ENH: show off using multiple inputs + renaming

Author: tacaswell

data_prototype/wrappers.py

@@ -280,7 +280,6 @@ def draw(self, renderer):
 
     def _update_wrapped(self, data):
         for k, v in data.items():
-            k = {"x": "xdata", "y": "ydata"}.get(k, k)
             getattr(self._wrapped_instance, f"set_{k}")(v)
 
 

examples/mapped.py

@@ -13,7 +13,7 @@
 
 from matplotlib.colors import Normalize
 
-from data_prototype.wrappers import LineWrapper
+from data_prototype.wrappers import LineWrapper, FormatedText
 from data_prototype.containers import ArrayContainer
 
 cmap = plt.colormaps["viridis"]
@@ -25,9 +25,9 @@
     # arbitrary functions
     "lw": lambda lw: min(1 + lw, 5),
     # standard color mapping
-    "color": lambda color: cmap(norm(color)),
+    "color": lambda j: cmap(norm(j)),
     # categorical
-    "ls": lambda ls: {"A": "-", "B": ":", "C": "--"}[ls[()]],
+    "ls": lambda cat: {"A": "-", "B": ":", "C": "--"}[cat[()]],
 }
 
 th = np.linspace(0, 2 * np.pi, 128)
@@ -36,21 +36,30 @@
 fig, ax = plt.subplots()
 
 for j in range(10):
+    ac = ArrayContainer(
+        **{
+            "x": th,
+            "y": np.sin(th + j * delta) + j,
+            "j": np.asarray(j),
+            "lw": np.asarray(j),
+            "cat": np.asarray({0: "A", 1: "B", 2: "C"}[j % 3]),
+        }
+    )
     ax.add_artist(
         LineWrapper(
-            ArrayContainer(
-                **{
-                    "x": th,
-                    "y": np.sin(th + j * delta) + j,
-                    "color": np.asarray(j),
-                    "lw": np.asarray(j),
-                    "ls": np.asarray({0: "A", 1: "B", 2: "C"}[j % 3]),
-                }
-            ),
+            ac,
             nus,
         )
     )
-
+    ax.add_artist(
+        FormatedText(
+            ac,
+            {"text": lambda j, cat: f"index={j[()]} class={cat[()]!r}", "y": lambda j: j},
+            x=2 * np.pi,
+            ha="right",
+            bbox={"facecolor": "gray", "alpha": 0.5},
+        )
+    )
 ax.set_xlim(0, np.pi * 2)
 ax.set_ylim(-1.1, 10.1)