First, we import everything from Dionysus:

```
>>> from __future__ import print_function # if you are using Python 2
>>> import dionysus as d
```

A simplex is simply a list of vertices. It’s represented by the `Simplex`

class:

```
>>> s = d.Simplex([0,1,2])
>>> print("Dimension:", s.dimension())
Dimension: 2
```

We can iterate over the vertices of the simplex.

```
>>> for v in s:
... print(v)
0
1
2
```

Or over the boundary:

```
>>> for sb in s.boundary():
... print(sb)
<1,2> 0
<0,2> 0
<0,1> 0
```

Simplices can store optional data, and the 0 reported after each boundary edge is the default value of the data:

```
>>> s.data = 5
>>> print(s)
<0,1,2> 5
```

We can use `closure()`

to generate all faces of a set of
simplices. For example, an 8-sphere is the 8-dimensional skeleton of the
closure of the 9-simplex.

```
>>> simplex9 = d.Simplex([0,1,2,3,4,5,6,7,8,9])
>>> sphere8 = d.closure([simplex9], 8)
>>> print(len(sphere8))
1022
```

A filtration is a nested sequence of simplicial complexes, \(K_1 \subseteq K_2 \subseteq \ldots \subseteq K_n\). Without loss of generality, we can assume that two consecutive complexes in the filtration differ by a single simplex, so we can think of a filtration as a sequence of simplices.

In Dionysus, a filtration is represented by a special class,
`Filtration`

, that supports both iterating over the
simplices and looking up an index given a simplex. A filtration can be
`sort()`

ed. By default this orders
simplices by their data, breaking ties by dimension, and then
lexicographically.

```
>>> simplices = [([2], 4), ([1,2], 5), ([0,2], 6),
... ([0], 1), ([1], 2), ([0,1], 3)]
>>> f = d.Filtration()
>>> for vertices, time in simplices:
... f.append(d.Simplex(vertices, time))
>>> f.sort()
>>> for s in f:
... print(s)
<0> 1
<1> 2
<0,1> 3
<2> 4
<1,2> 5
<0,2> 6
```

We can lookup the index of a given simplex. (Indexing starts from 0.)

```
>>> print(f.index(d.Simplex([1,2])))
4
```

Applying homology functor to the filtration, we get a sequence of homology groups, connected by linear maps:
\(H_*(K_1) \to H_*(K_2) \to \ldots \to H_*(K_n)\). To compute decomposition of this sequence, i.e., persistence barcode,
we use `homology_persistence()`

, which returns its internal representation of the reduced boundary matrix:

```
>>> m = d.homology_persistence(f)
>>> for i,c in enumerate(m):
... print(i, c)
0
1
2 1*0 + 1*1
3
4 1*1 + 1*3
5
```

We can manually extract the persistence pairing from the reduced matrix:

```
>>> for i in range(len(m)):
... if m.pair(i) < i: continue # skip negative simplices
... dim = f[i].dimension()
... if m.pair(i) != m.unpaired:
... print(dim, i, m.pair(i))
... else:
... print(dim, i)
0 0
0 1 2
0 3 4
1 5
```

But we can also use the `init_diagrams()`

function, by providing it access to the filtration:

```
>>> dgms = d.init_diagrams(m, f)
>>> print(dgms)
[Diagram with 3 points, Diagram with 1 points]
>>> for i, dgm in enumerate(dgms):
... for pt in dgm:
... print(i, pt.birth, pt.death)
0 1.0 inf
0 2.0 3.0
0 4.0 5.0
1 6.0 inf
```

Notice that `init_diagrams()`

uses the data stored in
the simplices, instead of the index pairing we printed out in the previous
example.

`homology_persistence()`

knows several methods of
persistence computation. These can be specified with the method keyword
argument:

```
>>> m = d.homology_persistence(f, method = 'column')
```

**Homology.**
Dionysus doesn’t compute homology directly, but we can get it as a by-product
of persistent homology.

```
>>> f = d.Filtration(sphere8)
>>> f.sort()
>>> m = d.homology_persistence(f, prime=2)
>>> dgms = d.init_diagrams(m, f)
>>> for i, dgm in enumerate(dgms):
... print("Dimension:", i)
... for p in dgm:
... print(p)
Dimension: 0
(0,inf)
Dimension: 1
Dimension: 2
Dimension: 3
Dimension: 4
Dimension: 5
Dimension: 6
Dimension: 7
Dimension: 8
(0,inf)
```

It’s possible to compute persistent homology of a filtration relative to a subcomplex:
\(H_*(K_1, L_1) \to H_*(K_2, L_2) \to \ldots \to H_*(K_n, L_n)\), where \(L_i = K_i \cap L_n\).
To accomplish this,
`homology_persistence()`

takes an extra argument,
relative, to specify the subcomplex, \(L_n\). This subcomplex is
represented by a `Filtration`

, but the ordering of
the simplices in it doesn’t matter, only their presence.
For example, homology of a triangle relative to its boundary has a single class in dimension 2:

```
>>> f = d.Filtration(d.closure([d.Simplex([0,1,2])], 2))
>>> f.sort()
>>> f1 = d.Filtration([s for s in f if s.dimension() <= 1])
>>> m = d.homology_persistence(f, relative = f1)
>>> dgms = d.init_diagrams(m, f)
>>> for i, dgm in enumerate(dgms):
... print("Dimension:", i)
... for p in dgm:
... print(p)
Dimension: 0
Dimension: 1
Dimension: 2
(0,inf)
```

`wasserstein_distance()`

computes q-th Wasserstein distance between a pair of persistence diagrams.
`bottleneck_distance()`

computes the bottleneck distance.

```
>>> f1 = d.fill_rips(np.random.random((20, 2)), 2, 1)
>>> m1 = d.homology_persistence(f1)
>>> dgms1 = d.init_diagrams(m1, f1)
>>> f2 = d.fill_rips(np.random.random((20, 2)), 2, 1)
>>> m2 = d.homology_persistence(f2)
>>> dgms2 = d.init_diagrams(m2, f2)
>>> wdist = d.wasserstein_distance(dgms1[1], dgms2[1], q=2)
>>> print("2-Wasserstein distance between 1-dimensional persistence diagrams:", wdist)
2-Wasserstein distance between 1-dimensional persistence diagrams: 0.037361082536
>>> bdist = d.bottleneck_distance(dgms1[1], dgms2[1])
>>> print("Bottleneck distance between 1-dimensional persistence diagrams:", bdist)
Bottleneck distance between 1-dimensional persistence diagrams: 0.0336918905377
```