@@ -8,7 +8,7 @@ from PIL import Image, ImageDraw

 sys.path.insert(0, os.path.abspath(".."))  # to get adaptive on the path

-import adaptive  # noqa: E402

+import adaptive  # noqa: E402, isort:skip

 holoviews.notebook_extension("matplotlib")

@@ -19,7 +19,7 @@ def create_and_run_learner():

         x, y = xy

         a = 0.2

-        return x + np.exp(-(x ** 2 + y ** 2 - 0.75 ** 2) ** 2 / a ** 4)

+        return x + np.exp(-((x ** 2 + y ** 2 - 0.75 ** 2) ** 2) / a ** 4)

     learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

     adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)

@@ -28,7 +28,7 @@ def create_and_run_learner():

 def plot_learner_and_save(learner, fname):

     fig, ax = plt.subplots()

-    tri = learner.ip().tri

+    tri = learner.interpolator(scaled=True).tri

     triang = mtri.Triangulation(*tri.points.T, triangles=tri.vertices)

     ax.triplot(triang, c="k", lw=0.8)

     ax.imshow(learner.plot().Image.I.data, extent=(-0.5, 0.5, -0.5, 0.5))

@@ -1,22 +1,25 @@

 import os

 import sys

-sys.path.insert(0, os.path.abspath('..'))  # to get adaptive on the path

-

-import adaptive

 import holoviews

 import matplotlib.pyplot as plt

 import matplotlib.tri as mtri

 from PIL import Image, ImageDraw

-holoviews.notebook_extension('matplotlib')

+

+sys.path.insert(0, os.path.abspath(".."))  # to get adaptive on the path

+

+import adaptive  # noqa: E402

+

+holoviews.notebook_extension("matplotlib")

 def create_and_run_learner():

     def ring(xy):

         import numpy as np

+

         x, y = xy

         a = 0.2

-        return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)

+        return x + np.exp(-(x ** 2 + y ** 2 - 0.75 ** 2) ** 2 / a ** 4)

     learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

     adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)

@@ -27,7 +30,7 @@ def plot_learner_and_save(learner, fname):

     fig, ax = plt.subplots()

     tri = learner.ip().tri

     triang = mtri.Triangulation(*tri.points.T, triangles=tri.vertices)

-    ax.triplot(triang, c='k', lw=0.8)

+    ax.triplot(triang, c="k", lw=0.8)

     ax.imshow(learner.plot().Image.I.data, extent=(-0.5, 0.5, -0.5, 0.5))

     ax.set_xticks([])

     ax.set_yticks([])

@@ -36,10 +39,10 @@ def plot_learner_and_save(learner, fname):

 def add_rounded_corners(fname, rad):

     im = Image.open(fname)

-    circle = Image.new('L', (rad * 2, rad * 2), 0)

+    circle = Image.new("L", (rad * 2, rad * 2), 0)

     draw = ImageDraw.Draw(circle)

     draw.ellipse((0, 0, rad * 2, rad * 2), fill=255)

-    alpha = Image.new('L', im.size, 255)

+    alpha = Image.new("L", im.size, 255)

     w, h = im.size

     alpha.paste(circle.crop((0, 0, rad, rad)), (0, 0))

     alpha.paste(circle.crop((0, rad, rad, rad * 2)), (0, h - rad))

@@ -49,9 +52,9 @@ def add_rounded_corners(fname, rad):

     return im

-if __name__ == '__main__':

+if __name__ == "__main__":

     learner = create_and_run_learner()

-    fname = 'source/_static/logo_docs.png'

+    fname = "source/_static/logo_docs.png"

     plot_learner_and_save(learner, fname)

     im = add_rounded_corners(fname, rad=200)

     im.thumbnail((200, 200), Image.ANTIALIAS)  # resize

@@ -1,3 +1,8 @@

+import os

+import sys

+

+sys.path.insert(0, os.path.abspath('..'))  # to get adaptive on the path

+

 import adaptive

 import holoviews

 import matplotlib.pyplot as plt

new file mode 100644

@@ -0,0 +1,53 @@

+import adaptive

+import holoviews

+import matplotlib.pyplot as plt

+import matplotlib.tri as mtri

+from PIL import Image, ImageDraw

+holoviews.notebook_extension('matplotlib')

+

+

+def create_and_run_learner():

+    def ring(xy):

+        import numpy as np

+        x, y = xy

+        a = 0.2

+        return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)

+

+    learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

+    adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)

+    return learner

+

+

+def plot_learner_and_save(learner, fname):

+    fig, ax = plt.subplots()

+    tri = learner.ip().tri

+    triang = mtri.Triangulation(*tri.points.T, triangles=tri.vertices)

+    ax.triplot(triang, c='k', lw=0.8)

+    ax.imshow(learner.plot().Image.I.data, extent=(-0.5, 0.5, -0.5, 0.5))

+    ax.set_xticks([])

+    ax.set_yticks([])

+    plt.savefig(fname, bbox_inches="tight", transparent=True, dpi=300, pad_inches=-0.1)

+

+

+def add_rounded_corners(fname, rad):

+    im = Image.open(fname)

+    circle = Image.new('L', (rad * 2, rad * 2), 0)

+    draw = ImageDraw.Draw(circle)

+    draw.ellipse((0, 0, rad * 2, rad * 2), fill=255)

+    alpha = Image.new('L', im.size, 255)

+    w, h = im.size

+    alpha.paste(circle.crop((0, 0, rad, rad)), (0, 0))

+    alpha.paste(circle.crop((0, rad, rad, rad * 2)), (0, h - rad))

+    alpha.paste(circle.crop((rad, 0, rad * 2, rad)), (w - rad, 0))

+    alpha.paste(circle.crop((rad, rad, rad * 2, rad * 2)), (w - rad, h - rad))

+    im.putalpha(alpha)

+    return im

+

+

+if __name__ == '__main__':

+    learner = create_and_run_learner()

+    fname = 'source/_static/logo_docs.png'

+    plot_learner_and_save(learner, fname)

+    im = add_rounded_corners(fname, rad=200)

+    im.thumbnail((200, 200), Image.ANTIALIAS)  # resize

+    im.save(fname)