diff --git a/nifty_core.py b/nifty_core.py
index 10da6c6858596bc0f4161814dbf83c52865ef88a..86f83f375eac498b0525173cce0657a54eb30d0a 100644
--- a/nifty_core.py
+++ b/nifty_core.py
@@ -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