nkdict_fast implemented.

__init__.py

@@ -23,7 +23,7 @@ from __future__ import division
 from nifty_core import *
 from nifty_cmaps import *
 from nifty_power import *
-#from nifty_tools impoert *
+from nifty_tools import *
 
 
 

nifty_core.py

@@ -2422,7 +2422,7 @@ class rg_space(space):
             if(kindex is None):
                 ## quick kindex
                 if(self.fourier)and(not hasattr(self,"power_indices"))and(len(kwargs)==0):
-                    kindex = gp.nklength(gp.nkdict(self.para[:(np.size(self.para)-1)//2],self.vol,fourier=True))
+                    kindex = gp.nklength(gp.nkdict_fast(self.para[:(np.size(self.para)-1)//2],self.vol,fourier=True))
                 ## implicit kindex
                 else:
                     try:

nifty_power.py

@@ -291,7 +291,7 @@ def _calc_inverse(tk,var,kindex,rho,b1,Amem): ## > computes the inverse Hessian
     ## inversion
     return np.linalg.inv(T2+np.diag(b2,k=0)),b2,Amem
 
-def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None,rho=None,q=1E-42,alpha=1,perception=(1,0),smoothness=True,var=10,bare=True,**kwargs):
+def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None,rho=None,q=1E-42,alpha=1,perception=(1,0),smoothness=True,var=10,force=False,bare=True,**kwargs):
     """
         Infers the power spectrum.
 
@@ -338,6 +338,9 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
             (default: True).
         var : {scalar, list, array}, *optional*
             Variance of the assumed spectral smoothness prior (default: 10).
+        force : bool, *optional*, *experimental*
+            Indicates whether smoothness is to be enforces or not
+            (default: False).
         bare : bool, *optional*
             Indicates whether the power spectrum entries returned are "bare"
             or not (mandatory for the correct incorporation of volume weights)
@@ -505,6 +508,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
             numerator = weight_power(domain,numerator,power=-1,pindex=pindex,pundex=pundex) ## bare(!)
 
         ## smoothness prior
+        permill = 0
         divergence = 1
         while(divergence):
             pk = numerator/denominator1 ## bare(!)
@@ -524,7 +528,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
                     absdelta = np.abs(delta).max()
                     tk += min(1,0.1/absdelta)*delta # adaptive step width
                     pk *= np.exp(min(1,0.1/absdelta)*delta) # adaptive step width
-                var_ /= 1.1 # lowering the variance when converged
+                var_ /= 1.1+permill # lowering the variance when converged
                 if(var_<var):
                     if(breakinfo): # making sure there's one iteration with the correct variance
                         break
@@ -538,6 +542,14 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
                         break
                     else:
                         divergence += 1
+                        if(force):
+                            permill = 0.001
+                elif(force)and(var_/var_OLD>1.001):
+                        permill = 0
+                        pot = int(np.log10(var_))
+                        var = int(1+var_*10**-pot)*10**pot
+                        about.warnings.cprint("WARNING: smoothness variance increased ( var = "+str(var)+" ).")
+                        break
                 else:
                     var_OLD = var_
             if(breakinfo):

powerspectrum.py

@@ -65,7 +65,7 @@ def draw_vector_nd(axes,dgrid,ps,symtype=0,fourier=False,zerocentered=False,kpac
 
     """
     if(kpack is None):
-        kdict = np.fft.fftshift(nkdict(axes,dgrid,fourier))
+        kdict = np.fft.fftshift(nkdict_fast(axes,dgrid,fourier))
         klength = nklength(kdict)
     else:
         kdict = kpack[1][np.fft.ifftshift(kpack[0],axes=shiftaxes(zerocentered,st_to_zero_mode=False))]
@@ -164,7 +164,7 @@ def draw_vector_nd(axes,dgrid,ps,symtype=0,fourier=False,zerocentered=False,kpac
 #        foufield = field
 #    fieldabs = np.abs(foufield)**2
 #
-#    kdict = nkdict(axes,dgrid,fourier)
+#    kdict = nkdict_fast(axes,dgrid,fourier)
 #    klength = nklength(kdict)
 #
 #    ## power spectrum
@@ -228,7 +228,7 @@ def calc_ps_fast(field,axes,dgrid,zerocentered=False,fourier=False,pindex=None,k
     if(rho is None):
         if(pindex is None):
             ## kdict
-            kdict = nkdict(axes,dgrid,fourier)
+            kdict = nkdict_fast(axes,dgrid,fourier)
             ## klength
             if(kindex is None):
                 klength = nklength(kdict)
@@ -253,7 +253,7 @@ def calc_ps_fast(field,axes,dgrid,zerocentered=False,fourier=False,pindex=None,k
                 rho[pindex[ii]] += 1
     elif(pindex is None):
         ## kdict
-        kdict = nkdict(axes,dgrid,fourier)
+        kdict = nkdict_fast(axes,dgrid,fourier)
         ## klength
         if(kindex is None):
             klength = nklength(kdict)
@@ -317,9 +317,9 @@ def get_power_index(axes,dgrid,zerocentered,irred=False,fourier=True):
 
     ## kdict, klength
     if(np.any(zerocentered==False)):
-        kdict = np.fft.fftshift(nkdict(axes,dgrid,fourier),axes=shiftaxes(zerocentered,st_to_zero_mode=True))
+        kdict = np.fft.fftshift(nkdict_fast(axes,dgrid,fourier),axes=shiftaxes(zerocentered,st_to_zero_mode=True))
     else:
-        kdict = nkdict(axes,dgrid,fourier)
+        kdict = nkdict_fast(axes,dgrid,fourier)
     klength = nklength(kdict)
     ## output
     if(irred):
@@ -372,9 +372,9 @@ def get_power_indices(axes,dgrid,zerocentered,fourier=True):
 
     ## kdict, klength
     if(np.any(zerocentered==False)):
-        kdict = np.fft.fftshift(nkdict(axes,dgrid,fourier),axes=shiftaxes(zerocentered,st_to_zero_mode=True))
+        kdict = np.fft.fftshift(nkdict_fast(axes,dgrid,fourier),axes=shiftaxes(zerocentered,st_to_zero_mode=True))
     else:
-        kdict = nkdict(axes,dgrid,fourier)
+        kdict = nkdict_fast(axes,dgrid,fourier)
     klength = nklength(kdict)
     ## output
     ind = np.empty(axes,dtype=np.int)
@@ -587,13 +587,11 @@ def shiftaxes(zerocentered,st_to_zero_mode=False):
 
 
 def nkdict(axes,dgrid,fourier=True):
-
     """
         Calculates an n-dimensional array with its entries being the lengths of
         the k-vectors from the zero point of the Fourier grid.
 
     """
-
     if(fourier):
         dk = dgrid
     else:
@@ -605,6 +603,25 @@ def nkdict(axes,dgrid,fourier=True):
     return kdict
 
 
+def nkdict_fast(axes,dgrid,fourier=True):
+    """
+        Calculates an n-dimensional array with its entries being the lengths of
+        the k-vectors from the zero point of the Fourier grid.
+
+    """
+    if(fourier):
+        dk = dgrid
+    else:
+        dk = np.array([1/dgrid[i]/axes[i] for i in range(len(axes))])
+
+    temp_vecs = np.array(np.where(np.ones(axes)),dtype='float').reshape(np.append(len(axes),axes))
+    temp_vecs = np.rollaxis(temp_vecs,0,len(temp_vecs.shape))
+    temp_vecs -= axes//2
+    temp_vecs *= dk
+    temp_vecs *= temp_vecs
+    return np.sqrt(np.sum((temp_vecs),axis=-1))
+
+
 def nklength(kdict):
     return np.sort(list(set(kdict.flatten())))