Graph from molecule similarity.

README.md

@@ -14,6 +14,7 @@ original LynxKite. The primary goals of this rewrite are:
 - `lynxkite-graph-analytics`: Graph analytics plugin. The classical LynxKite experience!
 - `lynxkite-pillow`: A simple example plugin.
 - `lynxkite-lynxscribe`: A plugin for building and running LynxScribe applications.
+- `lynxkite-bio`: Bioinformatics additions for LynxKite Graph Analytics.
 - `docs`: User-facing documentation. It's shared between all packages.
 
 ## Development
@@ -25,7 +26,7 @@ uv venv
 source .venv/bin/activate
 uvx pre-commit install
 # The [dev] tag is only needed if you intend on running tests
-uv pip install -e lynxkite-core/[dev] -e lynxkite-app/[dev] -e lynxkite-graph-analytics/[dev] -e lynxkite-lynxscribe/ -e lynxkite-pillow-example/
+uv pip install -e lynxkite-core/[dev] -e lynxkite-app/[dev] -e lynxkite-graph-analytics/[dev] -e lynxkite-bio -e lynxkite-lynxscribe/ -e lynxkite-pillow-example/
 ```
 
 This also builds the frontend, hopefully very quickly. To run it:

lynxkite-app/src/lynxkite_app/crdt.py

@@ -3,7 +3,6 @@
 import asyncio
 import contextlib
 import enum
-import pathlib
 import fastapi
 import os.path
 import pycrdt

lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py

@@ -1,3 +1,3 @@
-from . import lynxkite_ops  # noqa (imported to trigger registration)
+from .lynxkite_ops import *  # noqa (imported to trigger registration)
 from . import networkx_ops  # noqa (imported to trigger registration)
 from . import pytorch_model_ops  # noqa (imported to trigger registration)

lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py

@@ -80,9 +80,10 @@ class Bundle:
         # TODO: Use relations.
         graph = nx.DiGraph()
         if "nodes" in self.dfs:
-            graph.add_nodes_from(
-                self.dfs["nodes"].set_index("id").to_dict("index").items()
-            )
+            df = self.dfs["nodes"]
+            if df.index.name != "id":
+                df = df.set_index("id")
+            graph.add_nodes_from(df.to_dict("index").items())
         graph.add_edges_from(
             self.dfs["edges"][["source", "target"]].itertuples(index=False, name=None)
         )