Add ipycytoscape plotting for InterDependencies_ objects

docs/changes/newsfragments/7305.new

@@ -0,0 +1 @@
+Adds InterDependencies visualisation capability via ipywidgets and ipycytoscape

pyproject.toml

@@ -95,6 +95,7 @@ test = [
     "zhinst.qcodes>=0.5", # typecheck zhinst driver alias
     "libcst>=1.2.0", # refactor tests
     "jinja2>=3.1.3", # transitive dependency pin due to cve in earlier version
+    "qcodes[live_plotting]",
 ]
 docs = [
     "autodocsumm>=0.2.9",
@@ -114,6 +115,9 @@ docs = [
 refactor = [
     "libcst>=1.2.0"
 ]
+live_plotting = [
+    "ipycytoscape>=1.2.2"
+]
 
 [project.scripts]
 qcodes-monitor = "qcodes.monitor.monitor:main"

src/qcodes/dataset/descriptions/dependencies.py

@@ -322,15 +322,6 @@ def __getitem__(self, name: str) -> ParamSpecBase:
     def graph(self) -> nx.DiGraph[str]:
         return self._graph
 
-    def to_ipycytoscape_json(self) -> dict[str, list[dict[str, Any]]]:
-        graph_json: dict[str, list[dict[str, Any]]] = nx.cytoscape_data(self.graph)[
-            "elements"
-        ]
-        # TODO: Add different node types?
-        for edge_dict in graph_json["edges"]:
-            edge_dict["classes"] = edge_dict["data"]["interdep_type"]
-        return graph_json
-
     @staticmethod
     def validate_paramspectree(
         paramspectree: ParamSpecTree, interdep_type: str | None = None

src/qcodes/dataset/descriptions/dependencies_viz.py

@@ -0,0 +1,100 @@
+"""
+Utils for visualizing InterDependencies_. Note that these only work in
+Jupyter and require you to have ipycytoscape installed. This can be
+installed with ``pip install qcodes[live_plotting]``
+"""
+
+from typing import TYPE_CHECKING, Any
+
+import ipycytoscape
+import ipywidgets
+import networkx as nx
+
+if TYPE_CHECKING:
+    from qcodes.dataset.descriptions.dependencies import InterDependencies_
+
+INTERDEPENDENCIES_STYLE = [
+    {
+        "selector": "node",
+        "css": {
+            "content": "data(id)",
+            "text-valign": "center",
+            "font-weight": 10,
+            "color": "white",
+            "text-outline-width": 1,
+            "text-outline-color": "#11479e",
+            "background-color": "#8aa8d8",
+            "border-color": "#11479e",
+            "border-width": 1.5,
+            "border-style": "solid",
+        },
+    },
+    {
+        "selector": "node[node_type='dependency']",
+        "css": {
+            "text-outline-color": "#089222",
+            "background-color": "#90C79A",
+            "border-color": "#089222",
+        },
+    },
+    {
+        "selector": "node[node_type='inference']",
+        "css": {
+            "text-outline-color": "#920808",
+            "background-color": "#D37B7B",
+            "border-color": "#920808",
+        },
+    },
+    {
+        "selector": "edge",
+        "style": {
+            "width": 4,
+            "curve-style": "bezier",
+            "target-arrow-shape": "triangle",
+        },
+    },
+    {
+        "selector": "edge[interdep_type = 'depends_on'],  ",
+        "style": {
+            "line-color": "#089222",
+            "target-arrow-color": "#089222",
+        },
+    },
+    {
+        "selector": "edge[interdep_type = 'inferred_from']",
+        "style": {
+            "line-color": "#920808",
+            "target-arrow-color": "#920808",
+        },
+    },
+]
+
+
+def draw_interdepenencies(interdeps: "InterDependencies_") -> ipywidgets.HBox:
+    graphwidget = ipycytoscape.CytoscapeWidget()
+    graphwidget.graph.add_graph_from_json(interdeps_to_ipycytoscape_json(interdeps))
+    graphwidget.set_style(INTERDEPENDENCIES_STYLE)
+    graphwidget.user_zooming_enabled = True
+    graphwidget.min_zoom = 0.2
+    graphwidget.max_zoom = 5
+    graphwidget.set_layout(name="cola", animate=False, avoidOverlap=True)
+    graphwidget.wheel_sensitivity = 0.1
+    return ipywidgets.HBox([graphwidget])
+
+
+def interdeps_to_ipycytoscape_json(
+    interdeps: "InterDependencies_",
+) -> dict[str, list[dict[str, Any]]]:
+    graph_json: dict[str, list[dict[str, Any]]] = nx.cytoscape_data(interdeps.graph)[
+        "elements"
+    ]
+    interdeps_dict = interdeps._to_dict()
+    dependencies = list(interdeps_dict["dependencies"].keys())
+    inferences = list(interdeps_dict["inferences"].keys())
+
+    for node_dict in graph_json["nodes"]:
+        if node_dict["data"]["id"] in dependencies:
+            node_dict["data"]["node_type"] = "dependency"
+        elif node_dict["data"]["id"] in inferences:
+            node_dict["data"]["node_type"] = "inference"
+    return graph_json