# Operators functions¶

pygsp.operators.adj2vec(G)[source]

Prepare the graph for the gradient computation.

Parameters: G : Graph structure

## Divergence¶

pygsp.operators.div(G, s)[source]

Compute Graph divergence of a signal.

Parameters: G : Graph structure s : ndarray Signal living on the nodes di : float The graph divergence

pygsp.operators.grad(G, s)[source]

Parameters: G : Graph structure s : ndarray Signal living on the nodes gr : ndarray Gradient living on the edges

pygsp.operators.grad_mat(G)[source]

Gradient sparse matrix of the graph G.

Parameters: G : Graph structure D : ndarray Gradient sparse matrix

## Gwft¶

pygsp.operators.generalized_wft(G, g, f, lowmemory=True)[source]

Graph windowed Fourier transform

Parameters: G : Graph g : ndarray or Filter Window (graph signal or kernel) f : ndarray Graph signal lowmemory : bool use less memory (default=True) C : ndarray Coefficients

## Gwft2¶

pygsp.operators.gabor_wft(G, f, k)[source]

Graph windowed Fourier transform

Parameters: G : Graph f : ndarray Graph signal k : #TODO kernel C : Coefficient.

## Gwft Frame Matrix¶

pygsp.operators.gwft_frame_matrix(G, g)[source]

Create the matrix of the GWFT frame

Parameters: G : Graph g : window F : ndarray Frame

## Igft¶

pygsp.operators.igft(G, f_hat)[source]

Compute inverse graph Fourier transform.

Parameters: G : Graph or Fourier basis f_hat : ndarray Signal f : ndarray Inverse graph Fourier transform of f_hat

## Ngwft¶

pygsp.operators.ngwft(G, f, g, lowmemory=True)[source]

Normalized graph windowed Fourier transform

Parameters: G : Graph f : ndarray Graph signal g : ndarray Window lowmemory : bool Use less memory. (default = True) C : ndarray Coefficients

## Ngwft Frame Matrix¶

pygsp.operators.ngwft_frame_matrix(G, g)[source]

Create the matrix of the GWFT frame

Parameters: G : Graph g : ndarray Window Output parameters: F : ndarray Frame

## Compute Chebyshev Coefficient¶

pygsp.operators.compute_cheby_coeff(f, *args, **kwargs)

## Chebyshev Operator¶

pygsp.operators.cheby_op(G, c, signal, **kwargs)[source]

Chebyshev polynomial of graph Laplacian applied to vector.

Parameters: G : Graph c : ndarray or list of ndarrays Chebyshev coefficients for a Filter or a Filterbank signal : ndarray Signal to filter r : ndarray Result of the filtering

## Localize¶

pygsp.operators.localize(g, i)[source]

Localize a kernel g to the node i.

Parameters: g : Filter kernel (or filterbank) i : int Index of vertex gt : ndarray Translated signal

## Modulate¶

pygsp.operators.modulate(G, f, k)[source]

Tranlate the signal f to the node i.

Parameters: G : Graph f : ndarray Signal (column) k : int Index of frequencies fm : ndarray Modulated signal

## Translate¶

pygsp.operators.translate(G, f, i)[source]

Tranlate the signal f to the node i

Parameters: G : Graph f : ndarray Signal i : int Indices of vertex ft : translate signal