Added a warning if number of neurons will be changed in the initializ…

…ation of a neuron layer.

Fixed a small bug in VectorCells init: self.Agent was not set before being called.
Removed default value of None for `Other_Agent` in AgentVectorCells init, as None is not an allowed value.
Raised an error during setting of tuning types in ObjectVectorCells if there are not objects in the environment.

ratinabox/Neurons.py

@@ -1209,6 +1209,8 @@ def __init__(self, Agent, params={}):
             Agent. The RatInABox Agent these cells belong to. 
             params (dict, optional). Defaults to {}.
         """
+        self.Agent = Agent
+
         assert (
             self.Agent.Environment.dimensionality == "2D"
         ), "Vector cells only possible in 2D"
@@ -1245,18 +1247,28 @@ def __init__(self, Agent, params={}):
         self.sigma_angles = None
         self.sigma_distances = None
 
-
         # set the parameters of each cell. 
         (self.tuning_distances, 
          self.tuning_angles, 
          self.sigma_distances, 
          self.sigma_angles) = self.set_tuning_parameters(**self.params)
-        self.n = len(self.tuning_distances) #ensure n is correct
+
+        # records whether n was passed as a parameter.
+        if not hasattr(self, "_warn_n_change"):
+            self._warn_n_change = ("n" in params.keys() and params["n"] is not None)
+
+        # raises a warning if n was passed as a parameter, but will change.
+        if self._warn_n_change and params["n"] != len(self.tuning_distances):
+            warnings.warn(f"Ignoring 'n' parameter value ({params['n']}) that was passed, and setting number of {self.name} neurons to {len(self.tuning_distances)}, inferred from the cell arrangement parameter.")
+
+        self.n = len(self.tuning_distances) # ensure n is correct
+
+
         self.firingrate = np.zeros(self.n)
         self.noise = np.zeros(self.n)
         self.cell_colors = None 
 
-    
+
     def set_tuning_parameters(self, **kwargs):
         """Get the tuning parameters for the vector cells.
          Args:
@@ -1438,6 +1450,10 @@ def __init__(self, Agent, params={}):
         self.params = copy.deepcopy(__class__.default_params)
         self.params.update(params)
 
+        # records whether n was passed as a parameter.
+        if not hasattr(self, "_warn_n_change"):
+            self._warn_n_change = ("n" in params.keys() and params["n"] is not None)
+
         super().__init__(Agent, self.params)
 
         assert (
@@ -1483,7 +1499,6 @@ def __init__(self, Agent, params={}):
         return
 
 
-
     def get_state(self, evaluate_at="agent", **kwargs):
         """
         Here we implement the same type if boundary vector cells as de Cothi et al. (2020), 
@@ -1789,6 +1804,10 @@ def __init__(self, Agent, params={}):
             self.Agent.Environment.dimensionality == "2D"
         ), "object vector cells only possible in 2D"
 
+        # records whether n was passed as a parameter.
+        if not hasattr(self, "_warn_n_change"):
+            self._warn_n_change = ("n" in params.keys() and params["n"] is not None)
+
         super().__init__(Agent, self.params)
 
         self.object_locations = self.Agent.Environment.objects["objects"]
@@ -1834,6 +1853,8 @@ def set_tuning_types(self, tuning_types=None):
 
         if tuning_types == "random":
             self.object_types = self.Agent.Environment.objects["object_types"]
+            if len(self.object_types) == 0:
+                raise RuntimeError("Cannot initialize object vector cells randomly, as no objects were found in the environment.")
             self.tuning_types = np.random.choice(
                 np.unique(self.object_types), replace=True, size=(self.n,)
             )
@@ -2006,7 +2027,6 @@ def __init__(self,Agent,params={}):
             warnings.warn("For FieldOfViewOVCs you must specify the object type they are selective for with the 'object_tuning_type' parameter. This can be 'random' (each cell in the field of view chooses a random object type) or any integer (all cells have the same preference for this type). For now defaulting to params['object_tuning_type'] = 0.")
             self.params["object_tuning_type"] = 0
 
-
         self.params["reference_frame"] = "egocentric"
         assert self.params["cell_arrangement"] is not None, "cell_arrangement must be set for FOV Neurons"
 
@@ -2034,7 +2054,7 @@ class AgentVectorCells(VectorCells):
 
     def __init__(self, 
                  Agent,
-                 Other_Agent = None, #this must be another riab Agent object
+                 Other_Agent, #this must be another riab Agent object
                  params={}):
 
         self.Agent = Agent
@@ -2269,6 +2289,8 @@ def __init__(self, Agent, params={}):
                 [self.params["angular_spread_degrees"] * np.pi / 180] * self.n
             )
         if self.Agent.Environment.dimensionality == "1D":
+            if "n" in params.keys() and params["n"] != 2:
+                warnings.warn(f"Ignoring 'n' parameter value ({params['n']}) that was passed for {self.params['name']}. Only 2 head direction cells are needed for a 1D environment.")
             self.n = 2  # one left, one right
         self.params["n"] = self.n
         super().__init__(Agent, self.params)
@@ -2476,7 +2498,11 @@ def __init__(self, Agent, params={}):
         self.params.update(params)
 
         super().__init__(Agent, self.params)
+
+        if "n" in params.keys() and params["n"] != 1:
+            warnings.warn(f"Ignoring 'n' parameter value ({params['n']}) that was passed for {self.name}. Only 1 speed cell is needed.")
         self.n = 1
+
         self.one_sigma_speed = self.Agent.speed_mean + self.Agent.speed_std
 
         if ratinabox.verbose is True: