def test_gabor(self):
f = filters.Rectangular(self._G, None, 0.1)
f = filters.Gabor(self._G, f)
self._test_methods(f, tight=False, check=False)
self.assertRaises(ValueError, filters.Gabor, graphs.Sensor(), f)
f = filters.Regular(self._G)
self.assertRaises(ValueError, filters.Gabor, self._G, f)
def gft_windowed_gabor(self, s, k):
r"""Gabor windowed graph Fourier transform.
Parameters
----------
s : ndarray
Graph signal in the vertex domain.
k : function
Gabor kernel. See :class:`pygsp.filters.Gabor`.
Returns
-------
s : ndarray
Vertex-frequency representation of the signals.
Examples
--------
>>> G = graphs.Logo()
>>> s = np.random.normal(size=(G.N, 2))
>>> s = G.gft_windowed_gabor(s, lambda x: x/(1.-x))
>>> s.shape
(1130, 2, 1130)
"""
from pygsp import filters
return filters.Gabor(self, k).filter(s)
def test_modulation_gabor(self):
"""Both should be equivalent for deltas centered at the eigenvalues."""
f = filters.Rectangular(self._G, 0, 0)
f1 = filters.Modulation(self._G, f, modulation_first=True)
f2 = filters.Gabor(self._G, f)
s1 = f1.filter(self._signal)
s2 = f2.filter(self._signal)
np.testing.assert_allclose(s1, s2, atol=1e-5)
def test_gabor(self):
f = filters.Gabor(self._G, lambda x: x / (1. + x))
self._test_methods(f, tight=False, check=False)
def test_gabor(G, fu):
g = filters.Gabor(G, fu)
