AGENTS.md

@@ -34,13 +34,13 @@ CI=true SOURCE_FRAMES=30 dora run dataflow.yml --uv   # the same, headless
 - **`dora_image_view/payload.py`** — `as_float32()`: reinterprets a `uint8` payload rather than converting it, because a C++ node sending a raw byte slice produces a `UInt8` arrow array despite declaring `Float32`. Shared by `depth` and `intrinsics`, which both meet it.
 - **`dora_image_view/intrinsics.py`** — `Intrinsics`, `parse()` for the `camera_info` payload and `from_metadata()` for the copy senders attach to each frame. Both exist because dora-zed publishes the same numbers twice, and the per-frame copy is the one that survives a viewer starting after the camera did — `camera_info` goes out **once per session**. A nine-element row-major K is accepted alongside the compact four values, since that is how ROS spells the same model. The node needs none of this to draw; it is held for whatever turns depth into 3D, and drawn so a wrong camera model is visible rather than merely plausible.
 - **`dora_image_view/bbox.py`** — Parses the `bbox` struct into xyxy corners. `xywh` is centre-based (x, y are the box centre). `_flat()` unwraps the single-element list that `pa.array([{...}])` produces around each field, and reads a field stored flat as-is.
-- **`examples/source.py`** — The synthetic source node: an animated frame, a depth map of the same scene (a flat wall with the square in front of it), the box around the square, and a caption. It is both the demo and the CI gate, and the second role has been shaping it: it publishes depth and camera_info because those inputs are *typed*, and `dora validate --strict` fails on a typed input the dataflow does not wire. Before adding an output here, know which of the two reasons you are adding it for. `SOURCE_FRAMES` bounds the run, which is what lets CI wait for a clean exit instead of killing it on a timeout.
+- **`tests/source.py`** — The synthetic source node: an animated frame, a depth map of the same scene (a flat wall with the square in front of it), the intrinsics, the box around the square, and a caption. A **test fixture**, not an example — it lives in `tests/` because that is what it is, and it publishes depth and camera_info because those inputs are *typed* and `dora validate --strict` fails on a typed input the dataflow does not wire. It is a dora node rather than a pytest case because it exists to exercise the wiring and the event loop, which only a running dataflow covers; pytest does not collect it, since the filename does not start with `test_`. `SOURCE_FRAMES` bounds the run, which is what lets CI wait for a clean exit instead of killing it on a timeout.
 - **`dora-node.yml`** — Node manifest (`apiVersion: 1`). `dora validate` injects these types into any dataflow that reaches the node by `path:`; a `git:`-sourced node does **not** pick it up, which is why the example builds from the checkout. `image` and `text` carry type URNs naming the element type because each payload is a raw buffer described by its metadata, not an Arrow struct. `bbox` is deliberately **untyped**: senders disagree on whether its numeric fields are 32- or 64-bit, and the node reads either.
-- **`dataflow.yml`** — The example and the CI gate, with `strict_types: true`. The viewer is a `path:` node, so its manifest is injected; the source is typed inline with `output_types:` and the two are compared against each other. `bbox` is annotated on neither side, matching the manifest.
+- **`dataflow.yml`** — The end-to-end test, with `strict_types: true`. It sits at the repository root, not beside the fixture, because a node's working directory is the dataflow file's directory and both nodes build with `pip install .`, which needs `pyproject.toml` there. The viewer is a `path:` node, so its manifest is injected; the source is typed inline with `output_types:` and the two are compared against each other. `bbox` is annotated on neither side, matching the manifest.
 
   **One manifest, on purpose.** A `dora-node.yml` describes exactly one node (the schema is `NodeManifest`, singular) and is the unit `dora hub publish` operates on — it expects one beside `pyproject.toml`. This repository ships one node, so it has one manifest. The example source is a test fixture and gets none.
 
-  That is why the source runs as `path: python` + `args:` rather than `path: examples/source.py`. `dora validate` matches a manifest to a `path:` node **by entrypoint**, so a path node here would be tried against the root manifest, would fail to match its `dora-image-view` entrypoint, and would warn — and `strict_types: true` makes that warning fatal. Going through the interpreter leaves nothing to match. The cost is that the fixture gets no managed venv of its own (`.dora/python-envs/` stays empty for it) and runs against the project environment instead; the shipped node still uses `path:` + `build:`, which is the mechanism a consumer actually uses. Do not "fix" this back into a path node without re-reading this paragraph.
+  That is why the source runs as `path: python` + `args:` rather than `path: tests/source.py`. `dora validate` matches a manifest to a `path:` node **by entrypoint**, so a path node here would be tried against the root manifest, would fail to match its `dora-image-view` entrypoint, and would warn — and `strict_types: true` makes that warning fatal. Going through the interpreter leaves nothing to match. The cost is that the fixture gets no managed venv of its own (`.dora/python-envs/` stays empty for it) and runs against the project environment instead; the shipped node still uses `path:` + `build:`, which is the mechanism a consumer actually uses. Do not "fix" this back into a path node without re-reading this paragraph.
 - **`.gitea/workflows/validate.yml`** — CI: lints, runs the unit tests, validates the dataflow and runs it headless. Needs no camera and no display. One job on purpose — the gates themselves take about a second, and are dwarfed by preparing the runner.
 
 ## Why CI installs libgl1
@@ -55,7 +55,7 @@ CI=true SOURCE_FRAMES=30 dora run dataflow.yml --uv   # the same, headless
 
 ## A caveat on typed optional inputs
 
-`dora validate --strict` injects a contract type for *every* typed manifest input and then warns `input_types key "..." not found in inputs list` for any the dataflow does not wire. So a typed optional input forces the example dataflow to wire it. If a typed input is added to the manifest, either wire it in `dataflow.yml` or leave it untyped. This is why `examples/source.py` publishes a depth map and a camera_info at all: both are typed, so the example has to drive them, and a new output it sends needs a matching entry in the dataflow's `output_types:`.
+`dora validate --strict` injects a contract type for *every* typed manifest input and then warns `input_types key "..." not found in inputs list` for any the dataflow does not wire. So a typed optional input forces the example dataflow to wire it. If a typed input is added to the manifest, either wire it in `dataflow.yml` or leave it untyped. This is why `tests/source.py` publishes a depth map and a camera_info at all: both are typed, so the fixture has to drive them, and a new output it sends needs a matching entry in the dataflow's `output_types:`.
 
 ## Checking what it draws
 

README.md

@@ -35,13 +35,14 @@ wiring several shows them side by side, scaled to a common height. `bbox`, `text
 are overlays rather than panels — boxes are drawn on the `image` panel, in its own
 pixels, and the caption on the composite.
 
-### Runnable example
+### End-to-end test
 
-[`dataflow.yml`](dataflow.yml) is self-contained. [`examples/source.py`](examples/source.py)
+[`dataflow.yml`](dataflow.yml) is self-contained. [`tests/source.py`](tests/source.py)
 is a synthetic source node that emits an animated frame, a depth map of the same scene,
 the camera intrinsics, the box around the square moving in the frame, and a caption —
-every input this node draws — so the example needs no camera, no detector and no
-network:
+every input this node draws — so it needs no camera, no detector and no network. It is
+a test fixture rather than a worked example; the realistic wiring is the block at the
+top of this file. Run it to watch the node work:
 
 ```bash
 dora run dataflow.yml --uv
@@ -53,11 +54,8 @@ to quit.
 It builds the viewer from this checkout (`path:` + `build:`) rather than from a tag,
 which is what lets `dora validate` pick up the manifest described below.
 
-This example is also the end-to-end gate: CI runs the same dataflow headless on every
-push, which is what keeps it from rotting. It earns its place twice over — it is the
-only worked example of the *producing* side of these contracts, which is the fiddly
-half of wiring this node. The realistic wiring, against a real camera, is the block at
-the top of this file.
+CI runs the same dataflow headless on every push, which is what makes it a gate rather
+than a demo that rots.
 
 Set `SOURCE_FRAMES` to stop the source after a fixed number of frames instead of running
 until you quit; that plus `CI=true` is how the same dataflow runs headless as a test:

dataflow.yml

@@ -1,6 +1,11 @@
-# The repository's own example, and its end-to-end test: a synthetic source
-# feeding the viewer. Everything it needs is in this checkout -- no camera, no
-# detector, no network -- so it is also what CI runs.
+# The end-to-end test: a synthetic source feeding the viewer. Everything it
+# needs is in this checkout -- no camera, no detector, no network -- which is
+# what lets CI run it rather than only validate it.
+#
+# It lives at the repository root rather than beside tests/source.py because a
+# node's working directory is the directory of the dataflow file, and both
+# nodes build with `pip install .`, which needs pyproject.toml in that
+# directory.
 #
 # Both nodes are built from the checkout with `path:` + `build:`, which is what
 # lets `dora validate` pick up the typed contract in dora-node.yml. A consumer
@@ -11,7 +16,7 @@ strict_types: true
 
 nodes:
   - id: source
-    # Run through the interpreter rather than as `path: examples/source.py`.
+    # Run through the interpreter rather than as `path: tests/source.py`.
     #
     # A dora-node.yml describes exactly one node and is what gets published
     # (`dora hub publish` expects it beside pyproject.toml), and this
@@ -25,7 +30,7 @@ nodes:
     # interpreter leaves nothing for it to match, and `output_types` below is
     # the documented way to type an output without one.
     path: python
-    args: examples/source.py
+    args: tests/source.py
     # The source needs dora-rs, numpy and pyarrow; installing the project gives
     # it exactly the versions pinned in uv.lock.
     build: pip install .

tests/source.py

@@ -1,8 +1,13 @@
-"""Synthetic source node: this repository's stand-in for a camera and a detector.
+"""Synthetic source node: the test's stand-in for a camera and a detector.
 
-Emits an animated frame, a depth map of the same scene, the box around the square
-moving in it, and a caption -- every input dora-image-view draws -- so the example
-dataflow needs no camera, no detector and no network.
+Emits an animated frame, a depth map of the same scene, the intrinsics, the box
+around the square moving in it, and a caption -- every input dora-image-view draws --
+so dataflow.yml needs no camera, no detector and no network.
+
+This is a test fixture, not an example of how to wire the node; see the README for
+that. It is a dora node rather than a pytest case because what it exists to exercise
+is the wiring and the event loop, which only a running dataflow covers. pytest does
+not collect it: the filename does not start with `test_`.
 
 The depth map describes the same scene as the image: a flat back wall with the square
 standing in front of it. `camera_info` is published once, on the first tick, the way a