In [1]:

    

import numpy as np
import malis as m
import h5py
np.set_printoptions(precision=4)


    

In [127]:

    

def mknhood2d(radius=1):
    # Makes nhood structures for some most used dense graphs.

    ceilrad = np.ceil(radius)
    x = np.arange(-ceilrad,ceilrad+1,1)
    y = np.arange(-ceilrad,ceilrad+1,1)
    [i,j] = np.meshgrid(y,x)

    idxkeep = (i**2+j**2)<=radius**2
    i=i[idxkeep].ravel(); j=j[idxkeep].ravel();
    zeroIdx = np.ceil(len(i)/2).astype(int);

    # flipud and order (j,i,k) so that mknhood2(1) matches mknhood(6)
    nhood = np.vstack((i[:zeroIdx],j[:zeroIdx])).T.astype(np.int32)
    return np.ascontiguousarray(nhood)

def mknhood3d(radius=1):
    # Makes nhood structures for some most used dense graphs.
    # The neighborhood reference for the dense graph representation we use
    # nhood(1,:) is a 3 vector that describe the node that conn(:,:,:,1) connects to
    # so to use it: conn(23,12,42,3) is the edge between node [23 12 42] and [23 12 42]+nhood(3,:)
    # See? It's simple! nhood is just the offset vector that the edge corresponds to.

    ceilrad = np.ceil(radius)
    x = np.arange(-ceilrad,ceilrad+1,1)
    y = np.arange(-ceilrad,ceilrad+1,1)
    z = np.arange(-ceilrad,ceilrad+1,1)
    [i,j,k] = np.meshgrid(z,y,z)

    idxkeep = (i**2+j**2+k**2)<=radius**2
    i=i[idxkeep].ravel(); j=j[idxkeep].ravel(); k=k[idxkeep].ravel();
    zeroIdx = np.ceil(len(i)/2).astype(int);

    # flipud and order (j,i,k) so that mknhood2(1) matches mknhood(6)
    nhood = np.vstack((k[:zeroIdx],i[:zeroIdx],j[:zeroIdx])).T.astype(np.int32)
    return np.ascontiguousarray(nhood)

def mknhood3d_aniso(radiusxy=1,radiusxy_zminus1=1.8):
    # Makes nhood structures for some most used dense graphs.

    nhoodxyz = mknhood3d(radiusxy)
    nhoodxy_zminus1 = mknhood2d(radiusxy_zminus1)
    
    nhood = np.zeros((nhoodxyz.shape[0]+2*nhoodxy_zminus1.shape[0],3),dtype=np.int)
    nhood[:3,:3] = nhoodxyz
    nhood[3:,0] = -1
    nhood[3:,1:] = np.vstack((nhoodxy_zminus1,-nhoodxy_zminus1))

    return np.ascontiguousarray(nhood)


    

In [128]:

    

nh=mknhood3d()
print nh


    

    




[[ 0  0 -1]
 [ 0 -1  0]
 [-1  0  0]]

In [129]:

    

nh=mknhood3d_aniso()
print nh


    

    




[[ 0  0 -1]
 [ 0 -1  0]
 [-1  0  0]
 [-1 -1 -1]
 [-1  0 -1]
 [-1  1 -1]
 [-1 -1  0]
 [-1  1  1]
 [-1  0  1]
 [-1 -1  1]
 [-1  1  0]]

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