Fix HoloViews opts deprecation warnings (#357)

Author: basnijholt

adaptive/learner/learner2D.py

@@ -818,12 +818,9 @@ def plot(self, n=None, tri_alpha=0):
         else:
             im = hv.Image([], bounds=lbrt)
             tris = hv.EdgePaths([])
-
-        im_opts = {"cmap": "viridis"}
-        tri_opts = {"line_width": 0.5, "alpha": tri_alpha}
-        no_hover = {"plot": {"inspection_policy": None, "tools": []}}
-
-        return im.opts(style=im_opts) * tris.opts(style=tri_opts, **no_hover)
+        return im.opts(cmap="viridis") * tris.opts(
+            line_width=0.5, alpha=tri_alpha, tools=[]
+        )
 
     def _get_data(self) -> dict[tuple[float, float], Float | np.ndarray]:
         return self.data

adaptive/learner/learnerND.py

@@ -932,12 +932,9 @@ def plot(self, n=None, tri_alpha=0):
         else:
             im = hv.Image([], bounds=lbrt)
             tris = hv.EdgePaths([])
-
-        im_opts = {"cmap": "viridis"}
-        tri_opts = {"line_width": 0.5, "alpha": tri_alpha}
-        no_hover = {"plot": {"inspection_policy": None, "tools": []}}
-
-        return im.opts(style=im_opts) * tris.opts(style=tri_opts, **no_hover)
+        return im.opts(cmap="viridis") * tris.opts(
+            line_width=0.5, alpha=tri_alpha, tools=[]
+        )
 
     def plot_slice(self, cut_mapping, n=None):
         """Plot a 1D or 2D interpolated slice of a N-dimensional function.
@@ -1005,7 +1002,7 @@ def plot_slice(self, cut_mapping, n=None):
             else:
                 im = hv.Image([], bounds=lbrt)
 
-            return im.opts(style={"cmap": "viridis"})
+            return im.opts(cmap="viridis")
         else:
             raise ValueError("Only 1 or 2-dimensional plots can be generated.")
 
@@ -1199,10 +1196,7 @@ def plot_isoline(self, level=0.0, n=None, tri_alpha=0):
 
         vertices, lines = self._get_iso(level, which="line")
         paths = [[vertices[i], vertices[j]] for i, j in lines]
-        contour = hv.Path(paths)
-
-        contour_opts = {"color": "black"}
-        contour = contour.opts(style=contour_opts)
+        contour = hv.Path(paths).opts(color="black")
         return plot * contour
 
     def plot_isosurface(self, level=0.0, hull_opacity=0.2):

adaptive/learner/skopt_learner.py

@@ -98,12 +98,12 @@ def plot(self, nsamples=200):
                 xsp = self.space.transform(xs.reshape(-1, 1).tolist())
                 y_pred, sigma = model.predict(xsp, return_std=True)
                 # Plot model prediction for function
-                curve = hv.Curve((xs, y_pred)).opts(style={"line_dash": "dashed"})
+                curve = hv.Curve((xs, y_pred)).opts(line_dash="dashed")
                 # Plot 95% confidence interval as colored area around points
                 area = hv.Area(
                     (xs, y_pred - 1.96 * sigma, y_pred + 1.96 * sigma),
                     vdims=["y", "y2"],
-                ).opts(style={"alpha": 0.5, "line_alpha": 0})
+                ).opts(alpha=0.5, line_alpha=0)
 
             else:
                 area = hv.Area([])

docs/source/algorithms_and_examples.md

@@ -98,7 +98,7 @@ def plot_loss_interval(learner):
         x, y = [x_0, x_1], [y_0, y_1]
     else:
         x, y = [], []
-    return hv.Scatter((x, y)).opts(style=dict(size=6, color="r"))
+    return hv.Scatter((x, y)).opts(size=6, color="r")
 
 
 def plot(learner, npoints):
@@ -114,7 +114,7 @@ def get_hm(loss_per_interval, N=101):
 plot_homo = get_hm(uniform_loss).relabel("homogeneous sampling")
 plot_adaptive = get_hm(default_loss).relabel("with adaptive")
 layout = plot_homo + plot_adaptive
-layout.opts(plot=dict(toolbar=None))
+layout.opts(toolbar=None)
 ```
 
 ## {class}`adaptive.Learner2D`

docs/source/tutorial/tutorial.LearnerND.md

@@ -94,7 +94,7 @@ dm = dm.redim.values(
 
 # In a notebook one would run `dm` however we want a statically generated
 # html, so we use a HoloMap to display it here
-dynamicmap_to_holomap(dm).options(hv.opts.Path(framewise=True))
+dynamicmap_to_holomap(dm).opts(hv.opts.Path(framewise=True))
 ```
 
 The plots show some wobbles while the original function was smooth, this is a result of the fact that the learner chooses points in 3 dimensions and the simplices are not in the same face as we try to interpolate our lines.

docs/source/tutorial/tutorial.advanced-topics.md

@@ -316,7 +316,7 @@ adaptive.runner.replay_log(reconstructed_learner, runner.log)
 ```
 
 ```{code-cell} ipython3
-learner.plot().Scatter.I.opts(style=dict(size=6)) * reconstructed_learner.plot()
+learner.plot().Scatter.I.opts(size=6) * reconstructed_learner.plot()
 ```
 
 ## Adding coroutines