remove plot_signal(), everything done with graph.plot()

README.rst

@@ -78,7 +78,7 @@ structure!
 >>> s = np.zeros(G.N)
 >>> s[DELTAS] = 1
 >>> s = g.filter(s)
->>> fig, ax = G.plot_signal(s, highlight=DELTAS)
+>>> fig, ax = G.plot(s, highlight=DELTAS)
 
 .. image:: ../pygsp/data/readme_example_graph.png
     :alt:

doc/tutorials/intro.rst

@@ -151,7 +151,7 @@ which assign a set of values (a vector in :math:`\mathbb{R}^d`) at every node
     >>>
     >>> fig, axes = plt.subplots(1, 2, figsize=(10, 3))
     >>> for i, ax in enumerate(axes):
-    ...     _ = G.plot_signal(G.U[:, i+1], size=30, ax=ax)
+    ...     _ = G.plot(G.U[:, i+1], size=30, ax=ax)
     ...     _ = ax.set_title('Eigenvector {}'.format(i+2))
     ...     ax.set_axis_off()
     >>> fig.tight_layout()
@@ -168,9 +168,9 @@ plot best represents the graph.
     >>> G2 = graphs.Ring(N=50)
     >>> G2.compute_fourier_basis()
     >>> fig, axes = plt.subplots(1, 2, figsize=(10, 4))
-    >>> _ = G2.plot_signal(G2.U[:, 4], ax=axes[0])
+    >>> _ = G2.plot(G2.U[:, 4], ax=axes[0])
     >>> G2.set_coordinates('line1D')
-    >>> _ = G2.plot_signal(G2.U[:, 1:4], ax=axes[1])
+    >>> _ = G2.plot(G2.U[:, 1:4], ax=axes[1])
     >>> fig.tight_layout()
 
 Filters
@@ -227,9 +227,9 @@ low-pass filter.
     >>> s2 = g.filter(s)
     >>>
     >>> fig, axes = plt.subplots(1, 2, figsize=(10, 3))
-    >>> _ = G.plot_signal(s, size=30, title='noisy', ax=axes[0])
+    >>> _ = G.plot(s, size=30, title='noisy', ax=axes[0])
     >>> axes[0].set_axis_off()
-    >>> _ = G.plot_signal(s2, size=30, title='cleaned', ax=axes[1])
+    >>> _ = G.plot(s2, size=30, title='cleaned', ax=axes[1])
     >>> axes[1].set_axis_off()
     >>> fig.tight_layout()
 

doc/tutorials/optimization.rst

@@ -22,7 +22,7 @@ This tutorial focuses on the problem of recovering a label signal on a graph fro
     >>> # Create label signal
     >>> label_signal = np.copysign(np.ones(G.N), G.U[:, 3])
     >>>
-    >>> fig, ax = G.plot_signal(label_signal)
+    >>> fig, ax = G.plot(label_signal)
 
 The first figure shows a plot of the original label signal, that we wish to recover, on the graph.
 
@@ -39,7 +39,7 @@ The first figure shows a plot of the original label signal, that we wish to reco
     >>> sigma = 0.1
     >>> subsampled_noisy_label_signal = M * (label_signal + sigma * rs.standard_normal(G.N))
     >>>
-    >>> fig, ax = G.plot_signal(subsampled_noisy_label_signal)
+    >>> fig, ax = G.plot(subsampled_noisy_label_signal)
 
 This figure shows the label signal on the graph after the application of the subsampling mask and the addition of noise. The label of more than half of the vertices has been set to :math:`0`.
 
@@ -88,7 +88,7 @@ We start with the graph TV regularization. We will use the :class:`pyunlocbox.so
         objective function f(sol) = 2.024594e+02
         stopping criterion: MAXIT
     >>>
-    >>> fig, ax = G.plot_signal(prob1['sol'])
+    >>> fig, ax = G.plot(prob1['sol'])
 
 This figure shows the label signal recovered by graph total variation regularization. We can confirm here that this sort of regularization does indeed promote piecewise-constant solutions.
 
@@ -110,6 +110,6 @@ This figure shows the label signal recovered by graph total variation regulariza
         objective function f(sol) = 9.555135e+01
         stopping criterion: MAXIT
     >>>
-    >>> fig, ax = G.plot_signal(prob2['sol'])
+    >>> fig, ax = G.plot(prob2['sol'])
 
 This last figure shows the label signal recovered by Tikhonov regularization. As expected, the recovered label signal has smoother transitions than the one obtained by graph TV regularization.

doc/tutorials/wavelet.rst

@@ -76,7 +76,7 @@ scales.
     >>> for i in range(g.Nf):
     ...     ax = fig.add_subplot(1, g.Nf, i+1, projection='3d')
     ...     title = r'Heat diffusion, $\tau={}$'.format(taus[i])
-    ...     _ = G.plot_signal(s[:, i], colorbar=False, title=title, ax=ax)
+    ...     _ = G.plot(s[:, i], colorbar=False, title=title, ax=ax)
     ...     ax.set_axis_off()
     >>> fig.tight_layout()
 
@@ -124,7 +124,7 @@ a Kronecker delta placed at one specific vertex.
     >>> 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], title='Wavelet {}'.format(i+1), ax=ax)
+    ...     _ = G.plot(s[:, i], title='Wavelet {}'.format(i+1), ax=ax)
     ...     ax.set_axis_off()
     >>> fig.tight_layout()
 
@@ -165,6 +165,6 @@ curvature at different scales.
     >>> for i in range(4):
     ...     ax = fig.add_subplot(2, 2, i+1, projection='3d')
     ...     title = 'Curvature estimation (scale {})'.format(i+1)
-    ...     _ = G.plot_signal(s[:, i], title=title, ax=ax)
+    ...     _ = G.plot(s[:, i], title=title, ax=ax)
     ...     ax.set_axis_off()
     >>> fig.tight_layout()

playground.ipynb

@@ -60,7 +60,7 @@
     "s = np.zeros(G.N)\n",
     "s[DELTAS] = 1\n",
     "s = g.filter(s)\n",
-    "G.plot_signal(s, highlight=DELTAS, backend='matplotlib')"
+    "G.plot(s, highlight=DELTAS, backend='matplotlib')"
    ]
   },
   {

pygsp/__init__.py

@@ -36,13 +36,11 @@
 try:
     filters.Filter.plot.__doc__ = plotting._plot_filter.__doc__
     graphs.Graph.plot.__doc__ = plotting._plot_graph.__doc__
-    graphs.Graph.plot_signal.__doc__ = plotting._plot_signal.__doc__
     graphs.Graph.plot_spectrogram.__doc__ = plotting._plot_spectrogram.__doc__
 except AttributeError:
     # For Python 2.7.
     filters.Filter.plot.__func__.__doc__ = plotting._plot_filter.__doc__
     graphs.Graph.plot.__func__.__doc__ = plotting._plot_graph.__doc__
-    graphs.Graph.plot_signal.__func__.__doc__ = plotting._plot_signal.__doc__
     graphs.Graph.plot_spectrogram.__func__.__doc__ = plotting._plot_spectrogram.__doc__
 
 __version__ = '0.5.1'

pygsp/filters/abspline.py

@@ -38,7 +38,7 @@ class Abspline(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/expwin.py

@@ -40,7 +40,7 @@ class Expwin(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/filter.py

@@ -190,7 +190,7 @@ def filter(self, s, method='chebyshev', order=30):
 
         >>> fig, ax = plt.subplots()
         >>> G.set_coordinates('line1D')  # To visualize multiple signals in 1D.
-        >>> _ = G.plot_signal(s[:, 9, :], ax=ax)
+        >>> _ = G.plot(s[:, 9, :], ax=ax)
         >>> legend = [r'$\tau={}$'.format(t) for t in taus]
         >>> ax.legend(legend)  # doctest: +ELLIPSIS
         <matplotlib.legend.Legend object at ...>
@@ -218,8 +218,8 @@ def filter(self, s, method='chebyshev', order=30):
         Look how well we were able to reconstruct:
 
         >>> fig, axes = plt.subplots(1, 2)
-        >>> _ = G.plot_signal(s1, ax=axes[0])
-        >>> _ = G.plot_signal(s2, ax=axes[1])
+        >>> _ = G.plot(s1, ax=axes[0])
+        >>> _ = G.plot(s2, ax=axes[1])
         >>> print('{:.5f}'.format(np.linalg.norm(s1 - s2)))
         0.29620
 
@@ -351,7 +351,7 @@ def localize(self, i, **kwargs):
         >>> G.estimate_lmax()
         >>> g = filters.Heat(G, 100)
         >>> s = g.localize(DELTA)
-        >>> _ = G.plot_signal(s, highlight=DELTA)
+        >>> _ = G.plot(s, highlight=DELTA)
 
         """
         s = np.zeros(self.G.N)

pygsp/filters/gabor.py

@@ -43,7 +43,7 @@ class Gabor(Filter):
     >>> s = g.localize(G.N // 2, method='exact')
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0], sum=False)
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/halfcosine.py

@@ -29,7 +29,7 @@ class HalfCosine(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/heat.py

@@ -57,7 +57,7 @@ class Heat(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/held.py

@@ -41,7 +41,7 @@ class Held(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/itersine.py

@@ -34,7 +34,7 @@ class Itersine(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/mexicanhat.py

@@ -51,7 +51,7 @@ class MexicanHat(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/meyer.py

@@ -35,7 +35,7 @@ class Meyer(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/papadakis.py

@@ -37,7 +37,7 @@ class Papadakis(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/rectangular.py

@@ -38,7 +38,7 @@ class Rectangular(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/regular.py

@@ -47,7 +47,7 @@ class Regular(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/simoncelli.py

@@ -39,7 +39,7 @@ class Simoncelli(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/filters/simpletight.py

@@ -35,7 +35,7 @@ class SimpleTight(Filter):
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])
-    >>> _ = G.plot_signal(s, ax=axes[1])
+    >>> _ = G.plot(s, ax=axes[1])
 
     """
 

pygsp/graphs/__init__.py

@@ -84,7 +84,6 @@
 .. autosummary::
 
     Graph.plot
-    Graph.plot_signal
     Graph.plot_spectrogram
 
 Others

pygsp/graphs/fourier.py

@@ -70,7 +70,7 @@ def coherence(self):
         >>> # Plot some delocalized eigenvectors.
         >>> import matplotlib.pyplot as plt
         >>> graph.set_coordinates('line1D')
-        >>> _ = graph.plot_signal(graph.U[:, :5])
+        >>> _ = graph.plot(graph.U[:, :5])
 
         Localized eigenvectors.
 
@@ -83,7 +83,7 @@ def coherence(self):
         >>> # Plot the most localized eigenvector.
         >>> import matplotlib.pyplot as plt
         >>> idx = np.argmax(np.max(graph.U, axis=0))
-        >>> _ = graph.plot_signal(graph.U[:, idx])
+        >>> _ = graph.plot(graph.U[:, idx])
 
         """
         return self._check_fourier_properties('coherence',

pygsp/graphs/graph.py

@@ -683,21 +683,18 @@ def modulate(self, f, k):
         fm *= np.sqrt(self.N)
         return fm
 
-    def plot(self, edges=None, index=False, backend=None, vertex_size=None,
-             title=None, ax=None):
+    def plot(self, color=None, size=None, highlight=[], edges=None,
+             index=False, colorbar=True, limits=None, ax=None,
+             title=None, backend=None):
         r"""Docstring overloaded at import time."""
         from pygsp.plotting import _plot_graph
-        return _plot_graph(self, edges=edges, index=index, backend=backend,
-                           vertex_size=vertex_size, title=title, ax=ax)
+        return _plot_graph(self, color=color, size=size, highlight=highlight,
+                           edges=edges, index=index, colorbar=colorbar,
+                           limits=limits, ax=ax, title=title, backend=backend)
 
-    def plot_signal(self, color=None, size=None, highlight=[], edges=None,
-                    index=False, colorbar=True, limits=None, ax=None,
-                    title=None, backend=None):
-        r"""Docstring overloaded at import time."""
-        from pygsp.plotting import _plot_signal
-        return _plot_signal(self, color=color, size=size, highlight=highlight,
-                            edges=edges, index=index, colorbar=colorbar,
-                            limits=limits, ax=ax, title=title, backend=backend)
+    def plot_signal(self, *args, **kwargs):
+        r"""Deprecated, use plot() instead."""
+        return self.plot(*args, **kwargs)
 
     def plot_spectrogram(self, node_idx=None):
         r"""Docstring overloaded at import time."""