I was trying to plot the spectrogram plot but it doesn't work. There is no errors, but the plot does not show. here is the code I'm using:

import pygsp as pg
import matplotlib.pyplot as plt

G = pg.graphs.Ring(15)
G.estimate_lmax()
G.plot_spectrogram()
pg.plotting.show()

I also tried using plt.show() but no luck. I'm using version 0.5.1 and was trying to follow tutorial .

When you call graph.compute_fourier_basis(n_eigenvectors=k), only the first k-1 eigenvectors are computed, because of the design of the function sparse.linalg.eigsh.

In particular, we have as a consequence that graph.set_coordinates(kind='laplacian_eigenmap2D') sets graph.coords to a one-dimensional vector.

The release we get with pypi is 0.5.1 and the version on the master branch is also tagged 0.5.1 even if it has many new features. It confuses people, especially when looking at the doc. People mix the 'stable' and 'latest' tag in the doc. I would suggest to make a new release.
I guess it would solve some of the issues opened. I also had students confused during a course on networks and data science where they use it for their project.
Happy new year! :)

Hi,I try to use the extract_components(self) to get the connected components,but it always return the graph which I input. Then I read the source and find the code 'stack = set(np.nonzero(~visited)[0])' that build a stack with all nodes in graph, maybe it should be 'stack = set([np.nonzero(~visited)[0][0]])'? I think the stack should have only one node at the beginning, so that it can split the connected parts. Is that right?

I've forked pygsp and started implementing the tikhonov optimization on my branch, but it appears that

1. The current implementation of optimization.prox_tv does not work because
2. pyunlocbox does not yet support the non-tight frame problem.
   Regardless, I think I've fixed prox_tv up to this error with pyunlocbox. Once I get prox_tik done/documented, i'll switch over to pyunlocbox and see what I can do about the non-tight frame case.

This sample code in the Readme file

>>> from pygsp import graphs, filters
>>> G = graphs.Logo()
>>> G.compute_fourier_basis()  # Fourier to plot the eigenvalues.
>>> # G.estimate_lmax() is otherwise sufficient.
>>> g = filters.Heat(G, scale=50)
>>> fig, ax = g.plot()

throws the error line 79, in __init__ super(Heat, self).__init__(G, g, **kwargs) TypeError: __init__() got an unexpected keyword argument 'scale'.

The problem seems to be that filters. Heat doesn't have a scale parameter.

In a related note, I noticed that the content in Read the Docs page is not the same as the Readme file (perhaps that's how it should be, I'm not really familiar in how "Read the Docs" is built). In particular, in the read the docs, the example calls the filter function as g = filters.Heat(G, tau=100), which uses the correct parameter.

I'm running PyGSP 0.5.1 (installed using conda).

In 'synthesis()' there's a call to 'operator.cheby_op':

s = s + operator.cheby_op(self.G,
cheb_coeffs[i], c[i * N + tmpN])

but the function it's been moved to fast_filtering.py

I try to compute this example:

G = graphs.Torus()
G.compute_fourier_basis(n_eigenvectors=64)
G.U.shape

but got:

TypeError: compute_fourier_basis() got an unexpected keyword argument 'n_eigenvectors'

Howdy!

I am trying to run the tutorial as it is on the docs, and I get the following:

from pygsp import graphs, plotting
2
import numpy as np
3
​
4
G = graphs.Sensor(N=256, seed=42)
5
G.compute_fourier_basis()
6

Create label signal

7
label_signal = np.copysign(np.ones(G.N), G.U[:, 3])
8
​
1
G.plot_signal(label_signal)
(<Figure size 432x288 with 2 Axes>,
<matplotlib.axes._subplots.AxesSubplot at 0x1e2feb61780>)

1
rs = np.random.RandomState(42)
2

Create the mask

3
M = rs.rand(G.N)
4
M = (M > 0.6).astype(float) # Probability of having no label on a vertex.
5

Applying the mask to the data

6
sigma = 0.1
7
subsampled_noisy_label_signal = M * (label_signal + sigma * rs.standard_normal(G.N))
8
G.plot_signal(subsampled_noisy_label_signal)
(<Figure size 432x288 with 2 Axes>,
<matplotlib.axes._subplots.AxesSubplot at 0x1e2fee9a400>)

1
import pyunlocbox
2

Set the functions in the problem

3
gamma = 3.0
4
d = pyunlocbox.functions.dummy()
5
r = pyunlocbox.functions.norm_l1()
6
f = pyunlocbox.functions.norm_l2(w=M, y=subsampled_noisy_label_signal,
7
lambda_=gamma)
8

Define the solver

9
G.compute_differential_operator()
10
L = G.D.toarray()
11
step = 0.999 / (1 + np.linalg.norm(L))
12
solver = pyunlocbox.solvers.mlfbf(L=L, step=step)
13

Solve the problem

14
x0 = subsampled_noisy_label_signal.copy()
15
prob1 = pyunlocbox.solvers.solve([d, r, f], solver=solver, x0=x0, rtol=0, maxit=1000)
16

17
G.plot_signal(prob1['sol'])

ValueError Traceback (most recent call last)
in
13 # Solve the problem
14 x0 = subsampled_noisy_label_signal.copy()
---> 15 prob1 = pyunlocbox.solvers.solve([d, r, f], solver=solver, x0=x0, rtol=0, maxit=1000)
16
17 G.plot_signal(prob1['sol'])

c:\users\arash\anaconda3\envs\tf-gpu\lib\site-packages\pyunlocbox\solvers.py in solve(functions, x0, solver, atol, dtol, rtol, xtol, maxit, verbosity)
248
249 # Solver specific initialization.
--> 250 solver.pre(functions, x0)
251
252 while not crit:

c:\users\arash\anaconda3\envs\tf-gpu\lib\site-packages\pyunlocbox\solvers.py in pre(self, functions, x0)
376 self.smooth_funs = []
377 self.non_smooth_funs = []
--> 378 self._pre(functions, self.sol)
379 self.accel.pre(functions, self.sol)
380

c:\users\arash\anaconda3\envs\tf-gpu\lib\site-packages\pyunlocbox\solvers.py in _pre(self, functions, x0)
840
841 def _pre(self, functions, x0):
--> 842 super(mlfbf, self)._pre(functions, x0)
843
844 if len(functions) != 3:

c:\users\arash\anaconda3\envs\tf-gpu\lib\site-packages\pyunlocbox\solvers.py in _pre(self, functions, x0)
781 # Dual variable.
782 if self.d0 is None:
--> 783 self.dual_sol = self.L(x0)
784 else:
785 self.dual_sol = self.d0

c:\users\arash\anaconda3\envs\tf-gpu\lib\site-packages\pyunlocbox\solvers.py in (x)
761 else:
762 # Transform matrix form to operator form.
--> 763 self.L = lambda x: L.dot(x)
764
765 if Lt is None:

ValueError: shapes (256,916) and (256,) not aligned: 916 (dim 1) != 256 (dim 0)

Any suggestions?

Thank you,
Arash

I'm having trouble getting past assert self._e[-1] <= 2 with a graph when I try to compute its fourier basis, so I'm wondering in why this assertion would fail? Why does the largest eigenvalue have to be <= 2?

Taken from expwin.py in pygsp:

`def kernel_lowpass(x):
return h(0.5 - x/G.lmax + band_max)
def kernel_highpass(x):
return h(0.5 + x/G.lmax - band_min)

    if (band_min is None) and (band_max is None):
        kernel = lambda x: np.ones_like(x)
    elif band_min is None:
        kernel = kernel_lowpass  #Here what value of x is being passed
    elif band_max is None:
        kernel = kernel_highpass  #Here what value of x is being passed
    else:
        kernel = lambda x: kernel_lowpass(x) * kernel_highpass(x)`

This doubt came to my mind when I wanted to use expwin as high pass filter for my graph. Is this a glitch?

Use map to compute for several filters at once instead of the actual implementation of filterbanks

The Examples section of 'pygsp.filters.Itersine' repeats the one in 'pygsp.filters.HalfCosine'.

This is also the case when the backend is chosen as 'pyqtgraph' for any plotting function call. The screen gets stuck on an empty window, and the kernel dies on closing it.

A question.

I want to choose the degree of Community graph.
I tried to specify the value, but the min_deg argument is not used in the init function.

If someone knows about this, please tell me.

Community graph has a fixed epsilon-NN graph embedded in the construction of the classes.

We should replace 1e-3 by a parametrized value, potentially even depending on Nc and N.

Passing a 2D array to filter() is ambiguous when G.N == Nf. A 3D array should be used to be unambiguous, e.g.

s = np.identity(g.G.N)
tig = g.filter(s[..., np.newaxis], **kwargs)

I do not see any simple way to change the colormap of a plotting function.

Dear creators of Pygsp,

I would like to install the latest version with all the latest functionality, labeled as 'latest' on the pygsp website. However, I cannot seem to find a way to do so. I found your suggestion on issue 82 where pip install https://github.com/epfl-lts2/pygsp should do the trick, however this returns a lot of errors to me:

pip install https://github.com/epfl-lts2/pygsp
Collecting https://github.com/epfl-lts2/pygsp
  Downloading https://github.com/epfl-lts2/pygsp
     - 174kB 1.9MB/s
  ERROR: Cannot unpack file /private/var/folders/bw/qvtg52mj1pz37rcd5nr8k8qc0000gn/T/pip-unpack-ee5p_yel/pygsp (downloaded from /private/var/folders/bw/qvtg52mj1pz37rcd5nr8k8qc0000gn/T/pip-req-build-s0ije3vi, content-type: text/html; charset=utf-8); cannot detect archive format
ERROR: Cannot determine archive format of /private/var/folders/bw/qvtg52mj1pz37rcd5nr8k8qc0000gn/T/pip-req-build-s0ije3vi

Do you know how to solve such an error or is there an easier way to install the version with the latest functionality?

I think this might be my own fault from when I added the truncated eigendecomposition.

import pygsp
G = pygsp.graphs.RandomRegular()
G.set_coordinates(kind='laplacian_eigenmap2D')
G.plot()

throws

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-338-4a8fe3272aa9> in <module>
      2 G = pygsp.graphs.RandomRegular()
      3 G.set_coordinates(kind='laplacian_eigenmap2D')
----> 4 G.plot()

~/.local/lib/python3.7/site-packages/pygsp/graphs/graph.py in plot(self, vertex_color, vertex_size, highlight, edges, edge_color, edge_width, indices, colorbar, limits, ax, title, backend)
    897                            edges=edges, indices=indices, colorbar=colorbar,
    898                            edge_color=edge_color, edge_width=edge_width,
--> 899                            limits=limits, ax=ax, title=title, backend=backend)
    900 
    901     def plot_signal(self, *args, **kwargs):

~/.local/lib/python3.7/site-packages/pygsp/plotting.py in _plot_graph(G, vertex_color, vertex_size, highlight, edges, edge_color, edge_width, indices, colorbar, limits, ax, title, backend)
    382     check_2d_3d = (G.coords.ndim != 2) or (G.coords.shape[1] not in [2, 3])
    383     if G.coords.ndim != 1 and check_2d_3d:
--> 384         raise AttributeError('Coordinates should be in 1D, 2D or 3D space.')
    385     if G.coords.shape[0] != G.N:
    386         raise AttributeError('Graph needs G.N = {} coordinates.'.format(G.N))

AttributeError: Coordinates should be in 1D, 2D or 3D space.

and on inspection, G.coords.shape == (64, 1).

Issues epfl-lts2/gspbox#2 and epfl-lts2/gspbox#3 from the matlab version probably still apply.

In evaluate() 'fd' is assigned two times, the first seems useless:

fd = np.zeros(x.size)
fd = self.g[i](x)
return fd

In filterbank_bounds() if 'bounds' is not provided it does:

if not hasattr(self.G, 'lmax'):
    self.logger.info('FILTERBANK_BOUNDS: Has to estimate lmax.')
    self.G.estimate_lmax()  

if not hasattr(self.G, 'e'):
    self.logger.info(
    'FILTERBANK_BOUNDS: Has to compute Fourier basis.')
    self.G.compute_fourier_basis()
     
rng = self.G.e

but compute_fourier_basis() in graph.py already sets 'lmax' when returning, so I think the first if block should be removed.

Hope it helps. Nice toolbox 👍

pygsp.graphs.Graph.compute_fourier_basis raises a warning on large matrices.

if self.N > 3000:
    self.logger.warning('Computing the full eigendecomposition of a '
                        'large matrix ({0} x {0}) may take some '
'time.'.format(self.N))

Is it worth adding a function which computes the first n eigenvectors via sklearn.utils.extmath.randomized_svd? I'm happy to write the code for it. Alternatively, if you don't want the additional overhead of adding sklearn as a dependency, that function could be copied into PyGSP.

I'm happy to submit a PR if this is something you're interested in adding.

It looks like a factor of pi is missing in the numerical integration in

Hi developers of Pygsp,

After playing around with some heat diffusion code in your package, I was wondering how I could tweak the settings of:

G = graphs.Grid2d(10,10)
G.estimate_lmax()
taus = [0,1,2,3]
print(len(taus))
g = filters.Heat(G, taus)
DELTA = 25
s = np.zeros(G.N)
s[DELTA] = 1
s = g.filter(s, method='chebyshev', order = 10)
G.plot_signal(s[:,1], limits=[0,1])
for i in range(0,len(taus)):
    print(max(s[:,i]))

To not drop so fast in maximum values. Since the maximum value of the heat origin is dropping and reducing from the original delta 1 to [0.62, 0.41, 0.28]. Also, is there a way to create a filter that eventually fades out completely? Such that I can simulate pressure data originating from a source which then completely disappears after N tau values? That would be great for simulating training data for my machine learning model, which works with pressure sensors that can detect events. Hopefully my explanation is clear enough!

Hi,

I've read the docs but also.looked around and can't seem to work out how to do. So would appreciate any thoughts. I have a large numpy array that is really an array of voxels. The array is dense so there are alot of nodes. I can use another library to reduce the footprint to nonzero values to point clouds x,y,z,feature (segment label/number). I would really like to convert either of these into a network to use in pygsp to understand wave mechanics. Each node would be connected to it's neighbour but I would really like to understand how to encode the edge with the distance between nodes (perhaps collapsing similar nodes and multiply the edge to represent the distance) but have the nodes labelled as the segmentation label. Any thoughts how to proceed? Would appreciate any help.

Many doctest examples try to plot passing an 'ax' argument.
However by default pygsp.plotting sets:
BACKEND = 'pyqtgraph'
So I often get:
TypeError: _qtg_plot_signal() got an unexpected keyword argument 'ax'
The problem is that it takes the following arguments:
_qtg_plot_signal(G, signal, show_edges, plot_name, vertex_size, limits)
Making the matplotlib backend explicit in the calls could help improve the docs.
Another solution is to switch backend automatically when an 'ax' parameter is provided.

Hello!

I have just downloaded the package using pip install, and I have been trying to reproduce the code as shown on the Graphlet tutorial, but I keep encountering the same error when trying to place a Kronecker signal at a specific vertex by running:
Sf = Wk.synthesis(S)

Error:
cheb_coeffs = operator.compute_cheby_coeff(self, m=order, N=order + 1)
AttributeError: 'module' object has no attribute 'compute_cheby_coeff'.

It seems that the code calls the function filter.py, which, at line 221, calls:
cheb_coeffs = operator.compute_cheby_coeff(self, m=order, N=order + 1)
whereas it should probably call:
cheb_coeffs = fast_filtering.compute_cheby_coeff(self, m=order, N=order + 1)
(seems to work after correcting like this at least...)
I was thus wondering if the toolbox had been updated and if something had perhaps changed?
Thanks for your time and help.

C.D.

Details: anaconda/python2.7; Mac OS Sierra 10.12.3

I would like to plot only two of a graph's dimensions. Oftentimes this is desirable because working in 3D can be hard to wrap one's head around. This would be something like a keyword argument g.plot(dims=[1,2]) or such.

I poked my head in the plotting code and it appears that the bulk of the logic in the _plt* and _qtg* functions are built atop calls to _get_coords(G) and conditionals on G.coords.shape. This makes it a little bit difficult to just insert a dims parameter. It could be done, but it'd require a refactor. One hacky option is to alter _plot_graph and _plot_signal (and their upstream methods in graphs.py), to have dims=None as a keyword parameter and have

tmp = G.coords 
if dims and len(dims)<G.coords.shape[1]: 
     G.set_coordinates(tmp[:,dims])
## normal plotting (backend call)
G.set_coordinates(tmp)

Note that this doesn't consume any extra memory due to assignment by reference.

make lint should pass and then enforced by Travis

Goals:

• Import from and export to standard Python packages for network science. At least networkx and graph-tool. Optionally, NetworKit, igraph, SNAP.
• Use the above import & export functionality to load & save graphs and signals in standard formats such as:
  • GraphML
  • GML
  • GEXF (Gephi's format)
  • dot (Graphviz's format)
• Further leverage the import & export infrastructure. For example, we may want to use networkx's graph generators, or visualization tools.

@cgallay will implement those features. Any feedback or comment from the community is welcome.

Hello,

In update_graph_attr(self, *args, **kwargs) you import "from nngraphs import NNGraph" but do not list this in the requirements.txt and the function fails without it.

I believe this comes from torch?

Thanks!
-dave

demo.rst says:

You can now access the eigenvalues of the fourier basis with G.e and the eigenvectors G.U, they look like sinuses on the graph. Let's plot the second and third eigenvector, as the one is only constant.

But then, since the index starts at 0, it plots the third and the fourth ones:

pygsp.plotting.plt_plot_signal(G, G.U[:, 2], savefig=True, vertex_size=50, plot_name='doc/tutorials/img/logo_second_eigenvector')
pygsp.plotting.plt_plot_signal(G, G.U[:, 3], savefig=True, vertex_size=50, plot_name='doc/tutorials/img/logo_third_eigenvector')

Hi,
I'm using pygsp v0.5.1 installed through conda, and when running the LineGraph example

graph = graphs.Sensor(5)
line_graph = graphs.LineGraph(graph)

I get the following error:

AttributeError: module 'pygsp.graphs' has no attribute 'LineGraph'

Also, if i add the k=2 parameter to the graph = graphs.Sensor(5), i get the following:

TypeError: __init__() got an unexpected keyword argument 'k'

Hi,

I am not a Signal Processing expert, I am coming from a more Graph Theory background.
I have the code to create a low-pass filter:

def g(x):
    return 1. / (1. + tau * x)

But I cannot find a way (or example) to create a high-pass version. How could I achieve that?

Thank you for making this amazing package, and kind regards,

Stefan

In distanz():

# Size verification
if rx != ry:
    raise("The sizes of x and y do not fit")

when the condition is met the following error message is returned:

TypeError: exceptions must derive from BaseException

Something like the following should fix it:

# Size verification
if rx != ry:
    raise ValueError("The sizes of x and y do not fit")

Hi,

I am hoping to sparsify my graph which is a sparse matrix, and I also hope to update my node labels for generated sparsified graph.
Is there a way to update the node labels directly, or get the index of nodes that would be kept in sparsified graph?
Any suggestion to revise the code if the function is not provided yet?

Thank you a lot!

Hello,

Thank you very much for the nice library! I was wondering would it be possible to add the networkx io related function (from_networkx/to_networkx) to the stable version of pygsp? Those seem to be quite important/natural functions but right now those can only be used in the development version.

I prefer the stable version because I am currently using a library built upon pygsp stable. If I switch to dev version, some functions of the library won't work.

I downloaded the package using pip and I tried "Introduction to the PyGSP" in tutorials.
And I encountered the below error when running the code.

Code:
import numpy as np
from pygsp import graphs, filters, plotting
plotting.BACKEND = 'matplotlib'
G = graphs.Logo()
G.compute_fourier_basis()
print(G.U)
G.plot_signal(G.U[:, 1], vertex_size=50)

Error:
TypeError: pg_plot_signal() got an unexpected keyword argument 'show_plot'

I use:
MacOS Sierra 10.12.6
python 3.6
PyGSP (0.4.1)

Hi,

The learning.classification_tikhonov function (and its variants) take the labels y as input. The user of the function should not expect this array to be modified. But because of y[M == False] = 0 the array is modified, which is very annoying. Indeed, for validation purposes, it might be useful to store the real labels in y[M == False].

I suggest to either:

• document this behavior with a warning
• modify the function such that it starts by copying the data (that shouldn't be too expansive).

I'm having trouble getting pastassert -1e-12 < G1._e[0] < 1e-12 with a graph when I try to compute its fourier basis, so I'm wondering why this assertion would fail? Why does the largest eigenvalue have to be within that range? How to move past this error? I printed the e[0] and it was -6.426935757176126e-12
But why is it termed as erroneous?

edit: apologies, just encountered this on another package, don't think your package is to blame - please ignore

Hi there,

Was trying out the Gabor filter with the example code given in the comments:

G = graphs.Logo()
k = lambda x: x/(1.-x)
g = filters.Gabor(G, k);
2017-07-26 11:24:27,800:INFO: Gabor : has to compute lmax
2017-07-26 11:24:27,812:INFO: Filter Gabor will calculate and set the eigenvalues to normalize the kernel
Traceback (most recent call last):
File "", line 1, in
TypeError: init() should return None, not 'list'

Thanks, great package!
-dave

compute_jackson_cheby_coeff is indexing with a float. PR coming.

>>> import pygsp
>>> G = pygsp.graphs.Minnesota()
>>> G.estimate_lmax()
>>> pygsp.filters.compute_jackson_cheby_coeff([0, G.lmax/2], [0, G.lmax], 30)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/scottgigante/.local/lib/python3.7/site-packages/pygsp/filters/approximations.py", line 209, in compute_jackson_cheby_coeff
    (np.sin(i * np.arccos(filter_bounds[0])) - np.sin(i * np.arccos(filter_bounds[1])))
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

Description

No example works.

Most of them have an error related to the number of arguments of the plot() function.

Versions

Windows-10-10.0.17763-SP0
Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)]
NumPy 1.16.3
SciPy 1.2.1
Matplotlib 3.0.3
pygsp 0.5.1

Code to Reproduce (and Results)

D:/Documents/GitHub/pygsp/examples> python eigenvalue_concentration.py
Traceback (most recent call last):
  File "eigenvalue_concentration.py", line 20, in <module>
    graph.plot(graph.U[:, 1], ax=ax[0])
TypeError: plot() takes 1 positional argument but 2 were given

D:/Documents/GitHub/pygsp/examples> python filtering.py
Traceback (most recent call last):
  File "filtering.py", line 28, in <module>
    g = pg.filters.Expwin(G, band_max=0.5)
  File "D:/Programs/Miniconda3/lib/site-packages/pygsp/filters/expwin.py", line 56, in __init__
    super(Expwin, self).__init__(G, g, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'band_max'

D:/Documents/GitHub/pygsp/examples> python fourier_basis.py
Traceback (most recent call last):
  File "fourier_basis.py", line 33, in <module>
    plot_eigenvectors(G, axes[0])
  File "fourier_basis.py", line 27, in plot_eigenvectors
    G.plot(G.U[:, i], limits=limits, colorbar=False, vertex_size=50, ax=ax)
TypeError: plot() takes 1 positional argument but 2 were given

D:/Documents/GitHub/pygsp/examples> python fourier_transform.py
Traceback (most recent call last):
  File "fourier_transform.py", line 32, in <module>
    G.plot(x, limits=[-limit, limit], ax=axes[0, i])
TypeError: plot() takes 1 positional argument but 2 were given

D:/Documents/GitHub/pygsp/examples> python heat_diffusion.py
Traceback (most recent call last):
  File "heat_diffusion.py", line 23, in <module>
    x = np.zeros(G.n_vertices)
AttributeError: 'Grid2d' object has no attribute 'n_vertices'

D:/Documents/GitHub/pygsp/examples> python kernel_localization.py
Traceback (most recent call last):
  File "kernel_localization.py", line 28, in <module>
    g.plot(ax=axs[0], title='heat kernel')
  File "D:/Programs/Miniconda3/lib/site-packages/pygsp/filters/filter.py", line 510, in plot
    plotting.plot_filter(self, **kwargs)
  File "D:/Programs/Miniconda3/lib/site-packages/pygsp/plotting.py", line 85, in inner
    plot(obj, *args, **kwargs)
TypeError: plot_filter() got an unexpected keyword argument 'title'

D:/Documents/GitHub/pygsp/examples> python random_walk.py
Traceback (most recent call last):
  File "random_walk.py", line 28, in <module>
    graph.plot(state, ax=ax, title=r'$/delta P^{}$'.format(step))
TypeError: plot() takes 1 positional argument but 2 were given

D:/Documents/GitHub/pygsp/examples> python wave_propagation.py
Traceback (most recent call last):
  File "wave_propagation.py", line 23, in <module>
    x = np.zeros(G.n_vertices)
AttributeError: 'Grid2d' object has no attribute 'n_vertices'

Hi everyone. I just found myself having trouble with the pygsp.reduction.interpolate function, and the reason was in line 173 of the reduction.py file:

K_reg = getattr(G.mr, 'K_reg', kron_reduction(L_reg, keep_inds))
green_kernel = getattr(
            G.mr, 'green_kernel',
            filters.Filter(G, lambda x: 1. / (reg_eps + x)))

The error raised was claiming that G did not have any mr attribute. I assume an if-else could be added to fix it, as I have done to my local cloned version:

if hasattr(G, 'mr'):
    K_reg = getattr(G.mr, 'K_reg', kron_reduction(L_reg, keep_inds))
    green_kernel = getattr(
        G.mr, 'green_kernel',
    filters.Filter(G, lambda x: 1. / (reg_eps + x)))
else:
    K_reg = kron_reduction(L_reg, keep_inds)
    green_kernel = filters.Filter(G, lambda x: 1. / (reg_eps + x))

This solved for me. As I'm not from a computer science background and I'm not very familiar with the creation of test files, I thought of writing the problem here, instead of making a pull request. I hope this is helpful. I open this issue to raise attention to this problem and, if in fact you guys see that a fix is needed, a pull request is created.

Thanks.

Hi pygsp team,

I was trying to find a way to change the shape of a node, is that possible? I cannot seem to find the functionality to do so. I did find the way to change vertex size and color, but not the shape of the nodes. I have two types of sensors in my network and I would like to show that clearly in my figures.

Kind regards and thanks in advance!

Stefan

Howdy!

I am trying to use the reduction interpolate function, and I encounter the following:

    171     
    172     L_reg = G.L + reg_eps * sparse.eye(G.N)
--> 173     K_reg = getattr(G.mr, 'K_reg', kron_reduction(L_reg, keep_inds))
    174     green_kernel = getattr(G.mr, 'green_kernel',
    175                            filters.Filter(G, lambda x: 1. / (reg_eps + x)))

AttributeError: 'Graph' object has no attribute 'mr

The following is how I am using it; my graph is fully connected, and oneSignal and indeces are one full signal on the graph in np array format and the indices in np array format respectively.

pygsp.reduction.interpolate(graph, oneSignal, indeces)

Any suggestions?

I am kind of new to Python. I installed the pygsp package in conda. But when I tried to use the to_networkx() function, it always said that the 'Graph' object has no attribute 'to_networkx'. Could someone help me ? Thanks!

It seems to have to be corrected from
ch[0] = (2/(np.pi))*(np.arccos(filter_bounds[0])-np.arccos(filter_bounds[1]))
to
ch[0] = (1/(np.pi))*(np.arccos(filter_bounds[0])-np.arccos(filter_bounds[1])).