Multi camera support

behaviors/openfield.py

@@ -138,17 +138,49 @@ def setup(self, exp) -> None:
 
         self._initialize_dlc()
 
+    def _get_openfield_camera(self):
+        """Return the openfield camera (the one DLC consumes frames from).
+
+        DLC's corner detector and pose estimator both pull from a single
+        camera — the one with ``video_aim='openfield'``. Other cameras
+        configured for this setup (e.g. a passive ``'eye'`` camera) just
+        record to disk and must not be confused with the openfield camera.
+
+        Cameras are keyed as ``f"{video_aim}_{camera_idx}"`` in
+        ``Interface.cameras`` (stable per-physical-camera identity across
+        sessions), so we filter by the ``openfield_`` prefix.
+
+        Raises:
+            ValueError: if no camera with ``video_aim='openfield'`` is
+                configured, or if more than one is (DLC requires exactly one).
+        """
+        cameras = self.interface.cameras
+        matches = {k: c for k, c in cameras.items() if k.startswith("openfield_")}
+        if not matches:
+            configured = list(cameras.keys()) or "<none>"
+            raise ValueError(
+                "No camera with video_aim='openfield' is configured for this "
+                f"setup. Configured keys: {configured}. Add a row to "
+                "SetupConfiguration.Camera with video_aim='openfield' for the "
+                "camera DLC should track."
+            )
+        if len(matches) > 1:
+            raise ValueError(
+                f"Multiple openfield cameras configured: {list(matches.keys())}. "
+                "DLC requires exactly one camera with video_aim='openfield'."
+            )
+        return next(iter(matches.values()))
+
     def _initialize_dlc(self) -> None:
         """Initialize the DeepLabCut (DLC) object for pose estimation."""
-        if self.interface.camera is None:
-            raise ValueError("Camera is not initialized")
+        openfield_cam = self._get_openfield_camera()
         corners, affine_matrix = self.get_corners()
         dlc_body_path = self.logger.get(schema='interface',
                                                table='SetupConfigurationArena.Models',
                                                fields=['path'],
                                                key={'setup_conf_idx': self.exp.session_params['setup_conf_idx'],
                                                     'target': 'bodyparts'})[0]
-        self.dlc = DLCContinuousPoseEstimator(frame_queue=self.interface.camera.process_queue,
+        self.dlc = DLCContinuousPoseEstimator(frame_queue=openfield_cam.process_queue,
                                               model_path=dlc_body_path,
                                               logger=self.logger,
                                               shared_memory_conf=self.shm_conf,
@@ -362,7 +394,8 @@ def get_corners(self):
                                            fields=['path'],
                                            key={'setup_conf_idx': self.exp.session_params['setup_conf_idx'],
                                                 'target': 'corners'})[0]
-        dlcCorners = DLCCornerDetector(frame_queue=self.interface.camera.process_queue,
+        openfield_cam = self._get_openfield_camera()
+        dlcCorners = DLCCornerDetector(frame_queue=openfield_cam.process_queue,
                                        model_path=dlc_corners_path,
                                        arena_size=self.arena_size,
                                        result=corners_dict,
@@ -423,7 +456,7 @@ class ConfigurationArena(dj.Manual):
     definition = """
     # Camera information
     arena_idx                : tinyint
-    -> behavior.Configuration
+    -> interface.Configuration
     ---
     size                      : int
     discription               : varchar(256)

behaviors/vr_ball.py

@@ -1,12 +1,9 @@
-from core.Behavior import *
-
-
 import datajoint as dj
 import numpy as np
 
-from ethopy.core.behavior import Behavior
+from ethopy.core.behavior import Behavior, BehCondition
 from ethopy.core.logger import behavior
-from ethopy.interfaces.ball import Ball
+from ethopy.interfaces.Ball import Ball
 
 
 

experiments/approach.py

@@ -176,7 +176,7 @@ def entry(self):
         self.beh.punish()
         super().entry()
         self.punish_period = self.curr_cond["punish_duration"]
-        if self.params.get("incremental_punishment"):
+        if self.curr_cond["incremental_punishment"]:
             self.punish_period *= self.beh.get_false_history()
 
     def run(self):
@@ -196,7 +196,7 @@ def exit(self):
 
 class InterTrial(Experiment):
     def run(self):
-        if self.beh.is_licking() and self.params.get("noresponse_intertrial"):
+        if self.beh.is_licking() and self.curr_cond["noresponse_intertrial"]:
             self.state_timer.start()
 
     def next(self):

stimuli/olfactory.py

@@ -1,7 +1,7 @@
 import datajoint as dj
 
 from ethopy.core.logger import stimulus
-from ethopy.core.stimulus import Stimulus
+from ethopy.core.stimulus import Stimulus, StimCondition
 
 
 @stimulus.schema
@@ -36,12 +36,11 @@ class Channel(dj.Part):
         dutycycle            : int                      # odor dutycycle
         """
 
-    cond_tables = ['Olfactory', 'Olfactory.Channel']
-    required_fields = ['odor_duration', 'odorant_id', 'delivery_port']
-    default_key = {'dutycycle': 50}
-
     def __init__(self):
         super().__init__()
+        self.cond_tables = ['Olfactory', 'Olfactory.Channel']
+        self.required_fields = ['odor_duration', 'odorant_id', 'delivery_port']
+        self.default_key = {'dutycycle': 50}
         self.fill_colors.set({'background': (0, 0, 0),
                               'start': (0.2, 0.2, 0.2),
                               'ready':  (0.3, 0.3, 0.3),

stimuli/openfield_panda.py

@@ -361,10 +361,28 @@ def create_movies(self):
             os.remove(f)
 
     def get_cond(self, cond_name, idx=0):
+        """Extract the cond_name prefix conditions.
+
+        ``self.curr_cond`` is a flat dict holding the whole trial (lights,
+        background, and every object field prefixed with ``cond_name``, e.g.
+        ``"obj_"``). This returns just the fields for object ``idx``, with the
+        prefix stripped, so an ``Agent`` can read ``cond["mag"]`` instead of
+        ``cond["obj_mag"]``.
+
+        For each matching field:
+          - a scalar value is shared by every object (returned as-is);
+          - a sequence (tuple/list/array) is treated as one value per object,
+            so object ``idx`` gets ``v[idx]``.
+
+        Args:
+            cond_name: Field prefix to select and strip (e.g. ``"obj_"``).
+            idx: Index of the object whose values to pick from sequence fields.
+
+        Returns:
+            A dict of de-prefixed field names mapped to this object's value.
+        """
         return {
-            k.split(cond_name, 1)[1]: v
-            if type(v) is int or type(v) is float
-            else v[idx]
+            k.split(cond_name, 1)[1]: (v if np.ndim(v) == 0 else v[idx])
             for k, v in self.curr_cond.items()
             if k.startswith(cond_name)
         }

stimuli/psycho_grating.py

@@ -2,7 +2,7 @@
 import datajoint as dj
 
 from ethopy.core.logger import stimulus
-from ethopy.core.stimulus import Stimulus
+from ethopy.core.stimulus import Stimulus, StimCondition
 from ethopy.stimuli.psycho_presenter import Presenter
 from ethopy.utils.helper_functions import iterable
 

stimuli/vr_odors.py

@@ -3,7 +3,7 @@
 
 from ethopy.stimuli.olfactory import Odorants, Olfactory
 from ethopy.core.logger import stimulus
-from ethopy.core.stimulus import Stimulus
+from ethopy.core.stimulus import Stimulus, StimCondition
 
 
 @stimulus.schema

tasks/openfield_task.py

@@ -1,10 +1,9 @@
 import numpy as np
 from scipy import interpolate
 
-from ethopy.core import logger
-from ethopy.behaviors.openField import OpenField
+from ethopy.behaviors.openfield import OpenField
 from ethopy.experiments.approach import Experiment
-from ethopy.stimuli.panda import Panda
+from ethopy.stimuli.openfield_panda import Panda
 
 
 def interp(x):
@@ -37,7 +36,7 @@
 
 
 exp = Experiment()
 exp.setup(logger, OpenField, session_params)
 conditions = []
 rot_f = lambda: interp((np.random.rand(30) - 0.5) * 10)