>self.epsilon**2*np.dot(Gx.real.flatten(order='C'),Gx.real.flatten(order='C'),out=None)):
about.warnings.cprint("WARNING: discarding considerable imaginary part.")
Gx = np.real(Gx)
return Gx
elif(sigma==-1):
about.infos.cprint("INFO: invalid sigma reset.")
sigma = 1.5*np.max(self.vol) ## sqrt(2)*max(dist)
elif(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
## smooth
naxes = (np.size(self.para)-1)//2
Gx = gs.smooth_field(x,self.fourier,self.para[-naxes:].astype(np.bool).tolist(),bool(self.para[naxes]==1),self.vol,smooth_length=sigma)
## check complexity
if(not self.para[naxes]): ## purely real
## check imaginary part
if(np.any(Gx.imag!=0))and(np.dot(Gx.imag.flatten(order='C'),Gx.imag.flatten(order='C'),out=None)>self.epsilon**2*np.dot(Gx.real.flatten(order='C'),Gx.real.flatten(order='C'),out=None)):
about.warnings.cprint("WARNING: discarding considerable imaginary part.")
Gx = np.real(Gx)
return Gx
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -3931,14 +3933,16 @@ class lm_space(space):
"""
x = self.enforce_shape(np.array(x,dtype=self.datatype))
## check sigma
if(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
elif(sigma==0):
if(sigma==0):
return x
else:
## smooth
#return gl.smoothalm(x,lmax=self.para[0],mmax=self.para[1],fwhm=0.0,sigma=sigma,overwrite=False) ## no overwrite
return hp.smoothalm(x,fwhm=0.0,sigma=sigma,invert=False,pol=True,mmax=self.para[1],verbose=False,inplace=False) ## no overwrite
elif(sigma==-1):
about.infos.cprint("INFO: invalid sigma reset.")
sigma = 4.5/(self.para[0]+1) ## sqrt(2)*pi/(lmax+1)
elif(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
## smooth
#return gl.smoothalm(x,lmax=self.para[0],mmax=self.para[1],fwhm=0.0,sigma=sigma,overwrite=False) ## no overwrite
return hp.smoothalm(x,fwhm=0.0,sigma=sigma,invert=False,pol=True,mmax=self.para[1],verbose=False,inplace=False) ## no overwrite
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -4601,13 +4605,15 @@ class gl_space(space):
"""
x = self.enforce_shape(np.array(x,dtype=self.datatype))
## check sigma
if(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
elif(sigma==0):
if(sigma==0):
return x
else:
## smooth
return gl.smoothmap(x,nlat=self.para[0],nlon=self.para[1],lmax=self.para[0]-1,mmax=self.para[0]-1,fwhm=0.0,sigma=sigma)
elif(sigma==-1):
about.infos.cprint("INFO: invalid sigma reset.")
sigma = 4.5/self.para[0] ## sqrt(2)*pi/(lmax+1)
elif(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
## smooth
return gl.smoothmap(x,nlat=self.para[0],nlon=self.para[1],lmax=self.para[0]-1,mmax=self.para[0]-1,fwhm=0.0,sigma=sigma)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -5167,10 +5173,8 @@ class hp_space(space):
## weight if discrete
if(self.discrete):
x = self.calc_weight(x,power=-0.5)
## check keyword arguments
iterations = kwargs.get("iter",self.niter)
## transform
Tx = hp.map2alm(x.astype(np.float64),lmax=codomain.para[0],mmax=codomain.para[1],iter=iterations,pol=True,use_weights=False,regression=True,datapath=None)
Tx = hp.map2alm(x.astype(np.float64),lmax=codomain.para[0],mmax=codomain.para[1],iter=kwargs.get("iter",self.niter),pol=True,use_weights=False,regression=True,datapath=None)
else:
raise ValueError(about._errors.cstring("ERROR: unsupported transformation."))
......@@ -5205,15 +5209,15 @@ class hp_space(space):
"""
x = self.enforce_shape(np.array(x,dtype=self.datatype))
## check sigma
if(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
elif(sigma==0):
if(sigma==0):
return x
else:
## check keyword arguments
iterations = kwargs.get("iter",self.niter)
## smooth
return hp.smoothing(x,fwhm=0.0,sigma=sigma,invert=False,pol=True,iter=iterations,lmax=3*self.para[0]-1,mmax=3*self.para[0]-1,use_weights=False,regression=True,datapath=None)
elif(sigma==-1):
about.infos.cprint("INFO: invalid sigma reset.")
sigma = 1.5/self.para[0] ## sqrt(2)*pi/(lmax+1)
elif(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
## smooth
return hp.smoothing(x,fwhm=0.0,sigma=sigma,invert=False,pol=True,iter=kwargs.get("iter",self.niter),lmax=3*self.para[0]-1,mmax=3*self.para[0]-1,use_weights=False,regression=True,datapath=None)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -5243,8 +5247,7 @@ class hp_space(space):
if(self.discrete):
x = self.calc_weight(x,power=-0.5)
## power spectrum
iterations = kwargs.get("iter",self.niter)
return hp.anafast(x,map2=None,nspec=None,lmax=3*self.para[0]-1,mmax=3*self.para[0]-1,iter=iterations,alm=False,pol=True,use_weights=False,regression=True,datapath=None)
return hp.anafast(x,map2=None,nspec=None,lmax=3*self.para[0]-1,mmax=3*self.para[0]-1,iter=kwargs.get("iter",self.niter),alm=False,pol=True,use_weights=False,regression=True,datapath=None)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -6012,9 +6015,7 @@ class nested_space(space):
"""
x = self.enforce_shape(np.array(x,dtype=self.datatype))
## check sigma
if(sigma<0):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
elif(sigma==0):
if(sigma==0):
return x
else:
## reshape
......
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