Portable Python env + 360° LiDAR ablation flag

Two small features.

(1) Portable interpreter
* `tools/setup_env.sh` exports HERDING_PYTHON (default points to the
  project's conda env; override in your shell to retarget).
* Both `controllers/*/runtime.ini` files now use Webots' env-var
  expansion: `COMMAND = $(HERDING_PYTHON)` so the Webots-launched
  controllers pick up the same interpreter as the bash scripts.
* `tools/run_webots.sh`, `tools/webots_sweep{,_gt}.sh` and
  `tools/calibrate_mecanum.sh` all source `setup_env.sh` at the top
  instead of hard-coding `/home/jalf/miniconda3/envs/tir/bin`.
The hard-coded conda path is now exactly one line in `setup_env.sh`'s
fallback default — a single place to edit on a new machine, or
override-once via `export HERDING_PYTHON=...`.

(2) 360° LiDAR FOV ablation
* New `LIDAR_WEBOTS_360` preset matches the existing
  `protos/ShepherdDog360.proto` (360 rays / 2π FOV / 15 m range).
* `tools/run_webots.sh` reads `HERDING_LIDAR=140|360` and swaps the
  diff-drive proto accordingly (mecanum keeps 140° — the
  ShepherdDogMecanum proto has its own LiDAR section). The variant
  is written into `herding_runtime.cfg` so the controller can read
  it even when Webots strips env vars.
* `controllers/shepherd_dog/shepherd_dog.py` picks the matching
  `lidar_cfg` (`HERDING_WEBOTS.lidar` for 140°, `LIDAR_WEBOTS_360`
  otherwise) and feeds it to `detections_from_scan` so the
  perception pipeline interprets ray angles + max range correctly.

Smoke test: `HERDING_LIDAR=360 tools/run_webots.sh 5 strombom
differential field` launches with `ShepherdDog360.proto`, the
controller logs the new mode/drive/world line, and the dog is
penning sheep through 360° perception (4/5 at step 19200 before I
killed the test). No retraining required because the gym already
trains under `LIDAR_FULL` (360° preset).

126 pytest cases still pass.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

controllers/shepherd_dog/shepherd_dog.py

@@ -97,7 +97,7 @@ from herding.world.geometry import (
     DOG_SOUTH_LIMIT,
     PEN_ENTRY, is_penned,
 )
-from herding.config import HERDING_WEBOTS, RobotConfig
+from herding.config import HERDING_WEBOTS, LIDAR_WEBOTS_360, RobotConfig
 
 # Robot physical constants come from RobotConfig so they stay in sync with
 # the training environment.  The Webots preset uses action_smooth=0.55.
@@ -136,6 +136,18 @@ WORLD = (os.environ.get("HERDING_WORLD")
          or _runtime_cfg.get("HERDING_WORLD")
          or "field").lower()
 
+# LiDAR FOV variant — "140" (default, ShepherdDog.proto) or "360"
+# (ShepherdDog360.proto, FOV ablation). The launcher swaps the proto
+# in the temp world file; the controller picks the matching lidar_cfg
+# below so the perception pipeline interprets ray angles correctly.
+LIDAR_FOV_VARIANT = (os.environ.get("HERDING_LIDAR")
+                     or _runtime_cfg.get("HERDING_LIDAR")
+                     or "140").lower()
+if LIDAR_FOV_VARIANT == "360":
+    _LIDAR_CFG = LIDAR_WEBOTS_360
+else:
+    _LIDAR_CFG = HERDING_WEBOTS.lidar
+
 # Diagnostic: bypass LiDAR tracker and use GT emitter positions directly.
 # Set HERDING_USE_GT=1 to isolate perception sim-to-real gap from policy quality.
 USE_GT_PERCEPTION = bool(int(
@@ -409,7 +421,7 @@ while robot.step(timestep) != -1:
     detections = detections_from_scan(
         ranges, dog_xy[0], dog_xy[1], dog_heading,
         detection_cfg=HERDING_WEBOTS.detection,
-        lidar_cfg=HERDING_WEBOTS.lidar,
+        lidar_cfg=_LIDAR_CFG,
     )
     if USE_GT_PERCEPTION and _gt_sheep:
         # Bypass tracker: feed GT emitter positions directly to policy/teacher.