Print graphs and filters

pygsp/filters/abspline.py

@@ -35,6 +35,8 @@ class Abspline(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Abspline(G)
+    >>> g
+    Abspline(in=1, out=6, lpfactor=20.00)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/expwin.py

@@ -37,6 +37,8 @@ class Expwin(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Expwin(G, band_min=0.1, band_max=0.7, slope=5)
+    >>> g
+    Expwin(in=1, out=1, band_min=0.10, band_max=0.70, slope=5)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/filter.py

@@ -45,6 +45,8 @@ class Filter(object):
     --------
     >>> G = graphs.Logo()
     >>> my_filter = filters.Filter(G, lambda x: x / (1. + x))
+    >>> my_filter
+    Filter(in=1, out=1)
     >>>
     >>> # Signal: Kronecker delta.
     >>> signal = np.zeros(G.N)

pygsp/filters/gabor.py

@@ -67,6 +67,8 @@ class Gabor(Filter):
     ...     _ = g.plot(ax=ax[0], sum=False)
     ...     _ = G.plot(s, ax=ax[1])
     >>> fig.tight_layout()
+    >>> g
+    Gabor(in=1, out=7)
 
     """
 

pygsp/filters/halfcosine.py

@@ -26,6 +26,8 @@ class HalfCosine(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.HalfCosine(G)
+    >>> g
+    HalfCosine(in=1, out=6)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/heat.py

@@ -59,6 +59,8 @@ class Heat(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Heat(G, scale=[5, 10, 100])
+    >>> g
+    Heat(in=1, out=3, scale=[5.00, 10.00, 100.00], normalize=False)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/held.py

@@ -40,6 +40,8 @@ class Held(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Held(G)
+    >>> g
+    Held(in=1, out=2, a=0.67)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/itersine.py

@@ -31,6 +31,8 @@ class Itersine(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Itersine(G)
+    >>> g
+    Itersine(in=1, out=6, overlap=2.00)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/mexicanhat.py

@@ -48,6 +48,8 @@ class MexicanHat(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.MexicanHat(G)
+    >>> g
+    MexicanHat(in=1, out=6, lpfactor=20.00, normalize=False)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/meyer.py

@@ -32,6 +32,8 @@ class Meyer(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Meyer(G)
+    >>> g
+    Meyer(in=1, out=6)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/modulation.py

@@ -88,6 +88,8 @@ class Modulation(Filter):
     >>> g1 = filters.Modulation(G, g, modulation_first=False)
     >>> g2 = filters.Modulation(G, g, modulation_first=True)
     >>> _ = g1.plot(sum=False)
+    >>> g1
+    Modulation(in=1, out=90)
     >>>
     >>> # Signal.
     >>> s = np.empty(G.N)

pygsp/filters/papadakis.py

@@ -36,6 +36,8 @@ class Papadakis(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Papadakis(G)
+    >>> g
+    Papadakis(in=1, out=2, a=0.75)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/rectangular.py

@@ -48,6 +48,8 @@ class Rectangular(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Rectangular(G, band_min=0.1, band_max=0.5)
+    >>> g
+    Rectangular(in=1, out=1, band_min=0.10, band_max=0.50)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/regular.py

@@ -44,6 +44,8 @@ class Regular(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Regular(G)
+    >>> g
+    Regular(in=1, out=2)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/simoncelli.py

@@ -36,6 +36,8 @@ class Simoncelli(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Simoncelli(G)
+    >>> g
+    Simoncelli(in=1, out=2, a=0.67)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/simpletight.py

@@ -32,6 +32,8 @@ class SimpleTight(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.SimpleTight(G)
+    >>> g
+    SimpleTight(in=1, out=6)
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/filters/wave.py

@@ -62,6 +62,8 @@ class Wave(Filter):
     >>> G.estimate_lmax()
     >>> G.set_coordinates('line1D')
     >>> g = filters.Wave(G, time=[5, 15], speed=1)
+    >>> g
+    Wave(in=1, out=2, time=[5.00, 15.00], speed=[1.00])
     >>> s = g.localize(G.N // 2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = g.plot(ax=axes[0])

pygsp/graphs/airfoil.py

@@ -14,6 +14,8 @@ class Airfoil(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Airfoil()
+    >>> G
+    Airfoil(n_vertices=4253, n_edges=12289)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.5)
     >>> _ = G.plot(edges=True, ax=axes[1])

pygsp/graphs/barabasialbert.py

@@ -35,6 +35,8 @@ class BarabasiAlbert(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.BarabasiAlbert(N=150, seed=42)
+    >>> G
+    BarabasiAlbert(n_vertices=150, n_edges=149, m0=1, m=1, seed=42)
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)

pygsp/graphs/comet.py

@@ -22,6 +22,8 @@ class Comet(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Comet(15, 10)
+    >>> G
+    Comet(n_vertices=15, n_edges=14, k=10)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/community.py

@@ -53,6 +53,8 @@ class Community(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Community(N=250, Nc=3, comm_sizes=[50, 120, 80], seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    Community(n_vertices=250, n_edges=1607, Nc=3, min_comm=28, ..., seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.5)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/davidsensornet.py

@@ -22,6 +22,8 @@ class DavidSensorNet(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.DavidSensorNet()
+    >>> G
+    DavidSensorNet(n_vertices=64, n_edges=236, seed=None)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/erdosrenyi.py

@@ -32,6 +32,8 @@ class ErdosRenyi(StochasticBlockModel):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.ErdosRenyi(N=64, seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    ErdosRenyi(n_vertices=64, n_edges=219, k=1, p=0.10, q=0.30, ..., seed=42)
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)

pygsp/graphs/fullconnected.py

@@ -19,6 +19,8 @@ class FullConnected(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.FullConnected(N=20)
+    >>> G
+    FullConnected(n_vertices=20, n_edges=190)
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=5)

pygsp/graphs/grid2d.py

@@ -32,6 +32,8 @@ class Grid2d(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Grid2d(N1=5, N2=4)
+    >>> G
+    Grid2d(n_vertices=20, n_edges=31, N1=5, N2=4)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/linegraph.py

@@ -25,7 +25,11 @@ class LineGraph(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> graph = graphs.Sensor(5, k=2, seed=10)
+    >>> graph
+    Sensor(n_vertices=5, n_edges=6, k=2, distributed=False, seed=10)
     >>> line_graph = graphs.LineGraph(graph)
+    >>> line_graph
+    LineGraph(n_vertices=6, n_edges=10)
     >>> fig, ax = plt.subplots()
     >>> fig, ax = graph.plot('blue', edge_color='blue', indices=True, ax=ax)
     >>> fig, ax = line_graph.plot('red', edge_color='red', indices=True, ax=ax)

pygsp/graphs/logo.py

@@ -13,6 +13,8 @@ class Logo(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Logo()
+    >>> G
+    Logo(n_vertices=1130, n_edges=3131)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.5)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/lowstretchtree.py

@@ -22,6 +22,8 @@ class LowStretchTree(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.LowStretchTree(k=2)
+    >>> G
+    LowStretchTree(n_vertices=16, n_edges=30, k=2)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/minnesota.py

@@ -25,6 +25,8 @@ class Minnesota(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Minnesota()
+    >>> G
+    Minnesota(n_vertices=2642, n_edges=3304, connected=True)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.5)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/nngraphs/bunny.py

@@ -15,6 +15,8 @@ class Bunny(NNGraph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Bunny()
+    >>> G  # doctest: +ELLIPSIS
+    Bunny(n_vertices=2503, n_edges=78292, NNtype=radius, ..., order=0)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(121)
     >>> ax2 = fig.add_subplot(122, projection='3d')

pygsp/graphs/nngraphs/cube.py

@@ -26,6 +26,8 @@ class Cube(NNGraph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Cube(seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    Cube(n_vertices=300, n_edges=1722, radius=1.00, nb_pts=300, ..., order=0)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(121)
     >>> ax2 = fig.add_subplot(122, projection='3d')

pygsp/graphs/nngraphs/grid2dimgpatches.py

@@ -28,6 +28,8 @@ class Grid2dImgPatches(Graph):
     >>> from skimage import data, img_as_float
     >>> img = img_as_float(data.camera()[::64, ::64])
     >>> G = graphs.Grid2dImgPatches(img)
+    >>> G  # doctest: +ELLIPSIS
+    Grid2dImgPatches(n_vertices=64, n_edges=522, N1=8, N2=8, ..., order=0)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/nngraphs/imgpatches.py

@@ -39,6 +39,8 @@ class ImgPatches(NNGraph):
     >>> from skimage import data, img_as_float
     >>> img = img_as_float(data.camera()[::64, ::64])
     >>> G = graphs.ImgPatches(img, patch_shape=(3, 3))
+    >>> G  # doctest: +ELLIPSIS
+    ImgPatches(n_vertices=64, n_edges=473, patch_shape=(3, 3), ..., order=0)
     >>> print('{} nodes ({} x {} pixels)'.format(G.Xin.shape[0], *img.shape))
     64 nodes (8 x 8 pixels)
     >>> print('{} features per node'.format(G.Xin.shape[1]))

pygsp/graphs/nngraphs/nngraph.py

@@ -68,6 +68,8 @@ class NNGraph(Graph):
     >>> import matplotlib.pyplot as plt
     >>> X = np.random.RandomState(42).uniform(size=(30, 2))
     >>> G = graphs.NNGraph(X)
+    >>> G  # doctest: +ELLIPSIS
+    NNGraph(n_vertices=30, n_edges=187, NNtype=knn, ..., order=0)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=5)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/nngraphs/sensor.py

@@ -39,12 +39,16 @@ class Sensor(NNGraph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Sensor(N=64, seed=42)
+    >>> G
+    Sensor(n_vertices=64, n_edges=229, k=6, distributed=False, seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)
     >>> _ = G.plot(ax=axes[1])
 
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Sensor(N=64, distributed=True, seed=42)
+    >>> G
+    Sensor(n_vertices=64, n_edges=228, k=6, distributed=True, seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/nngraphs/sphere.py

@@ -26,6 +26,8 @@ class Sphere(NNGraph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Sphere(nb_pts=100, seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    Sphere(n_vertices=100, n_edges=581, radius=1.00, nb_pts=100, ..., order=0)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(121)
     >>> ax2 = fig.add_subplot(122, projection='3d')

pygsp/graphs/nngraphs/twomoons.py

@@ -38,6 +38,8 @@ class TwoMoons(NNGraph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.TwoMoons()
+    >>> G  # doctest: +ELLIPSIS
+    TwoMoons(n_vertices=2000, n_edges=6132, moontype=standard, ..., order=0)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.5)
     >>> _ = G.plot(edges=True, ax=axes[1])

pygsp/graphs/path.py

@@ -42,6 +42,8 @@ class Path(Graph):
     ...     G = graphs.Path(N=10, directed=directed)
     ...     _ = axes[i, 0].spy(G.W)
     ...     _ = G.plot(ax=axes[i, 1])
+    >>> G
+    Path(n_vertices=10, n_edges=9, directed=True)
 
     The GFT of the path graph is the classical DCT.
 

pygsp/graphs/randomregular.py

@@ -40,6 +40,8 @@ class RandomRegular(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.RandomRegular(N=64, k=5, seed=42)
+    >>> G
+    RandomRegular(n_vertices=64, n_edges=160, k=5, seed=42)
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=2)

pygsp/graphs/randomring.py

@@ -23,6 +23,8 @@ class RandomRing(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.RandomRing(N=10, seed=42)
+    >>> G
+    RandomRing(n_vertices=10, n_edges=10, seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/ring.py

@@ -36,6 +36,8 @@ class Ring(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.Ring(N=10)
+    >>> G
+    Ring(n_vertices=10, n_edges=10, k=1)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W)
     >>> _ = G.plot(ax=axes[1])

pygsp/graphs/stochasticblockmodel.py

@@ -52,6 +52,8 @@ class StochasticBlockModel(Graph):
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.StochasticBlockModel(
     ...     100, k=3, p=[0.4, 0.6, 0.3], q=0.02, seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    StochasticBlockModel(n_vertices=100, n_edges=795, k=3, ..., seed=42)
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)
     >>> _ = axes[0].spy(G.W, markersize=0.8)

pygsp/graphs/swissroll.py

@@ -35,6 +35,8 @@ class SwissRoll(Graph):
     --------
     >>> import matplotlib.pyplot as plt
     >>> G = graphs.SwissRoll(N=200, seed=42)
+    >>> G  # doctest: +ELLIPSIS
+    SwissRoll(n_vertices=200, n_edges=1244, a=1, b=4, dim=3, ..., seed=42)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(121)
     >>> ax2 = fig.add_subplot(122, projection='3d')