Brief Tutorial

It suffices to import only the necessary commands from module dionysus, but for the purposes of the tutorial, we import everything:

from dionysus import *

Read in a points file (for points_file() the points should appear one per line with coordinates separated with spaces; of course, one can read in the points however one wants):

points = [p for p in points_file('points_filename')]
print "Number of points:", len(points)


If the points are in \mathbb{R}^2 or \mathbb{R}^3, one can construct an alphashape filtration:

simplices = Filtration()
fill_alpha2D_complex(points, simplices)     # for 2D, or
fill_alpha3D_complex(points, simplices)     # for 3D

Functions fill_alpha*_complex fill the simplices with all the simplices of the Delaunay triangulation. Each one has its attribute data set to a pair: the smallest value of the squared distance function on the dual Voronoi cell and whether the simplex is critical or not (i.e. whether its dual cell does or does not contain a critical point of the distance function). See Alpha shapes for more details, and Alpha shape example for a full example.

As a result, if one wanted only those simplices whose alpha shape value did not exceed 10, one could obtain them as follows:

simplices10 = [s for s in simplices if[0] <= 10]

If the point set lies in higher dimensions, one may construct a Rips complex on it. This complex requires only pairwise distances, which makes it very versatile. One must first construct an instance of a class providing such distances (e.g. PairwiseDistances for explicit points, see Distances for more details), and then pass it to the Rips complex class:

distances = PairwiseDistances(points)
rips = Rips(distances)

Usually, because of space restrictions, generation of a Rips complex has to be restricted to a k-skeleton of the complex and some maximal parameter max. In the following example k = 3 and max = 50:

simplices = Filtration()
rips.generate(3, 50, simplices.append)

Rips.generate() takes a skeleton and a maximum distance cutoffs, and a function which is called with each of the simplices in the complex (in this case, Python list simplicesappend method).


There are two ways of computing persistence in Dionysus. The first offline way, required by StaticPersistence (and its derivatives), is to set up the entire filtration at once, compute its persistence in one operation, and then examine the pairings. The second way is to feed simplices one by one in an online manner and manually keep track of the pairs that are created. ZigzagPersistence and CohomologyPersistence accept their input this way,


For the first approach, i.e. to use StaticPersistence, one must put the sort the filtration with respect to some ordering (for example, data_dim_cmp() for alpha shapes or Rips.cmp() for the Rips complex):

simplices.sort(data_dim_cmp)     # for the alpha shapes
simplices.sort(rips.cmp)         # for the rips complex

Creating an instance of StaticPersistence initialized with the filtration really initializes a boundary matrix. The subsequent call to pair_simplices() reduces the matrix to compute the persistence of the filtration:

p = StaticPersistence(simplices)

Once the simplices are paired, one may examine the pairings by iterating over the instance of StaticPersistence. We can use an auxilliary map smap to remap the persistence indices into the actual simplices:

smap = p.make_simplex_map(simplices)
for i in p:
    if i.sign():
        birth = smap[i]
        if i.unpaired():
            print birth.dimension(),, "inf"

        death = smap[i.pair()]
        print birth.dimension(),,

The iterator i over the instance p of StaticPersistence is of type SPNode, and represents individual simplices taken in the filtration order. It knows about its own sign() and pair() as well as whether it is ~SPNode.unpaired. StaticPersistence.make_simplex_map() creates a map that we use to get the actual simplices: smap[i] and smap[i.pair()]. The previous code snippet prints out the persistence diagrams of the given filtration.


Class ZigzagPersistence accepts additions and removals of the simplices one by one, and returns an internal representation of the simplices. (CohomologyPersistence works in the same way, but accepts only additions of the simplices.) When one adds a simplex via ZigzagPersistence.add(), one must provide its boundary in this internal representation together with a birth value which ZigzagPersistence will store in case a class is born as a result of the addition. add() returns a pair (i,d). i is the internal representation of the newly added simplex, which one must record for future use in the boundaries of its cofaces (below it is recorded in the dictionary complex). d is None in case of the birth, otherwise, it is the previously recorded birth value of the class that dies as a result of the addition. The following code adds all the simplices to a zigzag:

complex = {}
zz = ZigzagPersistence()
for s in simplices:
    i,d = zz.add([complex[sb] for sb in s.boundary], (s.dimension(),
    complex[s] = i
    if d is not None:                   # we have a death
        dimension, birth = d            # we previously stored the (dimension, data) pair
        print dimension, birth,

Similarly, one can remove simplices by calling ZigzagPersistence.remove() with the internal index previously returned by add():

for s in reversed(simplices):
    d = zz.remove(complex[s], (s.dimension() - 1,
    del complex[s]
    if d is not None:
        dimension, birth = d
        print dimension, birth,

Naturally, remove() returns only d.

If one wants to add or remove an entire set of simplices “at once” (i.e. with all births being assigned the same value), there are two helper functions: add_simplices() and remove_simplices().

See the bindings reference for more details, and Triangle zigzag example for an example of ZigzagPersistence. See Python cohomology computation for an example of CohomologyPersistence.

Speed-up suggestions

Currently, when the choice comes between efficiency and flexibility, the Python bindings err on the side of flexibility. There is hope that in the future the choice won’t really be necessary. Meanwhile, one can use a few techniques that speed up computation at the expense of memory. Note, however, that since the recent switch of PairwiseDistances to C++ rather than pure Python, it is not clear whether these deliver a substantial speed-up:

  • To avoid (possibly expensive) computation of distances during Rips complex generation, store ExplicitDistances (see Distances):

    distances = PairwiseDistances(points)
    distances = ExplicitDistances(distances)
  • To avoid the computation of simplex sizes in the Rips complex during the initialization of a Filtration, store them explicitly in attribute (this is not done by default to save memory); then use data_dim_cmp() when sorting the Filtration:

    rips = Rips(distances)
    simplices = Filtration()
    rips.generate(..., simplices.append)
    for s in simplices: = rips.eval(s)