rif.index package

Module contents

docstring for rif.index

rif.index.stripe_index_3d(maxdis, points, payloads=None)[source]

create a 3D stripe index of points[payloads] based on dtype of inputs

stripe index is a fast index of 3D coords (possible enhanced with metadata) which can quickly get all the neighbors within a certain maxdis, which must be specified at construction

Parameters:
  • maxdis (float) – maximum distance for points to be neighbors
  • points (array) – can map to array of C++ structs via dtype
  • payloads (array) – optional, additional data attached to each point
Returns:

Wrapper, for example, see the *_V3 or *_V3_V3 versions
Return type:

stripe_index_3d_<points_t>_<payload_t=NULL>
Return value notes:
If payloads is a sequence of python objects, internally integers 1-N are used as the payloads, and the objects in the sequence are returned (not recommended, you should be using array style computing).

Examples

  • Example: simple points with python objects
    >>> array = np.random.uniform(size=(1000,3))
>>> points = [(x[0], x[1], x[2]) for x in array]
>>> type(points[13])  # converted to tuples
<class 'tuple'>
>>> index = stripe_index_3d(maxdis=0.1, points=points)
>>> index
<_rif._index._stripe_index.stripe_index_3d_V3 object at ...>
>>> test = (0.1, 0.2, 0.3)
>>> neighbors_fast = index.neighbor_count(test)
>>> neighbors_slow = index.neighbor_count_brute(test)
>>> # speedup for this example would be nothing
>>> # because python operations dominate runtime
  • Example: simple points with numpy
    >>> points = np.random.uniform(size=30000).astype('f').view(V3)
>>> points.shape
(10000,)
>>> index = stripe_index_3d(maxdis=0.05, points=points)
>>> index
<_rif._index._stripe_index.stripe_index_3d_V3 object at ...>
>>> testpts = np.random.uniform(low=-1, high=2, size=3000).astype('f').view(V3)
>>> tfast = clock()
>>> neighbors_fast = index.neighbor_count(testpts)
>>> tfast = clock() - tfast
>>> tslow = clock()
>>> neighbors_slow = index.neighbor_count_brute(testpts)
>>> tslow = clock() - tslow
>>> print('avg number of neighbors:', np.mean(neighbors_fast))  
avg number of neighbors: 0.881
>>> np.all(neighbors_fast == neighbors_slow)
True
>>> print('speedup is %fx vs brute force loop over points' % (tslow/tfast)) 
speedup is 81.904762x vs brute force loop over points
>>> # this example is faster because it uses a numpy vectorized query
>>> # this amortizes away the cost of python operations
  • Example: mapping points to arbitrary python payloads
    >>> points = [(0,0,0), (1,1,1), (2,2,2), (3,3,3)]
>>> values = ['string payloads: ' + str(x) for x in points]
>>> index = stripe_index_3d(maxdis=1.0, points=points, payloads=values)
>>> index.neighbors((0,0,0))  # returns python objects
['string payloads: (0, 0, 0)']
>>> index.neighbors((0.5,0.5,0.5))
['string payloads: (0, 0, 0)', 'string payloads: (1, 1, 1)']
>>> # this example will also be slow... no reason to use this over brute force
  • Example: mapping point-like structs to structs via numpy dtypes
    >>> N = 1000
>>> points = (np.arange(3*N).astype('f')/N).view(V3)
>>> atoms = np.empty(N, dtype=Atom)
>>> atoms['pos'] = points
>>> atoms['atype'] = np.arange(N) % 23
>>> atoms['rtype'] = np.arange(N) % 20
>>> atoms['anum'] = np.arange(N) % 7
>>> atoms[-3:]
array([(([ 2.99099994,  2.9920001 ,  2.99300003],),  8, 3, 17),
       (([ 2.99399996,  2.99499989,  2.99600005],),  9, 4, 18),
       (([ 2.99699998,  2.99799991,  2.99900007],), 10, 5, 19)],
      dtype=[('pos', [('raw', '<f4', (3,))]), ('atype', 'u1'), ('anum', 'u1'), ('rtype', '<u2')])
>>> index = stripe_index_3d(maxdis=0.05, points=points, payloads=atoms)
>>> index.neighbors((1, 1, 1))
array([(([ 0.972     ,  0.97299999,  0.97399998],),  2, 2,  4),
       (([ 0.97500002,  0.97600001,  0.977     ],),  3, 3,  5),
       (([ 0.97799999,  0.97899997,  0.98000002],),  4, 4,  6),
       (([ 0.98100001,  0.98199999,  0.98299998],),  5, 5,  7),
       (([ 0.98400003,  0.98500001,  0.986     ],),  6, 6,  8),
       (([ 0.98699999,  0.98799998,  0.98900002],),  7, 0,  9),
       (([ 0.99000001,  0.991     ,  0.99199998],),  8, 1, 10),
       (([ 0.99299997,  0.99400002,  0.995     ],),  9, 2, 11),
       (([ 0.99599999,  0.99699998,  0.99800003],), 10, 3, 12),
       (([ 0.99900001,  1.        ,  1.00100005],), 11, 4, 13),
       (([ 1.00199997,  1.00300002,  1.00399995],), 12, 5, 14),
       (([ 1.005     ,  1.00600004,  1.00699997],), 13, 6, 15),
       (([ 1.00800002,  1.00899994,  1.00999999],), 14, 0, 16),
       (([ 1.01100004,  1.01199996,  1.01300001],), 15, 1, 17),
       (([ 1.01400006,  1.01499999,  1.01600003],), 16, 2, 18),
       (([ 1.01699996,  1.01800001,  1.01900005],), 17, 3, 19),
       (([ 1.01999998,  1.02100003,  1.02199996],), 18, 4,  0),
       (([ 1.023     ,  1.02400005,  1.02499998],), 19, 5,  1),
       (([ 1.02600002,  1.02699995,  1.028     ],), 20, 6,  2)],
      dtype=[('pos', [('raw', '<f4', (3,))]), ('atype', 'u1'), ('anum', 'u1'), ('rtype', '<u2')])