Make the example's __main__ actually run

It claimed to run the pipeline with no engine involved, and could not: the
node bodies it called save and load artifacts, which raise outside a node by
design. Each node is now a thin wrapper over a plain function — make_rows,
train_curve, score — and __main__ calls those, which is the split the sandbox
already demonstrates and the one worth copying.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

examples/myresearch/data.py

@@ -8,6 +8,14 @@ import fluksio
 from fluksio import Port, node
 
 
+def make_rows(source: str = "builtin", limit: int = 512) -> dict:
+    """The training set itself, with no Fluksio in it."""
+    return {
+        "source": source,
+        "rows": [{"x": index / limit, "y": (index % 7) / 7} for index in range(limit)],
+    }
+
+
 @node(provides=[Port("dataset", "artifact"), Port("rows", "int")])
 def prepare(source: str = "builtin", limit: int = 512):
     """Make the training set and store it, returning a reference to it.
@@ -15,11 +23,11 @@ def prepare(source: str = "builtin", limit: int = 512):
     `source` and `limit` have defaults and are not ports, so they become the
     node's settings: the canvas can tune them without touching this file.
     """
-    rows = [{"x": index / limit, "y": (index % 7) / 7} for index in range(limit)]
-    payload = json.dumps({"source": source, "rows": rows}).encode()
+    payload = make_rows(source, limit)
+    blob = json.dumps(payload).encode()
     return {
-        "dataset": fluksio.save_artifact(payload, "dataset.json", "application/json"),
-        "rows": len(rows),
+        "dataset": fluksio.save_artifact(blob, "dataset.json", "application/json"),
+        "rows": len(payload["rows"]),
     }