running but not tested for speed

nifty/operators/smoothing_operator/Cython/extended.pyx

@@ -17,6 +17,12 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.fl
         # No smoothing requested, just return the input array.
         return power[startindex:endindex]
 
+    #excluded_power = np.array([])
+    #if (exclude > 0):
+    #    distances = distances[exclude:]
+    #    excluded_power = np.copy(power[:exclude])
+    #    power = power[exclude:]
+
     if (smooth_length is None) or (smooth_length < 0):
         smooth_length = distances[1]-distances[0]
 
@@ -26,7 +32,8 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.fl
         l = max(i-int(2*smooth_length)-1,0)
         u = min(i+int(2*smooth_length)+2,len(p_smooth))
         p_smooth[i-startindex] = GaussianKernel(power[l:u], distances[l:u], distances[i], smooth_length)
-
+    #if (exclude > 0):
+    #    p_smooth = np.r_[excluded_power,p_smooth]
     return p_smooth
 
 def getShape(a):
@@ -55,7 +62,9 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
     cdef np.int_t Ntot = np.product(outshape)
     cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
     slicedArr = arr[tuple(i.tolist())]
-    res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+    print "WHUT"
+    res = apply_kernel_along_array(slicedArr, startindex, endindex, distances[0], smooth_length)
+    print "Here", res
     outshape = np.asarray(getShape(arr))
     outshape[axis] = endindex - startindex
     outarr = np.zeros(outshape, dtype=np.float64)
@@ -71,7 +80,7 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarra
             n -= 1
         i.put(indlist, ind)
         slicedArr = arr[tuple(i.tolist())]
-        res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+        res = apply_kernel_along_array(slicedArr, startindex, endindex, distances[k], smooth_length)
         outarr[tuple(i.tolist())] = res
         k += 1
 

nifty/operators/smoothing_operator/Cython/test3.py

+import pyximport; pyximport.install()
+import extended
+
+import numpy as np
+
+
+print "///////////////////////////////////////First thing ////////////////////////"
+
+arr = np.ones(5, dtype=np.float)
+arr[2] = 10.0
+mk = np.arange(5, dtype=np.float)
+mi = 3.0
+smooth = 1.0
+
+a = extended.GaussianKernel(arr,mk,mi,smooth)
+
+print a
+
+print "///////////////////////////////////////Second thing ////////////////////////"
+
+
+n=12
+
+ksq=np.sqrt(np.arange(n), dtype=np.float)
+kk=np.arange(n, dtype=np.float)
+power = np.ones(n, dtype=np.float)
+# power[0][4][4]=1000
+# power[1][4][4]=1000
+# power[2][4][4]=1000
+# power[3][4][4]=1000
+power[n/2]=100
+# power[5][4][4]=1000
+# power[6][4][4]=1000
+# power[7][4][4]=1000
+k = kk
+sigma=k[1]-k[0]
+mirrorsize=7
+startindex=mirrorsize/2
+endindex=n-mirrorsize/2
+print power, k, power.shape
+
+smooth = extended.apply_kernel_along_array(power, startindex, endindex, k, sigma)
+
+print smooth
+
+print "///////////////////////////////////////Third thing ////////////////////////"
+n=10
+ksq=np.sqrt(np.arange(n))
+kk=[np.arange(0,1,1.0/n)]*n**2
+power = np.ones(n**3).reshape((n,n,n))
+# power[0][4][4]=1000
+# power[1][4][4]=1000
+# power[2][4][4]=1000
+# power[3][4][4]=1000
+power[n/2][n/2][n/2]=10000
+# power[5][4][4]=1000
+# power[6][4][4]=1000
+# power[7][4][4]=1000
+distances = np.asarray(kk,dtype=np.float64)
+sigma=k[1]-k[0]
+mirrorsize=5
+startindex=mirrorsize/2
+endindex=n-mirrorsize/2
+print k, power, power.shape
+# smooth = extended.smooth_something(datablock=power, axis=(2),
+#                                    startindex=startindex, endindex=endindex,
+#                                    kernelfunction=extended.GaussianKernel, k=k,
+#                                    sigma=sigma)
+
+
+smooth = extended.apply_along_axis(2, power,
+                               startindex, endindex, distances,
+                               sigma)
+
+print "Smoooooth", smooth
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