# Introduction to spectral graph wavelets¶

This tutorial will show you how to easily construct a wavelet frame, a kind of filter bank, and apply it to a signal. This tutorial will walk you into computing the wavelet coefficients of a graph, visualizing filters in the vertex domain, and using the wavelets to estimate the curvature of a 3D shape.

As usual, we first have to import some packages.

```
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pygsp import graphs, filters, plotting, utils
```

Then we can load a graph. The graph we’ll use is a nearest-neighbor graph of a point cloud of the Stanford bunny. It will allow us to get interesting visual results using wavelets.

```
>>> G = graphs.Bunny()
```

Note

At this stage we could compute the Fourier basis using
`pygsp.graphs.Graph.compute_fourier_basis()`

, but this would take some
time, and can be avoided with a Chebychev polynomials approximation to
graph filtering. See the documentation of the
`pygsp.filters.Filter.filter()`

filtering function and
[hammond2011wavelets] for details on how it is down.

## Simple filtering: heat diffusion¶

Before tackling wavelets, let’s observe the effect of a single filter on a graph signal. We first design a few heat kernel filters, each with a different scale.

```
>>> taus = [10, 25, 50]
>>> g = filters.Heat(G, taus)
```

Let’s create a signal as a Kronecker delta located on one vertex, e.g. the vertex 20. That signal is our heat source.

```
>>> s = np.zeros(G.N)
>>> DELTA = 20
>>> s[DELTA] = 1
```

We can now simulate heat diffusion by filtering our signal s with each of our heat kernels.

```
>>> s = g.filter(s, method='chebyshev')
```

And finally plot the filtered signal showing heat diffusion at different scales.

```
>>> fig = plt.figure(figsize=(10, 3))
>>> for i in range(g.Nf):
... ax = fig.add_subplot(1, g.Nf, i+1, projection='3d')
... G.plot_signal(s[:, i], colorbar=False, ax=ax)
... title = r'Heat diffusion, $\tau={}$'.format(taus[i])
... _ = ax.set_title(title)
... ax.set_axis_off()
>>> fig.tight_layout()
```

Note

The `pygsp.filters.Filter.localize()`

method can be used to visualize a
filter in the vertex domain instead of doing it manually.

## Visualizing wavelets atoms¶

Let’s now replace the Heat filter by a filter bank of wavelets. We can create a
filter bank using one of the predefined filters, such as
`pygsp.filters.MexicanHat`

to design a set of Mexican hat wavelets.

```
>>> g = filters.MexicanHat(G, Nf=6) # Nf = 6 filters in the filter bank.
```

Then plot the frequency response of those filters.

```
>>> fig, ax = plt.subplots(figsize=(10, 5))
>>> g.plot(ax=ax)
>>> _ = ax.set_title('Filter bank of mexican hat wavelets')
```

Note

We can see that the wavelet atoms are stacked on the low frequency part of
the spectrum. A better coverage could be obtained by adapting the filter
bank with `pygsp.filters.WarpedTranslates`

or by using another
filter bank like `pygsp.filters.Itersine`

.

We can visualize the atoms as we did with the heat kernel, by filtering a Kronecker delta placed at one specific vertex.

```
>>> s = g.localize(DELTA)
>>>
>>> fig = plt.figure(figsize=(10, 2.5))
>>> for i in range(3):
... ax = fig.add_subplot(1, 3, i+1, projection='3d')
... G.plot_signal(s[:, i], ax=ax)
... _ = ax.set_title('Wavelet {}'.format(i+1))
... ax.set_axis_off()
>>> fig.tight_layout()
```

## Curvature estimation¶

As a last and more applied example, let us try to estimate the curvature of the underlying 3D model by only using spectral filtering on the nearest-neighbor graph formed by its point cloud.

A simple way to accomplish that is to use the coordinates map \([x, y, z]\) and filter it using the above defined wavelets. Doing so gives us a 3-dimensional signal \([g_i(L)x, g_i(L)y, g_i(L)z], \ i \in [0, \ldots, N_f]\) which describes variation along the 3 coordinates.

```
>>> s = G.coords
>>> s = g.filter(s)
```

The curvature is then estimated by taking the \(\ell_1\) or \(\ell_2\) norm across the 3D position.

```
>>> s = np.linalg.norm(s, ord=2, axis=1)
```

Let’s finally plot the result to observe that we indeed have a measure of the curvature at different scales.

```
>>> fig = plt.figure(figsize=(10, 7))
>>> for i in range(4):
... ax = fig.add_subplot(2, 2, i+1, projection='3d')
... G.plot_signal(s[:, i], ax=ax)
... title = 'Curvature estimation (scale {})'.format(i+1)
... _ = ax.set_title(title)
... ax.set_axis_off()
>>> fig.tight_layout()
```