minor

demos/actor_critic_example.ipynb

@@ -79,7 +79,7 @@
     "from ratinabox.Agent import Agent\n",
     "from ratinabox.Neurons import PlaceCells\n",
     "from ratinabox.contribs.NeuralNetworkNeurons import NeuralNetworkNeurons #for the Actor and Critic\n",
-    "ratinabox.autosave_plots = True; ratinabox.figure_directory = \"./figures/\"; ratinabox.stylize_plots()\n",
+    "ratinabox.autosave_plots = True; ratinabox.figure_directory = \"../figures/\"; ratinabox.stylize_plots()\n",
     "\n",
     "#misc\n",
     "import torch \n",

ratinabox/Environment.py

@@ -151,7 +151,6 @@ def __init__(self, params={}):
             }
             self.n_object_types = 0
             self.object_colormap = "rainbow"
-            self.plot_objects = True
 
             # make some other attributes
             left = min([c[0] for c in b])
@@ -255,7 +254,8 @@ def plot_environment(self,
                          fig=None, 
                          ax=None, 
                          gridlines=False,
-                         autosave=None):
+                         autosave=None,
+                         **kwargs,):
         """Plots the environment on the x axis, dark grey lines show the walls
         Args:
             fig,ax: the fig and ax to plot on (can be None)
@@ -264,7 +264,6 @@ def plot_environment(self,
         Returns:
             fig, ax: the environment figures, can be used for further downstream plotting.
         """
-
         if self.dimensionality == "1D":
             extent = self.extent
             if fig is None and ax is None:
@@ -350,8 +349,10 @@ def plot_environment(self,
                     zorder=2,
                 )
 
-            # plot objects
-            if self.plot_objects == True:
+            # plot objects if there isn't a kwarg setting it to false
+            if 'plot_objects' in kwargs and kwargs['plot_objects'] == False:
+                pass
+            else: 
                 object_cmap = matplotlib.colormaps[self.object_colormap]
                 for i, object in enumerate(self.objects["objects"]):
                     object_color = object_cmap(

ratinabox/Neurons.py

@@ -27,13 +27,16 @@ class Neurons:
     • GridCells()
     • BoundaryVectorCells()
     • ObjectVectorCells()
+    • FieldOfViewBVCs()
+    • FieldOfViewOVCs()
     • VelocityCells()
     • HeadDirectionCells()
     • SpeedCells()
     • FeedForwardLayer()
+    • RandomSpatialNeurons()
     as well as (in  the contribs)
     • ValueNeuron()
-    • FieldOfViewNeurons()
+    • NeuralNetworkNeurons()
 
     The unique function in each child classes is get_state(). Whenever Neurons.update() is called Neurons.get_state() is then called to calculate and return the firing rate of the cells at the current moment in time. This is then saved. In order to make your own Neuron subclass you will need to write a class with the following mandatory structure:
 
@@ -381,7 +384,7 @@ def plot_rate_map(
                 else:
                     Nx, Ny = shape[0], shape[1]
                 env_fig, env_ax = self.Agent.Environment.plot_environment(
-                    autosave=False
+                    autosave=False, **kwargs,
                 )
                 width, height = env_fig.get_size_inches()
                 plt.close(env_fig)
@@ -415,7 +418,7 @@ def plot_rate_map(
                         cax = divider.append_axes("right", size="5%", pad=0.05)
             for i, ax_ in enumerate(axes):
                 _, ax_ = self.Agent.Environment.plot_environment(
-                    fig, ax_, autosave=False
+                    fig, ax_, autosave=False, **kwargs
                 )
             if len(chosen_neurons) != axes.size:
                 print(
@@ -463,9 +466,9 @@ def plot_rate_map(
                     cbar = plt.colorbar(ims[-1], cax=cax)
                     cbar.ax.tick_params(length=0)
                     cbar.set_label("Firing rate / Hz",labelpad=-10)
-                    lim_v = vmax if vmax > -vmin else vmin
-                    cbar.set_ticks([0, lim_v])
-                    cbar.set_ticklabels([0.0, round(lim_v, 1)])
+                    # lim_v = vmax if vmax > -vmin else vmin
+                    cbar.set_ticks([vmin,vmax])
+                    cbar.set_ticklabels([f"{vmin:.1f}", f"{vmax:.1f}"])
                     cbar.outline.set_visible(False)
 
             if spikes is True:
@@ -2386,7 +2389,8 @@ class RandomSpatialNeurons(Neurons):
                       'max_fr':1, #maximum firing rate 
                       'min_fr':0, #minimum firing rate
                       'n':10, #number of neurons
-                      'wall_geometry':'geodesic' #how to account for walls when calculating distance between points (only relevant in 2D)
+                      'wall_geometry':'geodesic', #how to account for walls when calculating distance between points (only relevant in 2D)
+                      'name':'RandomSpatialNeurons', #name of the class
                       }
 
     def __init__(self, Agent, params={}):

ratinabox/__init__.py

@@ -19,7 +19,7 @@
 
 DARKGREY = [0.3,0.3,0.3,1]
 GREY = [0.5,0.5,0.5,1]
-LIGHTGREY = [0.92,0.92,0.92,1]
+LIGHTGREY = [0.9,0.9,0.9,1]
 
 from .Environment import *
 from .Agent import *

ratinabox/utils.py

@@ -569,6 +569,7 @@ def mountain_plot(
     fig=None,
     ax=None,
     norm_by="max",
+    linewidth=1,
     width=ratinabox.MOUNTAIN_PLOT_WIDTH_MM,
     overlap=ratinabox.MOUNTAIN_PLOT_OVERLAP,
     shift=ratinabox.MOUNTAIN_PLOT_SHIFT_MM,
@@ -590,6 +591,8 @@ def mountain_plot(
         ax (_type_, optional): ax to plot on if desider. Defaults to None.
         norm_by: what to normalise each line of the mountainplot by.
            If "max", norms by the maximum firing rate found across all the neurons. Otherwise, pass a float (useful if you want to compare different neural datsets apples-to-apples)
+        linewidth: width of lines
+        width: width of figure in mm
         overlap: how much each plots overlap by (> 1 = overlap, < 1 = no overlap) (overlap is not relevant if you also set "norm_by")
         shift: distance between lines in mm
 
@@ -615,7 +618,7 @@ def mountain_plot(
 
     zorder = 1
     for i in range(len(NbyX)):
-        ax.plot(X, NbyX[i] + i + 1, c=c, zorder=zorder)
+        ax.plot(X, NbyX[i] + i + 1, c=c, zorder=zorder, lw=linewidth)
         zorder -= 0.01
         ax.fill_between(
             X, NbyX[i] + i + 1, i + 1, color=fc, zorder=zorder, alpha=0.8, linewidth=0

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = ratinabox
-version = 1.10.0
+version = 1.10.1
 author = Tom George
 author_email = tomgeorge1@btinternet.com
 project_urls =