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diff --git a/README.md b/README.md
index eefc0fa3..c0a1f09a 100644
--- a/README.md
+++ b/README.md
@@ -72,15 +72,17 @@ Here is a list of features loosely organised into three categories: those pertai
(i) the [`Environment`](#i-environment-features)
* [Adding walls](#walls)
+* [Polygon-shaped Environments](#polygon-shaped-environments)
+* [Holes](#holes)
* [Boundary conditions](#boundary-conditions)
* [1- or 2-dimensions](#1--or-2-dimensions)
-
(ii) the [`Agent`](#ii-agent-features)
* [Random motion](#random-motion-model)
* [Importing trajectories](#importing-trajectories)
* [Policy control](#policy-control)
* [Wall repelling](#wall-repelling)
+* [Advanced `Agent` classes](#advanced-agent-classes)
(iii) the [`Neurons`](#iii-neurons-features).
* [Cell types](#multiple-cell-types)
@@ -105,8 +107,28 @@ Here are some easy to make examples.
+#### Polygon-shaped `Environments`
+By default, `Environments` in RatInABox are square (or rectangular if `aspect != 1`). It is possible to create arbitrary environment shapes using the `"boundary"` parameter at initialisation:
+```python
+Env = Environment(params={'boundary':[[0,-0.2],[0,0.2],[1.5,0.5],[1.5,-0.5]]})
+```
+
+
+
+#### Holes
+One can add holes to the `Environment` using the `"holes"` parameter at initialisation
+```python
+Env = Environment(params={
+ 'aspect':1.8,
+ 'holes' : [[[0.2,0.2],[0.8,0.2],[0.8,0.8],[0.2,0.8]],
+ [[1,0.2],[1.6,0.2],[1.6,0.8],[1,0.8]]]
+})
+```
+
+
+
#### Boundary conditions
-Boundary conditions can be "periodic" or "solid". Place cells and the motion of the Agent will respect these boundaries accordingly.
+Boundary conditions (for default square/rectangular environments) can be "periodic" or "solid". Place cells and the motion of the Agent will respect these boundaries accordingly.
```python
Env = Environment(
params = {'boundary_conditions':'periodic'} #or 'solid' (default)
@@ -172,7 +194,13 @@ Under the random motion policy, walls in the environment mildly "repel" the `Age
Αgent.thigmotaxis = 0.8 #1 = high thigmotaxis (left plot), 0 = low (right)
```
-
+
+
+
+#### Advanced `Agent` classes
+One can make more advanced Agent classes, for example `ThetaSequenceAgent()` where the position "sweeps" (blue) over the position of an underlying true (regular) `Agent()` (purple), highly reminiscent of theta sequences observed when one decodes position from the hippocampal populaton code on sub-theta (10 Hz) timescales. This class can be found in the [`contribs`](./ratinabox/contribs/) directory.
+
+
### (iii) `Neurons` features
diff --git a/demos/decoding_position_example.ipynb b/demos/decoding_position_example.ipynb
index 38d2e1e8..8e97f6c4 100644
--- a/demos/decoding_position_example.ipynb
+++ b/demos/decoding_position_example.ipynb
@@ -24,7 +24,7 @@
"import ratinabox\n",
"from ratinabox.Environment import Environment\n",
"from ratinabox.Agent import Agent\n",
- "from ratinabox.Neurons import PlaceCells,GridCells,BoundaryVectorCells\n",
+ "from ratinabox.Neurons import PlaceCells, GridCells, BoundaryVectorCells\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
@@ -39,10 +39,11 @@
"metadata": {},
"outputs": [],
"source": [
- "#Leave this as False. \n",
- "#For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save. \n",
- "if False: \n",
+ "# Leave this as False.\n",
+ "# For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save.\n",
+ "if False:\n",
" import tomplotlib.tomplotlib as tpl\n",
+ "\n",
" tpl.figureDirectory = \"../figures/\"\n",
" tpl.setColorscheme(colorscheme=2)\n",
" save_plots = True\n",
@@ -65,43 +66,61 @@
"metadata": {},
"outputs": [],
"source": [
- "def train_decoder(Neurons,t_start=None,t_end=None):\n",
+ "def train_decoder(Neurons, t_start=None, t_end=None):\n",
" \"\"\"t_start and t_end allow you to pick the poritions of the saved data to train on.\"\"\"\n",
- " #Get training data\n",
- " t = np.array(Neurons.history['t'])\n",
- " if t_start is None: i_start = 0\n",
- " else: i_start = np.argmin(np.abs(t-t_start))\n",
- " if t_end is None: i_end = -1\n",
- " else: i_end = np.argmin(np.abs(t-t_end))\n",
- " t = t[i_start:i_end][::5] #subsample data for training (most of it is redundant anyway)\n",
- " fr = np.array(Neurons.history['firingrate'])[i_start:i_end][::5]\n",
- " pos = np.array(Neurons.Agent.history['pos'])[i_start:i_end][::5]\n",
- " #Initialise and fit model\n",
+ " # Get training data\n",
+ " t = np.array(Neurons.history[\"t\"])\n",
+ " if t_start is None:\n",
+ " i_start = 0\n",
+ " else:\n",
+ " i_start = np.argmin(np.abs(t - t_start))\n",
+ " if t_end is None:\n",
+ " i_end = -1\n",
+ " else:\n",
+ " i_end = np.argmin(np.abs(t - t_end))\n",
+ " t = t[i_start:i_end][\n",
+ " ::5\n",
+ " ] # subsample data for training (most of it is redundant anyway)\n",
+ " fr = np.array(Neurons.history[\"firingrate\"])[i_start:i_end][::5]\n",
+ " pos = np.array(Neurons.Agent.history[\"pos\"])[i_start:i_end][::5]\n",
+ " # Initialise and fit model\n",
" from sklearn.gaussian_process.kernels import RBF\n",
- " model_GP = GaussianProcessRegressor(alpha=0.01, kernel=RBF(1\n",
- " *np.sqrt(Neurons.n/20), #<-- kernel size scales with typical input size ~sqrt(N)\n",
- " length_scale_bounds=\"fixed\"\n",
- " ))\n",
+ "\n",
+ " model_GP = GaussianProcessRegressor(\n",
+ " alpha=0.01,\n",
+ " kernel=RBF(\n",
+ " 1\n",
+ " * np.sqrt(\n",
+ " Neurons.n / 20\n",
+ " ), # <-- kernel size scales with typical input size ~sqrt(N)\n",
+ " length_scale_bounds=\"fixed\",\n",
+ " ),\n",
+ " )\n",
" model_LR = Ridge(alpha=0.01)\n",
- " model_GP.fit(fr,pos) \n",
- " model_LR.fit(fr,pos) \n",
- " #Save models into Neurons class for later use\n",
+ " model_GP.fit(fr, pos)\n",
+ " model_LR.fit(fr, pos)\n",
+ " # Save models into Neurons class for later use\n",
" Neurons.decoding_model_GP = model_GP\n",
" Neurons.decoding_model_LR = model_LR\n",
- " return \n",
+ " return\n",
"\n",
- "def decode_position(Neurons,t_start=None,t_end=None):\n",
+ "\n",
+ "def decode_position(Neurons, t_start=None, t_end=None):\n",
" \"\"\"t_start and t_end allow you to pick the poritions of the saved data to train on.\n",
" Returns a list of times and decoded positions\"\"\"\n",
- " #Get testing data\n",
- " t = np.array(Neurons.history['t'])\n",
- " if t_start is None: i_start = 0\n",
- " else: i_start = np.argmin(np.abs(t-t_start))\n",
- " if t_end is None: i_end = -1\n",
- " else: i_end = np.argmin(np.abs(t-t_end))\n",
+ " # Get testing data\n",
+ " t = np.array(Neurons.history[\"t\"])\n",
+ " if t_start is None:\n",
+ " i_start = 0\n",
+ " else:\n",
+ " i_start = np.argmin(np.abs(t - t_start))\n",
+ " if t_end is None:\n",
+ " i_end = -1\n",
+ " else:\n",
+ " i_end = np.argmin(np.abs(t - t_end))\n",
" t = t[i_start:i_end]\n",
- " fr = np.array(Neurons.history['firingrate'])[i_start:i_end]\n",
- " #decode position from the data and using the decoder saved in the Neurons class \n",
+ " fr = np.array(Neurons.history[\"firingrate\"])[i_start:i_end]\n",
+ " # decode position from the data and using the decoder saved in the Neurons class\n",
" decoded_position_GP = Neurons.decoding_model_GP.predict(fr)\n",
" decoded_position_LR = Neurons.decoding_model_LR.predict(fr)\n",
" return (t, decoded_position_GP, decoded_position_LR)"
@@ -120,16 +139,22 @@
"metadata": {},
"outputs": [],
"source": [
- "np.random.seed(10) #make reproducible\n",
+ "np.random.seed(10) # make reproducible\n",
"\n",
"Env = Environment()\n",
- "Env.add_wall(np.array([[0.4,0],[0.4,0.4]]))\n",
- "Ag = Agent(Env, params={'dt':50e-3})\n",
- "\n",
- "\n",
- "PCs = PlaceCells(Ag,params={'description':'gaussian_threshold','widths':0.4,'n':20,'color':'C1'})\n",
- "GCs = GridCells(Ag,params={'n':20,'color':'C2'},)\n",
- "BVCs = BoundaryVectorCells(Ag,params={'n':20,'color':'C3'})"
+ "Env.add_wall(np.array([[0.4, 0], [0.4, 0.4]]))\n",
+ "Ag = Agent(Env, params={\"dt\": 50e-3})\n",
+ "\n",
+ "\n",
+ "PCs = PlaceCells(\n",
+ " Ag,\n",
+ " params={\"description\": \"gaussian_threshold\", \"widths\": 0.4, \"n\": 20, \"color\": \"C1\"},\n",
+ ")\n",
+ "GCs = GridCells(\n",
+ " Ag,\n",
+ " params={\"n\": 20, \"color\": \"C2\"},\n",
+ ")\n",
+ "BVCs = BoundaryVectorCells(Ag, params={\"n\": 20, \"color\": \"C3\"})"
]
},
{
@@ -164,8 +189,9 @@
],
"source": [
"np.random.seed(9)\n",
- "from tqdm import tqdm \n",
- "for i in tqdm(range(int(5*60/Ag.dt))):\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "for i in tqdm(range(int(5 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
@@ -211,12 +237,15 @@
}
],
"source": [
- "fig, ax = PCs.plot_rate_map(chosen_neurons='all')\n",
- "if save_plots == True: tpl.saveFigure(fig, \"PCs\")\n",
- "fig, ax = GCs.plot_rate_map(chosen_neurons='all')\n",
- "if save_plots == True: tpl.saveFigure(fig, \"GCs\")\n",
- "fig, ax = BVCs.plot_rate_map(chosen_neurons='all')\n",
- "if save_plots == True: tpl.saveFigure(fig, \"BVCs\")"
+ "fig, ax = PCs.plot_rate_map(chosen_neurons=\"all\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"PCs\")\n",
+ "fig, ax = GCs.plot_rate_map(chosen_neurons=\"all\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"GCs\")\n",
+ "fig, ax = BVCs.plot_rate_map(chosen_neurons=\"all\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"BVCs\")"
]
},
{
@@ -270,15 +299,18 @@
],
"source": [
"np.random.seed(10)\n",
- "for i in tqdm(range(int(60/Ag.dt))):\n",
+ "for i in tqdm(range(int(60 / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
" BVCs.update()\n",
"\n",
- "fig_t, ax_t = Ag.plot_trajectory(fig=fig_t, ax=ax_t,t_start=Ag.t-60,color='black',alpha=0.5)\n",
- "if save_plots == True: tpl.saveFigure(fig_t,\"data\")\n",
- "fig_t\n"
+ "fig_t, ax_t = Ag.plot_trajectory(\n",
+ " fig=fig_t, ax=ax_t, t_start=Ag.t - 60, color=\"black\", alpha=0.5\n",
+ ")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig_t, \"data\")\n",
+ "fig_t"
]
},
{
@@ -287,9 +319,9 @@
"metadata": {},
"outputs": [],
"source": [
- "t, pos_PCs_GP, pos_PCs_LR = decode_position(PCs,t_start=Ag.t-60)\n",
- "t, pos_GCs_GP, pos_GCs_LR = decode_position(GCs,t_start=Ag.t-60)\n",
- "t, pos_BVCs_GP, pos_BVCs_LR = decode_position(BVCs,t_start=Ag.t-60)"
+ "t, pos_PCs_GP, pos_PCs_LR = decode_position(PCs, t_start=Ag.t - 60)\n",
+ "t, pos_GCs_GP, pos_GCs_LR = decode_position(GCs, t_start=Ag.t - 60)\n",
+ "t, pos_BVCs_GP, pos_BVCs_LR = decode_position(BVCs, t_start=Ag.t - 60)"
]
},
{
@@ -316,27 +348,32 @@
}
],
"source": [
- "fig, ax = plt.subplots(2,3,figsize=(12,8))\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[0,0],color='black',alpha=0.5)\n",
- "ax[0,0].scatter(pos_PCs_GP[:,0],pos_PCs_GP[:,1],s=5,c='C1',alpha=0.2,zorder=3.1)\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[1,0],color='black', alpha=0.5)\n",
- "ax[1,0].scatter(pos_PCs_LR[:,0],pos_PCs_LR[:,1],s=5,c='C1',alpha=0.2,zorder=3.1)\n",
- "ax[0,0].set_title(\"Place cells\")\n",
- "\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[0,1],color='black', alpha=0.5)\n",
- "ax[0,1].scatter(pos_GCs_GP[:,0],pos_GCs_GP[:,1],s=5,c='C2',alpha=0.2,zorder=3.1)\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[1,1],color='black', alpha=0.5)\n",
- "ax[1,1].scatter(pos_GCs_LR[:,0],pos_GCs_LR[:,1],s=5,c='C2',alpha=0.2,zorder=3.1)\n",
- "ax[0,1].set_title(\"GAUSSIAN PROCESSS REGRESSION\\n\\nGrid cells\")\n",
- "ax[1,1].set_title(\"LINEAR REGRESSION\")\n",
- "\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[0,2],color='black', alpha=0.5)\n",
- "ax[0,2].scatter(pos_BVCs_GP[:,0],pos_BVCs_GP[:,1],s=5,c='C3',alpha=0.5,zorder=3.1)\n",
- "Ag.plot_trajectory(t_start=Ag.t-60,fig=fig, ax=ax[1,2],color='black', alpha=0.5)\n",
- "ax[1,2].scatter(pos_BVCs_LR[:,0],pos_BVCs_LR[:,1],s=5,c='C3',alpha=0.5,zorder=3.1)\n",
- "ax[0,2].set_title(\"Boundary vector cells\")\n",
- "\n",
- "if save_plots == True: tpl.saveFigure(fig, \"decoded\")"
+ "fig, ax = plt.subplots(2, 3, figsize=(12, 8))\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[0, 0], color=\"black\", alpha=0.5)\n",
+ "ax[0, 0].scatter(pos_PCs_GP[:, 0], pos_PCs_GP[:, 1], s=5, c=\"C1\", alpha=0.2, zorder=3.1)\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[1, 0], color=\"black\", alpha=0.5)\n",
+ "ax[1, 0].scatter(pos_PCs_LR[:, 0], pos_PCs_LR[:, 1], s=5, c=\"C1\", alpha=0.2, zorder=3.1)\n",
+ "ax[0, 0].set_title(\"Place cells\")\n",
+ "\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[0, 1], color=\"black\", alpha=0.5)\n",
+ "ax[0, 1].scatter(pos_GCs_GP[:, 0], pos_GCs_GP[:, 1], s=5, c=\"C2\", alpha=0.2, zorder=3.1)\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[1, 1], color=\"black\", alpha=0.5)\n",
+ "ax[1, 1].scatter(pos_GCs_LR[:, 0], pos_GCs_LR[:, 1], s=5, c=\"C2\", alpha=0.2, zorder=3.1)\n",
+ "ax[0, 1].set_title(\"GAUSSIAN PROCESSS REGRESSION\\n\\nGrid cells\")\n",
+ "ax[1, 1].set_title(\"LINEAR REGRESSION\")\n",
+ "\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[0, 2], color=\"black\", alpha=0.5)\n",
+ "ax[0, 2].scatter(\n",
+ " pos_BVCs_GP[:, 0], pos_BVCs_GP[:, 1], s=5, c=\"C3\", alpha=0.5, zorder=3.1\n",
+ ")\n",
+ "Ag.plot_trajectory(t_start=Ag.t - 60, fig=fig, ax=ax[1, 2], color=\"black\", alpha=0.5)\n",
+ "ax[1, 2].scatter(\n",
+ " pos_BVCs_LR[:, 0], pos_BVCs_LR[:, 1], s=5, c=\"C3\", alpha=0.5, zorder=3.1\n",
+ ")\n",
+ "ax[0, 2].set_title(\"Boundary vector cells\")\n",
+ "\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"decoded\")"
]
},
{
@@ -488,62 +525,71 @@
"source": [
"from tqdm.notebook import tqdm # notebook compatible loading bars\n",
"\n",
- "N_features = [320,160,80,40,20,10,5]\n",
+ "N_features = [320, 160, 80, 40, 20, 10, 5]\n",
"N_repeats = 15\n",
"\n",
- "results_array = np.zeros(shape=(3,len(N_features),N_repeats,2))\n",
+ "results_array = np.zeros(shape=(3, len(N_features), N_repeats, 2))\n",
"\n",
"Env = Environment()\n",
- "Env.add_wall(np.array([[0.4,0],[0.4,0.4]]))\n",
- "\n",
- "for (i,N) in enumerate(tqdm(N_features, desc=\"Features\")): \n",
- " for j in tqdm(range(N_repeats),leave=False, desc=\"Repeats\"):\n",
- " #Initialise agent and features\n",
- " Ag = Agent(Env, params={'dt':50e-3})\n",
- " PCs = PlaceCells(Ag,params={'n':N,'description':'gaussian_threshold','widths':0.4})\n",
- " GCs = GridCells(Ag,params={'n':N,'gridscale':0.4},)\n",
- " BVCs = BoundaryVectorCells(Ag,params={'n':N})\n",
- "\n",
- " #Generate training data \n",
- " for _ in range(int(5*60/Ag.dt)):\n",
+ "Env.add_wall(np.array([[0.4, 0], [0.4, 0.4]]))\n",
+ "\n",
+ "for (i, N) in enumerate(tqdm(N_features, desc=\"Features\")):\n",
+ " for j in tqdm(range(N_repeats), leave=False, desc=\"Repeats\"):\n",
+ " # Initialise agent and features\n",
+ " Ag = Agent(Env, params={\"dt\": 50e-3})\n",
+ " PCs = PlaceCells(\n",
+ " Ag, params={\"n\": N, \"description\": \"gaussian_threshold\", \"widths\": 0.4}\n",
+ " )\n",
+ " GCs = GridCells(\n",
+ " Ag,\n",
+ " params={\"n\": N, \"gridscale\": 0.4},\n",
+ " )\n",
+ " BVCs = BoundaryVectorCells(Ag, params={\"n\": N})\n",
+ "\n",
+ " # Generate training data\n",
+ " for _ in range(int(5 * 60 / Ag.dt)):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
" BVCs.update()\n",
- " \n",
- " #Train\n",
+ "\n",
+ " # Train\n",
" train_decoder(PCs)\n",
" train_decoder(GCs)\n",
" train_decoder(BVCs)\n",
"\n",
- " #Generate test data \n",
- " steps = int(1*60/Ag.dt)\n",
+ " # Generate test data\n",
+ " steps = int(1 * 60 / Ag.dt)\n",
" for _ in range(steps):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
" BVCs.update()\n",
- " \n",
- " #Test\n",
- " t, pos_PCs_GP, pos_PCs_LR = decode_position(PCs,t_start=Ag.t-60)\n",
- " t, pos_GCs_GP, pos_GCs_LR = decode_position(GCs,t_start=Ag.t-60)\n",
- " t, pos_BVCs_GP, pos_BVCs_LR = decode_position(BVCs,t_start=Ag.t-60)\n",
- " pos_groundtruth = np.array(Ag.history['pos'])[-steps:,:]\n",
- "\n",
- " #Save results (error in cm) for both gaussian process and linear regression\n",
- " PC_error_GP = 100*np.linalg.norm(pos_PCs_GP-pos_groundtruth,axis=1).mean()\n",
- " GC_error_GP = 100*np.linalg.norm(pos_GCs_GP-pos_groundtruth,axis=1).mean()\n",
- " BVC_error_GP = 100*np.linalg.norm(pos_BVCs_GP-pos_groundtruth,axis=1).mean()\n",
- " PC_error_LR = 100*np.linalg.norm(pos_PCs_LR-pos_groundtruth,axis=1).mean()\n",
- " GC_error_LR = 100*np.linalg.norm(pos_GCs_LR-pos_groundtruth,axis=1).mean()\n",
- " BVC_error_LR = 100*np.linalg.norm(pos_BVCs_LR-pos_groundtruth,axis=1).mean()\n",
- "\n",
- " results_array[0,i,j,0] = PC_error_GP\n",
- " results_array[1,i,j,0] = GC_error_GP\n",
- " results_array[2,i,j,0] = BVC_error_GP\n",
- " results_array[0,i,j,1] = PC_error_LR\n",
- " results_array[1,i,j,1] = GC_error_LR\n",
- " results_array[2,i,j,1] = BVC_error_LR\n"
+ "\n",
+ " # Test\n",
+ " t, pos_PCs_GP, pos_PCs_LR = decode_position(PCs, t_start=Ag.t - 60)\n",
+ " t, pos_GCs_GP, pos_GCs_LR = decode_position(GCs, t_start=Ag.t - 60)\n",
+ " t, pos_BVCs_GP, pos_BVCs_LR = decode_position(BVCs, t_start=Ag.t - 60)\n",
+ " pos_groundtruth = np.array(Ag.history[\"pos\"])[-steps:, :]\n",
+ "\n",
+ " # Save results (error in cm) for both gaussian process and linear regression\n",
+ " PC_error_GP = 100 * np.linalg.norm(pos_PCs_GP - pos_groundtruth, axis=1).mean()\n",
+ " GC_error_GP = 100 * np.linalg.norm(pos_GCs_GP - pos_groundtruth, axis=1).mean()\n",
+ " BVC_error_GP = (\n",
+ " 100 * np.linalg.norm(pos_BVCs_GP - pos_groundtruth, axis=1).mean()\n",
+ " )\n",
+ " PC_error_LR = 100 * np.linalg.norm(pos_PCs_LR - pos_groundtruth, axis=1).mean()\n",
+ " GC_error_LR = 100 * np.linalg.norm(pos_GCs_LR - pos_groundtruth, axis=1).mean()\n",
+ " BVC_error_LR = (\n",
+ " 100 * np.linalg.norm(pos_BVCs_LR - pos_groundtruth, axis=1).mean()\n",
+ " )\n",
+ "\n",
+ " results_array[0, i, j, 0] = PC_error_GP\n",
+ " results_array[1, i, j, 0] = GC_error_GP\n",
+ " results_array[2, i, j, 0] = BVC_error_GP\n",
+ " results_array[0, i, j, 1] = PC_error_LR\n",
+ " results_array[1, i, j, 1] = GC_error_LR\n",
+ " results_array[2, i, j, 1] = BVC_error_LR"
]
},
{
@@ -577,79 +623,116 @@
}
],
"source": [
- "#Get means and std from the results data frame \n",
- "means_GP = np.mean(results_array[:,:,:,0],axis=2)\n",
- "stds_GP = np.std(results_array[:,:,:,0],axis=2) / np.sqrt(15)\n",
- "means_LR = np.mean(results_array[:,:,:,1],axis=2)\n",
- "stds_LR = np.std(results_array[:,:,:,1],axis=2) / np.sqrt(15)\n",
+ "# Get means and std from the results data frame\n",
+ "means_GP = np.mean(results_array[:, :, :, 0], axis=2)\n",
+ "stds_GP = np.std(results_array[:, :, :, 0], axis=2) / np.sqrt(15)\n",
+ "means_LR = np.mean(results_array[:, :, :, 1], axis=2)\n",
+ "stds_LR = np.std(results_array[:, :, :, 1], axis=2) / np.sqrt(15)\n",
"\n",
- "#Make figure for Gaussian process regression\n",
+ "# Make figure for Gaussian process regression\n",
"fig, ax = plt.subplots()\n",
- "ax.scatter(N_features,means_GP[0,:],c='C1')\n",
- "ax.plot(N_features,means_GP[0,:],c='C1',label='Place cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_GP[0,:]+stds_GP[0,:],means_GP[0,:]-stds_GP[0,:],facecolor='C1',alpha=0.3)\n",
- "\n",
- "ax.scatter(N_features,means_GP[1,:],c='C2')\n",
- "ax.plot(N_features,means_GP[1,:],c='C2',label='Grid cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_GP[1,:]+stds_GP[1,:],means_GP[1,:]-stds_GP[1,:],facecolor='C2',alpha=0.3)\n",
- "\n",
- "ax.scatter(N_features,means_GP[2,:],c='C3')\n",
- "ax.plot(N_features,means_GP[2,:],c='C3',label='Boundary vector cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_GP[2,:]+stds_GP[2,:],means_GP[2,:]-stds_GP[2,:],facecolor='C3',alpha=0.3)\n",
- "\n",
- "log2_cms = np.logspace(0,4,5,base=2,dtype=int)\n",
+ "ax.scatter(N_features, means_GP[0, :], c=\"C1\")\n",
+ "ax.plot(N_features, means_GP[0, :], c=\"C1\", label=\"Place cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_GP[0, :] + stds_GP[0, :],\n",
+ " means_GP[0, :] - stds_GP[0, :],\n",
+ " facecolor=\"C1\",\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "\n",
+ "ax.scatter(N_features, means_GP[1, :], c=\"C2\")\n",
+ "ax.plot(N_features, means_GP[1, :], c=\"C2\", label=\"Grid cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_GP[1, :] + stds_GP[1, :],\n",
+ " means_GP[1, :] - stds_GP[1, :],\n",
+ " facecolor=\"C2\",\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "\n",
+ "ax.scatter(N_features, means_GP[2, :], c=\"C3\")\n",
+ "ax.plot(N_features, means_GP[2, :], c=\"C3\", label=\"Boundary vector cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_GP[2, :] + stds_GP[2, :],\n",
+ " means_GP[2, :] - stds_GP[2, :],\n",
+ " facecolor=\"C3\",\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "\n",
+ "log2_cms = np.logspace(0, 4, 5, base=2, dtype=int)\n",
"\n",
"ax.set_xlabel(\"Number of cells \\n (log scale)\")\n",
"ax.set_xscale(\"log\")\n",
"ax.set_yscale(\"log\")\n",
- "ax.tick_params(axis='x', which='minor', bottom=False)\n",
- "ax.tick_params(axis='y', which='minor', left=False)\n",
- "ax.set_xbound(lower=N_features[-1]*0.8, upper=N_features[0]/0.8)\n",
+ "ax.tick_params(axis=\"x\", which=\"minor\", bottom=False)\n",
+ "ax.tick_params(axis=\"y\", which=\"minor\", left=False)\n",
+ "ax.set_xbound(lower=N_features[-1] * 0.8, upper=N_features[0] / 0.8)\n",
"ax.set_ylabel(\"Average decoding error / cm, \\n (log scale)\")\n",
"ax.set_title(\"Gaussian process regression\")\n",
- "ax.spines['right'].set_color('none')\n",
- "ax.spines['top'].set_color('none')\n",
+ "ax.spines[\"right\"].set_color(\"none\")\n",
+ "ax.spines[\"top\"].set_color(\"none\")\n",
"ax.set_xticks(N_features)\n",
"ax.set_yticks(log2_cms)\n",
"ax.set_xticklabels(N_features)\n",
"ax.set_yticklabels(log2_cms)\n",
"ax.legend()\n",
"\n",
- "if save_plots is True: tpl.saveFigure(fig, \"GPanalysis\")\n",
+ "if save_plots is True:\n",
+ " tpl.saveFigure(fig, \"GPanalysis\")\n",
"\n",
"\n",
- "\n",
- "#Make identical figure for linear ridge regression\n",
+ "# Make identical figure for linear ridge regression\n",
"fig, ax = plt.subplots()\n",
- "ax.scatter(N_features,means_LR[0,:],c='C1')\n",
- "ax.plot(N_features,means_LR[0,:],c='C1',label='Place cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_LR[0,:]+stds_LR[0,:],means_LR[0,:]-stds_LR[0,:],facecolor='C1',alpha=0.3)\n",
- "\n",
- "ax.scatter(N_features,means_LR[1,:],c='C2')\n",
- "ax.plot(N_features,means_LR[1,:],c='C2',label='Grid cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_LR[1,:]+stds_LR[1,:],means_LR[1,:]-stds_LR[1,:],facecolor='C2',alpha=0.3)\n",
- "\n",
- "ax.scatter(N_features,means_LR[2,:],c='C3')\n",
- "ax.plot(N_features,means_LR[2,:],c='C3',label='Boundary vector cells',linewidth=1)\n",
- "ax.fill_between(N_features,means_LR[2,:]+stds_LR[2,:],means_LR[2,:]-stds_LR[2,:],facecolor='C3',alpha=0.3)\n",
+ "ax.scatter(N_features, means_LR[0, :], c=\"C1\")\n",
+ "ax.plot(N_features, means_LR[0, :], c=\"C1\", label=\"Place cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_LR[0, :] + stds_LR[0, :],\n",
+ " means_LR[0, :] - stds_LR[0, :],\n",
+ " facecolor=\"C1\",\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "\n",
+ "ax.scatter(N_features, means_LR[1, :], c=\"C2\")\n",
+ "ax.plot(N_features, means_LR[1, :], c=\"C2\", label=\"Grid cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_LR[1, :] + stds_LR[1, :],\n",
+ " means_LR[1, :] - stds_LR[1, :],\n",
+ " facecolor=\"C2\",\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "\n",
+ "ax.scatter(N_features, means_LR[2, :], c=\"C3\")\n",
+ "ax.plot(N_features, means_LR[2, :], c=\"C3\", label=\"Boundary vector cells\", linewidth=1)\n",
+ "ax.fill_between(\n",
+ " N_features,\n",
+ " means_LR[2, :] + stds_LR[2, :],\n",
+ " means_LR[2, :] - stds_LR[2, :],\n",
+ " facecolor=\"C3\",\n",
+ " alpha=0.3,\n",
+ ")\n",
"\n",
"ax.set_xlabel(\"Number of cells \\n (log scale)\")\n",
"ax.set_xscale(\"log\")\n",
"ax.set_yscale(\"log\")\n",
- "ax.tick_params(axis='x', which='minor', bottom=False)\n",
- "ax.tick_params(axis='y', which='minor', left=False)\n",
- "ax.set_xbound(lower=N_features[-1]*0.8, upper=N_features[0]/0.8)\n",
+ "ax.tick_params(axis=\"x\", which=\"minor\", bottom=False)\n",
+ "ax.tick_params(axis=\"y\", which=\"minor\", left=False)\n",
+ "ax.set_xbound(lower=N_features[-1] * 0.8, upper=N_features[0] / 0.8)\n",
"ax.set_ylabel(\"Average decoding error / cm, \\n (log scale)\")\n",
"ax.set_title(\"Linear ridge regression\")\n",
- "ax.spines['right'].set_color('none')\n",
- "ax.spines['top'].set_color('none')\n",
+ "ax.spines[\"right\"].set_color(\"none\")\n",
+ "ax.spines[\"top\"].set_color(\"none\")\n",
"ax.set_xticks(N_features)\n",
"ax.set_yticks(log2_cms)\n",
"ax.set_xticklabels(N_features)\n",
"ax.set_yticklabels(log2_cms)\n",
"ax.legend()\n",
"\n",
- "if save_plots is True: tpl.saveFigure(fig, \"LRanalysis\")"
+ "if save_plots is True:\n",
+ " tpl.saveFigure(fig, \"LRanalysis\")"
]
},
{
diff --git a/demos/extensive_example.ipynb b/demos/extensive_example.ipynb
index dace2f0b..a87b477b 100644
--- a/demos/extensive_example.ipynb
+++ b/demos/extensive_example.ipynb
@@ -26,7 +26,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#Import ratinabox\n",
+ "# Import ratinabox\n",
"import ratinabox\n",
"from ratinabox.Environment import Environment\n",
"from ratinabox.Agent import Agent\n",
@@ -48,40 +48,41 @@
],
"source": [
"# 1 Initialise environment.\n",
- "Env = Environment(\n",
- " params = {'aspect':2,\n",
- " 'scale':1})\n",
+ "Env = Environment(params={\"aspect\": 2, \"scale\": 1})\n",
"\n",
- "# 2 Add walls. \n",
- "Env.add_wall([[1,0],[1,0.35]])\n",
- "Env.add_wall([[1,0.65],[1,1]])\n",
+ "# 2 Add walls.\n",
+ "Env.add_wall([[1, 0], [1, 0.35]])\n",
+ "Env.add_wall([[1, 0.65], [1, 1]])\n",
"\n",
"# 3 Add Agent.\n",
"Ag = Agent(Env)\n",
- "Ag.pos = np.array([0.5,0.5])\n",
+ "Ag.pos = np.array([0.5, 0.5])\n",
"Ag.speed_mean = 0.2\n",
"\n",
- "# 4 Add place cells. \n",
- "PCs = PlaceCells(Ag,\n",
- " params={'n':100,\n",
- " 'description':'gaussian_threshold',\n",
- " 'widths':0.40,\n",
- " 'wall_geometry':'line_of_sight',\n",
- " 'max_fr':10,\n",
- " 'min_fr':0.1,\n",
- " 'color':'C1'})\n",
- "PCs.place_cell_centres[-1] = np.array([1.1,0.5])\n",
+ "# 4 Add place cells.\n",
+ "PCs = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 100,\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"widths\": 0.40,\n",
+ " \"wall_geometry\": \"line_of_sight\",\n",
+ " \"max_fr\": 10,\n",
+ " \"min_fr\": 0.1,\n",
+ " \"color\": \"C1\",\n",
+ " },\n",
+ ")\n",
+ "PCs.place_cell_centres[-1] = np.array([1.1, 0.5])\n",
"\n",
"# 5 Add boundary vector cells.\n",
- "BVCs = BoundaryVectorCells(Ag,\n",
- " params = {'n':30,\n",
- " 'color':'C2'})\n",
+ "BVCs = BoundaryVectorCells(Ag, params={\"n\": 30, \"color\": \"C2\"})\n",
"\n",
- "# 6 Simulate. \n",
- "dt = 50e-3 \n",
- "T = 10*60\n",
- "from tqdm import tqdm #gives time bar\n",
- "for i in tqdm(range(int(T/dt))):\n",
+ "# 6 Simulate.\n",
+ "dt = 50e-3\n",
+ "T = 10 * 60\n",
+ "from tqdm import tqdm # gives time bar\n",
+ "\n",
+ "for i in tqdm(range(int(T / dt))):\n",
" Ag.update(dt=dt)\n",
" PCs.update()\n",
" BVCs.update()"
@@ -106,9 +107,9 @@
}
],
"source": [
- "# 7 Plot trajectory. \n",
+ "# 7 Plot trajectory.\n",
"fig, ax = Ag.plot_position_heatmap()\n",
- "fig, ax = Ag.plot_trajectory(t_start=50,t_end=60,fig=fig,ax=ax)"
+ "fig, ax = Ag.plot_trajectory(t_start=50, t_end=60, fig=fig, ax=ax)"
]
},
{
@@ -130,8 +131,10 @@
}
],
"source": [
- "# 8 Plot timeseries. \n",
- "fig, ax = BVCs.plot_rate_timeseries(t_start=0,t_end=60,chosen_neurons='12',spikes=True)"
+ "# 8 Plot timeseries.\n",
+ "fig, ax = BVCs.plot_rate_timeseries(\n",
+ " t_start=0, t_end=60, chosen_neurons=\"12\", spikes=True\n",
+ ")"
]
},
{
@@ -153,7 +156,7 @@
}
],
"source": [
- "# 9 Plot place cells. \n",
+ "# 9 Plot place cells.\n",
"fig, ax = PCs.plot_place_cell_locations()"
]
},
@@ -188,9 +191,9 @@
}
],
"source": [
- "# 10 Plot rate maps. \n",
- "fig, ax = PCs.plot_rate_map(chosen_neurons='3',method='groundtruth')\n",
- "fig, ax = PCs.plot_rate_map(chosen_neurons='3',method='history',spikes=True)"
+ "# 10 Plot rate maps.\n",
+ "fig, ax = PCs.plot_rate_map(chosen_neurons=\"3\", method=\"groundtruth\")\n",
+ "fig, ax = PCs.plot_rate_map(chosen_neurons=\"3\", method=\"history\", spikes=True)"
]
},
{
@@ -223,8 +226,8 @@
],
"source": [
"# 11 Display BVC rate maps and polar receptive fields\n",
- "fig, ax = BVCs.plot_rate_map(chosen_neurons='2')\n",
- "fig, ax = BVCs.plot_BVC_receptive_field(chosen_neurons='2')"
+ "fig, ax = BVCs.plot_rate_map(chosen_neurons=\"2\")\n",
+ "fig, ax = BVCs.plot_BVC_receptive_field(chosen_neurons=\"2\")"
]
},
{
@@ -256,21 +259,25 @@
}
],
"source": [
- "# 12 Multipanel figure \n",
- "fig, axes = plt.subplots(2,8,figsize=(24,6))\n",
- "Ag.plot_trajectory(t_start=0, t_end=60,fig=fig,ax=axes[0,0])\n",
- "axes[0,0].set_title(\"Trajectory (last minute)\")\n",
- "Ag.plot_position_heatmap(fig=fig,ax=axes[1,0])\n",
- "axes[1,0].set_title(\"Full trajectory heatmap\")\n",
- "PCs.plot_rate_timeseries(t_start=0,t_end=60,chosen_neurons='6',spikes=True,fig=fig, ax=axes[0,1])\n",
- "axes[0,1].set_title(\"Place cell activity\")\n",
- "axes[0,1].set_xlabel(\"\")\n",
- "BVCs.plot_rate_timeseries(t_start=0,t_end=60,chosen_neurons='6',spikes=True,fig=fig, ax=axes[1,1])\n",
- "axes[1,1].set_title(\"BVC activity\")\n",
- "PCs.plot_rate_map(chosen_neurons='6',method='groundtruth',fig=fig,ax=axes[0,2:])\n",
- "axes[0,2].set_title(\"Place cell receptive fields\")\n",
- "BVCs.plot_rate_map(chosen_neurons='6',method='groundtruth',fig=fig,ax=axes[1,2:])\n",
- "axes[1,2].set_title(\"BVC receptive fields\")"
+ "# 12 Multipanel figure\n",
+ "fig, axes = plt.subplots(2, 8, figsize=(24, 6))\n",
+ "Ag.plot_trajectory(t_start=0, t_end=60, fig=fig, ax=axes[0, 0])\n",
+ "axes[0, 0].set_title(\"Trajectory (last minute)\")\n",
+ "Ag.plot_position_heatmap(fig=fig, ax=axes[1, 0])\n",
+ "axes[1, 0].set_title(\"Full trajectory heatmap\")\n",
+ "PCs.plot_rate_timeseries(\n",
+ " t_start=0, t_end=60, chosen_neurons=\"6\", spikes=True, fig=fig, ax=axes[0, 1]\n",
+ ")\n",
+ "axes[0, 1].set_title(\"Place cell activity\")\n",
+ "axes[0, 1].set_xlabel(\"\")\n",
+ "BVCs.plot_rate_timeseries(\n",
+ " t_start=0, t_end=60, chosen_neurons=\"6\", spikes=True, fig=fig, ax=axes[1, 1]\n",
+ ")\n",
+ "axes[1, 1].set_title(\"BVC activity\")\n",
+ "PCs.plot_rate_map(chosen_neurons=\"6\", method=\"groundtruth\", fig=fig, ax=axes[0, 2:])\n",
+ "axes[0, 2].set_title(\"Place cell receptive fields\")\n",
+ "BVCs.plot_rate_map(chosen_neurons=\"6\", method=\"groundtruth\", fig=fig, ax=axes[1, 2:])\n",
+ "axes[1, 2].set_title(\"BVC receptive fields\")"
]
},
{
@@ -300,7 +307,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.9"
},
"orig_nbformat": 4
},
diff --git a/demos/list_of_plotting_fuctions.md b/demos/list_of_plotting_fuctions.md
index b1010a60..c6b4e463 100644
--- a/demos/list_of_plotting_fuctions.md
+++ b/demos/list_of_plotting_fuctions.md
@@ -12,7 +12,7 @@ Displays the environment. Works for both 1 or 2D environments.
Examples:
* `Env.plot_environment()`
-
+
@@ -24,37 +24,37 @@ Plots the agent trajectory. Works for 1 or 2D.
* `Ag.plot_trajectory(t_end=120)`
-
+
* `Ag1D.plot_trajectory(t_end=120)`
-
+
## `Agent.animate_trajectory()`
Makes an animation of the agents trajectory.
-
+
## `Agent.plot_position_heatmap()`
Plots a heatmap of the Agents past locations (2D and 1D example shown)
-
+
-
+
## `Agent.plot_histogram_of_speeds()`
-
+
## `Agent.plot_histogram_of_rotational_velocities()`
-
+
# `Neurons`
@@ -62,18 +62,18 @@ Plots a heatmap of the Agents past locations (2D and 1D example shown)
## `Neurons.plot_rate_timeseries()`
Plots a timeseries of the firing rates
-
+
## `Neurons.plot_rate_timeseries(imshow=True)`
Plots a timeseries of the firing rates as an image
-
+
Plots a timeseries of the firing rates
-
+
## `Neurons.animate_rate_timeseries()`
Makes an animation of the firing rates timeseries
-
+
## `Neurons.plot_ratemap()`
@@ -86,21 +86,21 @@ As an example here we show this function for a set of 3 (two dimensional) grid c
* `Neurons.plot_ratemap(method=`analytic`)
-
+
-
+
* `Neurons.plot_ratemap(method=`history`)
-
+
-
+
* `Neurons.plot_ratemap(method=`neither`, spikes=True)
-
+
-
+
@@ -108,12 +108,12 @@ As an example here we show this function for a set of 3 (two dimensional) grid c
Scatters where the place cells are centres
-
+
## `BoundaryVectorCells.plot_BVC_receptive_field()`
-
+
# Other details:
@@ -126,7 +126,7 @@ fig, ax = Neurons.plot_rate_map(chosen_neuron="1")
fig, ax = Ag.plot_trajectory(fig=fig, ax=ax)
```
-
+
2. Multipanel figures:
```python
@@ -136,7 +136,7 @@ Neurons.plot_rate_map(fig=fig,ax=[axes[1],axes[2],axes[3]],chosen_neurons='3') #
Neurons.plot_rate_timeseries(fig=fig,ax=axes[4])
```
-
+
* For rate maps and timeseries' by default **all** the cells will be plotted. This may take a long time if the number of cells is large. Control this with the `chosen_neurons` argument
diff --git a/demos/paper_figures.ipynb b/demos/paper_figures.ipynb
index 13744b11..0d22492c 100644
--- a/demos/paper_figures.ipynb
+++ b/demos/paper_figures.ipynb
@@ -44,15 +44,17 @@
"metadata": {},
"outputs": [],
"source": [
- "#Leave this as False. \n",
- "#For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save. \n",
- "if False: \n",
+ "# Leave this as False.\n",
+ "# For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save.\n",
+ "if False:\n",
" import tomplotlib.tomplotlib as tpl\n",
+ "\n",
" tpl.figureDirectory = \"../figures/\"\n",
" tpl.setColorscheme(colorscheme=2)\n",
" save_plots = True\n",
" from matplotlib import rcParams, rc\n",
- " rcParams['figure.dpi']= 300\n",
+ "\n",
+ " rcParams[\"figure.dpi\"] = 300\n",
"else:\n",
" save_plots = False"
]
@@ -94,43 +96,32 @@
}
],
"source": [
- "ratinabox.verbose=False\n",
+ "ratinabox.verbose = False\n",
"Env = Environment()\n",
- "Env.add_wall(np.array([[0.4,0],[0.4,0.4]]))\n",
+ "Env.add_wall(np.array([[0.4, 0], [0.4, 0.4]]))\n",
"\n",
"Ag = Agent(Env)\n",
"\n",
- "PCs = PlaceCells(Ag,\n",
- " params={'n':4,\n",
- " 'description':'gaussian_threshold',\n",
- " 'widths':0.4,\n",
- " 'color':'C1'\n",
- " }\n",
+ "PCs = PlaceCells(\n",
+ " Ag,\n",
+ " params={\"n\": 4, \"description\": \"gaussian_threshold\", \"widths\": 0.4, \"color\": \"C1\"},\n",
")\n",
"\n",
- "GCs = GridCells(Ag,\n",
- " params={'n':4,\n",
- " 'color':'C2'\n",
- " }\n",
- ")\n",
+ "GCs = GridCells(Ag, params={\"n\": 4, \"color\": \"C2\"})\n",
"\n",
- "BVCs = BoundaryVectorCells(Ag,\n",
- " params={'n':4,\n",
- " 'color':'C3'\n",
- " }\n",
- ")\n",
+ "BVCs = BoundaryVectorCells(Ag, params={\"n\": 4, \"color\": \"C3\"})\n",
"\n",
- "VCs = VelocityCells(Ag,\n",
- " params={'color':'C5'\n",
- " }\n",
- ")\n",
+ "VCs = VelocityCells(Ag, params={\"color\": \"C5\"})\n",
"\n",
"fig, ax = PCs.plot_rate_map()\n",
- "if save_plots == True: tpl.saveFigure(fig,'PCs')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"PCs\")\n",
"fig, ax = GCs.plot_rate_map()\n",
- "if save_plots == True: tpl.saveFigure(fig,'GCs')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"GCs\")\n",
"fig, ax = BVCs.plot_rate_map()\n",
- "if save_plots == True: tpl.saveFigure(fig,'BVCs')\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"BVCs\")"
]
},
{
@@ -147,7 +138,7 @@
}
],
"source": [
- "for i in tqdm(range(int(60/Ag.dt))):\n",
+ "for i in tqdm(range(int(60 / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
@@ -213,17 +204,21 @@
],
"source": [
"fig, ax = Ag.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,'trajectory')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"trajectory\")\n",
"\n",
"fig, ax = VCs.plot_rate_timeseries()\n",
- "if save_plots == True: tpl.saveFigure(fig,'VCs_ts')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"VCs_ts\")\n",
"fig, ax = BVCs.plot_rate_timeseries()\n",
- "if save_plots == True: tpl.saveFigure(fig,'BVCs_ts')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"BVCs_ts\")\n",
"fig, ax = GCs.plot_rate_timeseries()\n",
- "if save_plots == True: tpl.saveFigure(fig,'GCs_ts')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"GCs_ts\")\n",
"fig, ax = PCs.plot_rate_timeseries()\n",
- "if save_plots == True: tpl.saveFigure(fig,'PCs_ts')\n",
- "\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"PCs_ts\")"
]
},
{
@@ -289,68 +284,70 @@
],
"source": [
"Env1 = Environment()\n",
- "Env1.add_wall([[0,0.5],[0.2,0.5]])\n",
- "Env1.add_wall([[0.3,0.5],[0.7,0.5]])\n",
- "Env1.add_wall([[0.8,0.5],[1,0.5]])\n",
- "Env1.add_wall([[0.5,0],[0.5,0.2]])\n",
- "Env1.add_wall([[0.5,0.3],[0.5,0.7]])\n",
- "Env1.add_wall([[0.5,0.8],[0.5,1]])\n",
+ "Env1.add_wall([[0, 0.5], [0.2, 0.5]])\n",
+ "Env1.add_wall([[0.3, 0.5], [0.7, 0.5]])\n",
+ "Env1.add_wall([[0.8, 0.5], [1, 0.5]])\n",
+ "Env1.add_wall([[0.5, 0], [0.5, 0.2]])\n",
+ "Env1.add_wall([[0.5, 0.3], [0.5, 0.7]])\n",
+ "Env1.add_wall([[0.5, 0.8], [0.5, 1]])\n",
"Ag1 = Agent(Env)\n",
- "Ag1.pos = np.array([0.4,0.25])\n",
- "Ag1.velocity = 0.3*np.array([1,0])\n",
+ "Ag1.pos = np.array([0.4, 0.25])\n",
+ "Ag1.velocity = 0.3 * np.array([1, 0])\n",
"\n",
"\n",
"Env2 = Environment()\n",
- "Env2.add_wall([[0.2,0],[0.2,0.8]])\n",
- "Env2.add_wall([[0.4,1],[0.4,0.2]])\n",
- "Env2.add_wall([[0.6,0],[0.6,0.8]])\n",
- "Env2.add_wall([[0.8,1],[0.8,0.2]])\n",
+ "Env2.add_wall([[0.2, 0], [0.2, 0.8]])\n",
+ "Env2.add_wall([[0.4, 1], [0.4, 0.2]])\n",
+ "Env2.add_wall([[0.6, 0], [0.6, 0.8]])\n",
+ "Env2.add_wall([[0.8, 1], [0.8, 0.2]])\n",
"Ag2 = Agent(Env2)\n",
- "Ag2.pos = np.array([0.1,0.1])\n",
- "Ag2.velocity = 0.3*np.array([0,1])\n",
+ "Ag2.pos = np.array([0.1, 0.1])\n",
+ "Ag2.velocity = 0.3 * np.array([0, 1])\n",
"\n",
"\n",
- "Env3 = Environment(params={'aspect':2,\n",
- " 'scale':0.5}) \n",
- "Env3.add_wall([[0.5,0],[0.5,0.4]])\n",
- "Env3.add_wall([[0,0.4],[0.2,0.4]])\n",
- "Env3.add_wall([[0.3,0.4],[0.7,0.4]])\n",
- "Env3.add_wall([[0.8,0.4],[1,0.4]])\n",
+ "Env3 = Environment(params={\"aspect\": 2, \"scale\": 0.5})\n",
+ "Env3.add_wall([[0.5, 0], [0.5, 0.4]])\n",
+ "Env3.add_wall([[0, 0.4], [0.2, 0.4]])\n",
+ "Env3.add_wall([[0.3, 0.4], [0.7, 0.4]])\n",
+ "Env3.add_wall([[0.8, 0.4], [1, 0.4]])\n",
"Ag3 = Agent(Env3)\n",
- "Ag3.pos = np.array([0.22,0.35])\n",
- "Ag3.velocity = 0.3*np.array([0.5,1])\n",
+ "Ag3.pos = np.array([0.22, 0.35])\n",
+ "Ag3.velocity = 0.3 * np.array([0.5, 1])\n",
"\n",
"\n",
- "Env4 = Environment(params={'aspect':2,\n",
- " 'scale':0.5})\n",
- "Env4.add_wall([[0.1,0.25],[0.5,0.45]])\n",
- "Env4.add_wall([[0.4,0.3],[0.65,0.05]])\n",
- "Env4.add_wall([[0.65,0.25],[0.9,0.3]])\n",
+ "Env4 = Environment(params={\"aspect\": 2, \"scale\": 0.5})\n",
+ "Env4.add_wall([[0.1, 0.25], [0.5, 0.45]])\n",
+ "Env4.add_wall([[0.4, 0.3], [0.65, 0.05]])\n",
+ "Env4.add_wall([[0.65, 0.25], [0.9, 0.3]])\n",
"\n",
"Ag4 = Agent(Env)\n",
- "Ag4.pos = np.array([0.5,0.05])\n",
- "Ag4.velocity = 0.3*np.array([0,1])\n",
+ "Ag4.pos = np.array([0.5, 0.05])\n",
+ "Ag4.velocity = 0.3 * np.array([0, 1])\n",
"\n",
"\n",
"train_time = 10\n",
- "for i in tqdm(range(int(train_time/Ag1.dt))): \n",
+ "for i in tqdm(range(int(train_time / Ag1.dt))):\n",
" Ag1.update()\n",
" Ag2.update()\n",
" Ag3.update()\n",
" Ag4.update()\n",
"\n",
"\n",
- "fig1,ax1=Ag1.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig1,'fourroom')\n",
+ "fig1, ax1 = Ag1.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig1, \"fourroom\")\n",
"\n",
- "fig2,ax2=Ag2.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig2,'hairpin')\n",
+ "fig2, ax2 = Ag2.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig2, \"hairpin\")\n",
"\n",
- "fig3,ax3=Ag3.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig3,'tworoom')\n",
+ "fig3, ax3 = Ag3.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig3, \"tworoom\")\n",
"\n",
- "fig4,ax4=Ag4.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig4,'random')"
+ "fig4, ax4 = Ag4.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig4, \"random\")"
]
},
{
@@ -377,18 +374,15 @@
}
],
"source": [
- "Env = Environment(params={'dimensionality':'1D',\n",
- " 'boundary_conditions':'periodic'})\n",
- "Ag = Agent(Env,\n",
- " params={'speed_mean':0.1,\n",
- " 'speed_std':0.2}\n",
- ")\n",
+ "Env = Environment(params={\"dimensionality\": \"1D\", \"boundary_conditions\": \"periodic\"})\n",
+ "Ag = Agent(Env, params={\"speed_mean\": 0.1, \"speed_std\": 0.2})\n",
"\n",
- "for i in range(int(60/Ag.dt)):\n",
+ "for i in range(int(60 / Ag.dt)):\n",
" Ag.update()\n",
"\n",
"fig, ax = Ag.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,'1Dtrajectory')\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1Dtrajectory\")"
]
},
{
@@ -431,50 +425,63 @@
"from scipy import io\n",
"from scipy.optimize import curve_fit\n",
"\n",
- "def rayleigh(x,sigma,K):\n",
- " return K*x*np.e**(-x**2/(2*(sigma**2)))\n",
- "def exponential(t,tau,K):\n",
- " return K*np.e**(-t/tau)\n",
- "def gaussian(x,sigma,K):\n",
- " return K*np.e**(-x**2/(2*(sigma**2)))\n",
- "def lagged_autocorrelation(t,x,max_t=10):\n",
+ "\n",
+ "def rayleigh(x, sigma, K):\n",
+ " return K * x * np.e ** (-(x**2) / (2 * (sigma**2)))\n",
+ "\n",
+ "\n",
+ "def exponential(t, tau, K):\n",
+ " return K * np.e ** (-t / tau)\n",
+ "\n",
+ "\n",
+ "def gaussian(x, sigma, K):\n",
+ " return K * np.e ** (-(x**2) / (2 * (sigma**2)))\n",
+ "\n",
+ "\n",
+ "def lagged_autocorrelation(t, x, max_t=10):\n",
" from scipy.stats.stats import pearsonr\n",
+ "\n",
" R, T = [], []\n",
" time, i = 0, 0\n",
" while time < max_t:\n",
- " if i == 0:r = pearsonr(x,x)[0]\n",
- " else: r = pearsonr(x[i:],x[:-i])[0]\n",
+ " if i == 0:\n",
+ " r = pearsonr(x, x)[0]\n",
+ " else:\n",
+ " r = pearsonr(x[i:], x[:-i])[0]\n",
" i += 1\n",
" T.append(t[i])\n",
" R.append(r)\n",
" time = t[i]\n",
" return np.array(T), np.array(R)\n",
"\n",
- "#import data\n",
- "mat = io.loadmat(\"../rawdata//8F6BE356-3277-475C-87B1-C7A977632DA7_1/11084-03020501_t2c1.mat\")\n",
- "x = ((mat['x1'] + mat['x2'])/2).reshape(-1)\n",
- "y = ((mat['y1'] + mat['y2'])/2).reshape(-1)\n",
- "t = (mat['t']).reshape(-1)\n",
- "#remove nans \n",
+ "\n",
+ "# import data\n",
+ "mat = io.loadmat(\n",
+ " \"../rawdata//8F6BE356-3277-475C-87B1-C7A977632DA7_1/11084-03020501_t2c1.mat\"\n",
+ ")\n",
+ "x = ((mat[\"x1\"] + mat[\"x2\"]) / 2).reshape(-1)\n",
+ "y = ((mat[\"y1\"] + mat[\"y2\"]) / 2).reshape(-1)\n",
+ "t = (mat[\"t\"]).reshape(-1)\n",
+ "# remove nans\n",
"y = y[np.logical_not(np.isnan(x))]\n",
"t = t[np.logical_not(np.isnan(x))]\n",
"x = x[np.logical_not(np.isnan(x))]\n",
- "#normalise and put in metres\n",
- "x = (x-min(x))/100\n",
- "y = (y-min(y))/100\n",
- "x = x + 0.5*(1-max(x))\n",
- "y = y + 0.5*(1-max(y))\n",
- "#downsample (so my code will later smooth it) (currently at 50Hz --> 2.5Hz)\n",
+ "# normalise and put in metres\n",
+ "x = (x - min(x)) / 100\n",
+ "y = (y - min(y)) / 100\n",
+ "x = x + 0.5 * (1 - max(x))\n",
+ "y = y + 0.5 * (1 - max(y))\n",
+ "# downsample (so my code will later smooth it) (currently at 50Hz --> 2.5Hz)\n",
"x = x[::20]\n",
"y = y[::20]\n",
"t = t[::20]\n",
- "#concatenate\n",
- "pos = np.stack((x,y)).T\n",
- "#make env, pass data to agent, and then upsample\n",
+ "# concatenate\n",
+ "pos = np.stack((x, y)).T\n",
+ "# make env, pass data to agent, and then upsample\n",
"Env = Environment()\n",
"Ag_s = Agent(Env)\n",
- "Ag_s.import_trajectory(times=t,positions=pos)\n",
- "for i in tqdm(range(int(max(t)/Ag_s.dt))):\n",
+ "Ag_s.import_trajectory(times=t, positions=pos)\n",
+ "for i in tqdm(range(int(max(t) / Ag_s.dt))):\n",
" Ag_s.update()"
]
},
@@ -559,75 +566,73 @@
}
],
"source": [
- "#plot sargolini trajectory\n",
- "fig, ax = Ag_s.plot_trajectory(t_end=5*60)\n",
- "if save_plots == True: tpl.saveFigure(fig,'sarg_trajectory')\n",
+ "# plot sargolini trajectory\n",
+ "fig, ax = Ag_s.plot_trajectory(t_end=5 * 60)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"sarg_trajectory\")\n",
"\n",
"\n",
- "#plot sargolini speed histogram \n",
+ "# plot sargolini speed histogram\n",
"fig, ax, y_v, x_v, patches = Ag_s.plot_histogram_of_speeds(return_data=True)\n",
"ax.set_xlim(right=0.6)\n",
- "x_v = (x_v[1:]+x_v[:-1])/2\n",
- "sigma, K = curve_fit(rayleigh,x_v,y_v)[0]\n",
- "print(\"best Rayleigh sigma:\",sigma)\n",
- "y_fit = rayleigh(x_v,sigma,K)\n",
- "ax.plot(x_v,y_fit)\n",
- "if save_plots == True: \n",
- " tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'sarg_rayleigh')\n",
- "\n",
- "\n",
- "#plot sargolini rotational speed histogram \n",
- "fig, ax, y_v, x_v, patches = Ag_s.plot_histogram_of_rotational_velocities(return_data=True)\n",
- "ax.set_xlim(left=-1000,right=1000)\n",
- "x_v = (x_v[1:]+x_v[:-1])/2\n",
- "sigma, K = curve_fit(gaussian,x_v,y_v,p0=np.array([1000,500]))[0]\n",
- "print(\"best gaussian sigma:\",sigma)\n",
- "y_fit = gaussian(x_v,sigma,K)\n",
- "ax.plot(x_v,y_fit)\n",
- "if save_plots == True: \n",
+ "x_v = (x_v[1:] + x_v[:-1]) / 2\n",
+ "sigma, K = curve_fit(rayleigh, x_v, y_v)[0]\n",
+ "print(\"best Rayleigh sigma:\", sigma)\n",
+ "y_fit = rayleigh(x_v, sigma, K)\n",
+ "ax.plot(x_v, y_fit)\n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'sarg_normal')\n",
+ " tpl.saveFigure(fig, \"sarg_rayleigh\")\n",
"\n",
"\n",
+ "# plot sargolini rotational speed histogram\n",
+ "fig, ax, y_v, x_v, patches = Ag_s.plot_histogram_of_rotational_velocities(\n",
+ " return_data=True\n",
+ ")\n",
+ "ax.set_xlim(left=-1000, right=1000)\n",
+ "x_v = (x_v[1:] + x_v[:-1]) / 2\n",
+ "sigma, K = curve_fit(gaussian, x_v, y_v, p0=np.array([1000, 500]))[0]\n",
+ "print(\"best gaussian sigma:\", sigma)\n",
+ "y_fit = gaussian(x_v, sigma, K)\n",
+ "ax.plot(x_v, y_fit)\n",
+ "if save_plots == True:\n",
+ " tpl.xyAxes(ax)\n",
+ " tpl.saveFigure(fig, \"sarg_normal\")\n",
"\n",
- "t = np.array(Ag_s.history['t'])\n",
- "speed = np.linalg.norm(np.array(Ag_s.history['vel']),axis=1)\n",
- "speed = (speed - np.mean(speed))/np.std(speed)\n",
- "lag, speed_autocorr = lagged_autocorrelation(t,speed)\n",
+ "\n",
+ "t = np.array(Ag_s.history[\"t\"])\n",
+ "speed = np.linalg.norm(np.array(Ag_s.history[\"vel\"]), axis=1)\n",
+ "speed = (speed - np.mean(speed)) / np.std(speed)\n",
+ "lag, speed_autocorr = lagged_autocorrelation(t, speed)\n",
"lag = lag[10:]\n",
"speed_autocorr = speed_autocorr[10:]\n",
"fig, ax = plt.subplots()\n",
- "ax.plot(lag,speed_autocorr)\n",
- "tau, K = curve_fit(exponential,lag,speed_autocorr)[0]\n",
- "print(\"best tau for speed is:\",tau)\n",
- "y_fit = exponential(lag,tau,K)\n",
- "ax.plot(lag,y_fit)\n",
- "ax.set_xlim(left=0,right=4)\n",
- "if save_plots == True: \n",
+ "ax.plot(lag, speed_autocorr)\n",
+ "tau, K = curve_fit(exponential, lag, speed_autocorr)[0]\n",
+ "print(\"best tau for speed is:\", tau)\n",
+ "y_fit = exponential(lag, tau, K)\n",
+ "ax.plot(lag, y_fit)\n",
+ "ax.set_xlim(left=0, right=4)\n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'sarg_speedac')\n",
- "\n",
- "\n",
+ " tpl.saveFigure(fig, \"sarg_speedac\")\n",
"\n",
"\n",
- "\n",
- "rot_vel = np.array(Ag_s.history['rot_vel'])\n",
- "rot_vel = (rot_vel - np.mean(rot_vel))/np.std(rot_vel)\n",
- "lag, rot_vel_autocorr = lagged_autocorrelation(t,rot_vel)\n",
+ "rot_vel = np.array(Ag_s.history[\"rot_vel\"])\n",
+ "rot_vel = (rot_vel - np.mean(rot_vel)) / np.std(rot_vel)\n",
+ "lag, rot_vel_autocorr = lagged_autocorrelation(t, rot_vel)\n",
"lag = lag[10:]\n",
"rot_vel_autocorr = rot_vel_autocorr[10:]\n",
"fig, ax = plt.subplots()\n",
- "ax.plot(lag,rot_vel_autocorr)\n",
- "tau, K = curve_fit(exponential,lag,rot_vel_autocorr)[0]\n",
- "print(\"best tau for rotational_vel is:\",tau)\n",
- "y_fit = exponential(lag,tau,K)\n",
- "ax.plot(lag,y_fit)\n",
+ "ax.plot(lag, rot_vel_autocorr)\n",
+ "tau, K = curve_fit(exponential, lag, rot_vel_autocorr)[0]\n",
+ "print(\"best tau for rotational_vel is:\", tau)\n",
+ "y_fit = exponential(lag, tau, K)\n",
+ "ax.plot(lag, y_fit)\n",
"ax.set_xlim(right=4)\n",
- "if save_plots == True: \n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'sarg_rotac')\n",
- "\n"
+ " tpl.saveFigure(fig, \"sarg_rotac\")"
]
},
{
@@ -654,7 +659,7 @@
"source": [
"Env = Environment()\n",
"Ag_r = Agent(Env)\n",
- "for i in tqdm(range(int(600/Ag_r.dt))):\n",
+ "for i in tqdm(range(int(600 / Ag_r.dt))):\n",
" Ag_r.update()"
]
},
@@ -731,53 +736,54 @@
}
],
"source": [
- "fig, ax = Ag_r.plot_trajectory(t_end = 60*5)\n",
- "if save_plots == True: tpl.saveFigure(fig,'riab_trajectory')\n",
+ "fig, ax = Ag_r.plot_trajectory(t_end=60 * 5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"riab_trajectory\")\n",
"\n",
"fig, ax = Ag_r.plot_histogram_of_speeds()\n",
- "ax.set_xlim(0,0.60)\n",
- "if save_plots == True: \n",
+ "ax.set_xlim(0, 0.60)\n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'riab_rayleigh')\n",
+ " tpl.saveFigure(fig, \"riab_rayleigh\")\n",
"\n",
"fig, ax = Ag_r.plot_histogram_of_rotational_velocities()\n",
- "ax.set_xlim(-1000,1000)\n",
- "if save_plots == True: \n",
+ "ax.set_xlim(-1000, 1000)\n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'riab_normal')\n",
+ " tpl.saveFigure(fig, \"riab_normal\")\n",
"\n",
- "t = np.array(Ag_r.history['t'])\n",
- "speed = np.linalg.norm(np.array(Ag_r.history['vel']),axis=1)\n",
- "speed = (speed - np.mean(speed))/np.std(speed)\n",
- "lag, speed_autocorr = lagged_autocorrelation(t,speed)\n",
+ "t = np.array(Ag_r.history[\"t\"])\n",
+ "speed = np.linalg.norm(np.array(Ag_r.history[\"vel\"]), axis=1)\n",
+ "speed = (speed - np.mean(speed)) / np.std(speed)\n",
+ "lag, speed_autocorr = lagged_autocorrelation(t, speed)\n",
"lag = lag[10:]\n",
"speed_autocorr = speed_autocorr[10:]\n",
"fig, ax = plt.subplots()\n",
- "ax.plot(lag,speed_autocorr)\n",
- "tau, K = curve_fit(exponential,lag,speed_autocorr)[0]\n",
- "print(\"best tau for speed is:\",tau)\n",
- "y_fit = exponential(lag,tau,K)\n",
- "ax.plot(lag,y_fit)\n",
- "ax.set_xlim(left=0,right=4)\n",
- "if save_plots == True: \n",
+ "ax.plot(lag, speed_autocorr)\n",
+ "tau, K = curve_fit(exponential, lag, speed_autocorr)[0]\n",
+ "print(\"best tau for speed is:\", tau)\n",
+ "y_fit = exponential(lag, tau, K)\n",
+ "ax.plot(lag, y_fit)\n",
+ "ax.set_xlim(left=0, right=4)\n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'riab_speedac')\n",
+ " tpl.saveFigure(fig, \"riab_speedac\")\n",
"\n",
- "rot_vel = np.array(Ag_r.history['rot_vel'])\n",
- "rot_vel = (rot_vel - np.mean(rot_vel))/np.std(rot_vel)\n",
- "lag, rot_vel_autocorr = lagged_autocorrelation(t,rot_vel)\n",
+ "rot_vel = np.array(Ag_r.history[\"rot_vel\"])\n",
+ "rot_vel = (rot_vel - np.mean(rot_vel)) / np.std(rot_vel)\n",
+ "lag, rot_vel_autocorr = lagged_autocorrelation(t, rot_vel)\n",
"lag = lag[10:]\n",
"rot_vel_autocorr = rot_vel_autocorr[10:]\n",
"fig, ax = plt.subplots()\n",
- "ax.plot(lag,rot_vel_autocorr)\n",
- "tau, K = curve_fit(exponential,lag,rot_vel_autocorr)[0]\n",
- "print(\"best tau for rotational_vel is:\",tau)\n",
- "y_fit = exponential(lag,tau,K)\n",
- "ax.plot(lag,y_fit)\n",
+ "ax.plot(lag, rot_vel_autocorr)\n",
+ "tau, K = curve_fit(exponential, lag, rot_vel_autocorr)[0]\n",
+ "print(\"best tau for rotational_vel is:\", tau)\n",
+ "y_fit = exponential(lag, tau, K)\n",
+ "ax.plot(lag, y_fit)\n",
"ax.set_xlim(right=4)\n",
- "if save_plots == True: \n",
+ "if save_plots == True:\n",
" tpl.xyAxes(ax)\n",
- " tpl.saveFigure(fig,'riab_rotac')\n"
+ " tpl.saveFigure(fig, \"riab_rotac\")"
]
},
{
@@ -802,13 +808,23 @@
],
"source": [
"Env = Environment()\n",
- "Ag1 = Ag = Agent(Env,params={'thigmotaxis':0.8,})\n",
- "Ag2 = Ag = Agent(Env,params={'thigmotaxis':0.2,})\n",
+ "Ag1 = Ag = Agent(\n",
+ " Env,\n",
+ " params={\n",
+ " \"thigmotaxis\": 0.8,\n",
+ " },\n",
+ ")\n",
+ "Ag2 = Ag = Agent(\n",
+ " Env,\n",
+ " params={\n",
+ " \"thigmotaxis\": 0.2,\n",
+ " },\n",
+ ")\n",
"\n",
- "Ag1.dt=100e-3\n",
- "Ag2.dt=100e-3\n",
+ "Ag1.dt = 100e-3\n",
+ "Ag2.dt = 100e-3\n",
"\n",
- "for i in tqdm(range(int(90*60/Ag1.dt))):\n",
+ "for i in tqdm(range(int(90 * 60 / Ag1.dt))):\n",
" Ag1.update()\n",
" Ag2.update()"
]
@@ -861,14 +877,18 @@
],
"source": [
"fig, ax = Ag1.plot_position_heatmap()\n",
- "if save_plots == True: tpl.saveFigure(fig,'highthigmotaxis')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"highthigmotaxis\")\n",
"fig, ax = Ag2.plot_position_heatmap()\n",
- "if save_plots == True: tpl.saveFigure(fig,'lowthigmotaxis')\n",
- "\n",
- "fig, ax = Ag1.plot_trajectory(t_end = 60*10,alpha=0.5)\n",
- "if save_plots == True: tpl.saveFigure(fig,'highthigmotaxis_traj')\n",
- "fig, ax = Ag2.plot_trajectory(t_end = 60*10,alpha=0.5)\n",
- "if save_plots == True: tpl.saveFigure(fig,'lowthigmotaxis_traj')"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"lowthigmotaxis\")\n",
+ "\n",
+ "fig, ax = Ag1.plot_trajectory(t_end=60 * 10, alpha=0.5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"highthigmotaxis_traj\")\n",
+ "fig, ax = Ag2.plot_trajectory(t_end=60 * 10, alpha=0.5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"lowthigmotaxis_traj\")"
]
},
{
@@ -932,56 +952,61 @@
}
],
"source": [
- "#import data\n",
- "from scipy import io \n",
- "mat = io.loadmat(\"../rawdata//8F6BE356-3277-475C-87B1-C7A977632DA7_1/11084-03020501_t2c1.mat\")\n",
- "x = ((mat['x1'] + mat['x2'])/2).reshape(-1)\n",
- "y = ((mat['y1'] + mat['y2'])/2).reshape(-1)\n",
- "t = (mat['t']).reshape(-1)\n",
- "#remove nans \n",
+ "# import data\n",
+ "from scipy import io\n",
+ "\n",
+ "mat = io.loadmat(\n",
+ " \"../rawdata//8F6BE356-3277-475C-87B1-C7A977632DA7_1/11084-03020501_t2c1.mat\"\n",
+ ")\n",
+ "x = ((mat[\"x1\"] + mat[\"x2\"]) / 2).reshape(-1)\n",
+ "y = ((mat[\"y1\"] + mat[\"y2\"]) / 2).reshape(-1)\n",
+ "t = (mat[\"t\"]).reshape(-1)\n",
+ "# remove nans\n",
"y = y[np.logical_not(np.isnan(x))]\n",
"t = t[np.logical_not(np.isnan(x))]\n",
"x = x[np.logical_not(np.isnan(x))]\n",
- "#normalise and put in metres\n",
- "x = (x-min(x))/100\n",
- "y = (y-min(y))/100\n",
- "x = x + 0.5*(1-max(x))\n",
- "y = y + 0.5*(1-max(y))\n",
- "#save_data\n",
- "pos = np.stack((x,y)).T\n",
+ "# normalise and put in metres\n",
+ "x = (x - min(x)) / 100\n",
+ "y = (y - min(y)) / 100\n",
+ "x = x + 0.5 * (1 - max(x))\n",
+ "y = y + 0.5 * (1 - max(y))\n",
+ "# save_data\n",
+ "pos = np.stack((x, y)).T\n",
"# np.savez(\"../ratinabox/data/sargolini.npz\",t=t,pos=pos) #(did this once but dont do it again)\n",
- "#data is 10 mins, we want 10 secs\n",
- "startid = np.argmin(np.abs(t-2)) #start at 2s\n",
- "endid = np.argmin(np.abs(t-2-25)) #end at 27s \n",
+ "# data is 10 mins, we want 10 secs\n",
+ "startid = np.argmin(np.abs(t - 2)) # start at 2s\n",
+ "endid = np.argmin(np.abs(t - 2 - 25)) # end at 27s\n",
"x = x[startid:endid]\n",
"y = y[startid:endid]\n",
"t = t[startid:endid]\n",
- "print(t[0],t[-1])\n",
- "#downsample (so my code will later smooth it) (currently at 50Hz --> 2.5Hz)\n",
- "print((t[1]-t[0])**-1)\n",
+ "print(t[0], t[-1])\n",
+ "# downsample (so my code will later smooth it) (currently at 50Hz --> 2.5Hz)\n",
+ "print((t[1] - t[0]) ** -1)\n",
"x_ds = x[::30]\n",
"y_ds = y[::30]\n",
"t_ds = t[::30]\n",
- "print((t_ds[1]-t_ds[0])**-1)\n",
- "#concatenate\n",
- "pos = np.stack((x,y)).T\n",
- "pos_ds = np.stack((x_ds,y_ds)).T\n",
+ "print((t_ds[1] - t_ds[0]) ** -1)\n",
+ "# concatenate\n",
+ "pos = np.stack((x, y)).T\n",
+ "pos_ds = np.stack((x_ds, y_ds)).T\n",
"\n",
"Env = Environment()\n",
"Ag1 = Agent(Env)\n",
"Ag2 = Agent(Env)\n",
- "Ag1.import_trajectory(times=t,positions=pos)\n",
- "Ag2.import_trajectory(times=t_ds,positions=pos_ds)\n",
+ "Ag1.import_trajectory(times=t, positions=pos)\n",
+ "Ag2.import_trajectory(times=t_ds, positions=pos_ds)\n",
"\n",
- "for i in tqdm(range(int(t_ds[-1]/Ag2.dt))):\n",
+ "for i in tqdm(range(int(t_ds[-1] / Ag2.dt))):\n",
" Ag1.update()\n",
" Ag2.update()\n",
"\n",
"fig, ax = Ag1.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,'imported')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"imported\")\n",
"fig, ax = Ag2.plot_trajectory()\n",
- "ax.scatter(x_ds,y_ds,c='C1',s=15,linewidth=1,zorder=11,alpha=0.7)\n",
- "if save_plots == True: tpl.saveFigure(fig,'upsampled')"
+ "ax.scatter(x_ds, y_ds, c=\"C1\", s=15, linewidth=1, zorder=11, alpha=0.7)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"upsampled\")"
]
},
{
@@ -1010,23 +1035,17 @@
"Ag = Agent(Env)\n",
"\n",
"Ntest = 1000\n",
- "PCs = PlaceCells(Ag,\n",
- " params={'n':Ntest,\n",
- " 'color':'C1'\n",
- " }\n",
- ")\n",
+ "PCs = PlaceCells(Ag, params={\"n\": Ntest, \"color\": \"C1\"})\n",
"\n",
- "GCs = GridCells(Ag,\n",
- " params={'n':Ntest,\n",
- " 'color':'C2'\n",
- " }\n",
- ")\n",
+ "GCs = GridCells(Ag, params={\"n\": Ntest, \"color\": \"C2\"})\n",
"\n",
- "BVCs = BoundaryVectorCells(Ag,\n",
- " params={'n':Ntest,\n",
- " 'color':'C3',\n",
- " }\n",
- ")\n"
+ "BVCs = BoundaryVectorCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": Ntest,\n",
+ " \"color\": \"C3\",\n",
+ " },\n",
+ ")"
]
},
{
@@ -1043,7 +1062,7 @@
}
],
"source": [
- "import time \n",
+ "import time\n",
"\n",
"motion = []\n",
"pc = []\n",
@@ -1051,48 +1070,47 @@
"bvc = []\n",
"matmul = []\n",
"inverse = []\n",
- " \n",
+ "\n",
"for i in tqdm(range(100)):\n",
" t0 = time.time()\n",
" Ag.update()\n",
" t1 = time.time()\n",
- " motion.append(t1-t0)\n",
+ " motion.append(t1 - t0)\n",
"\n",
" t0 = time.time()\n",
" PCs.update()\n",
" t1 = time.time()\n",
- " pc.append(t1-t0)\n",
+ " pc.append(t1 - t0)\n",
"\n",
" t0 = time.time()\n",
" GCs.update()\n",
" t1 = time.time()\n",
- " gc.append(t1-t0)\n",
+ " gc.append(t1 - t0)\n",
"\n",
" t0 = time.time()\n",
" BVCs.update()\n",
" t1 = time.time()\n",
- " bvc.append(t1-t0)\n",
+ " bvc.append(t1 - t0)\n",
"\n",
" a = np.random.normal(size=(Ntest,))\n",
- " b = np.random.normal(size=(Ntest,Ntest))\n",
+ " b = np.random.normal(size=(Ntest, Ntest))\n",
" t0 = time.time()\n",
- " c = np.matmul(b,a)\n",
+ " c = np.matmul(b, a)\n",
" t1 = time.time()\n",
- " matmul.append(t1-t0)\n",
+ " matmul.append(t1 - t0)\n",
"\n",
- " a = np.random.normal(size=(Ntest,Ntest))\n",
+ " a = np.random.normal(size=(Ntest, Ntest))\n",
" t0 = time.time()\n",
" b = np.linalg.inv(a)\n",
" t1 = time.time()\n",
- " inverse.append(t1-t0)\n",
+ " inverse.append(t1 - t0)\n",
"\n",
"motion = np.array(motion)\n",
"pc = np.array(pc)\n",
"gc = np.array(gc)\n",
"bvc = np.array(bvc)\n",
"matmul = np.array(matmul)\n",
- "inverse = np.array(inverse)\n",
- "\n"
+ "inverse = np.array(inverse)"
]
},
{
@@ -1112,17 +1130,32 @@
}
],
"source": [
- "positions = [1,2,3,4,5.2,6.2]\n",
- "heights = [motion.mean(),pc.mean(),gc.mean(),bvc.mean(),matmul.mean(),inverse.mean()]\n",
- "uncertainties = [motion.std(),pc.std(),gc.std(),bvc.std(),matmul.std(),inverse.std()]\n",
- "color = ['C0','C0','C0','C0','C1','C1']\n",
+ "positions = [1, 2, 3, 4, 5.2, 6.2]\n",
+ "heights = [\n",
+ " motion.mean(),\n",
+ " pc.mean(),\n",
+ " gc.mean(),\n",
+ " bvc.mean(),\n",
+ " matmul.mean(),\n",
+ " inverse.mean(),\n",
+ "]\n",
+ "uncertainties = [\n",
+ " motion.std(),\n",
+ " pc.std(),\n",
+ " gc.std(),\n",
+ " bvc.std(),\n",
+ " matmul.std(),\n",
+ " inverse.std(),\n",
+ "]\n",
+ "color = [\"C0\", \"C0\", \"C0\", \"C0\", \"C1\", \"C1\"]\n",
"\n",
"fig, ax = plt.subplots()\n",
- "ax.bar(positions,heights,color=color,yerr=uncertainties,ecolor=color)\n",
- "ax.set_yscale('log')\n",
+ "ax.bar(positions, heights, color=color, yerr=uncertainties, ecolor=color)\n",
+ "ax.set_yscale(\"log\")\n",
"ax.set_ylim(bottom=1e-5)\n",
"ax.set_xticks([])\n",
- "if save_plots == True: tpl.saveFigure(fig,'clocktimes')\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"clocktimes\")"
]
},
{
@@ -1148,20 +1181,22 @@
],
"source": [
"Env = Environment()\n",
- "Env.add_wall(np.array([[0.3,0],[0.3,0.4]]))\n",
+ "Env.add_wall(np.array([[0.3, 0], [0.3, 0.4]]))\n",
"Ag = Agent(Env)\n",
"Ag.dt = 50e-3\n",
- "PCs = PlaceCells(Ag,params={'n':100})\n",
- "GCs = GridCells(Ag,params={'n':3,'color':None},)\n",
- "BVCs = BoundaryVectorCells(Ag,params={'n':3,'color':None})\n",
+ "PCs = PlaceCells(Ag, params={\"n\": 100})\n",
+ "GCs = GridCells(\n",
+ " Ag,\n",
+ " params={\"n\": 3, \"color\": None},\n",
+ ")\n",
+ "BVCs = BoundaryVectorCells(Ag, params={\"n\": 3, \"color\": None})\n",
"\n",
- "Env1D = Environment(params={'dimensionality':'1D'})\n",
- "Ag1D = Agent(Env1D,params={'speed_mean':0.0})\n",
+ "Env1D = Environment(params={\"dimensionality\": \"1D\"})\n",
+ "Ag1D = Agent(Env1D, params={\"speed_mean\": 0.0})\n",
"Ag1D.dt = 50e-3\n",
- "PCs1D = PlaceCells(Ag1D,params={'n':10,\n",
- " 'widths':0.2})\n",
+ "PCs1D = PlaceCells(Ag1D, params={\"n\": 10, \"widths\": 0.2})\n",
"\n",
- "for i in tqdm(range(int(3*60/Ag.dt))):\n",
+ "for i in tqdm(range(int(3 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
@@ -1418,59 +1453,58 @@
"fig8, ax8 = Ag.plot_histogram_of_rotational_velocities()\n",
"fig9, ax9 = GCs.plot_rate_map()\n",
"fig10, ax10 = PCs1D.plot_rate_map()\n",
- "fig11, ax11 = GCs.plot_rate_map(method='history')\n",
- "fig12, ax12 = PCs1D.plot_rate_map(method='history')\n",
- "fig13, ax13 = GCs.plot_rate_map(method='neither',spikes=True)\n",
- "fig14, ax14 = PCs1D.plot_rate_map(method='neither',spikes=True)\n",
+ "fig11, ax11 = GCs.plot_rate_map(method=\"history\")\n",
+ "fig12, ax12 = PCs1D.plot_rate_map(method=\"history\")\n",
+ "fig13, ax13 = GCs.plot_rate_map(method=\"neither\", spikes=True)\n",
+ "fig14, ax14 = PCs1D.plot_rate_map(method=\"neither\", spikes=True)\n",
"fig15, ax15 = GCs.plot_rate_timeseries(t_end=120)\n",
"fig16, ax16 = PCs.plot_place_cell_locations()\n",
"fig17, ax17 = BVCs.plot_BVC_receptive_field()\n",
"fig18, ax18 = BVCs.plot_rate_map(chosen_neurons=\"1\")\n",
- "fig18, ax18 = Ag.plot_trajectory(t_end=120,fig=fig18,ax=ax18[0])\n",
- "fig19, axes19 = plt.subplots(1,5,figsize=(20,4))\n",
- "fig20, ax20 = GCs.plot_rate_timeseries(t_end=120,imshow=True)\n",
- "Ag.plot_trajectory(fig=fig19,ax=axes19[0],t_end=30)\n",
- "BVCs.plot_rate_map(fig=fig19,ax=[axes19[1],axes19[2],axes19[3]],chosen_neurons='3') \n",
- "BVCs.plot_rate_timeseries(fig=fig19,ax=axes19[4],t_end=30) \n",
+ "fig18, ax18 = Ag.plot_trajectory(t_end=120, fig=fig18, ax=ax18[0])\n",
+ "fig19, axes19 = plt.subplots(1, 5, figsize=(20, 4))\n",
+ "fig20, ax20 = GCs.plot_rate_timeseries(t_end=120, imshow=True)\n",
+ "Ag.plot_trajectory(fig=fig19, ax=axes19[0], t_end=30)\n",
+ "BVCs.plot_rate_map(fig=fig19, ax=[axes19[1], axes19[2], axes19[3]], chosen_neurons=\"3\")\n",
+ "BVCs.plot_rate_timeseries(fig=fig19, ax=axes19[4], t_end=30)\n",
"\n",
"\n",
"anim = True\n",
"if anim == True:\n",
- " anim1 = Ag.animate_trajectory(t_end=60,speed_up=5)\n",
+ " anim1 = Ag.animate_trajectory(t_end=60, speed_up=5)\n",
" anim1.save(\"../figures/plotting_examples_save/trajectory_animation.gif\")\n",
- " anim2 = GCs.animate_rate_timeseries(t_end=60,speed_up=5)\n",
+ " anim2 = GCs.animate_rate_timeseries(t_end=60, speed_up=5)\n",
" anim2.save(\"../figures/plotting_examples_save/animate_rate_timeseries.gif\")\n",
"\n",
"\n",
- "if save_plots == True: \n",
+ "if save_plots == True:\n",
" tpl.figureDirectory = \"../figures/plotting_examples_save/\"\n",
- " \n",
- " tpl.saveFigure(fig1,\"plot_env\")\n",
- " tpl.saveFigure(fig2,\"plot_env_1D\")\n",
- " tpl.saveFigure(fig3,\"plot_traj\")\n",
- " tpl.saveFigure(fig4,\"plot_traj_1D\")\n",
- " tpl.saveFigure(fig5,\"plot_heatmap\")\n",
- " tpl.saveFigure(fig6,\"plot_heatmap_1D\")\n",
- " tpl.saveFigure(fig7,\"plot_histogram_speed\")\n",
- " tpl.saveFigure(fig8,\"plot_histogram_rotvel\")\n",
- " tpl.saveFigure(fig9,\"gc_plotrm\")\n",
- " tpl.saveFigure(fig10,\"pc1d_plotrm\")\n",
- " tpl.saveFigure(fig11,\"gc_plotrm_history\")\n",
- " tpl.saveFigure(fig12,\"pc1d_plotrm_history\")\n",
- " tpl.saveFigure(fig13,\"gc_plotrm_spikes\")\n",
- " tpl.saveFigure(fig14,\"pc1d_plotrm_spikes\")\n",
- " tpl.saveFigure(fig15,\"gc_plotrts\")\n",
- " tpl.saveFigure(fig16,\"pc_locations\")\n",
- " tpl.saveFigure(fig17,\"bvc_rfs\")\n",
- " tpl.saveFigure(fig18,\"trajectory_on_ratemap\")\n",
- " tpl.saveFigure(fig19,\"multipanel_riab\")\n",
- " tpl.saveFigure(fig19,\"multipanel_riab\")\n",
- " tpl.saveFigure(fig20,\"gcs_plotrts_imshow\")\n",
+ "\n",
+ " tpl.saveFigure(fig1, \"plot_env\")\n",
+ " tpl.saveFigure(fig2, \"plot_env_1D\")\n",
+ " tpl.saveFigure(fig3, \"plot_traj\")\n",
+ " tpl.saveFigure(fig4, \"plot_traj_1D\")\n",
+ " tpl.saveFigure(fig5, \"plot_heatmap\")\n",
+ " tpl.saveFigure(fig6, \"plot_heatmap_1D\")\n",
+ " tpl.saveFigure(fig7, \"plot_histogram_speed\")\n",
+ " tpl.saveFigure(fig8, \"plot_histogram_rotvel\")\n",
+ " tpl.saveFigure(fig9, \"gc_plotrm\")\n",
+ " tpl.saveFigure(fig10, \"pc1d_plotrm\")\n",
+ " tpl.saveFigure(fig11, \"gc_plotrm_history\")\n",
+ " tpl.saveFigure(fig12, \"pc1d_plotrm_history\")\n",
+ " tpl.saveFigure(fig13, \"gc_plotrm_spikes\")\n",
+ " tpl.saveFigure(fig14, \"pc1d_plotrm_spikes\")\n",
+ " tpl.saveFigure(fig15, \"gc_plotrts\")\n",
+ " tpl.saveFigure(fig16, \"pc_locations\")\n",
+ " tpl.saveFigure(fig17, \"bvc_rfs\")\n",
+ " tpl.saveFigure(fig18, \"trajectory_on_ratemap\")\n",
+ " tpl.saveFigure(fig19, \"multipanel_riab\")\n",
+ " tpl.saveFigure(fig19, \"multipanel_riab\")\n",
+ " tpl.saveFigure(fig20, \"gcs_plotrts_imshow\")\n",
"\n",
" # anim1.save(\"../figures/plotting_examples_save/trajectory_animation.gif\")\n",
"\n",
- " tpl.figureDirectory = \"../figures/\"\n",
- "\n"
+ " tpl.figureDirectory = \"../figures/\""
]
},
{
diff --git a/demos/path_integration_example.ipynb b/demos/path_integration_example.ipynb
index 335b8612..43ac0884 100644
--- a/demos/path_integration_example.ipynb
+++ b/demos/path_integration_example.ipynb
@@ -81,7 +81,7 @@
}
],
"source": [
- "#Import ratinabox\n",
+ "# Import ratinabox\n",
"import ratinabox\n",
"from ratinabox.Environment import Environment\n",
"from ratinabox.Agent import Agent\n",
@@ -90,7 +90,7 @@
"\n",
"import numpy as np\n",
"import matplotlib\n",
- "import matplotlib.pyplot as plt \n",
+ "import matplotlib.pyplot as plt\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
@@ -104,10 +104,11 @@
"metadata": {},
"outputs": [],
"source": [
- "#Leave this as False. \n",
- "#For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save. \n",
- "if False: \n",
+ "# Leave this as False.\n",
+ "# For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save.\n",
+ "if False:\n",
" import tomplotlib.tomplotlib as tpl\n",
+ "\n",
" tpl.figureDirectory = \"../figures/\"\n",
" tpl.setColorscheme(colorscheme=2)\n",
" save_plots = True\n",
@@ -130,11 +131,11 @@
"source": [
"class PyramidalNeurons(Neurons):\n",
" \"\"\"The PyramidalNeuorn class defines a layer of Neurons() whos firing rates are derived from the firing rates in two DendriticCompartments. They are theta modulated, during early theta phase the apical DendriticCompartment (self.apical_compartment) drives the soma, during late theta phases the basal DendriticCompartment (self.basal_compartment) drives the soma.\n",
- " \n",
- " Must be initialised with an Agent and a 'params' dictionary. \n",
"\n",
- " Check that the input layers are all named differently. \n",
- " List of functions: \n",
+ " Must be initialised with an Agent and a 'params' dictionary.\n",
+ "\n",
+ " Check that the input layers are all named differently.\n",
+ " List of functions:\n",
" • get_state()\n",
" • update()\n",
" • update_dendritic_compartments()\n",
@@ -142,154 +143,166 @@
" • plot_loss()\n",
" • plot_rate_map()\n",
" \"\"\"\n",
- " def __init__(self,Agent,params={}):\n",
+ "\n",
+ " def __init__(self, Agent, params={}):\n",
" \"\"\"Initialises a layer of pyramidal neurons\n",
"\n",
" Args:\n",
" Agent (_type_): _description_\n",
" params (dict, optional): _description_. Defaults to {}.\n",
- " \"\"\" \n",
+ " \"\"\"\n",
" default_params = {\n",
- " 'n':10,\n",
- " 'name':'PyramidalNeurons',\n",
- " #theta params \n",
- " 'theta_freq':5,\n",
- " 'theta_frac':0.5, #-->0 all basal input, -->1 all apical input\n",
+ " \"n\": 10,\n",
+ " \"name\": \"PyramidalNeurons\",\n",
+ " # theta params\n",
+ " \"theta_freq\": 5,\n",
+ " \"theta_frac\": 0.5, # -->0 all basal input, -->1 all apical input\n",
" }\n",
" default_params.update(params)\n",
" self.params = default_params\n",
" super().__init__(Agent, self.params)\n",
"\n",
- " self.history['loss']=[]\n",
- " self.error=None\n",
- " \n",
- " self.basal_compartment = DendriticCompartment(self.Agent,\n",
- " params={\n",
- " 'soma':self,\n",
- " 'name':f\"{self.name}_basal\",\n",
- " 'n':self.n,\n",
- " 'color':self.color,\n",
- " })\n",
- " self.apical_compartment = DendriticCompartment(self.Agent,\n",
- " params={\n",
- " 'soma':self,\n",
- " 'name':f\"{self.name}_apical\",\n",
- " 'n':self.n,\n",
- " 'color':self.color\n",
- " })\n",
+ " self.history[\"loss\"] = []\n",
+ " self.error = None\n",
+ "\n",
+ " self.basal_compartment = DendriticCompartment(\n",
+ " self.Agent,\n",
+ " params={\n",
+ " \"soma\": self,\n",
+ " \"name\": f\"{self.name}_basal\",\n",
+ " \"n\": self.n,\n",
+ " \"color\": self.color,\n",
+ " },\n",
+ " )\n",
+ " self.apical_compartment = DendriticCompartment(\n",
+ " self.Agent,\n",
+ " params={\n",
+ " \"soma\": self,\n",
+ " \"name\": f\"{self.name}_apical\",\n",
+ " \"n\": self.n,\n",
+ " \"color\": self.color,\n",
+ " },\n",
+ " )\n",
"\n",
" def update(self):\n",
- " \"\"\"Updates the firing rate of the layer. Saves a loss (lpf difference between basal and apical). Also adds noise.\n",
- " \"\"\" \n",
- " super().update() #this sets and saves self.firingrate \n",
- "\n",
- " dt = self.Agent.dt \n",
- " tau_smooth = 10 \n",
- " #update a smoothed history of the loss\n",
- " fr_b, fr_a = self.basal_compartment.firingrate, self.apical_compartment.firingrate\n",
+ " \"\"\"Updates the firing rate of the layer. Saves a loss (lpf difference between basal and apical). Also adds noise.\"\"\"\n",
+ " super().update() # this sets and saves self.firingrate\n",
+ "\n",
+ " dt = self.Agent.dt\n",
+ " tau_smooth = 10\n",
+ " # update a smoothed history of the loss\n",
+ " fr_b, fr_a = (\n",
+ " self.basal_compartment.firingrate,\n",
+ " self.apical_compartment.firingrate,\n",
+ " )\n",
" error = np.mean(np.abs(fr_b - fr_a))\n",
- " if self.Agent.t < 2/self.theta_freq:\n",
+ " if self.Agent.t < 2 / self.theta_freq:\n",
" self.error = None\n",
" else:\n",
" # loss_smoothing_timescale = dt\n",
- " self.error = (dt / tau_smooth) * error + (\n",
- " 1 - dt / tau_smooth\n",
- " ) * (self.error or error) \n",
+ " self.error = (dt / tau_smooth) * error + (1 - dt / tau_smooth) * (\n",
+ " self.error or error\n",
+ " )\n",
" self.history[\"loss\"].append(self.error)\n",
- " return \n",
+ " return\n",
"\n",
" def update_dendritic_compartments(self):\n",
- " \"\"\"Individually updates teh basal and apical firing rates.\n",
- " \"\"\" \n",
+ " \"\"\"Individually updates teh basal and apical firing rates.\"\"\"\n",
" self.basal_compartment.update()\n",
" self.apical_compartment.update()\n",
" return\n",
"\n",
" def get_state(self, evaluate_at=\"last\", **kwargs):\n",
- " \"\"\"Returns the firing rate of the soma. This depends on the firing rates of the basal and apical compartments and the current theta phase. By default the theta is obtained from self.Agent.t but it can be passed manually as an kwarg to override this. \n",
+ " \"\"\"Returns the firing rate of the soma. This depends on the firing rates of the basal and apical compartments and the current theta phase. By default the theta is obtained from self.Agent.t but it can be passed manually as an kwarg to override this.\n",
"\n",
- " theta (or theta_gating) is a number between [0,1] controlling flow of information into soma from the two compartment.s 0 = entirely basal. 1 = entirely apical. Between equals weighted combination. he function theta_gating() takes a time and returns theta. \n",
+ " theta (or theta_gating) is a number between [0,1] controlling flow of information into soma from the two compartment.s 0 = entirely basal. 1 = entirely apical. Between equals weighted combination. he function theta_gating() takes a time and returns theta.\n",
" Args:\n",
" evaluate_at (str, optional): 'last','agent','all' or None (in which case pos can be passed directly as a kwarg). Defaults to \"last\".\n",
" Returns:\n",
" firingrate\n",
- " \"\"\" \n",
- " #theta can be passed in manually as a kwarg. If it isn't ithe time from the agent will be used to get theta. Theta determines how much basal and how much apical this neurons uses. \n",
- " if 'theta' in kwargs:\n",
- " theta = kwargs['theta']\n",
- " else: \n",
- " theta = theta_gating(t = self.Agent.t,\n",
- " freq=self.theta_freq,\n",
- " frac=self.theta_frac) \n",
+ " \"\"\"\n",
+ " # theta can be passed in manually as a kwarg. If it isn't ithe time from the agent will be used to get theta. Theta determines how much basal and how much apical this neurons uses.\n",
+ " if \"theta\" in kwargs:\n",
+ " theta = kwargs[\"theta\"]\n",
+ " else:\n",
+ " theta = theta_gating(\n",
+ " t=self.Agent.t, freq=self.theta_freq, frac=self.theta_frac\n",
+ " )\n",
" fr_basal, fr_apical = 0, 0\n",
- " #these are special cases, no need to even get their fr's if they aren't used\n",
- " if theta != 0: fr_apical = self.apical_compartment.get_state(evaluate_at, **kwargs)\n",
- " if theta != 1: fr_basal = self.basal_compartment.get_state(evaluate_at, **kwargs)\n",
- " firingrate = (1-theta)*fr_basal + (theta)*fr_apical\n",
+ " # these are special cases, no need to even get their fr's if they aren't used\n",
+ " if theta != 0:\n",
+ " fr_apical = self.apical_compartment.get_state(evaluate_at, **kwargs)\n",
+ " if theta != 1:\n",
+ " fr_basal = self.basal_compartment.get_state(evaluate_at, **kwargs)\n",
+ " firingrate = (1 - theta) * fr_basal + (theta) * fr_apical\n",
" return firingrate\n",
- " \n",
+ "\n",
" def update_weights(self):\n",
- " \"\"\"Trains the weights, this function actually defined in the dendrite class.\n",
- " \"\"\" \n",
- " if self.Agent.t > 2/self.theta_freq:\n",
+ " \"\"\"Trains the weights, this function actually defined in the dendrite class.\"\"\"\n",
+ " if self.Agent.t > 2 / self.theta_freq:\n",
" self.basal_compartment.update_weights()\n",
" self.apical_compartment.update_weights()\n",
- " return \n",
+ " return\n",
"\n",
" def plot_loss(self, fig=None, ax=None):\n",
- " \"\"\"Plots the loss against time to see if learning working\n",
- " \"\"\" \n",
- " if fig is None and ax is None: \n",
+ " \"\"\"Plots the loss against time to see if learning working\"\"\"\n",
+ " if fig is None and ax is None:\n",
" fig, ax = plt.subplots(figsize=(1.5, 1.5))\n",
- " ylim=0\n",
- " else: ylim = ax.get_ylim()[1]\n",
+ " ylim = 0\n",
+ " else:\n",
+ " ylim = ax.get_ylim()[1]\n",
" t = np.array(self.history[\"t\"]) / 60\n",
" loss = self.history[\"loss\"]\n",
" ax.plot(t, loss, color=self.color, label=self.name)\n",
- " ax.set_ylim(bottom=0, top=max(ylim, np.nanmax(np.array(loss, dtype=np.float64))))\n",
+ " ax.set_ylim(\n",
+ " bottom=0, top=max(ylim, np.nanmax(np.array(loss, dtype=np.float64)))\n",
+ " )\n",
" ax.set_xlim(left=0)\n",
" ax.legend(frameon=False)\n",
" ax.set_xlabel(\"Training time / min\")\n",
" ax.set_ylabel(\"Loss\")\n",
" return fig, ax\n",
- " \n",
- " def plot_rate_map(self,route='basal',**kwargs):\n",
- " \"\"\"This is a wrapper function for the general Neuron class function plot_rate_map. It takes the same arguments as Neurons.plot_rate_map() but, in addition, route can be set to basal or apical in which case theta is set correspondingly and teh soma with take its input from downstream or upstream sources entirely. \n",
"\n",
- " The arguments for the standard plottiong function plot_rate_map() can be passed as usual as kwargs. \n",
+ " def plot_rate_map(self, route=\"basal\", **kwargs):\n",
+ " \"\"\"This is a wrapper function for the general Neuron class function plot_rate_map. It takes the same arguments as Neurons.plot_rate_map() but, in addition, route can be set to basal or apical in which case theta is set correspondingly and teh soma with take its input from downstream or upstream sources entirely.\n",
+ "\n",
+ " The arguments for the standard plottiong function plot_rate_map() can be passed as usual as kwargs.\n",
"\n",
" Args:\n",
" route (str, optional): _description_. Defaults to 'basal'.\n",
- " \"\"\" \n",
- " if route=='basal':theta=0\n",
- " elif route=='apical':theta=1\n",
- " fig, ax = super().plot_rate_map(**kwargs,theta=theta)\n",
+ " \"\"\"\n",
+ " if route == \"basal\":\n",
+ " theta = 0\n",
+ " elif route == \"apical\":\n",
+ " theta = 1\n",
+ " fig, ax = super().plot_rate_map(**kwargs, theta=theta)\n",
" return fig, ax\n",
- " \n",
+ "\n",
"\n",
"class DendriticCompartment(Neurons):\n",
- " \"\"\"The DendriticCompartment class defines a layer of Neurons() whos firing rates are an activated linear combination of input layers. This class is a subclass of Neurons() and inherits it properties/plotting functions. \n",
+ " \"\"\"The DendriticCompartment class defines a layer of Neurons() whos firing rates are an activated linear combination of input layers. This class is a subclass of Neurons() and inherits it properties/plotting functions.\n",
"\n",
- " Must be initialised with an Agent and a 'params' dictionary. \n",
- " Input params dictionary must contain a list of input_layers which feed into these Neurons. This list looks like [Neurons1, Neurons2,...] where each is a Neurons() class. \n",
+ " Must be initialised with an Agent and a 'params' dictionary.\n",
+ " Input params dictionary must contain a list of input_layers which feed into these Neurons. This list looks like [Neurons1, Neurons2,...] where each is a Neurons() class.\n",
"\n",
- " Currently supported activations include 'sigmoid' (paramterised by max_fr, min_fr, mid_x, width), 'relu' (gain, threshold) and 'linear' specified with the \"activation_params\" dictionary in the inout params dictionary. See also activate() for full details. \n",
+ " Currently supported activations include 'sigmoid' (paramterised by max_fr, min_fr, mid_x, width), 'relu' (gain, threshold) and 'linear' specified with the \"activation_params\" dictionary in the inout params dictionary. See also activate() for full details.\n",
"\n",
- " Check that the input layers are all named differently. \n",
- " List of functions: \n",
+ " Check that the input layers are all named differently.\n",
+ " List of functions:\n",
" • get_state()\n",
" • add_input()\n",
" \"\"\"\n",
"\n",
" def __init__(self, Agent, params={}):\n",
" default_params = {\n",
- " \"soma\":None,\n",
+ " \"soma\": None,\n",
" \"activation_params\": {\n",
" \"activation\": \"sigmoid\",\n",
" \"max_fr\": 1,\n",
" \"min_fr\": 0,\n",
" \"mid_x\": 1,\n",
- " \"width_x\": 2,},\n",
+ " \"width_x\": 2,\n",
+ " },\n",
" }\n",
" self.Agent = Agent\n",
" default_params.update(params)\n",
@@ -300,28 +313,22 @@
" self.firingrate_prime_temp = None\n",
" self.inputs = {}\n",
"\n",
- " def add_input(self, \n",
- " input_layer,\n",
- " eta = 0.001,\n",
- " w_init = 0.1,\n",
- " L1 = 0.0001,\n",
- " L2 = 0.001,\n",
- " tau_PI = 100e-3):\n",
- " \"\"\"Adds an input layer to the class. Each input layer is stored in a dictionary of self.inputs. Each has an associated matrix of weights which are initialised randomly. \n",
+ " def add_input(\n",
+ " self, input_layer, eta=0.001, w_init=0.1, L1=0.0001, L2=0.001, tau_PI=100e-3\n",
+ " ):\n",
+ " \"\"\"Adds an input layer to the class. Each input layer is stored in a dictionary of self.inputs. Each has an associated matrix of weights which are initialised randomly.\n",
"\n",
" Args:\n",
" input_layer (_type_): the layer which feeds into this compartment\n",
- " eta: learning rate of the weights \n",
- " w_init: initialisation scale of the weights \n",
+ " eta: learning rate of the weights\n",
+ " w_init: initialisation scale of the weights\n",
" L1: how much L1 regularisation\n",
" L2: how much L2 regularisation\n",
" tau_PI: smoothing timescale of plasticity induction variable\n",
" \"\"\"\n",
" name = input_layer.name\n",
" n_in = input_layer.n\n",
- " w = np.random.normal(\n",
- " loc=0, scale=w_init / np.sqrt(n_in), size=(self.n, n_in)\n",
- " )\n",
+ " w = np.random.normal(loc=0, scale=w_init / np.sqrt(n_in), size=(self.n, n_in))\n",
" I = np.zeros(n_in)\n",
" PI = np.zeros(n_in)\n",
" if name in self.inputs.keys():\n",
@@ -332,29 +339,31 @@
" self.inputs[name][\"layer\"] = input_layer\n",
" self.inputs[name][\"w\"] = w\n",
" self.inputs[name][\"w_init\"] = w.copy()\n",
- " self.inputs[name][\"I\"] = I #input current\n",
- " self.inputs[name][\"I_temp\"] = None #input current\n",
- " self.inputs[name][\"PI\"] = PI #plasticity induction variable\n",
- " self.inputs[name][\"eta\"] = eta \n",
- " self.inputs[name][\"L2\"] = L2 \n",
- " self.inputs[name][\"L1\"] = L1 \n",
+ " self.inputs[name][\"I\"] = I # input current\n",
+ " self.inputs[name][\"I_temp\"] = None # input current\n",
+ " self.inputs[name][\"PI\"] = PI # plasticity induction variable\n",
+ " self.inputs[name][\"eta\"] = eta\n",
+ " self.inputs[name][\"L2\"] = L2\n",
+ " self.inputs[name][\"L1\"] = L1\n",
" self.inputs[name][\"tau_PI\"] = tau_PI\n",
"\n",
" def get_state(self, evaluate_at=\"last\", **kwargs):\n",
- " \"\"\"Returns the \"firing rate\" of the dendritic compartment. By default this layer uses the last saved firingrate from its input layers. Alternatively evaluate_at and kwargs can be set to be anything else which will just be passed to the input layer for evaluation. \n",
+ " \"\"\"Returns the \"firing rate\" of the dendritic compartment. By default this layer uses the last saved firingrate from its input layers. Alternatively evaluate_at and kwargs can be set to be anything else which will just be passed to the input layer for evaluation.\n",
" Once the firing rate of the inout layers is established these are multiplied by the weight matrices and then activated to obtain the firing rate of this FeedForwardLayer.\n",
"\n",
" Args:\n",
" evaluate_at (str, optional). Defaults to 'last'.\n",
" Returns:\n",
- " firingrate: array of firing rates \n",
+ " firingrate: array of firing rates\n",
" \"\"\"\n",
- " if evaluate_at == 'last':\n",
+ " if evaluate_at == \"last\":\n",
" V = np.zeros(self.n)\n",
- " elif evaluate_at == 'all': \n",
- " V = np.zeros((self.n,self.Agent.Environment.flattened_discrete_coords.shape[0]))\n",
+ " elif evaluate_at == \"all\":\n",
+ " V = np.zeros(\n",
+ " (self.n, self.Agent.Environment.flattened_discrete_coords.shape[0])\n",
+ " )\n",
" else:\n",
- " V = np.zeros((self.n,kwargs['pos'].shape[0]))\n",
+ " V = np.zeros((self.n, kwargs[\"pos\"].shape[0]))\n",
"\n",
" for inputlayer in self.inputs.values():\n",
" w = inputlayer[\"w\"]\n",
@@ -362,10 +371,12 @@
" I = inputlayer[\"layer\"].firingrate\n",
" else: # kick can down the road let input layer decide how to evaluate the firingrate\n",
" I = inputlayer[\"layer\"].get_state(evaluate_at, **kwargs)\n",
- " inputlayer['I_temp'] = I\n",
+ " inputlayer[\"I_temp\"] = I\n",
" V += np.matmul(w, I)\n",
" firingrate = utils.activate(V, other_args=self.activation_params)\n",
- " firingrate_prime = utils.activate(V, other_args=self.activation_params, deriv=True) \n",
+ " firingrate_prime = utils.activate(\n",
+ " V, other_args=self.activation_params, deriv=True\n",
+ " )\n",
"\n",
" self.firingrate_temp = firingrate\n",
" self.firingrate_prime_temp = firingrate_prime\n",
@@ -373,49 +384,47 @@
" return firingrate\n",
"\n",
" def update(self):\n",
- " \"\"\"Updates firingrate of this compartment and saves it to file\n",
- " \"\"\" \n",
+ " \"\"\"Updates firingrate of this compartment and saves it to file\"\"\"\n",
" self.get_state()\n",
" self.firingrate = self.firingrate_temp.reshape(-1)\n",
" self.firingrate_deriv = self.firingrate_prime_temp.reshape(-1)\n",
" for inputlayer in self.inputs.values():\n",
- " inputlayer['I'] = inputlayer['I_temp'].reshape(-1)\n",
+ " inputlayer[\"I\"] = inputlayer[\"I_temp\"].reshape(-1)\n",
" self.save_to_history()\n",
" return\n",
- " \n",
+ "\n",
" def update_weights(self):\n",
- " \"\"\"Implements the weight update: dendritic prediction of somatic activity. \n",
- " \"\"\" \n",
- " target = self.soma.firingrate \n",
+ " \"\"\"Implements the weight update: dendritic prediction of somatic activity.\"\"\"\n",
+ " target = self.soma.firingrate\n",
" delta = (target - self.firingrate) * (self.firingrate_deriv)\n",
" dt = self.Agent.dt\n",
" for inputlayer in self.inputs.values():\n",
- " eta = inputlayer['eta']\n",
- " if eta != 0: \n",
- " tau_PI = inputlayer['tau_PI']\n",
+ " eta = inputlayer[\"eta\"]\n",
+ " if eta != 0:\n",
+ " tau_PI = inputlayer[\"tau_PI\"]\n",
" assert (dt / tau_PI) < 0.2\n",
- " I = inputlayer['I']\n",
- " w = inputlayer['w']\n",
- " #first updates plasticity induction variable (smoothed delta error outer product with the input current for this input layer)\n",
- " PI_old = inputlayer['PI']\n",
+ " I = inputlayer[\"I\"]\n",
+ " w = inputlayer[\"w\"]\n",
+ " # first updates plasticity induction variable (smoothed delta error outer product with the input current for this input layer)\n",
+ " PI_old = inputlayer[\"PI\"]\n",
" PI_update = np.outer(delta, I)\n",
- " PI_new = (dt / tau_PI) * PI_update + (\n",
- " 1 - dt / tau_PI) * PI_old\n",
- " inputlayer['PI'] = PI_new\n",
- " #updates weights\n",
- " dw = eta * (PI_new - inputlayer['L2']*w - inputlayer['L1']*np.sign(w)) \n",
- " inputlayer['w'] = w + dw\n",
+ " PI_new = (dt / tau_PI) * PI_update + (1 - dt / tau_PI) * PI_old\n",
+ " inputlayer[\"PI\"] = PI_new\n",
+ " # updates weights\n",
+ " dw = eta * (\n",
+ " PI_new - inputlayer[\"L2\"] * w - inputlayer[\"L1\"] * np.sign(w)\n",
+ " )\n",
+ " inputlayer[\"w\"] = w + dw\n",
" return\n",
"\n",
- "def theta_gating(t,\n",
- " freq=10,\n",
- " frac=0.5):\n",
- " T = 1/freq\n",
- " phase = ((t/T) % 1) % 1\n",
- " if phase < frac:\n",
- " return 1\n",
- " elif phase >= frac:\n",
- " return 0"
+ "\n",
+ "def theta_gating(t, freq=10, frac=0.5):\n",
+ " T = 1 / freq\n",
+ " phase = ((t / T) % 1) % 1\n",
+ " if phase < frac:\n",
+ " return 1\n",
+ " elif phase >= frac:\n",
+ " return 0"
]
},
{
@@ -434,69 +443,77 @@
"metadata": {},
"outputs": [],
"source": [
- "#Initialise the 1D environment \n",
- "Env = Environment(params={'dimensionality':'1D',\n",
- " 'boundary_conditions':'periodic'})\n",
+ "# Initialise the 1D environment\n",
+ "Env = Environment(params={\"dimensionality\": \"1D\", \"boundary_conditions\": \"periodic\"})\n",
"\n",
- "#Put agent (who will move randomly under the ratinabox Ornstein Uhlenbeck random motion policy) inside the environement\n",
+ "# Put agent (who will move randomly under the ratinabox Ornstein Uhlenbeck random motion policy) inside the environement\n",
"Ag = Agent(Env)\n",
"Ag.speed_mean = 0\n",
- "Ag.speed_std=0.3\n",
+ "Ag.speed_std = 0.3\n",
"\n",
"n_cells = 50\n",
- "#Place cells provide the target signal \n",
- "PlaceCells_ = PlaceCells(Ag, params={'n':n_cells,\n",
- " 'widths':0.1,\n",
- " 'name':'PlaceCells'})\n",
- "\n",
- "#The key neuron class: Ring attractor at the centre of the network made from our bespoke, custom-define PyramidalNeurons class. \n",
- "RingAttractor = PyramidalNeurons(Ag,params={'n':n_cells,\n",
- " 'name':'RingAttractor'})\n",
- "\n",
- "#Velocity cells encode agent velocity\n",
- "VelocityCells_ = VelocityCells(Ag,params={'name':'VelocityCells'})\n",
- "\n",
- "#Conjuctive cells \n",
- "ConjunctiveCells_left = FeedForwardLayer(Ag,\n",
- " params={'n':n_cells,\n",
- " 'name':'ConjunctiveCells_left',\n",
- " })\n",
- "\n",
- "ConjunctiveCells_right = FeedForwardLayer(Ag,\n",
- " params={'n':n_cells,\n",
- " 'name':'ConjunctiveCells_right',\n",
- " })\n",
- "\n",
- "#Set inputs into ring attractor compartments\n",
- "#Make their activation functions linear \n",
- "#Set the fixed weights from place celles to Ring attractor to be fixed \n",
+ "# Place cells provide the target signal\n",
+ "PlaceCells_ = PlaceCells(Ag, params={\"n\": n_cells, \"widths\": 0.1, \"name\": \"PlaceCells\"})\n",
+ "\n",
+ "# The key neuron class: Ring attractor at the centre of the network made from our bespoke, custom-define PyramidalNeurons class.\n",
+ "RingAttractor = PyramidalNeurons(Ag, params={\"n\": n_cells, \"name\": \"RingAttractor\"})\n",
+ "\n",
+ "# Velocity cells encode agent velocity\n",
+ "VelocityCells_ = VelocityCells(Ag, params={\"name\": \"VelocityCells\"})\n",
+ "\n",
+ "# Conjuctive cells\n",
+ "ConjunctiveCells_left = FeedForwardLayer(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": n_cells,\n",
+ " \"name\": \"ConjunctiveCells_left\",\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "ConjunctiveCells_right = FeedForwardLayer(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": n_cells,\n",
+ " \"name\": \"ConjunctiveCells_right\",\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# Set inputs into ring attractor compartments\n",
+ "# Make their activation functions linear\n",
+ "# Set the fixed weights from place celles to Ring attractor to be fixed\n",
"RingAttractor.apical_compartment.add_input(RingAttractor)\n",
"RingAttractor.apical_compartment.add_input(ConjunctiveCells_left)\n",
"RingAttractor.apical_compartment.add_input(ConjunctiveCells_right)\n",
"RingAttractor.apical_compartment.activation_params = {\"activation\": \"linear\"}\n",
"\n",
- "RingAttractor.basal_compartment.add_input(PlaceCells_,eta=0) #eta=0, these are fixed \n",
- "RingAttractor.basal_compartment.inputs['PlaceCells']['w'] = np.identity(n_cells)\n",
+ "RingAttractor.basal_compartment.add_input(PlaceCells_, eta=0) # eta=0, these are fixed\n",
+ "RingAttractor.basal_compartment.inputs[\"PlaceCells\"][\"w\"] = np.identity(n_cells)\n",
"RingAttractor.basal_compartment.activation_params = {\"activation\": \"linear\"}\n",
"\n",
- "#Set inputs into the conjuctive cells\n",
- "#Set the (fixed) weights into the conjunctive cells to be their correct values (identity or just 1's)\n",
+ "# Set inputs into the conjuctive cells\n",
+ "# Set the (fixed) weights into the conjunctive cells to be their correct values (identity or just 1's)\n",
"ConjunctiveCells_left.add_input(VelocityCells_)\n",
"ConjunctiveCells_left.add_input(RingAttractor)\n",
"ConjunctiveCells_right.add_input(VelocityCells_)\n",
"ConjunctiveCells_right.add_input(RingAttractor)\n",
- "ConjunctiveCells_left.inputs['VelocityCells']['w'] = np.ones((n_cells,2)) * np.array([1,-1]) #thus left velocity excites these cells and right velocity shuts them off\n",
- "ConjunctiveCells_right.inputs['VelocityCells']['w'] = np.ones((n_cells,2)) * np.array([-1,1])#thus right velocity excites these cells and rigleftht velocity shuts them off\n",
- "ConjunctiveCells_left.inputs['RingAttractor']['w'] = np.identity(n_cells)\n",
- "ConjunctiveCells_right.inputs['RingAttractor']['w'] = np.identity(n_cells)\n",
- "ConjunctiveCells_left.activation_params={\n",
- " \"activation\": \"relu\",\n",
- " \"threshold\": 1,\n",
- " \"width_x\": 2}\n",
- "ConjunctiveCells_right.activation_params={\n",
- " \"activation\": \"relu\",\n",
- " \"threshold\": 1,\n",
- " \"width_x\": 2}"
+ "ConjunctiveCells_left.inputs[\"VelocityCells\"][\"w\"] = np.ones((n_cells, 2)) * np.array(\n",
+ " [1, -1]\n",
+ ") # thus left velocity excites these cells and right velocity shuts them off\n",
+ "ConjunctiveCells_right.inputs[\"VelocityCells\"][\"w\"] = np.ones((n_cells, 2)) * np.array(\n",
+ " [-1, 1]\n",
+ ") # thus right velocity excites these cells and rigleftht velocity shuts them off\n",
+ "ConjunctiveCells_left.inputs[\"RingAttractor\"][\"w\"] = np.identity(n_cells)\n",
+ "ConjunctiveCells_right.inputs[\"RingAttractor\"][\"w\"] = np.identity(n_cells)\n",
+ "ConjunctiveCells_left.activation_params = {\n",
+ " \"activation\": \"relu\",\n",
+ " \"threshold\": 1,\n",
+ " \"width_x\": 2,\n",
+ "}\n",
+ "ConjunctiveCells_right.activation_params = {\n",
+ " \"activation\": \"relu\",\n",
+ " \"threshold\": 1,\n",
+ " \"width_x\": 2,\n",
+ "}"
]
},
{
@@ -522,18 +539,18 @@
}
],
"source": [
- "for i in tqdm(range(int(10*60/Ag.dt))):\n",
- " #update agent\n",
+ "for i in tqdm(range(int(10 * 60 / Ag.dt))):\n",
+ " # update agent\n",
" Ag.update()\n",
- " #update firing rates of all the cell layers\n",
+ " # update firing rates of all the cell layers\n",
" PlaceCells_.update()\n",
" VelocityCells_.update()\n",
" ConjunctiveCells_left.update()\n",
" ConjunctiveCells_right.update()\n",
" RingAttractor.update_dendritic_compartments()\n",
" RingAttractor.update()\n",
- " #finally, update the weights\n",
- " RingAttractor.update_weights()\n"
+ " # finally, update the weights\n",
+ " RingAttractor.update_weights()"
]
},
{
@@ -566,8 +583,8 @@
"source": [
"fig, ax = RingAttractor.plot_loss()\n",
"\n",
- "if save_plots == True: \n",
- " tpl.saveFigure(fig,\"PI_loss\")"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"PI_loss\")"
]
},
{
@@ -601,18 +618,18 @@
}
],
"source": [
- "#pull out the weight as they were at initialisation \n",
- "w_ccl_init = RingAttractor.apical_compartment.inputs['ConjunctiveCells_left']['w_init']\n",
- "w_ccr_init = RingAttractor.apical_compartment.inputs['ConjunctiveCells_right']['w_init']\n",
- "w_rec_init = RingAttractor.apical_compartment.inputs['RingAttractor']['w_init']\n",
- "\n",
- "#pull out the weights after training\n",
- "w_ccl = RingAttractor.apical_compartment.inputs['ConjunctiveCells_left']['w']\n",
- "w_ccr = RingAttractor.apical_compartment.inputs['ConjunctiveCells_right']['w']\n",
- "w_rec = RingAttractor.apical_compartment.inputs['RingAttractor']['w']\n",
- "\n",
- "#plot them \n",
- "fig, ax = plt.subplots(1,3,figsize=(12,4))\n",
+ "# pull out the weight as they were at initialisation\n",
+ "w_ccl_init = RingAttractor.apical_compartment.inputs[\"ConjunctiveCells_left\"][\"w_init\"]\n",
+ "w_ccr_init = RingAttractor.apical_compartment.inputs[\"ConjunctiveCells_right\"][\"w_init\"]\n",
+ "w_rec_init = RingAttractor.apical_compartment.inputs[\"RingAttractor\"][\"w_init\"]\n",
+ "\n",
+ "# pull out the weights after training\n",
+ "w_ccl = RingAttractor.apical_compartment.inputs[\"ConjunctiveCells_left\"][\"w\"]\n",
+ "w_ccr = RingAttractor.apical_compartment.inputs[\"ConjunctiveCells_right\"][\"w\"]\n",
+ "w_rec = RingAttractor.apical_compartment.inputs[\"RingAttractor\"][\"w\"]\n",
+ "\n",
+ "# plot them\n",
+ "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
"ax[0].imshow(w_rec_init)\n",
"ax[1].imshow(w_ccl_init)\n",
"ax[2].imshow(w_ccr_init)\n",
@@ -621,7 +638,7 @@
"ax[1].set_title(\"Left conjunctive velocity cells \\nto ring attractor\")\n",
"ax[2].set_title(\"Right conjunctive velocity cells \\nto ring attractor\")\n",
"\n",
- "fig1, ax1 = plt.subplots(1,3,figsize=(12,4))\n",
+ "fig1, ax1 = plt.subplots(1, 3, figsize=(12, 4))\n",
"ax1[0].imshow(w_rec)\n",
"ax1[1].imshow(w_ccl)\n",
"ax1[2].imshow(w_ccr)\n",
@@ -630,9 +647,9 @@
"ax1[1].set_title(\"Left conjunctive velocity cells \\nto ring attractor\")\n",
"ax1[2].set_title(\"Right conjunctive velocity cells \\nto ring attractor\")\n",
"\n",
- "if save_plots == True: \n",
- " tpl.saveFigure(fig,\"PIweights_beforelearning\") \n",
- " tpl.saveFigure(fig1,\"PIweights_afterlearning\")"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"PIweights_beforelearning\")\n",
+ " tpl.saveFigure(fig1, \"PIweights_afterlearning\")"
]
},
{
diff --git a/demos/readme_figures.ipynb b/demos/readme_figures.ipynb
index ce88f1b2..36dcfc36 100644
--- a/demos/readme_figures.ipynb
+++ b/demos/readme_figures.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -27,19 +27,21 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "#Leave this as False. \n",
- "#For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save. \n",
- "if True: \n",
+ "# Leave this as False.\n",
+ "# For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save.\n",
+ "if True:\n",
" import tomplotlib.tomplotlib as tpl\n",
+ "\n",
" tpl.figureDirectory = \"../figures/\"\n",
" tpl.setColorscheme(colorscheme=2)\n",
" save_plots = True\n",
" from matplotlib import rcParams, rc\n",
- " rcParams['figure.dpi']= 300\n",
+ "\n",
+ " rcParams[\"figure.dpi\"] = 300\n",
"else:\n",
" save_plots = False"
]
@@ -58,77 +60,84 @@
"outputs": [
{
"data": {
- "image/png": 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"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
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0N+0MZXKUFZwidhq10jTXguYiMXvpsS+2pKubd7q24naL/Kc/Ug+fYVYL6uMKN1cEK0hSIBKoKaK7iFlNqMsNvHhFWD1urXgH0Y9IrUBLkhYgKeWEQsK7ukDpzdiViaIXRCMJXqFUZFQRpSJS5gavHVLp8whBEguJJy9Jk0Q4+XqNeMg1Yt2n3Ek95CYh1UdU55C9QwwjqetJw4ePikVVIdpd17QltLcRsW8ErhGERuBrSmSciFWJim0Ek37aDuARkcaR+PIVKiXqeIScavSgcVuJa3P6PNqcDYEcoUqXN0WmA7uJ2KXHXvbIV0vCi4vH6UwOAcYJMUWkS6gx9weEUbEdLTdTw6Wb8b0+ppYTgTUz4ZEkxmS4iQ0X4YgX7piracZqqnCTRoy7Hg+QLiFceKd0upzPiPMaN9NMszx18HP4pNPUF/6ITcgR8Y1vWbqG9VSznSzDZPBOwSSRk0DtGmpc7rRUb3ZZunukMQ9470jeo27WVNct1bHEzXNH5zf6mJgEV1PLme3QMjBFzcthzjebYy6ujhDXhupGUN1E7M1Iul4+KOsRVivUd5pGCUhzpNPoXuCWBt/eRgUqQNUJzAaq60R74Wm+3SK+v3gUUozDQPzdH1CXC+rjI6q2JlUapMwpu8kjuoG02hCur9/5/X4KaRgQWiGUQqlMwpmMdwX6BEkUIhalxFAWbiOJOhJ1yulNmRAylYA6Py/tutcLieNFLjm43X2cu6bVmIlYDYWI+4TpIroPqN4jB5fPSTeQ+v7DjzJJVaLiMr5Ul2atKjdr+boQcUOOiuuUI+I6kUxEVBFpf9nNROw60vcBNTnq/gi9bXIUP8tNXcHkjRjkkp9ytxtDvXbomw5xvSK8uny0YCd5TxpGVDeh+ypnzjqB6BV9b7msW74xJ2iZyx5r3bBQfRltMlz6eRl/Peain7PqamKnsb1AlQ2d6jyiL2WNB0LUNaE2uQegEfgmwc+cgulTJuNXbsGmzCEuXcPN2LCaKvrJME2aVMYe1L65g/3oQx57yF2zyTnSdEhRf2wIF6+wx3PamSJUmqQUY2z5w6S5nLe01qFkxAXFdrD06xpxZagvJPVForlw6Jerd6pThcsrVEy07im6mzOuDNNcEmqI5YqVPpOC3eS5dXuxhe8vCJePGMaklIl9R+5SIaQgxfSLRnyp60EppJQg88jIToZeJJlHCGNJH+6JGILPXap5/CmBgqRSLs/JO99NItfuIvsUpHSiZLXEnU7pXW04L6Cmi+guE7HqJkRXIuKue6dF9bEgmzqPylQ2zxHvuqZLjTg/biPiUBcirgKyCmgbsNbzS6sGp3HEf/c9atthVgv0oiXWllhrkpWvkbFwuWFOdhNi05OWK/xm++jXZxpHxHbAbBvMVmI6gd8KXGW51i2qjNL1wXBlZrRqzGQcDcvQ8HJc8F13xPfrBf2mQm4UeiMw24TZRtTWkbb9u20gKku0qoymQbT8LBkP6Rfq0HsL3JuMX0xH9MGwdhXrqWY1VWxHy9BbQp+7LeVY0lpTvnn1mPJNPIZMxlMm4k+qXizu1B9+xdF8Gkf4/oK61kQzg6SQk2La1mzmlrWJeW0KAjFKzEZgb0oT0/ee+rsN8fuX7xwVhetr5DhSrc8wp3PCvCLUOqdpAekicgio9Yi8KeIx3f2btu6FGEgfYBIvdh1SKYQQCCkyGUNOS8eEiBIRd81zmYijz9HsriN41+yV1J2u9x1KU46IZfrBg/QCUcpL+w31mHIUM6aSpg6orhDxdkBsOmLXE99ljOaRILRG1BXUFanOgh6+VjkirgoJ74i4yUQcmghVRNUBYz21ddTWAc0H+Qy7jaCsa+SsRVkL1uTsjBC5CdaHPD7WdYSue2/nPU0TYr1Fr1rsTOf0fiVIWjGqmoskmIJiNdUc2YFaOZRIjEGz8RU3Q8N117Bd17A02KXErqFaJezSoZZb4gP7PIB8PpQiaZmbGzV5A/oz+KRrxi+HOZ239N6wnfIM4jgY/KARg0L2EjWUlNbAbaflGJFjQIwOhpHU9x/8hv1RCIHQBmE0KJVb7ZW6JeOUcgd4iqQQwbnSiPbrqX+HyyuU1rSAnFp0r5lWAt8qolG5iSnkRdpsEtUyUr9yVN9v4ZvviX3/KMcRu47YdYiXFbptMXVFbiVO4D1pGIjbHv+h06G/AOJmk8m4XIcqJURKiKRLRCuRHryH6CC4WyKOekfE5FEomXITa8lwixIZ77roRbjt8djV817bWA93UtMlIhabjrjNjw9dJ4ZSK7aWZA2pyspa0cocNe3GlyqIVcpRVJ0yETeeqsokPLOO1kx8KDLeIQ4DDG90kYm3nN9/JCTvSesNcjnDtpZQ536EJAQkzRQEl5Ni01TU1mF1QIiED4rRK4be4jqDXOfxK3sD1U2kuvHoZY7o45uf8SEQeUQqvbnh/BNwu7rXR4D7p6n7OYPPYw9DadgKgypELPYErPtSWxpKp2UfSnPHnZGHjwhC6/0NjDV5+FyrPHur5A8iYxEiYrcznVwmh3EkTdOvgpjDi5co72m7c8xqhjsy+FYSTF7YZSjzpduAvR5RVxvSdy/fy+48jSPhI7tefnGkRNhsUWTRnJyWLg+fkEEhnUR6QSip5dfIWJVxqNL89Zrb6k6UJ3AnMk6ZjN1tiSlHxfleVkMh4n6Erv+oiBip8n1cWVKlibYQ8V11rZLGDBXEOpJsjoit9bSVY2Yn5nbkyAzA6Yf+RD/EBwhkYt+jLm8wlc1ELE0ZdRJIp/CdpG8NvYkInVNIKQpwEjHKPH61EdhV7vOoL0NuNrtc4a/ese9iJyC1zxblzeXPYfilBsnfAvcm46uuwXuVSdjJ1+YPdQ+qz232uk+YfkfEEdU7RD9BP3xUUbEwFtnUUFWIKhNxMppoNKgsNbgfehe7KCJl4ZIY92NaYnK5Fr4b6RjuKTrxESJcXiFWG8zVCeZoTmosyZQsQUjIySO2PWm5JixXH8dC/GtGDIT1GhlCIeSIjBG8RXiNrBXSK5S5lXMMRpBUImr20XHa1Zx3zdjsFrA7ZBxuGy9zr0dCDyHPEfceOWQiTttSI+6Hj+b7l9bs7+NkMhFH8zoJ7+eIbSKZhKhyarqpJmZ24qgaODJDIeMDgL0wjnxlsEIgUosIeRZf9wI3E4Ra5nO66y2Mu/4Oge6LfvkqUV+X8auXS8Kry8fhAx9y+Wo3uePeZrTpE46Mu21N9II0KXAia9SOAj2IPIPYgelSVkLqc+SkO1fSWQOp6z+KqFhojWgaRNvcNnlYQ6pUvoG1LPWHTMI7paOsJnZHwMRHpI8IFxBjyDOhU53JuOtzV+knHCknNxFevISXFwh1K1eXQiTsav4fycbqzwIp5dGrEMB7hM8zoMJVSKeRoyJWCmnzGFQsZgd5PnkXGRethYKdHriICVlEW3ZkvNOa3vd79A4xFiLue9K2NGt9LNeAEGAMwpRNtdV5vMvm8xDM64ScbAIb0SZgjacxfh8Rn5ieI/04JZdfC5L3xKsbJGBjLGUsk41M2jyrHU251kQZv/Ls+wzMNuXxq6s8fhVfXb61mczPHts4ZbW6Ke2bh38On3QDV1ibPKztxK1I/FhqxP1u/jBhuoTZBnTnkZtCxNvu3dVg3hVCIKsKMZ8hmmY/8hBrU9JZZSetRW4CkLcycLs6jdg3vOTFSgT24vhqNIjRISqLtJZU2bwBed/NRe8bKf3qauO/KOTj7sDjMCBCRDoPrkY6T5oswhpkpZBWk7QsEpqiNHCVa1q8kabmVqp2/9NngQc55WhDjA4xesQ45Z6Pck1/jNfDrsSUjCKpshnRgrBL2yuKoEci6Ty+ZKynsY6ZmZibTMZHuufUbH8pzY9PBslNxMsr5OQw/RFqO8PMbZ7frvP1FlW+xnIfQmn66wJ6M6GWPdysCFc3j5o9TF2H7CZ036B7ieoF2J9+zicdGauNzKMPpVtTOvbNWnooRNxH9CYTsdqMyG1P2myJ76Hl/j7YRcNy1kLbENua1BhCrYttWlZZ2t24SQmi4o3uU3Gbzot3amteokZJGBVq1KjK5IXRaITJNeiPdfE64D1CCNSzp3By9OgvndxEcBNymmCsEHWNqCxpzNddMiXLU0iJHSGXLM/+EO9ExrkOncsvwpWMTynBMIw54zOOxI9A0OPHILQBrUm6dNaWzUjUuw7bW0JOJoFJKB2wOlBrT6MdMzUxUyNzNbKQw4GMfwTJe8L1NWKzRa4X2PkM09bEqmQXpbgNXnzRK+8nxLYnrtbEzebRg7K43aK3PXrTYrcKtxFw9tPPcfET7qY2G1kaPO46tWRVlkzGZfaw83nsZNuTNh1x/WH9TPeOLfM5qa33QvGhVvjmTes0ys1byPjH0nox19X258IJVCVQk8y7QCtRNi+G0miEUkiliJvtJ19L/tQhjM3d8mSpv+T8eyEWYSzqq89xz0+YzizwflKecRhgHJHDmMd5jEVU+TMmna+/pGVpRhSv9UDAD/sgCCmTb3HSYnK5MXEYP6ra8I9CZrOBJOWt8YkUZWN9p5FNJ5JOCB3ROmJ0oFKeVk80ymUiVgMLNXDxwEPRnz8n9T1htfm4z9k7ILkp13yvbpB1lbOOlb2dQCkZNSaXz8V7Lmmk1Qa9mmEWBtv+PBmPnzQZr28lLpVLe0EPPSZUn0ce9LbUiHcR8XrzaHWBh0AYm4n4aF6E4itCq3FtkcWrxO2og8kD40mX3XOps/2g+7SMgciQxRHUBMGVlL1NRCtQVmK0AC2RSiKURErxcRmt/zlACGTbIk+Os3VebXIjGiUdu1OOWq4eT9JSKtRvvmD6zSnb5xX9ueR9kTGQa8llBEaYnK6+Oxkg3pwM2D3Kc/dmACE3JOI9yYcsUTq5jzYSfhNCqUwESu5T1Ptaudrd06mMeSWkSigVsSpQaU+lPI3Kvs+tHJnJh69b/i+eITcTetPlJsebm4+ntv7YiCGX4rru9cmTHX6hzx1ubtDzGVVriZWAv/jp3/+k09R2lfbRYJa4LOMORShedg7ZjXlxK6npDxoR3yXieUuYWcLM4GY6i8Q3klCBr4tq0d46Lb12E3NHy5ckbsdAQo6KgyvRsQVlM6lrBUkZtJLoYrYtyJoZB0L+ZSCMRT17Qjw/YjxrcfP8vcedeEgA3UfMxmOuj9CPpOmrP/+M6YsTNl9UdM8lw5MEv9DXndyUj7/r9jPzQus9SSFkjiDLopnenJ0PxcTFFSOXT4VA7m4ySlTMTjpU3m6q93+WCSkjujys9FjpqaSnko5aOmrxcGGi7ZcNZlth1jV61qDrivDq6td/33/I6yUl4tU1qrZU5ueJ9pOOjO0qIUPad1ruxDzUmDstZTfk8aVds9YHrhHLWYOYzzIRLyp8a/AzhZvJrCJTkx1bqtJhWWzTcnNHyopFkh+Q8evSgTlboMad0EIx4FZyr3iEsCAFpQSNjImwDJ9EtPFYeK2DXRed5xizGtswPPqcqqgq1BfPcZ+fMDyrGE4U00IQmltZTRFADRK7VtSnhnpusdYQvvnuwdkcOZsRnp3Sf1bRPZP0nyXcUwd/eLSP9nZI6ZaYoRCVzNGjfCN62ZNx2D/3k0RRp0p3UvF7AYg7XrzIfF+LQsiqELIR2b+7Fg4jPEY8vMdj+1yhO0k1l9hGYa1GaU14efFBM4W/dsTtFvXqGlPcr34Kn3RkXC3D652WU0AMuctSDFMed9hsP/y4g1R54Z/PSfOGMLN7Ip7m2bHFt0UkfufWYrN1WrIRdAIdEaoI69/R8k1R5EcQJC+JXiAmSTS5mS2V2hT7TuxsvVcMQFEx5k5Y799NAu4TgTAWeXIMp0fEowbfGKKVt6MPU0BtHXq5JV0vH2VmWRiLev6M6ctTtl/W9E8l4ylMR4nYRlIRJSAIVCeZ1hI3V/iqZq5OsTHi//DNg45Dnp0ynNUMp5LxDNy55+TJBv7wgXfhKUEKpD+TDeBeZWy377g1typ/zu5VUoAWtx7PRgSkSFgRUO9gtNt/ltDbPHsbrCFpSSVBxYj/9rtPd8PzCSDc3JBp9suf/D0fP2EyNssJ6TIhiykX5sXocnPHe4huHgppDXLWlmatKluntSUibgsRt0WftiluLVUEGxE2onRE6YDWESljjmZLdBySIEaJ99meLvjctRlMmU3Wd7SASypQJFlS26YQcUC4Bunc48jA3fsEqX0T0y4t+V7eZjZDPnuC/+yY8TT7jLo2lwR2ZKxGMF1FtWywFzP0yya7zjw0ehACebzAf3ZC93nF9rlkeJaYzgP6aGLRjjQ2px9Hp9l2FcPKZu1rJRGpQkyn6H7IM9b3+sCKdDRjOtZMRwJ3HKlPBn5zcgM8edjnOeDd8fP6DwCv+Tzn/3+4GPn4LODXilhmnPP13lC5gBqGxzU1OeB1pJRr9D9Dxp90mlpfbXOXpc8el8k5Yj9kKcgQPordntAaMbvjYdpkIvatvPUvbSE04OtEaCOpiog6O7UY67E6YHTAqoAuaawdQpT4mI21nVdMXuGMxhtFVCqbv4tbCU2RBC5SBP0VwpucXSidqkzuF9nAyLZFHi2gbUi1zR225CYm0ZfZ0Zvlo6XQZNsiPn/G+NUp/TNLfyaYjvO5jzbdIWOB3iqqG0kzUzS1xgjx4NqtrCo4P2V8WtM/kQxPE9Mzz/zJli+OVnzWrjjS+TNug+VFv+Cb5piVnkHaKQpVzFenyHtq5spZ7ktwxZovtoHjWc9X7Q0HMv5l8RqvfoBlqTrrGW3FqDVJSESUSK9RY4PpjhGb7XtPVwutETY39KFKFBhT3oBP04fPYL5PvMXn8m8jYP0L4f7bgptV7rR0/vbLhI/qCxVVnrlMTUVsDKFR+EaWBfLWv9Q32aklNRFZe0zlqaynrab9zKGVAas8UiQkiYggJsEUNFNU9N4weM2gDb0yOJWIUhME7GaiRASiKOpG+YaUPiKmKit2jdV7FwXRnz8nnp8wnTa4ee4i32VopC9NTKsJfX0MF1d5XOFdIBXy6TnTlydsv7B0n0mG84Q7DqRZQFVZSD5EgRsUcqPxM0moFUnVzMIZyjnCxeX9NipCIJoGfzZjOFGMJwJ3GmjPOv7q7Ip/sXjBb6srzvQGgJvQ8ofqnFo5/h5YuwVjp7ErRXXaYI6PfijS/1Nvb02Z7c3Rv7CRxjhO9Ccu+vIpYK9PnE008t/98CH2fy9ISRAT+b5G4KPCJZXv8aTeqab4+emKV2bGRjS4aLIWwSQwnUFtWuTyiHDx0MGpn4as6yxsNGuz8JA1oLOxg0gJfERMDj1O2X96u80GLx/ROv5LYAqfcGQcL68+6iaPvXVaZYnVraBHqEQxEL+Tmt4RceOpakdTTSyqibao8NTK0ShHJTMZKyIBSUyCMWr6YBiCYeMqOhXQKrKViUlAQBNSSU8XeztfrOnCJFGVQlYGUVeIoYL3fCO4v/qM4WluYnLzvCFJGkh5TE33ErtS1CeWurVorfHfv3jw+6nTY8KzE7pn9raJ6YmjPe05nfUcVQNaRkavWY411+uWoa6JOs+fSNcw686QXX+vurpQCjGf4WcaNxe4RYKF49nRhr+ev+JfNt/xN/YlZyqT402sWciemARrV9Nta9xcMc0FbmGws/Z+H/xO01CWakt5Iyc+vnvlV4k7Y1rEnVpeQiSxH0mkbI6Juf8jlkyXjxKXJC4pXNL7x0Px14tLtIx8GwWdl7hJoHvBNJfYuUXNW7hUj9u0qDVysUCcHBGPWvy8ItSKUMl96WyniiWnPAGjtg656VGrzaNmxj4FfNKR8ceuILVLy6TKEAsR+1q8Nksc6lwjTtUtEc/qiaN6YGFGjmzPkR5p1ESrJirhUSIiRSQmSUiSMWm6YOmDpVaOlWxQMiJEYgOMCWLUhGL4vjNs9w7UJAiVQtYa0Zss0GDte70Jtl81dKWJyS3KRsSkTMZTlo4zK5nF3qs5MylRIT5s5y4V4viI8bxmOCsR8RPHydMNf3FyzW9nV5ybLUYEumi5mOb8vj7jj/qELs1Qk0b3CrtsqW6O7qXvLbQmNXkB8rUgVhFbO06rjs/tkt+YS36jV5xJiRSCk7ghJslVNeeP9SnfNUd0TUWoc+NNqm1+zbe97kPcazsLL0hOMnjNJlRU9z+TB9wTKQQIoXwP8TUDDBm41Qh4g4hdUExR7yPjIWmGaBjkw7WLf9tc4ZKkd4ZxNPhO4RuBawWu1Zi2RjY1cbt9lM8uqgp1ekLaZcCODG4m8/imLRMExRxEhizapIeE6SxmXWFmNaquiFc370Uh62OEj58wGX/UEOLWOs1qkpWve5haiLufda4R28rvifjY9pzanhPTcaQHWjXSyolauEzGRCKZjIdk6JSlCxWVdJhC1gCppL/GIIhRELxCVkUUZKIofUmUUciqyGW+ZzLefH7bxKSOJmbNhNU+H6fT9NsKPzfEKte8RWyYTU+Qw3Dvjm85awnHM6YjxXQscMeBpjQx/auj7/jnzfc81Stq4djGim/tKY1yhCT53WiYthKzkkzHGns0Q1xXb0+GUoJWe7H6qEHrSKsdczVwJAcWUjCXFRKBEz0LOdDKrLxkVAB1Z8Zclrnct0Tqe1TvinWoQAyKVV/z/XDE+b3O4gEPQgh5VjrGW3nPogewV80LAhESREEMghBue0D6YOiDoZMVrZzYxodvoT63N2xCxVU946Zu2NYmb/Kq4m5U6+wY9whkLIxFnZ0Sn50yPWkZTzXjUdlcN7f6CYgESdzaY/YC0wnsLI9fVVahS235z4GQp0+5m/pjhtB3rNOsJuzcWszuAcGWrmkT0TYUE/GJhRk5tT1ndsup7jjWHTM50sqRWjisuE0lTUkxJMMQDWvZ7EchgBw5R4kPkuAlzpUNgQNpdpKb5A5LK7MS1E6U4T3ibhPT86M1T+sNCzMQk2Tpal52C76vFwwqD+HKSaG3Lc3y9N43pWgb/MyUrnVIs8DpvOO37TV/U7/kb+33PFcdRsA2SmZyxCXNVdtyOW+5ntncbNdK4qxCmnucmzs1w5IlJqWcjnJJMaGYUsKlgBKCCPsaYSiPfW0xgUiJlN6+ozaOI2rTY9Yz9Eai15LtquaPzQn/6dt/igMeiBTy7L4IRdYzpDyGuctW7DJVRSMg+TwVMXnFGDRD0PTB0inHJtQY8fAU8rna8FIfcWQGKuPYmnRr36ghWo0yj+AaJBXqPBPx+NmM/lwznuRmSTdLhCYR66ybsCNjEQRiFKhB4DcCX+e1KamGWgp0zOIvn7zBzc/gEBm/Jwglc0pRq+JWI/cknP1Li22aSQgbMdZTW0drJo5KRHyqO870hhPVsVA9tXBFACDPHAbEvqa0lRYbAoq8WMckcEkyRcUYcpe1d4rkImlUd8zeyVZuRhYxfw1a3y8dek9MZ4H5ky1/c3bJ3y5e8mV1zYnqCElyFWb8vnpCpTy/S4JpatGdZFxp7OkCedHeK5UmTHbACjZvfmQVmJuJc7vhqV7xVPV8pjRGKDrhcGx5qlec2Y7WOq5tLN+VIFqFvMdGJaWsqyynLNUqJ8E0GlZTzSu34MIccSRGYpHDuomal2HBK79gNTWMo0FOAjWBnFLW1L3Pd5ISXC+prhfUJwo/l4Ta8kIfAw9XczrgHtjpaocyghl2TlR3tOQ92QrWC2JQTF4zeE2nLLXKKlx5k/3w0aZaTrRyolIeoyJCxdxPoPLoI1Lcdji/A9TxUU5NP2npzzXDuWQ8gek4ERYB0ebmVGMCUkZSEjiXPeldpwlNqSlrQRIqj/a5BWqccqPur1gxzB0i4/cEKbNjy268qBg9xJ1TSxGHRyeUjnunlrkZOdIjR3rgWHecqPxYyJ6ZcEWVJ+7JOCTBmBQm+b0oQCBHXmM0DMbkLmsdmHTE61Qs24r5hGYvWB+1RGmVtYOVyl1e7+PUHDmeLTb8zfyC/7T9hr80rziRPQHBZZjRygmXJKup4vuNxS0s01wSjix2Mc875LeNjoW41fMu6oRKZlEFKwKGhESiURgRMGShhX2GYSfO8IBNa3Iese2zXVun0Z1g2BoutnP+UJ+yUAMBwXncdVPP+P30hH/qnvCimzNtLbYT2Y+7C4j+/qWDcL1EvzyimRt8bYlKMqSKAxn/AijGBMIHhAvInUCRT8iilid9rucLB5SGykkpBmUwMpZxxlsifpda/269eF+QdY04OWI6bXJq+kQwnsB4FonHnmoxcjQbWFQjjXZoEYkIRq9ZTRXrtqarKiZtcjkmCqRXqKFG9kfIbUd4z2QsqmwwwS4oESJLtBbP7jS599bpHQ6R8XvCTntXFyK+80hyZ6GWlbW0CVSmWKbpqTRrZWH4hepZyJ6FnJgJTyVAkbWlQ0oEkbApItOOiPMYRKcsjaqY6YmNdvTGMJiA39m5FUvGHSFHXWztlMiNZ+pd9H5+GnUz8bTZ8Jv6ir80r/gLveJESgKJhbzBoXhVLfimOeFVsyDUJo+B1Qpb33M5ihG5i0YCxCAYg6aLlm2s6JKiSw5HoIuBLlnWsaYPFhcllFSi9DvN5HuclRiImy3mZqBaGaalws0VV82Mf9BPcrOWn3GsepSILH3D18MJf1if8ep6gVxq7FJQrSJmOZBWD1BIi4H47ffUTUXSR4ikkU7C7P4vdcD9kccuHTifCdnF7KbmEqE4rMkJpIE0SaJKBK0YtUaruN84Qi47ffbA49jGii5UTFHjgiQFua9f55r1Pa/tH4FYLIjHM9yxYTySTMeC6TgRTzyzk54n8y3P2jVPqi0LPWBEICaRa9nTjAs756Was5INU6wQXuTxq16jtzV6MUf0w+NHx0IgmwYxmyHmLampcp+PUVl/IJHr/mNAjBO6H0nb7tG9Dg5k/D4gsnXaziburjj8zuwh/0yI4tSSxeEDVnpalVNKuxrxTDhmwtMKqEXuvFUIgkjElEpq2hOFwImJrXT5+WoqgvNFLERlSc2k0uvHISnkLEm7TcQjpKz+FKwOzNTEQg6cyJ4TKTmSNZGES7mJ6Vh3tHpCm4DTKZ9HLUj6fseVJoccQvG5Fky94qaveTku+KM9KzXiFUZEtqniW3/Kt9MpL8c5675GDDI/d0yoPi+q93r/YURdrqhP6yx9WksGXfF9OqafDN+1R8zMhCSx9ZarrmG5bomvKupXkvoqUV051NUG/0C50jgMqD9+RwNIN0f3Bv7lg17qgHsieQduQuzIeIq3WvpTLlspA2kSZYMuiZNiUgkpdxKZ707GV37OtW9ZTTXDZGCStx7wLiFdyJuGB0IYi5g1uLllmmfddTfPqel6MfJ0seE382t+21zzmVlxrLZYEXLgECtemGNmeszz/kmwdgo/GlwvmDYSOzOoeYu4MY9LxlKhjo8Qp8fEoxY3t/iZJtpcVtxJB8uQkFPKJkR9tuRVy82juqu5Axm/B4jc9bqziNuJw++E4e/+WapbtxZb6kOV8Pv6cF1S01Uh4kpolBDI0k8dSIAnEJlEwNx5biXy61nl0bJIacofIeI7LjLZiOLWRed9IKYsahCQOXVGIpKIxDu/k2eo004gAdiZzt8HabtFrUfspsZsFdNasVq3/KHJXdMuKV7qJVYEtrHiO3fCP3RP+eP6hG5d5canDZhNRGz6W2GZt31/NxGvrrHfN7S1IikNSKap5nprWM5aVNGmDl4SO41aK5orSXORaF8Gqpdb4qurd4pcwnqN/CdP3T9DrxcHMv6lkBJpcojJIUaPrAJyUoWI097MRWqQRbo2SEmQirtXWojyneZQv57OeDksuB4axsEie4ka8jiRGhKyzwYpD4WoK1Jb44sDXWjAtwk5cxzPej5r1/y2ueavqguemxtOZEctPC4pVrFmoXKk7KNi8IZhKEqFjcK34FuFbWzWbXis8SutkedncH7CdNbijg3TXN2a9RTLS1IxcXEJNYDpNGZj8nx2U6GMJlxdv3Nm4RAZvy/snGhEaY4QItce94Lx+xZZlEg5HVV2wUpElIi5pilCqRGTI2Ih0CiUkLn+IwISgUqgRMKIUJ63m0dOaJHTXUoUk4n9e98e0604hLg95veEyWnWruLaz7gMMxbyhshASImLqLnwR1z5Geupxjtd6mtZGOC+kWnse/RyQ3XTUl1L3EwyNBVfqxNClFxNLWe2w0pPHwyX44xvNsdcXC8QVxZ7I6hvIvZmguWGNN1/Vx67DvXdSxqjEGmB9JqhE7hVbljZzVwqD1UvMBuorhPthaf+tkN8d0nYbO79vq8hJWLXkb75DnW9hP/sp3VyD3g8pGkiTVMm5MlkMh4l0SSCESidiFqgdhryUhIl7K70lCBE8U7dtr/rzvl2e8zNpiWsTelFANMl9NYjuoGwfXi3sigjnNFmG9hQ5ZHNuvYcVwPPqjWf2xu+NNc8VyuOpaMWuXNhESesCLikWIaGy2rGdd2wqqpsbFHlDutYa7S956z9n4JUyPMz0mdnTE9mjGdl/Gpe5Imr4pZXTrn0uQFTjaA7hV1LQiOpjMIoiYJ31vcO4UDGvyzedG+5A0lOOcsyR7yDIqFKaloiUWXWVAlZ0tSZpFVKrzm7SBH33dV/Cq/1c7xHAr6LsTO87Bb8sT5jrgYcihPZEZBchTn/OD3lD/0pV31L2GrsVuRFow+k+zRvATtPUXMxp5lpfK1ISjHGhj86xeWsZVZNKBmZvKYbDf26Rlwb6oscndaXHv1qTVqvH7b7TYmwXKH+AI0LqGGBXZvi2MWtn7FP6CFhN5Hq2mEuOsSLy3yTP1LDSBpHwjTxc6L1BzweUgikYUTUNWJwKKtJZbpC70owiqJYK8raIIkJ/E4mM0p8eHjp6HerM642LcOqQq0VZi2w64RdB/R6JK0376a+pdSt9KrOUT86YYxnZnJD6onqOJEdZ8pxLBW10LgUMHhc6jlRHceqZ25GKuMRNpC03ve0RKNgp2v9LmQsFershPT0jOnpjP6JYTgt41eLtFdERCdQWYyIkN3w5CAxW5FntK0iKgFyjokJObl3cr4L8ZdZf98Gvy4yfjOdmhJ7z7S7qde7Tymp25gkkdtdUu6CzM1akUhIokTGcZ+qDql0V99h+ZgkgXvstu6ZAn4wloaXzZz/qJ8SEVz7GXOVU2TXfsYf+jN+tzrnejlDLTVmDXYdUcuBuLp/hBi7DvXikqbWJN0ikkJOimlbs51bNibm9HwQiFGit4LqRlC/Ssy+99TfbeDV9b1T1K+hELJ0nmpzijmdE+YVodZEU8jYJdTgUZsRucyp6dAPj2/c8SsXT/jokFI2rxkGhNGIXiO1RBmZSWZfMhK32SmAlAk5xNx4GLwEHnYtvLxe4DYWtVTYG4G9SVSriL1xyOWWcLN8p48o3ijHpeLTrPfezKGUzzy1ENRCo1FIIXEiUu3+XWbJX6NCKamlW9c5JUDK/F4PP1DkrIXzU9yTluHc0J8XNcDjhD8KyJmjqjzW5vIegAuKcdT4wTA2WU0x6uLJHS3CzTDjKWkYH1zTjoc09fuD2HUoFl3anXjDThh+9whJEGKukWbBh6ystROHd0kSRCCmlGvE5c+ZmFP5+2yn6JIqz9OEUnf1RfwjpOx9fPveRY7vTuZ6r6X7HlFdKgbd8Lsk2DjL180JrZ5yZ6WreNXNuLqZE19VNFeC+ipRX03I6xXeP6DJJCXi9TVKK9p0jnQtutNMa4FvFcGobO5eZPnMJlEtI/VVJmLx7UX2NX7X85IScZvdccRVha4rTF3DrinNB9IwkIaRMLlf9UzlnxtSCKR+yAp3RiONKmlpUZTVKBHxjoh396ckFq9yZx5Oxv6yRm8kdiWwN1BfJ6orj77aki6v3zntm9Lra9xO/jyVNQ3YBwsh5eBBC/bBRNz3j4i9AU5KYq/jDdwabrwDhFLIk2P8act4ahhOBNMJTKeRcOKpjvL41XE9sDDDnoyHYFhPFcu+Zm0bnLYgFCIKpJOo0aK2M+R6/uB0dTxExu8BKSvG7Ihtf5EWYfidHi1lxxv3NoiaMWrGpLOqVnm45BhT2HdNS0o3NZmIhxQZE4yFhHfPG1N+vSlofJTEKDMZ70Ya4u1x7f5MZL+BeF9oXiZAM7qWbzvDRTNHlyYmN2n81qCWpYnpZaK58JiXm+ze9MAbMnlPuHiF9IG2P8WsZrgjnZW1StekCKCmhNlGzHJCX26ya9Ry9ajRafI+L35dh7jTtZ5iytfOIXL99SEl4jAirMmNQ1qBKuOEQr8uc1rc1UQUhKIrHm0i2Ycv1tWFQm/BrhL1TaK+dNhXW3h1Q9g8QkNUCOB3Y4QJ4QX4rLM9BMMmVKxDw1Zb1smhyjihS5F1TGyTZh0aulDRB8PkFWk3VlgehAQx5rnfh0AIZNsWj297O351lAhFJvfZ0YZn7Zpn1YYT01HLLI3bRcuNa3hhj3hhPJdijksVwqlsu9orzFGNPVrA9fJB68WBjN8HUso7xbhT3LmVwJPhlghFEKS7erQxC8R3wWat6VgxkyPbZMocce6azs1aZYdJNoLYJs02GbbJMkSTBT+iYYrZXtFHSQiyXOAi7153xxOLm0zMx0yIt25Y7wHz7wLSK3QvmVaWUFtGXYwinKDuBGYN9VWifelpvt3Ady/fWS87eU+4vEJut9jrI8xiRmqr23nCmBCDR3Z5pjdttu/XY7WIQhzwZ4IYiNseqRRC5RHFPG1R6sS7bqHE3l1NhGx/GT1E9/DFunmRMF0u91TXDvOqQ1xcEa+vH2ejObk8ujXu1OZATIJp1GymihvXcmVnXPgjFBGXRmoRmJJknSzf+2Mu/BGXbsbN2OTxq1FlYZSJ0sAZst/6A9cmoRRiMccvKtxc4WYCPwN/FDCLkSeLLV/Nb/htc8XndsmZ3lALR0DQxYorM2euRrQMhCi59BI/SFwncDOBm2v0rEHO2gfVjuOhges9IQTwebeYd4rcSuFFUeTwih6tU9kgwRi23jIrpg9bOVGHZt+UFYVgEiF3X6fXFbi2ybCODevQlJ81W1+x9ZYxaEan8U6Bl1nxx+82Bun2+HxE+Cxs/9AL/m3Q/n6L6hvsWjPNczNEVLu6Keg+Ua0j1ZXHvtwivnmRo9PHQMwat3EYkdc3oBSyRKc7pZ1QzM4PEeoBj43kHWnbZTcxIZBC3C58iWJzyl6kRnpBcOyldH+s8fNtMHsRMduAWU2o6w4ur7NF4SNtBuM4IocRPbToMUeLqpe43rDsa15UCxo1oUgMyXCittTC4ZJmFWsu/BFfT6d8PxxxMzQMvUUOEt0L1JDQfUQOLtdkH3rMSmUXtUZn97w2+8jTBObtyLN2zZfNDX9Zv+JLc8W53FKL/F6rVHGiOqSIuKTYuIpta+kag29FGeeSpMYg2wYeQMbpEBm/H+zs0+5q0u50aLMMXiZEfG7MmLzK9nauolauuC/dak0HsqCHEf7HtamTZR0abkLL0resfM3KV3Te0rmc9gleQrFPlE68po0rfSZlfMhp1PBwHdyfg/ynr2k251QnM9zC7rVoc2Qc0UNArSbU9Zp4eZ3Heh6bGGMgDu9vw3HAAT+KlDJxyRwFCSGQ5MUvuzopRJBIL/FeEFxCOorJDA9WTpt93SG3I2LdEW+WxG33uKWXaUJse9R2jukMZpvwM0HYKtZ1zfd6gSThy/jSseoxIhAQbELNKzfn++GI77dH3Gwa4sZgt3fGr7qA6MZ3yo4JrcEags3jUtFkC1tdBY7qbM7zmVnxXN/wpVpyphx1yUAuksMSmIzKx1vNuapb+rrO2vcVxRpXoax92Dk8kPF7QggklxWbZJHBk14WItylXnLqKTmJmzSDNnQqsJJNsUG81ZoekmH7c65NsWHpW659y41r2biKzVQxTAY3adIkEZPMJOxuH1mBJyJduNVgfUij1NuempslchiRFxX1bJZtJpXM9XWfG11S1xPeoTPxgAM+WpT68S4pKVJCxgjRIqJBelX0qyXBCYITyCm9ExmrP74kbjviNL0fe9SUSNsOtR4wK4tts4RttJJJW67Jozu9N1zVLTM1oWUgJkkfDDdTw2Xfcr1uGZc1elXGr1aJahXQyxHWW+LwwGMXIkv8FnnipHZGPVmOuNaOI91zXLwAjuXt+BWAiRNTGjmSA3M1MNMTtfZIE4t4S5E6NjLrWgtx/wDiQMbvBymEokebZfCUi6gpFSm8HBmrSRBHSTAJrxWDNrd6tOKu+1LWmm5lVRS5btM0u4atMRrWoWbla25cy9LVLKeG7WQZJoOfFDhZXICKDN6U8jFNWVBDTPl44+Tee4o2DgMMA6w2uYlpN+McU94IHFLEbw1hLMLoWwnTGHN24z16Uh/wjoiB2PeZkFNEhIgMsWTRCiE7hbQSZUVxVoOH6mGGV5fvvT8hbLbo1RY7rwmNwldFwlYqXLJce0k/Wq7qlkrnsaGYBIPX9JOh7y1hbdBL/dr4lVk61HJLfNdZaHlbo79rHrNTQMwjWB5LwAgwKCphCClSC4W9q24oPUpGhEivvVaSInuwK3X/830g4/eElO6o7njEFJFTRE0CNZamDF10aQdJlFmPtitfMOQ5YZckYzQ0qqJVE9VeWSvmOeIkc/d1NGx9xcpXbFzFcmpYjxXdaJhGTRoUcpDZjm+k2PJRNgg5KhYuwDg9SGXqwYiB9NhztH8mkItFnplsmyxur7P8Kj4ixwmx3uYMw0PFSg54v0iJ2PeIEAoR3zo8icogao2sFNFKVPFBf/Bb/RKNgjEQb5bIpsJWimiqW/clp3CDpG81fVUhTZbmJYlsHzkqRK8wW1HGrxLNVe76Npdb0vUyuyW90/HtplvSa3oPu/GrLNMry3hVHrtyKa9NLsW9CY9LGpckKZV55zckex888REOZPzekLzPhDw65OBRlUIPkmATccxNGWkQxcJQEqVm3I0ZltnjKSoGY5jpbPpQSZ8lM4lFIETk8aWo2XpL5y2bqWI7WbrRMA6W2GvEKHNTxSCKaUJCjykbIIwROXjEMJF+aTI+4N4QxiLPTuDJKdNpg1sYQiP3Sl4i5IYXsz5GX/eoiyvi1c0h5f8xogiCxBDukLFHOoucDNFqUqWIJvuif+yIXYe6vMEoVVK1FullDkJ6QWhyXTXptDdhUMVOUvUCswWzTtQ3uYHTXHTw6iZ3J7/LhnJvZxlLY1xpkvMC7yW9N2zL+NU6NqzjmJXBRJ6D7lJiHS3r2NBFSx8Mg9dEL3P/T2mGlSFnOR4yjfIOdtWPjl8fGe9l8CbkaJGjRlmJHsSt8k4x905CEgQENEOiyN9JxqDovWGjHbVyOZ1y51vzKY9FDcFka0BnGKb8mEadiXiQqF6ieoEayI+dE9EYUYNHjA6G0iBxiKLeGsJk8XpRWVDqdf/TyREf2fJNVBXq2VP8l2cMT2uG0+yQE+pcA4NsWq97hdlomitDPbdoawnfvzwQ8keK5D1p2yGmCTlWMNUIa5GVgdEgTZab/OiREnG9RkqBAUSYoUaL7ssoUTFhSPqOI5LPWTo9JHR3qwxmrjp4mcevHiOyTyEgJocaA6qYdchR4kfNdrJcTS2v7JyFOsYIj0sdlQiAYJ0sF2HBhV/wclpwM7V0kyGNCjVRHgkxxTx+9ZA19JCmfo8oji1pGBDWZE1aLdE7X+M9EcPOMinPtWvGcKfLWgd6Y3JNQ4W9ocQuteJCniMenWbyKgtnTIo0qBwR97sRAdB9Hh0yfY6eVOeRvUP0I2kYH94g8WcGYSzyaI44WhCPWnxrSVaSpMjzylNEdRNquSXdLLP36bsuKEKgnpzjfnNO90VN91QynBU93TaSTF4AhBOoTmLWAjfT+LphJs8xMeK//e6w2fpYEQNpDATnEeOYTe6rKguFWFPsQ9sPfZQ/i+Q9cblChoCZHKqbYRYVbq4Jjcyd4TrXV/f2hC6h+9w1bdYT8mYLVzfE5erxUuwhIPoROYRM/H0OTEKvWXc1F9WcmZpyl3eSrO/MGW9jxYU/4pvxlO+GYy77lq6rECXI0X0+fvUu7leHNPX7RfLuNRk8te/myzJ4t0RMVt5JkhAEMQqcyzPIk44MJqBUtkFU4nYxjSWdHYIk+PyIk8p+pUNJTe+IuAO1I+IuoruA6l0eGegHUt8/vg7yLwShdR5dKCMjKQSS8+/l84iqQj19Qnh6wnReMx1nJa9gyRrXMe+UTVdT3bTYyznqxRXh6vqdmqrU8RHh8zO6z2s2nyv6Z4np3COPHG07UZncAT95Td9V9EtDKMYYItbMpzPU9t11iD84/pS9569lk3GXlPuhkLHNJgmfABlDEdhZrpDDiNgssIsZuq1JjclpaiOz2Ank1O6US2VyOyDWW+Jm+/jjVyFkS9XtEXpr0V0xfWgkQ2W5MDO0iEQE66rmWPXUMt9TXbSvjV9drWeEtcFsJboD3SXM1iO2A/GBimaHNPX7xk4Gr5CF1Aold5q0wO2Aw14CTwQIPjduJBfxOndbSx2zap5IWbAHIAlShOgllDli4XL6RU3kGnGJiFWfivtRIeLOIbsJ0Q3E7v5evR8cUiGbGtHUiFlLqmzWeS4jUqIfSX1P2naPpqQlqgr1/BnuyzP65zX9WZbU8y0Em27JeBToTlEdS5qFoqk1WinCi4sHpYqF1vD0nPFJQ/9EMjyB6Wmgfbrl+fGap82Ghc7f39ZbXvQLvq8XdKpFJI2aJLpraJfn8ACzjQ8GUcT4pdiXAfZd4/JODbUI1aSdlGsqdbtPmaBLc2PyDjGO5XM/+9BH9fZIKU9NTA65XpdyTpXncLXK44yACNkaNQ0DaZyI72ukMSViP6BWXZaunEt8owhWEo1hIxu+TXn86rpus3uU9EQEfTCspprroeFm0zIsK9RKY1a78auIXpemyQeuo+KQpv4FEAOxH7IMnlZIKVHqdiERSRZ5SrFvKpAVWXlnKs0OWhJ1uuOFzB3DifLcHRE7Sj2k1IaH11PTZhPQnUNtJ8SmJ223OSr+hBYuUVXIk2M4P8GfNPi5uXVSAdQUUX3MKa/rDery5p01poXWOU38xRnbL2u6zxTDOUwnkTgLiKo4zUTBNCjURuHmEl9romqYxTOUc4SXF/c+17JtiSczhlPFeCqYzgL1ec9fnF3zz49e8tvqimOV/WiXoeUP9RmNfso/JsHgZtmDdaWoTmeoo/mDz8EvBqmyfKEpGQ+jy9/JTEpvRsep6Bbv1ONCHu/CuVyPjemTzfrsZVM/VenUncDOMNx+r0reZrFiJIX4i4w0pmkiLdfoeUvV6pIyz8cyRcvGS8bBcNPU1Ca7R8Uk8EHRT4aht4SNRq3y+FV1k01l7I1D3mwJ1zcPP7hPOTIWOj8l7UwNPuKbLbmJ2Amkkq8N+xcTJkSUyHK/SZcl8KS5HYHaNTzsrMn2iGJv9JAVtcReUCRHxgk97JojckSsO4daj4hNR+o64rb/pDSSZV0jnz/DPz9heFIznsi9rGZSFCUvhe4Tdm2oLy1VW6GUJFzdPPg6kfMZ8ekJ/fNMxN3zxPQkYE4Gzhc9i2pEi8gUFZuxYrluGNqKpBUiSdTU0HZnyK6/n3atEIjFHLewuLnAzRMcOZ4ebfjr+SX/qv2Wv7EvOJE9isRlbDlWXXbBmir+sLW4hWRaCPzCoucfMRnLQsDW5vSsMWBMjqS0yg5HZV70NULeuY2FLOcqfED4ImDjXO7dmKb3Vro44C2xi/bfn6bQTyMl4maLul5ha03UTe7zSALhJW40+JliXWdrVaGK3oOXMEnEkMevzDoTcX2V/c7N5RYub95NIexTJmN5cnzrMBR2Mo5ZRepjTFGlaSKVekLONhd7xWgQUWWFLi/whUh3Eng7V6FUuq9fX4R4Q8v2VlhdjaDHbFiv+ojuA3rrsizets9GCKvNJ9VhK7RGfvaU6aszus8ruqeK6YTcxFRFks6zi9KB7gRmJXEzS6gVrZRZ0/vq+t7XhtAacXzEeN7Qn8kcET/JaeKvTpb8dn7Nmd1SS8cQDa/GOX9oTvnGHNOnFjnp3OG8bqmWx8T7SHwKWTR1Jb4WhDpias9Z3fGb+orfmkv+Ui85ltnNaxGXhCR5VS34uj7h+2aBry2hloQ6v9ZHByEQ2iCbOjctVRaMJpkcFWflpBzB5KZH8bpOc8r3kyjOPnud9b3wTiZlxtKoOLkDKf+ZIrmJeH2DMppaCEg1MqgsiDRkCc9QyxwEqbye6FACnEFgujx+VS0j9WXWz+fi+p3188Un3cB1mslYxLTfDb+5E96R9EeBoksrUkKmhAgRFfNcmvAGOWXTe1ULghUEm8dVoiZ3X8sdGd99zVsyzhJ6O5nLO3PEfUANAbV1yG5EdEMm4s32kyJiAHl+hv/8lO6Lms3nkuFpwp1GxNGErT3GeFISjKNm7CzuJhuB5yamGa17ihxG4vZ+TRaiaYjHM6ZjnW3XjiPmZOCrkyX/ycn3/G3zgud6SS0nhmj51p4yKzXc302aqZOMa8l0orFHM2TTELvu7d5bCjA6XwMakgZjAq2emKuBE9mxkIJjWQMQGLKkn+potcOYwKTLdWTErX/yRwKhNaKqcu2/rkm1JVmT3bSsJhpJUqXxUe6aH/NNkMStD3cu9RT3MZ/vL+liFtGYfCbmqYJqRI1jGTs7RMp/joh9DxeXqJSo/RFqqtG9ZtpKfMNeu3p3nck9Gee+G7u+M3716vpR3K8+6cg4nhSh1mJVmB2SQtGD9llNapxyE8/OQ/ZDoyhzxVVChoCIEek8wlXIyaCcJkzqVsxci0LGJTLOE1B3Xu8OGe/MKNxO5jKbLsjeI4fcNS22paGp6z6O83EPCGPh/IThaU33JBPx9MzTnnc8O9pwXm9p9YSPio2veLmdc1nPGXSFSKpEpzOq1SlpvJ/7i6gsflYxzSS+hTgLnM0Hvprd8LfNC/5l9S3P1YZaBIakWMiegODGNVzOW67nFt/K3HU9s6imhrckY+AH/tK7oDomuTdsj6XoFPd/X2pyxdx956v9Pr2q7wUh8ixtUyOaBuoqE3FliLXeC11Ek2UVo75tfMwShK+nqUVkT8jZ+ISiCR8Rk0FOHjkYsAYxWKSxuWmo7z/KTNoB7xEp5c3wRUJNDjks0NsGOzO5qasW+wAI8horXUIPpdS3npDL7lHHr8RHtCe8NxlPp1VO8yZe3w1PYb8bFsOEqKucmuq6j2MnnBLJTYRVyEQ8tfnnWCFHg6w0qtJEKwlW3lmIKI/bRWj/2UtkLHyWt1RjlriUg0f0E2IYSd1A3Bax9Q94DuRi8SC/T3l2gjtrSxMTTOeB2ZOOvzy74p8tLviyumYhB1zSXPkZf6jO+Af9hK85YZxqdC8ZNgZztkBc3U9IQGhNrBTBQqgSog7Mq5En1YbneslzteELlaiEpUuOwJqrsOSp3TCvJq5tJFal9GAVSr/95Z5iKqpMRdd8ErhJs3I1V37GZZhzIkcgR+I3UXIZZ1z7GStXM005BZefm1/rg0MqZF0h2gbRFDnPxhIrTaxyNiNaSbS7DelOJKfcB0ULeI9UPLrjTglpR8oSNcXih6uQViMHjTS6NIcpktZ5bfhztM0s5YF9Q9Vug5PSR13yexQUQk7ThOx79GqGmjXYtsqbQS2hWLsSsp+yHH0OajYdcbXOhP5I5+aTjozHY7Xf7Yv4Bhm53eyaRQ4WMVSoyhK33T5S/uAo3rrCe+Q45oVpzPUyWWmS1Shz6zQSlQApXiNjSp0s79zi65uRwSHGKStrldGlj8E8QDx/injAdyBmLW5hmBYCd5RQR47Pj1f8i8UL/lX7Lb8xlxzJgYDgIhyxUAMRwXayvNoYppVkmkv8osK0LfTD229KlMrnXQIShExYGailo5YTtQhUwlIJTSRSi/xve0F5FUky7SM68admZX8MKUI/lOY7jeoFY2d41c34ujnlWOcI+7J0U9+Elt9PT/l9f85FN8N1lqqMtuk+QNcDH7CJazeSNp8hmprU1sTGEOs8Fx0qmcnYCELpmdhlhvI9wC0hF+yImLhbC3INTnpQTpQeComy+ZFGhTR5ukFojVQy3yOf2FTBgyEEsqryuNEuK2E07KY8Qs7YMeQaexrHD76Jf19I3hNulojNNpdL6gq9axy8cz4o8sZpGPNa+tgc8hFddvcn4yP5Ohnv07Ry75CkKoWsNarPohvSGJI1uV76MeyEizZtcB455SieqkLanK6TRpOUgtLAkgnhzioUS808pL13MpPL0c845TTcR9aw4p8u0NfHhFeXb/0coTWxrfFtqem0kVk78rTe8Jv6ir+xL/mNXrGQxX9UToQkuapnfNsccz2bEVpdnquxbY24zjPab4UQSm8CECGFrBveBcs2VgxJ0SVHJDKknKoeoqGLFhcUKcgyQ56/r3Sf6650gOrliF0bzFrhlprLZs7fmyfEJFg27Z6Ul77l6+GUf1yfc3kzRy7zPGS1jujlSFytgadv//6PCamQs3ZvcBHbmtgaYqPxjSZUIpOxfb2BcV+mUfyJyDj/yJGxyJFxKdmEnUOaScQpGy4onccLpZZIVeaYRZ50+LUTsjAWOWsQJ8fEeUuY25z1Mbk5Dm5T/XIMqG5CbAfUckNarx9tZv9jw76Uud3uLRcRpXs/lkzBe1xDP+k0tTva2e7d7oalz3O6akooB6oSqEGSrEQZldNTWiGlIm23b3Xj/du/+9ev/f9//e/+w30P9eexm8WbHMIMr412CK1z043MjSz7sY594TBHxNxpYIvF8OGjSMu/genYok+O4PLqrW9qoTXJqJyuNJBMojaehcnNSiey50xK5rIiEgmMnKsN1f/5v8W/Bf4tDnD8n/RR8R3V+UZ7SyTvkWPITXGDZBoUq6Hi1TTnhTvhRHYE1vua8bf+mG/dKa/GOdvRwiiLW1ZCjh7umaGI2w59vaa+qpkWuSt6MBVfc0rvDC/aBa2ekCKxcRWXfcvlzRx/WVNfyTKC4VDXa/x9atWPiTtEnOYtqa0Is4rQaEIt8U3uk/CV4H/429fLGP9Hd3RLyDKLq+wJOfHapnw/rx/Y+4dHkwhGoIzYy9FGLdBlTEreyVb8mglZGIs6PyWdHeNOc6bJt6VL35TRQO7USMeE7i1mU6PnDfKmgcvrP6mO9Yuslb8EdrPdvyA+6TT1tMg/96M9Uey7iYMr4uNWoo0gGonWsuyI882HFB/fjVek8NKYVbtQOY0mlMpiB0q+TiIp7us7yflS4/llBugfCjeXpFmN0Obtu7nl2xPn20Dcs4kpjRNqPWK2NaaDaStZrVu+bk+YqYmA4CosqaVjGyteuGP+vn/GN9tj1psGvVaYLdhNRG5zJ++9EAPx8ho7b2kbRdQKUIyu5kWfo2Rj8+LhncJ1BrnS1FeS5mWivQjYiy3x4u2zEY8KIZBNfUvEs5rQ2pKtUPhG4GtJqHMn65tw81Qi45Lq35Nx+R7TnXn7QsR35+6jzW5l0aRMwkqWZrBSehB5LRAAKSJj/NVFgMJY1GdPiU+Omc4bxhPNtJC42a3RyC7rkMk4nzPdS+xGYWca2xi00aiLyzzK8ys6Px8an3ZkvEhv7Ihvd8LSZwWqaCAYgdEpj4WUuisyp6Z2zckfFSEX7FR30jjeygKqHxlLSfGTUhgKVhArjayrtybj5DzChZw6cyCcYHCara+4CS03seE4rggMuJRYRsVN/KGOr/A5/UYIvH2OGlLfo5abrDV9I3FzSd9YvjYnxCRY+YqndoOWkTFqXo1zvtke8+3VMfHa0hS1HrtyiNXDJPPiZoP6/oLGKGCG9Ao1SNzaEhrDWIwipBNUvcCsobpOzF4Gmu86xLcXhHuOdD0KhEA2DWI+y97LbXVLxK3C7SKzGnwtiD8yBh3aVJToShhcZGHvKtGlRBbByfZnWcnOgTS56S1qUDuDln0zpOTu0rOf/y++tx9Dj8VjIKvHnRGenjA+axjONeOxYDoqUq51ItqU52pFyS7syLgT+Fbk78YKainQZE3phzRiHvDj+KQj47Ao5LPTdPaiNHCJ3D26qzeZXfNH2Q2XjbcCZCzCGyF83DdeSpCyes2njqQEScncwfm2z3ETcpyyrvagUJ2k77MG8x+rM05UR0CwkAMxSV6GBf84/lDHV/egumxXdx/P0eQ9abnCXM5p55pQK6LWDDT83imuZi2zasKogAuK7WhZbxritaW6UNSXiebSYy62xIcaNaREuLxCCUkbzlHDDLsxTPNsTbeTApU+y5/aTaK68VQvtohvX92rRv+YENaWrum61IjfIOJG5Fp+LfA1RPvD14izADoiVEKqhBD5sUNKIj9KPT95QfL5Z5wEUmchnTynXCYS9nXnO4QcYzYu8HnSIXyEZZ6HQJ4cEwsR9080w5nci+X4RYA6oKqQmw0FxJ3xTK/wXR71CVaQpAIsdVxgXGlo+pjXzU8InzQZq4XLHBUFREH0Arwk7sh4yvOJqdh1pX0e6k50GSMyJWQMxI9JIORXjDyKlfVo7/W89RazPsasNWYt6JeW75sFjc6ke1EtWMiBgOTaz/h9f85v33gNu4node4uv28mJGy26JfXNJUhmhaQSKeZtoqbueWmilmbOgiYJHqtqJeC5lWifRmpX/RwcX1vwZHXkBLh4gLlJprNKfZmjp9bfKP2zTfSJ9QQ0OsRdb0lXS8J19cPf893wd7MYze+ZAjNLjV9S8Q7n9vQpOx+1b/+Mqr1SBXQOmb3smIjusPOTjRGURzMFMFLkpP7aYSkJEqlIqcJ+w6wRNaHjwrhTdYsCLn/Qnr/1uIsHzXOTnDnLeOpZjyVjGdFU/3YU81HZs3IzDoqndc/FxSdM2wHy9BZJmuLsYPIaoGTRQ4L5LYnvHr1frOKb+pZ3xm/ouha/xqMQT7pNPVsNmQNg52FYBDZ7WjKnYHR5Bswi2WIOzchQBmLilXucN3thNfrT/oL/RSgJpBTIN3TOzneLDFXJzQnpnijKjZ6xj8mwdZZzutTZnrCRcXK1Vx0sx+QcX3lkFdrwvQA5bEYCJfXKKVoBUjXoHvFuBb4VhNtrmmKkOuUZgt2mWguA82LHvXd1aORYrhZIrY98npG1TZUe79b7nTSj4QPrLIm62qvqhVrS6jv1ohzJOybkqJuUp7FruMPyLhpR4wKWJ2jNyUSSt5u5lIS+CgJSWRP76DwXuFdfiSliIo8GijY2/eB2DeAiSgRQSGCRjmbz+PkENP0yW/Sw0nLdJxT0+NxJuJ06pgf9zyZbzmvt5zankbla8Unxc3UcDW2XNgZS5WYhIWoSi1ZobsKu54jVqv3Ex1LhbQGMWsRbUuqi0Rq6R8RMTetyinb1Kauz2NYH3G/zE9BfESHfG8yPpt1+5vQR7m/CSej8SY3uQR1NyoW7F2OIoggEV4jnUG6CqYa8ZHM4v6aYbYh+37ekyTiMKBeXVMvKnzT5qF8NFs343ed5bv6CKMDCRhHw9QZdkIY+/e+6Ij3FPy4i+QmwstX6BBoh1PMJi9yrpimJ5lH7NSUMNuIXTn0ZY+4uCJc3Tzqop7cRLie4Pp631MAfDRpVaE1oqnzDGtliU3eQIW6dE3XOTUdqlKzrCA0kVT9MGNy1AxYFaiUR8uIlf4HkbFPCh8lY9CMXjMFxaA1k9I4mYhSEYQklfMk9muB2K8HeRRK5/4EZxGVRYz2kydjv7BM82wW4haJuPDMj3ueH6357eyaz+slZ3rLXOXGwjEallXD9/aYWnmkgKso8JPEDQLXCdxcoxctcj7Lm9tHJEBhLPJojjg+Ii4a/Cxv5HKAdZvR2AkcqcEjtyNy3ZGWK8Jm+9HcB2+LTzoy/qxd55swSqaoGYOmd4ZeawZtmJTBSwhi135J6boUuHB788lJI5xFTjVyHB/9wjrgdZilg5uHNX6Ei0t0XdFqCdRZYanT+JVirCsGnZ2w5CSwneBNMhYvLgn37WR+A8lN+JevUF1PtTzGzltia4lWlU7UotazHRHrjrhcEe5jDPGgg8o9BR8TRFUhrCVVllQXBbNK4sv4UihNW7mD+paIZfND4juuBmrlqJXPhCwiRt5+3pAEPirGqJmiYgiG3huMMvQyMsjEJBNBaCI5LR32DV+l+dPnjm7hy5owmiyKMU6ffHTsW4WblWatecTMJ85nHV/Nbvir9hVf2Sue6hUzMSFFZIiGqzCnlXnDPEXF4DSbXuM7gWsFbiapWoNqW7hePs71V8RI5OkJ8fwEd1Ljjgxuljdwwd6OX2UyzuOCerCYbYVZ1ai2Rl0tiTfLT0p7/5OuGT+vV/sd8Rg022DptGWrLVsV6VRkEAknNAEgqTvjDyLbFXpZDBp03gXXNaIfDtHxe4S5fIcmphgI37/EAiKcofuKYaVwc7LTiirNti43MfHF608Pl1ePs2OOgbBawWaLrKu8gOwkLlNW64l/xteR0LoI2FjSXuJS7RfU/JNshlIlQp1INiLqgKl+SHpnVUejHI2aqKTHiPAGGUtc2pGxpg+GrbIYWWFkRMnc8DUCIWliEoWIE6F0YGejFYGykljEgtJY1oVef9pkXJdzXidSHWhqx1m95Vm15nNzw2/MJU/VloXIn3GQkpkc98S88hXLpqZrqqyStn89ha4tQqlHaS6VTYM8PyM8K+NXx4ppIXM3d8O+GXc3fiUCqFFkv/atxM4UVa0xlc6WqZfXnwwhf9Jk/LldvnYTbkLFVlXUqsLI8FpNycXb5g7pc2pMOghTvqDUqJFVufGs/TjUuX6teHn5TjdIGkfCty8w44RenWBPG/zcEKqSwkpFsKAPPyDjR09dFUnTe5k+/BlAaI0oanfJZp31aETR5ub2USWCTaTqlogr63mtyRI4s1vmaqSSnlaNGBFQJKSIe7MMlxQuarpo6aXByhxB6zdqy2NZC2KAECQiQPQQnNhbl0orkSbXLDEGYW0WavlE14R87jOZCRtpq4ljO/DErHmqVzxVW55KTysVCsGQAkr0DMlwpTecmCNa4zDWM1lbRkYhWJmNN+w9NAP+BERVIc9OCZ+dMDxtGM4U47HEHYGbpTwLbRPoRBIplxecQI4C3QvcFnwticaSlMACMiXCxeUnkbL+tNPU5paMh2RoQ81auX0aC3ItKSVRmrwEsRaEsFPpyqQcR0G0kmSzf6qoLGzlR5f2+7UgXF6982skN+G/f4Fcb7CXc2xTk2w2oSelbJc3TPBvT979gA+4N4S1WdvXZPelYHNNPZj82JOxgWQTmIgyEWs9bTUBzWuvd262tGqklRO1cNTSoYh3yLisA9HQJkMnK3Rp9LpbWw6FiKcoSEEQfSJ69spuwVKkM7NqXzIKUTyV7yVS85Fhr+ldRsNMqbu3cmImR2YiE/Fc5CFviWcQEzMx0cqRpqyrSsUsvLKTJlV5A5ylI9/Bj1eqPH715JjxScNwrhjOJOMJuKNImEdE49E2oHREiESMIjfpDZrQKUKza9oFMIjYYl1AjSPhoZm4h+DN8/C2KoOfcmT8VK8KGetMxnKikg5TthiRTMQhSrxXRJ/Hn+Kkbm88Q/EOlihbuvdM1rFO44GMP3bE7XY/KrRTLCOmOx2VJx/0+P4cIXQmL7TKnsQ7oxOz05reeSsnokkkE5E2YKynsY65/SEZH+uOhRyYyTGbcwiHJKJEJCRJRDIlxSANQzQ5chYRxe2m3EeJK82ewSu8iSQriK5sDlw+rp1sZtSyHL/Ka4KSJPcBTugjYW+sUTYo6k+07yoh91rf8qcYYv96b+jlPwBqPoOTI6azhuFUMbw2fuWo5xPzZmRejVQqN++FKOm9YT1UbJsKZy1R6awrHUF6gxxbTHeUrWPf50bqzviVsDavQ5BHrpwnTT9/4Yjw8WRd7k/Gak1MkglFFytqkXfLikRMojR15O5KZ8uYg8lKM3lXXnbpOhGtIJmyE9Y6ayEfUtWfFHaKZQd8YCgFOmeZkpZ7T+KdL/eejHXWGMcklAlUxlNrT6N/uHCdqQ2zEsW1csQSkCLf68A+TT0kw1ZUWBH2RBzII08+SVwsY0/2dg45S2TuBILKeqApxy2RRsNuo/eJQoZUNLtzRsBFSR8MYzQMyTAkxZgiigmZJGPyjAm6WDEkyxg1Lqic1o93pEeLSc27eGQLYxFHC/xpnkyYjgTTMUzHkXQ6sTjueTrf8rTZcGx65mpEioRLirWruaxnXFQzrvSMQdRMUefa/yjRnUVtZsjVLE8ePDZkzpzIoyPErMzSmzxKB1nNLdv5vgUZf8qR8YmcCEngkNTCYcTtQuySwiVFHwytmRi8ZjCBcHenriHpnKJKquyEtQRdUlMHHPAhcDfN9QluBkXRU09Kvmb/GUtKMyn20pZJJ4SOaB0xOtBoR6t/uGgeqYGF7JmJiVp4jIiYO6tXSAInAiblWrISMZNwSV/7KJm0zl3WyqB1Tnd6HYswkLiTeuX2OMtnELs1QapPov74JvZe2B7cpOgnw9pV2Q/bz3MXNRsG6ZHAmOAyVlyGOdd+xtI1bJ3FO42YRHHEKra1O7GkB16roq5IixY3N7fjV/NEPPLMjwY+X6z5anbD5/WSJ2bNQg4oEZmSZhkaXtojWn2CAC6iYPICNyp0J5gWEjOvMLMWlqtH/e6E1siTYzg5Ii5awswQWl02dKLIimYJXjVF3pzs+MHrfdpkDFOKuBQxxKxqR94lDymnq7a6otMTnbZ0xjNpnXe+akfEu4copCyzeMInvAs+4BPELs1lNMKa23T7NJEm997t2x4VaucwpkhKvkbCe1/ict+hElJHtA55jlh7GvXDKKIVY6lfOmbCYwSYO/8eRNqvA0pmq8sgc0TsVK4lN9FRq6wyZZRm0oGgFdwh4Vvf5NtNepJF+UkphLyH7eZHBD0k9JBQg8APim1fcdnMeGGPaOWEEokpKWZyRJEYkuEyzPnWnfLddMzlOGM9VIRBYUaBHkCNCTUGxDARHyKiUyDqLJHqZwrfcjt+1TpO254vZkt+21zxF9UrnuvlPjMyJMNNbFnIASkSU1SMXnM56OxE1YrsBNZodFsj6+rR1NSE1qjPnhHPj/CnDdMij1/5RuwzP0mIPObos9DRz5Gx/Ihu73uTcSsUlUg5vZIiMXqimBjkSCdHNrKmkRNWBowKuaFDRaJKd5oQxB3R+EzGSJl3wOLQxHXAe0YxUZCnJ6SjWdZtrjRJitt55W5CrLbEq+vHMTR5l0abt3l5VTxgyz3F7v5S7P+MBGTKZCxzx3OlPVb+OBnn9HQm4kpALQSK4r4GxJQYSg2Z6Aly3G/K6+ho5cRGVliZhUOMymuBeGM9QJYIfr8eCFC7daFs0j/BUojqA7pX6A78VjI1lquq5Rt9jBQRlxQ3pmUmM2EM0bAMLd9Nx3zdnfByO2e7rRFbhd4KdJcwXURtXVa+emCaWujcMBsrfTvyVufu+qaZOKs7nlVrvrJXr41fGQFj6lnEAUlkSIZ1XXM9tqzqGlebPLte5ZHHVBtEVT3K1IPQGvX589vxqxPNeCSy+1WTe5GSIs9YJlFmod/idd8h1f/YuDcZV8IQiYAHIk4EnPC521I4KumoZBYJMDKgVdYORqY3nFvuLBCCspCIT3YXfMA7QJZaT2lISTHtbSofG8JY5Mkx6bNzxidFyau9VfISMQsamC5il3PMqwXqxeU7z0qrk5PH+xBvYqcEtosmZY4QdtZ8r91zKiFkQqmEVhEtIlYG9I/MeNTCU4uwJ+JaKCQSVcjYUZ6TIrWIOAKDCPvOayN9XgtkVvDSMiJlyqJlMv1gHdj/v9htKLKxiRCCj2fJfHuYjcNu9D56C5VmY2q+EwkXc+31hT1ipjIZj1Gz9jUXw5xX3Yyr5YywNti1xGzAbBJmHZDrgfQualdKlUY/eWvqo0HYkJv5zMix6jnXG85lx1PpWUiNJNe1JROD2nCjWk50x8KMWOuZTMwd/CY35+56gd4ZUqE+e0Z4dszwrM3jV6eSaQF+nvBNIlU796tMxsIL5CDgZ3SOxEe0x7v3mTJCEZLAkIgkTOkSNCLsH1IktLgVlpeFjLMYbc7rZyenlPVqy0PclRc84NcNkUczZNsiZm2WbyyOUsIHGEbiepM9iB8pVby3tPvqKf3zhv5cMR0J3IxbjetYZig7SbVUNHNNU2u0kvgXLx+2QRACnp49ymf4OSQhyj11h4iLZ2naOSYVMQ5ZtKa1DPtpiLswImJIWCEwQmIKGctdS2/5EUgEEiZFLAF7Zy0wIqAL2WuZ1wSxs2Esj3TnWBHcsVwV7z2j8D6hlj12ZnCNyZs9LZiEZRmzlve6qZjZiUoVo4iYjSLWQ0W3rQlrg7lR2KXALhPVMmKWA2K5fidFOyHyRmcnWbzfpKmUsyVlrrwWjkoEaiGphEEiUELgmJjFvOGqpMcWuVRUKqNcu9e8n0vcnzhY1NkJ8fxoP37Vnxf3q6NImAfkzGOtx5hQxq8kzincqH+ejD/lyHgHJYrQA+y7K4HcWV2q4vsb7w7S3XtL3Pk78WnfeAfcA1KhTo/h/JR43OLnBl+/7oCke49aTejrNfHymviu0pZlpjJ89ZTtVy3bzyTDuWA6SYR5gCqnT1OQMErURuHmAl8ropkxA5RzD5rXVsdHhLMZ8B5TPm8Q161bGnvC23kR76wQxZ1xG/kjIze5KSvf44ZbIla7DXPKrx1IuBTK68Q8/lQmLJS4nTsWd34i0p6E3yRm4HaTDjna/wQhrleY1lJXMl/bIrsvOSfoek3fVmjrUcWecu981StEp7BriV0KqqtEfR2prkbU1YZwffNOG9RUvKNfSzek134Q058+5yElwq37T1ZWe0+QVQWnx7iz7H41nGYiHs8iHDvaxcBRO3BkRxrtkCLik6L3huVQwz/8iFH3HXzSDVw7hLIwvvlZ8khD/iJ/7EsSP3IBHPABIXbiATJ3rioFIeQmpveQLhZao55/Rnh+yvC0YTpWTPOsmRxL/16W1dRUa0t1VWPnLeqbF4Sbmwcfi6wr+OwJ/WcN288k/XPB+DQgT0dO5z3zO77Im9Gy2jQMbR6ZAImaGtruCWKzvbfcplgscEcWeDd97o8B6mcyV+oxbuq7m/JPlIgBwvUNqrJUSoKoIeXxHzlKfC8ItcRbg1fcqltNIjdrdQKzyQ5k9XWkvhjRrzaky+s8/vkuiNkGUfi41wgXQRC9xIU8DTNEwzZWdEmzTRMyTXuVsC7BNlm6WNFFyxQ1Pkrw8vb1ygjWfS1bX4MQeQN9XNyvjgTTUZ6D5iSPXz1bbPisWe/V4owM2UHON7yqZ/xQDvB1yE95ztilQCz/ORIhgSuKXC4pYpJFuzr/DFHm9TPeujdl+7SUN+rlJ7vd2gG/DIRAWIs6OyUtZqR2N6sn8k06edRmIC1XxOXqcTSChUA9/wz3myd0X9T055LxNI9UhDqP3GRZzbwYjWtFPZe0taRRAgUPskMUWiNPT5ietPRPFMN5JuLqWccXp0u+nN3w1G6opGeMmotpzjftCd+aY0ZahFPoXmM2M+zyHP/Nt/d6/9TW+OY9kkoqM6cpIe7eQ3f+KIphS/7116Oa8BaRTb7j8+8qIQkpEkmElIgp7arHr0VMkPWrQ/E9Tnd+vn78b/78dawDaRyJr67QUua1zlfoUaN6gW/LBvQN3WfpQY3kZq1NTk1X1xP6Yg0vXz2K3WzyDsYJ6SNqN37lwE+SfjKsXM2lm3GhF6XTe8WYHBIYkmQZa67CnCs/58Y1rKeKcdRZJnMSKJfHioR7t2kEoQ1p3uIXFjeTOVNV3K9mi5HnizW/nV/zVX3NZ2bJQg0Y4RmiYR0bvjMn/BwZf9KR8ZhcSUtFppQYkmQoalxTUgxJ52H1qAhRZim8ILNA/H5ovZyEQsy7hSSnTz6is/NrhRCoZ09JT8+YzvOIgG8lweRavgwJNSbMdoG9OUK/XBG/f/nOIwrqyRPC52d0X9RsP1f0T2E6DXDk0bXDmEBKAjdphk7jljobUWgNzGnd02w8v76f+5TQmnQ8ZzwxjMc5Na1OR744XfKfHL/gn7Uv+MJcUwvHkAwv3AlHOke/v3eKqWsYN4LxxGCO5/DdPeZehSAZnWcg3yd2GYxCyCKUrHS6vdeyl3j+c4ySEPOGOZbN9JvIm+yAI2UZTCSIQNxnxXLTliMRubMpRzHtN+eibNDze+1ueaLYH18+fl77f7HbnMdPez2I2y28SCgfqMc5umswpakrVFkAKXcBA4WM9ZjQfcSsA2aVU9Pp1dXj+b6nRBonZO/QfYUeJGoQiEEy9JbrpuGFPaJRDinyyNVC9ijyKNZNbHnhTvhmPOFFf8Syr/GDQfUSNYIaQA0R0U/39k+/CzlrSG2FbxSumFb4NqJbz0kZv/qL5pLf2jx+daI6FFmY5CbmLvU//sx7CP/xbPzuTcZdCoSUcMCQREljWIZY0hahygozIdsr+qCIUUIQ+3TI3j4tZDkyESKEmNOj4TDW9F4hBPqLz/FfnjN81tCf5SYmPytzejJ/R2oEs5HUN4pmZqhqi/z9N3sZzPtC1jU8OWF4VtM9lfTPEuOzQH3ec77YctZ0tHoiJsHGVbmbtJ3RmwpQSK/R/Zxqe0Lq+/tF6sYQZ1Xuap1BWARO5z2/nV/zL9rv+RfVt3ypV9QiMCTFicybjm2wXHcN1wuLb/MCGmcVaj7L7lFvg1+qITGmPRkTSoR8l4gjEEFEQdoZuERBSBKfJD7+kIwnJFOSmJ3qVvIYJJQ4+PVNuShkrO88MiH78h6h6NWnWDJku2NKr2/OxU5dKv5KCLnrSNOE7I+xmwV60WBbS6yzfniSRawigvDFK7h3yPWAWG2I1zfE4XFLHGkcEd2I7hp0l8U69FbiasO1bbEqf8cuKZamoZUTRoTsuRwaXk4Lvu2PedHNWW8a2OjyGuTxq84juoH4UAc1IaCqSJUhVHnjEqs8flXVjpO654nd8Lm54UtzzRdqzUJGrBAMaaKNWbr1Z8n4U27gWsdUZPAkQ1KsY806NuVnTRct22AZgmEMClf0qUXI7ea3NYodESdETAgf3q2+cMBbQT17iv/ynO1vWrpnuYnJHSX8PIApXe9eIEaJWUvcQuJrS9RHNCkh/v73D9KbFcdH+LMZ/ZlmPBNM54HmScdvz675Z4tXfFHdsFADAcG1n/GH5ox/0E/4lmNGV6MGid0YzPUCuVy9vQi9EIjK4iuV/XwroAoc1SNP7IbPzA1f6hVfqEQlLGPywIqb2PLEnnFUj1xXgVDvZjJVbip5W8SAcB75nnfgKQREiPv7iVgEDYrl3Y70iEDIhOxDlqmcQnZgexNdrIqYhwcZM/mSdjo/RGBKiTHBkBTbZPbCP7ufY/E8d+W9QpCksLNSzD7nucaY3sic3WbL0q8gbZ28J7y6RCzXyPkM0zbZ6rLoiedfSuCzjGPq8/hSeODm9+cQhxG93qLXLXajc9q8EkSj6HXNy6JDvXEVL6sFM5U9l8eo2fqKq7HlVTfjZt3gVxazlpg12E3CrgN6NZC23TuVt4TWRC1fk3TFJCpTxq90z4nqOJcdJzJyLC1KCNoUsumG+vlM3idNxjfR7jVpc4G/4ia03ISWpW9Z+5qtt2ydZXQa7xTJyTz3FXIaZv8IKUu7+RwVc4iK3ytkXZM+O6P/vGH7maJ/nhifeNTJxMlsoK0mBDAFxXawdKuaUBuilpAM0h1R98/xf/jmfrUgIRCzlunIMC0E09H/v70z15FrS7Pzt6czRUQOvHXr9jxAaKFQECBPLUNWAwJalmRIgGTIE9rqB5Cnp5BThmwB8uQJeoS2ZTTUKNXA4iWZU4xn3IOMvU9kJMkiGcwkM3nrLOCATJIZEYyMs9fe//+vtQLitOe70w3/+OQ1P61e8GfZJQvZ4JBcuzmnqgGgtZqLnWFYS7qFpDzNyYoC+FgylrFMreUdCYdRjlxGfXwlHLnIqGSGCoLKd8xkjA40yiFUICRdLKPs5giItkM3n3mjOd4/3kfjkkTK0sX7bsyhHX2SnYtl6s6paFfpzFsPuQsZMrWNvHcUwiFDnLAGGAL0QdKl9tTO5/ur9lmMVXTZvko2WIVzguDkvkIm7e1r268He+/ltC48oQXzvghDH/2ab27ihP/o/Cbl/v/r+uHzO795h9/VyFVNPjPYImnttaQXmtpVDL1mU+Vc5DMy5fZBEa3VbNucps7wG4NZKbLlKL9ymGWHWG1x23tuJA5ksKM0T8iAkoFMWnI5UMj+QH6lUSLO/Bcifl4/+BRfc5n6ys32aS11uvFWrmLlSta2YNmXrPuCZjC0vcEOCgaJ7GNzPw4LxJtO9gE5pEa/dVNIxGeG/PZHtD+a0XyjaH8E3Y/j6fQPz1f8frXiWVZjhGPncl63c35TnfLanNCRo3qJaQxmfYK6Xn58mZYYXu6rItnlgas8VdXzXbXhz4or/iJ/yR/rNWcyTulfyFiSu7Yzvi9PuJ7NsKXGlgJbarKyQJjso0/oYX9ivCWjIZ0G22Cog6ILFhUEtR+og6L1t0b9YTzBjSRx5KYxrLeY9flR33MsgvMEG+8jYT3ChjsVKGkF0kZ/Y5wgWIm1kt5GGUj7DnOGjYspTl5IBjEwiJjco5K0xQdBj9yfgg8rZFtXsLM5O5tR2yxWyZzCOxUrL2lzHn2E36iWWY/w/rZa9kOdI/EO3z7eAcRvt6ibDF1mFLkiSA1IhJP0g8B2kmVtWOcOpWJpJXgZwz5ahawV2VaQrQX5TaC89uRXPeo6ltbvndiUuEDsBw1u6WGcQ4jpYYI4VhxnjnyypfoYydVXfTK+cCe4IFKqiKH2GRtXsBwqlkPJeijY9jl1b+h7TegUopeRhHv2k3sqEbIYHKS4qweZ2J3wTgit8edzunNNdybozz3ZecufPLvhJ6ev+EfFBd/qNUY4Nr7gRX5OpXtCELzqFV1jMBtBfpZTnp/CEQ5AQsd83TG/NmSx1HRqGp4ll59nEk5lgcfj6FiqLc/0jpOsxRhHn4W9GXww+uOj9byDoUd2FtUHVCegU2y6jMt+zqvhjG/UFsmGwne0QfIy+QNf9nM2XQadioMpffQFDt1xi4y7uUFdnQPVUd93DIIdwFpEImPp/O2m14p4Ak0VKT9IwhCwVtFbTWsd2+Ht0vvSVTH0Qeq9w55MEYmOuBj2KbWpDYaNi2S8shUrW7JzkYhba+gGzTAo/CAjGQ+3bavDStm+dWV93KBbO82RfC6EgN9sUVc5mVJAiQgamaai7ZhXnGncOPGd1A6qB1ULzC7Jr5ae4rLHXGzh8vqTZ0vuvDxr42HNhf2MEVbSDZrWaTZubJFmVKJB0SGFYAiejQ+sw4fbScI+nY3e0WT8YjjbT1+2Ppa3ti5nNcQT8bor2LQ5bWtwiYhVK1Bd+gF2Ad2FveG57GzskQz9dNN9RsjFAjvPk0QA/Nxxvqj50/k1Pym/5yf593yrdhjh2XjDmarxxGGq1aKkn2uGuWRYaIp5dbxt6aEJBW9XehUCicAD740L+YSh5NB2qF1PtivRtURtJetNxfPqjIWOp/CluaGQPa3PeDGc83+bH/N8d8Z6U6G2Mg6m7Dxy2+J2nzBV/uoS+JPjv+9jEQJhJOPBIQafiDjEU2hKDxo3wy5ljPdK00qPkW//MFeJjFtvmMlun1csx7ziMc/Ym6Q5zdm4grUt2AwFmyGdjAdz27LqJWKQd17LftMwjFfcoGNtrEJM1bLPhtB1+KtrFJB7jxxKVGfQTaxi2ULgs5i2B2nYbpRfNQGzC+RrR3bToy+3cHH98fMc731hAbouSiy7gOoDshcwCLpes+pLlkPFhVmwkA1SeNowkKUhzKXPubAnH3yar/pk/LI7xRGnLzuvaZyhtobdkLMdsthLaA22NdAqZBMnc+9cfUB1Htk5RG+hSyPwT/mmEwKhTTLkl/s/G6c9g/NPOuVHFDk+V8nEPSBLy2ne8nv5mj8wN/yB3vCdkhgMWzkA11yZOd8W5/yy6GiL8naqsdCILPvoSkZw6bRmY5qKGASdVexszsaVLH3JqV/jaPDA0kuWrmLjCmqbYQcVd+s29njEYPFHDPsFa5GrLdlyRr5UDHNJU2U8N2cAbGzBr7Jne53xZT/n+e6M59dn2GVGuZTkq0C+HBDrT/MEPqas/8kYhnTFE4UcPGqIuk8/ENtEJhKz7yVBBaxWdEqj1dvv57Wd0XpDpTpqn2OEvWObOUYldj6WqbcuZ+tyNjYS8aYv2PYZTW/oO4PvVSTig5aV6sdKWSLlftxMWMIwEIapWva54duWcHmFtJasPUHtSrJFxjBX8Z7PwKvYvyVEhzzVh0jGW4tedahlNCR5yM+5b1pk06PaGLihWlC1pG8M13XJC3NCqfp94tWJjAEWA4qlm/FqOP3wk3zNph/ft6d4RMwq9ZrOahprYo940HStwXaa0Chkk07FrUjasxgpphuPahyytYg2EvEnj8B/RggdSUdkZh90LlKsW/wH4kBOkvpb1t5G8N23Z/KQUCrl20b5kpSBXFsq2XMiWxYiUIkMIxTeeyph90NMmXZ7Y3+viJuRI6xLQ9chmx7d+HhTNZK6znnVLPhV8YyFanAIzmT89cIt+GX/Lc/bc66aCtto8lagm4BuHDTtUe9tsBZ/eYU5W1AuNLZUeKNpKfnFoLiaV5wU3d6Ba9NlrDcVdpmRX2iKq0B56TCXNf4T7DC/FHw/IIYBMVhEb5G9RvYS1YMzILPUKtIiGq0piVeKXr5tWwtw3c9oVGxFFXLYe02PcGEk4+gtsHU5O5tTW8NmiES86zK6TuN6Bb1EpAqZ7EciDsl4Im3QrX+jdfUxvYgJ90XoOtzrC2TdoLcL1LzCVDm+1PgsmgHty9TWI/oovxK7FtZb/Gr98PKrvkfULXpnMbXG7BS2kvSlYZOVfK/jUFnnNdd2lkw/XJRjuZKLfsHvf+A5xBOqxh7fM27ne2etwUt6q+msou81dtC4VkVv35GIG4FuSAtpQLce1XpUaxFNHxfWh4ioeygkZyqZ55DniDwDo2PeslYEKaNv7oG+Q4xaSOdTmdDCMBC6PpaB2u7xT8yj41mSuPgQN1TDvucX3dUAhuDpQjRusCGatxyatuCPs8gM1iI2O8z6BLPRmLWgWWe8LBf8vf4OHwTX+Zy5auPv7Zxfts/4+eYbrlYz5FpH2cTGozcd4RNuet8PqJcXlKXBmwqQyEHT15KbecZN7hAyRA1sp1BbSbmUFFeB2StP+aqB19cPvuA8KLwj9AOiHyIZdxaVSXwmUEbiOwha7HOEgxIxf1jolPp69xR61VUUKqNUA7my6DfJ+KBC1vk4lV0nJUUzGOre0I6b804iOonqYxDHnpC7MaPXI3uPbC306d6ZBjq/LELAbzb47RaZ58hZhSoKyEwMcUkbcGFd/Bm1Ub702e6JEAjrDXo9x5wYskqmsrlkUDlXB/Krs7xJ8quADZKdzbjpqo8g46fz+TqajC+3MwBciPIIaxXOxsGM0Mk4rNXKZOmWTsRNQNdgmoBOgnBZx11PqJuncSoeSbgsEEURU4Qyg89M9CfWMkp8pNiP2gPJ0WjUSvuo9RxSqa1LC2PbEpr2QROIjkXoB2TnUt9eMHSKdVfwul/wMjvlTNZ4GoyAnZe8dnNe2VOu+4q6yxD7smJAdO5of1y/3qBvziiWhmGucaViq2b8PAh2Q8avi3Nmuscl049X9YKL5Rx7VVJcS/KbQH5jo1H+rjn+DfAOv1yhnhtmfIscSnSj6DYSW0l8rvEqSYE60DXkq0B55ShfNqjnF9iLi+Of9wsjtB2hKBDdgMxNJLhOokyIA3R6jAgeIxYlXoBD8yYZ33QVubIUKiXz7JPY/N5RyyPonaL3sULWWU0zaLrB0Hca2ytCqxCtuq2S3SHhWPJUrU/zI6la1nVPokQtjtGU/1AQQiTYQ5KV6jbi9Av28d1mg14tyBY5rpS4dEpHKIZQcDUodlXGRTajMHaspNP0hqbL+GAAwtd8Mt7uimiU46NEJNhxOjL2gvY735bbE3EdXVl07dE7ixqJuGmixeIj736F1jHKryojCZfR+cVnKpZojMQbGfsmYwD62DbeOxyFJCEJ+zKO7C2ic4guRxYFokybjwcI2z4WYbdD7QZMXaBrgdgpbnYlvyqfcZKGmK70GoNj40teDOf8v+ZbXmxPqbc5eicxuxCHmHYN9sjBB7/boS6uKRY5Lk8bm6DZDTN+UWd8X5xgtCMAfa/p6wxWhvxKUlwEZq8d+UVNuIdkIliLfXWBso6q+QazqejPNEN5mGccDvKMLeayRlxcx/jErwBh6Alti8gzRDegjMIbmU7EMVMcOUYsjq0G+U4p76opyLQjUw6jHFr4O+XskDzo36ySDWly2nUKOhVPxImI5d4uMbWsukjE+2HOJlY+fPf41aRxXZhArLo8xuBxCPibJarMyY1KvWsZpYaDwjaSujLUuUNqv+9r+yFWaOH9a+1XfTK2G7M3nB9dtcahHDlwOzXdHpSmm7iI6+2A2vWIXSSlsN09rpxJCGSeI+YzRFkSqoJQZvhC4wqNz2L4tsuiC0xQB/mfBz3T0T1otPccNdSq1/E02mpEniFrgzAmSn3q+7nTHAtf1+jVjmxVkq0M/UJSlyW/1FH/uhwqvsm2UdrkCl53C36xecar5QKWJka5rTxm1RPWm0/Sfvqra0yRU6lvgAI5SHStGRaKrshpD4IiTJP0i9eB6sJRfl8jXlziVvccEPEOd3GB3O3Il+dkJzN8lcVNl4xaYtk75K5DrHf4myXuETZP90FoO0LeIoxGGIVKZOz1G59fAWK8l/3btp27JqfTUWOqpUcd5CBDCplITl7OR92yswo3SMIgoZfITsbN+Zsb9BZ0G4lYtw7ZDrFt1XbRfeq+yUT3hRC3WdsTHhV+t0Nd3mC0AuYInyEHGSt8dUy/8rlK8ywBvMA4UO1HzLU8IdfHo8lYbVU6CYpbV59ROjEclp/Gmy6ga4fZ2kjE2waxrWNw/GP236RClgVyMYcqmlL4yuBLjS0ULjnSeBPN3CMZp4UsnSxG7C38ksPRmISiBonq4gdF5ZpgFDKPZCyNxn9CHN99EC6vyc9nlAsV84ONYRkWdIPmYj5jbnqU9DTWsG5zVpsKf52TXyqKq0Bx7dCXG9zN6pOqGcFa3MvXZIC05+i6oN0ohhkpEGLUMcbPTrbx5DeW4lWN+P4Sf3PzYFUUX9exQpEWXZ1l8cToYt/VPaU5hiMRhp7QtHEAUSukUqixvSLkQab4mKIm3lmt62uD1QqhAlJ6pAwpdjwQgkizizEIxo+VsiFF6Y0T030i4u5wg04a5vPoxt22rZou+o53j6+skGWJqEpC+TtYpn6CcDc3KCkwPiDcDNVm6GYMkDiQXwmRJr4jD/HjDz3wV1ymNht5x3heJgedPRn3iYhbvydi1dj9iZjNLhLxI5429mXpxZyQkkHcLMOWClcqbBF9WiMZk8wm2BPyYQg6xNPFoee2THIS2Qt0JlCZRGUSbSTKSJSUcZGUirDZfLFNiVuv0S9v4hCTLgCJ7DVtM+NFVSBzh5DgrSA0GrVRlDeC4jIwe+koXu6iXvY+weZdh/31C1TbMVufkZ+VDAsTqxAp2Uj1HtV4zKZH3kQTAbdaf54FOoRoUPCZPIAfC75JJ2OtkVLGGQclCCK6LAFvbKoFZHcfQ9Sxj44KOBl9y4U8jGiMHtej3zVjpWyIph5xSOu2UhZL05GIY9vKoXbDHSL2Tfvo5j/CZLFlVcZK2YSnAXd1jRospjtD7WaYTY6dG1xxUL3cT3zHdeSHTcapSrg/DY4e0wdkrDsfb7zGIkcirlvCtv6i5PMu7In4ZEGYlfh5jqsy7ExHu8ZC7AMFXC7wWSLhPRmH5JeaHnCMfhsTqWzcoLi0CPkMVCbQRqS+nSAoiVICKaLPsYQv9p7Y37wgMxp4hrQ5upGYjYpynzwQBGgbSzx6B/kyUF1aipc14jcXMcbtvvAO9+o1YrlCL+aYWUXIM1Ay+hA7FxfnTfK3fexJ9K8R3uF3DVIphJLIw2nYkGwPw2FV520yVltJ0CFNXo+f+1syFiHGIB4mskkHYgDVi705xFgt0wfSxnGTLushrg271LZ6AsOcosgRRUEoMkJu+OAQ0CNAjFLLIySGPwS49RpR18jNCdlijqkKfGnwmY6DXUqkcBSP7B380w88oH06a8vRZJytwy0BjQbv9lArmJy1Whd1aM04NV1/8bL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"text/plain": [
- ""
+ ""
]
},
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},
{
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"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
- "#Initialise 2D environment and agent\n",
+ "# Initialise 2D environment and agent\n",
"Env = Environment()\n",
- "Env.add_wall(np.array([[0.3,0.0],[0.3,0.4]]))\n",
+ "Env.add_wall(np.array([[0.3, 0.0], [0.3, 0.4]]))\n",
"\n",
"Ag = Agent(Env)\n",
- "Ag.pos=np.array([0.5,0.5])\n",
+ "Ag.pos = np.array([0.5, 0.5])\n",
"Ag.dt = 50e-3\n",
"Ag.speed_mean = 0.16\n",
"Ag.rotational_velocity_coherence_time = 0.3\n",
"\n",
- "#Initialise neuronal populations\n",
- "PCs = PlaceCells(Ag,\n",
- " params={'n':4,\n",
- " 'widths':0.18,\n",
- " 'color':'C1',})\n",
- "PCs.place_cell_centres = np.array([[0.2,0.3],[0.8,0.3],[0.4,0.8],[0.4,0.3]])\n",
+ "# Initialise neuronal populations\n",
+ "PCs = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 4,\n",
+ " \"widths\": 0.18,\n",
+ " \"color\": \"C1\",\n",
+ " },\n",
+ ")\n",
+ "PCs.place_cell_centres = np.array([[0.2, 0.3], [0.8, 0.3], [0.4, 0.8], [0.4, 0.3]])\n",
"# np.random.shuffle(PCs.place_cell_centres)\n",
"\n",
- "GCs = GridCells(Ag,\n",
- " params={'n':4,\n",
- " 'color':'C2',})\n",
- "\n",
- "BVCs = BoundaryVectorCells(Ag,\n",
- " params={'n':4,\n",
- " 'color':'C3',})\n",
- "\n",
+ "GCs = GridCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 4,\n",
+ " \"color\": \"C2\",\n",
+ " },\n",
+ ")\n",
"\n",
+ "BVCs = BoundaryVectorCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 4,\n",
+ " \"color\": \"C3\",\n",
+ " },\n",
+ ")\n",
"\n",
"\n",
"fig, ax = PCs.plot_rate_map(spikes=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,\"pcs\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"pcs\")\n",
"fig, ax = GCs.plot_rate_map(spikes=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,\"gcs\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"gcs\")\n",
"fig, ax = BVCs.plot_rate_map(spikes=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,\"bvcs\")\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"bvcs\")"
]
},
{
@@ -140,31 +149,29 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 1200/1200 [00:01<00:00, 824.75it/s]\n"
+ "100%|██████████| 1200/1200 [00:01<00:00, 1001.09it/s]\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_time = 60\n",
- "for i in tqdm(range(int(train_time/Ag.dt))): \n",
+ "for i in tqdm(range(int(train_time / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
" GCs.update()\n",
" BVCs.update()\n",
"\n",
- "fig, ax = Ag.plot_trajectory(t_end=60)\n"
+ "fig, ax = Ag.plot_trajectory(t_end=60)"
]
},
{
@@ -173,9 +180,9 @@
"metadata": {},
"outputs": [],
"source": [
- "if save_plots == True: \n",
- " anim = Ag.animate_trajectory(t_end=60,speed_up=2)\n",
- " anim.save(\"../figures/animations/trajectory.mp4\",dpi=250)"
+ "if save_plots == True:\n",
+ " anim = Ag.animate_trajectory(t_end=60, speed_up=2)\n",
+ " anim.save(\"../figures/animations/trajectory.mp4\", dpi=250)"
]
},
{
@@ -184,20 +191,20 @@
"metadata": {},
"outputs": [],
"source": [
- "if save_plots == True: \n",
- " anim = PCs.animate_rate_timeseries(t_end=60,speed_up=2)\n",
- " anim.save(\"../figures/animations/pcs.mp4\",dpi=250)\n",
+ "if save_plots == True:\n",
+ " anim = PCs.animate_rate_timeseries(t_end=60, speed_up=2)\n",
+ " anim.save(\"../figures/animations/pcs.mp4\", dpi=250)\n",
" print(\"pcs\")\n",
"\n",
- "if save_plots == True: \n",
- " anim = GCs.animate_rate_timeseries(t_end=60,speed_up=2)\n",
- " anim.save(\"../figures/animations/gcs.mp4\",dpi=250)\n",
+ "if save_plots == True:\n",
+ " anim = GCs.animate_rate_timeseries(t_end=60, speed_up=2)\n",
+ " anim.save(\"../figures/animations/gcs.mp4\", dpi=250)\n",
" print(\"gcs\")\n",
"\n",
- "if save_plots == True: \n",
- " anim = BVCs.animate_rate_timeseries(t_end=60,speed_up=2)\n",
- " anim.save(\"../figures/animations/bvcs.mp4\",dpi=250)\n",
- " print(\"bvcs\")\n"
+ "if save_plots == True:\n",
+ " anim = BVCs.animate_rate_timeseries(t_end=60, speed_up=2)\n",
+ " anim.save(\"../figures/animations/bvcs.mp4\", dpi=250)\n",
+ " print(\"bvcs\")"
]
},
{
@@ -209,193 +216,184 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 500/500 [00:01<00:00, 256.99it/s]\n"
+ "100%|██████████| 500/500 [00:01<00:00, 351.42it/s]\n"
]
},
{
"data": {
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
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1EjOXp+L85cloCmcAOBJimb1rNKJ95Uq0r1zZeKiIiPFrs/HhX30c1VoZEeWWQ+r15dEitodyHesB2S2jKrOIKOPurcVYWWrH6XNjMX3u1IF/CWurnbj2D3fii8/mtqxlXEcd83dXolyP3vzefOz7X0h0O1VvKkUveaMo8qiqKspuFc1WseW1D24q0nuvOrlqSF31bhSMiMjyiKgjOmtl3PpiIW5/uRgff/Bl1HXvCnhe5DE80oips2Px/Evn4/ylyQP5vQEA7hPLHJiZy1Px/Evn4/Pf3Y3ZW8ux+dJp3a13iOT7V5sjIrL8/vNlWcfyUjv+x3/7bfzBa5fiytfO7ntKRqddxs1rsxsRXHaruPPVYhR5vuXKcETE0vzaxuCqbhV13lve7d5n9+Ym3x9z1a0iq+soqzryIttyZ2NV1vfnKEcvlO9dWd6Nuro3lDqiuz6odd2qjuhU0V7txvzd1fj8kzsxPjkSr/yTKzFzeWpPvz8AQJpY5kBdenY6vrq+EI1GvpF2Zbd8dChn9x+PiKjLOuqsjpXlTvzuw1vRXuvG11++sONndjtlzN5ajk6njKqqYmlhrTeNoV3G3O3lWF3pRKvViCzPoqqqmLuzElVVRxYRzfX1j4sij263jLXVbuR5FlWeRV3VUVe9GxeLRh5ZrAfwg59f9q4yF8XmqRF1lPduhlyfXlI/5IryQ+3imLJbx+zt5Xjr//4wXvvOs/F7f3C401cA4EkhljlQE6dH4vdeeioW51dibbU3nWCnQNwplKv1VSKyLDZuhouImL29FO21ToyMtuLy82c2Hl9aWIvrv7sbX16fi067jOXFdu9mvDoiz3sBGevzo7NsLYaGm1FV1cY85Doi2mu91SrGJ4djbbWzMaBGI49ut9oI5rqq081a11E0i61Xlav7c5Kz6N3IF0ewhHS3U8Xf//XvYmSsFRee2b5UHwCwN2KZA/f0c9OxstSO9//us/Wl4rY+v3kaQ5btHNObX9dtl7E4vxbv/fRajI0Pxe0vFuP6p3dj9vZyZNHbJrrTLnsrSmzcJFhuLM/WW0Uii9XldnQ7ZRTNYsuV7qqqY2FuZT2iNyZWR6ORR1n24nrbjX7r8iKLqlqP/60XlrfItj90aMpuFT/7ySfx/f9j6shXEwGAk0Yscyi+9s2ZmL29HLdvLsRit4x60/1+vai9/89bZNunaHS7VWRlFe21bvy3//P9ODXZitXl9Tes61jtlBtvWjTyqKtq03yO3jSNRrPoXR1eX56u0cy3hGRZ1lGVVRSNTcWbZb2d9qKOquxN26g23aSY5705ylW7u+UHgONgeXEtblybjYuuLgPAY7HTAYfmW398OU6fG4vR8aFoNIsomsXG3N8sVcv1Do/FvQ07eldN526vRqfdW9atLDeHcR3l+gYhD75n2d10Y15d7zj3uKrqRPT2Vp4omo/epvphjvIib11H/Prtz4/uAwHghBLLHJqh4Wa88k+uxNnz45Hl967E3g/lx7kWW5V1dNvdLcu+9d6zjrqKbdFbPzCVoqq2rmqRrQ+oTky3iFjfaW/Hx3dYM3qHx3qPJ9/+wM3dXj66DwOAE0osc6iGhpvx7f/5+bj07OloDTUii02hvH4zX+zzimtd3/u1KXDvXzx+xMEPhHGWPXT+dEREa2jnWUt5nm8L5izLtgbzpl37jkq5w9VzAGBvxDJH4pt/9HScPjcak2dGotEqoiiy5E5+e7UtmDce32mLvK3HbdZbc7k336Oqquh2yui0u9Fpd6Pb7ka3W0ajsf1bJsuzaO4Q0pu3zr63rNxR5utxm0cNAINILHMkRk8Nxct/dDlGx4aisb7JR5ZlO66WcWge0eV53htPp11G2ektG7cxhzrLor1WRqddRrdTbintZrOIicnhbXOSszyPvOhdYc6L7MivLO80PQQA2BuxzJEZnxqJV7/zbFy8cvp+WB7Q1c+Nq8sPvVLdi9ZN/7rFvY1Kdgrae8dleRZFkUWnU0Zd15HnWYxNDEXRKGJiaiQenNY8NNKM8cnh3uoZxdHG68ho60g/DwBOIrHMkRoe6d30d+b8eBSN3lzfLDu4lSKyjf/Z4bksoijyTUspb15ruYqyrHurXTS3flv0bk7MN/17Ho31JebGp4Z77xm99Zwnp0djZLQZRZHHyGgzJqZGYvTUUExNj8TY+ND6knUH87U+ypmZU0fzQQBwgllnmSPXaBbx2v/0bPzkR7+OtdVu1HVvRYuDmmOb2gDk3nJvzWYRZVlvmaZQlfXGld88zyNvZetL0G2/Itxo5DE00oyh4UY887UzkUUWK0vtKMsqGs0iTp8dizPnT8Xs7eVYW+1EXUU0W0VMP3Uqvvh8Lt79m09jcX4lqkPe0e/sebEMAI9LLNMX45Mj8dJrl+Ldn17btvzb4+pdMX5wC73sfhxnWUxMDUUdEWsrneh2ep+/OZ4brSJOjQxHs1VE2e1tdx1Z7zW9HQF7bt9cjD/8s9/bcRxnZ8a3PXbhman49De3otstY22lu31N6AOS5RFnZiYO5b0B4EkilumbKy+ci7m7K3H1N7ei0y4jq+stU5gftZTbxmsitrfx+vSO9YvWUWxaxaI1VMTQSDOyLIuR0WZ0Or0b9yJ6xzSbxZYgzh8y9Xd5sR3zd1di4vTIrr7mosjjwpXTMX93pbdFd50lt9J+HCNjrZg6M3rg7wsATxpzlumbosjjm390Oc6eH4/GeqDem7/cm8ucbdvc495zsf66LMsiX3/d5jnIeZFHo9mIRrOIoZFGFOvv0xpqxKmJ4cjyLE6fG4tv/OHleOriRIyODcXoWCtGRltbQnk3Vlc6e3r9s18/GzPPTO17felHKYosnn5u+kCW5QOAJ50ry/RVs1XEt//8+fibN34Ts7eXe1tOb7rSmkVE5PcvMd8LwHx9R8BqfXm3ZjOPuu6taBHrN/I1m0WMjLWi2Sri1ORQTJweiVMTw9FoFnFqYiiG11eLuH71zmN9DXuda51lWXzrj5+J2VtL8eX1+cf67O3v3VuB48VXLx7o+wLAk+qJiOWlpaV44403+j0MHqJuRmSTnSi6VVRVtTGtIttY3uLeFI0soiwiq1uRdUejqFrRbBaRF3nUdR0TUyNxZuZUjI4P96ZTNPIYnxqO0VNDyc/e65XkBzX3cXyeZ/En/+vX4ic//iBmby9FtcNue1mjHdnQamSNbmRFd3dXorOIbLgZb/1/t/c8JvqrLMvodrtRVb3vgd7OkHk0Go2NTW04mfI8j7GxsRgfH4+LFy/G9PR0v4d0aO7cuRPXr1+PhYWFWFpa6p3vGXhLS0v9HsKheiJiuaqqWFhY6Pcw2I0sIkt0wUYrNjoRsRp1zEdWjcapsYtx6ZnzcfHK6RgbT0dxytmZ8fjq+v7++2i08pjc59zgoeFm/PH/8nz81f/161hZ7kRdrf9AUHSiGJ+PfGhtX+/b7nSjvbeZIRxD9yKi3W73eSQchbm5uYiI+PDDD2NmZiZefvnlmJg4OTfpzs/Px3vvvRc3b97s91Bgz8xZZqBV+XLMda7GmYutfYVyRMSZ8+PRGt7fz43nL01urLO8H5PTo/En3/t6nBof6s27brajMX1r36EMDL6bN2/Gm2++GXfuPN4UsePizp078eabbwplBpZYZuB1u5146623Yn5+f/N/8zyLi1dOH9lxDzp7fjy+87+9GFPnmtE4fTuy/Aj3xAaOpU7n8c5rx8X8/Hy89dZb0en46y4Gl1jmROh0OvHee+/t+/jLz0/Hmb1s4pFFvPDKhRgZO5gtpSdOj8TY+dWITCgDPY97XjsO3nvvPaHMwBuIWPaNxm7cvHlz339tmWVZ/MFrl+L805OPfG1eZPHSa5fi3IWDm094584df0UJbPM457V+c15jt4575w3EDX5ucGG3bty4se87yfM8ixf+0YW49OzpuH71bnx5fX7LKhXDY824+MxUnH96Kpqtg12d4Pr16wf6fsDJ8TjntX5yXmO3jnvnDUQsd7vdfg+BAXEQq56cmhyOr3/rQjz/0vlYXelEVdXRaOQxPNo8tI0+rNYCpAzq+WFQx83RO+6dNxCxbB1Gdmt5efnA3qto5PteYWOvVlZWjuRzgMFzkOe1o+S8xm4d984biDnLeT4Qw+QYGB3d+5rHayudWJhbicW51Wiv9een25GRkb58LnD87ee8dhw4r7Fbx73zBuLKcqPROPbzWTgexsfHd/W6sqziqxsLcePq3ViYXb3/RBZx+uxYXLxyOqafGju0aRcP2u24gSfPoJ4fBnXcHL1G43jn6PFO+XWt1sEsz8XJd+HChUe+ZnF+NX72k4/jo1/c2BrKERF1xN2vluL9n30WP//vV4/sSvPFixeP5HOAwbOb89px5LzGbh33zhuIWG42m/0eAgNgZmbmkXeMLy2sxbt/+2msrTw6ghdmV+MXf3M1Ou3DD+bp6emYmZk59M8BBstuzmvHlfMau3XcO28gYhkepdlsxssvv/zQ11RVHe//7LPodnZ/I8HKUic+/MWNxx3errz88svH/oQBHJ3dnNeOO+c1TgKxzMBrNpvxne98JyYmHr5JyO0vFmJ1ee8Ln9/5aimWF9f2O7xdm5iYiO985zv+YAF2fV477pzXOAnEMgNtZmYmvvvd7+7qrylvXJ3d34fUETc+3eexezQ9PR3f/e53/dUlPMH2cl4bBM5rDLrjffvhAcnzPMbGxvo9DA5Anudx6tSpGB8fjwsXLuz6D5Nup4zZ2/tfq/TWzYV4/qXz+z5+LyYmJuJP//RP486dO3Hjxo1YWFiIxcXFY78OJbu3tLT0WP9/OqedLPs9rw0S57WT7XHPacfdExHLY2Nj8b3vfa/fw6CPup2yr8fvx/T09In8Q5OIN95447F2N3NOY1A5r51Mj3tOO+5Mw+CJ8LjrJR/VessAwPEilnkiNFpF5MX+g3doxM0pAPAkEss8EYoij6cu7v+u8pnLkwc4GgBgUIhlnhgXr5ze13FFI4vzl8QyADyJxDJ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- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
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nfTH6ut08kkgiKSLSRv97mxvdSJKIRrMWmxvduHVtNT5JbsSR+amo1bNIkojjp2bjmReOxfxT03v+sCEAYhngQJx95URceH8hNteL2OvF5XufEUwiyrKMoigjTZOIKGN1eTOSNN36uv/4ytJmHD3WiixL49b11bh5fSWOzrfi2RePR1ZLXG0G2AOxDHAApmeb8fRzc3Hpk9tR5P21y/tYkbF1fBkRSeS9Ioq8jKQsIk2ze48XRRkba52Ynm3E5kY32hvduH1jLRYu3onZo1ORJElsdn1QEGA37IYBcEC+86cvRKOZRZIkW3/2/5pF0Q/liK01zV8p8M31bty5uRYba90otj5f2Gnn0d7obh1vKzqA3RDLAAekNd2IV799JrIs3d2ey1vPTZLYsd74bijfdX8sF0URvW4eeb4ziDc3uvv6sCHAYSWWAQ7Qa995Jk6cno1aPYs0237KrbzSXNG0SfSvJj/wuWUZeffuco+dL5DnZXTavXtLQgB4PGIZ4AClaRLff+vVmDve2grmJNK0/ydJkm1XnPtXku//8+WDSfrwS9Pbllc8KLbLMlYWN/a9bhrgsBHLAAes3qzFn//w9Tj7yvGYatW3Srj/WBLb4zhJkkizdMfyizRNHhjBdyM6v++K8YOuWOd5Ebn1ygC7JpYBhqDRrMWbf/ZS/PFfvBJHj01FvVGLerP/J0mSSNJ+JNcaWWS1ryzXSPuPf3Xdc7J1dbosy20hvWNf5bLs755xQD8bwJPM1nEAQ5KmSZw+Ox9/cXIm3v+Xi7GyuBkREeur7ej1ih3LLsqijCRNIqv1rzSXSbnj9XZIdi7ZuLdEw81JAHbNlWWAIWtO1eN7//mlePXbZ+LI/FQcmZvaEb71rSvMd0M5YiuO7y7fuHu1+SvSdOcSjrux/MC4BuChXFkGGIFaLYvnv3Yizr5yPG7fWIsvri7HpY9vRVGUUWtkUa9nsbayGZsbvfuOSiJL0yjKcltE303g/lKOBwdxkvWXbBSl5RgAuyGWAUYoSZI48fRsnHh6Np7/2on4/KNb8cW15SiLiJkjzYiIe8GcpklMHW1GZ/PLG45svUjUGtm919v5d0R/2zpbYQDsmlgGGBPTs814/Y1n4uX2qbh+eSk21jqR50V02r1ob/Ric70TEUlMteqxfGejfxW6nsZUqx5plsTync0dr5kkEa2ZRnTauT2WAfZALAOMmUazFmdfObHj++2NbiwvbkSvW0SRF3Hz+kos39mIcquBm1NZtDfzL1+nkcX0bDMiiei016PXzSMb1g8B8IQQywATotmqx8lW/d7Xz750PDrtXly7tBhLtzfiyPxU3L6xGt1OHs1WvX+b7S1lUVqFAbAHYhlggjWatXj+a0/d+7ooyvj845uxcPFO9Dr9S87tzW70ennVSwDwEGIZ4AmSpkm8+OrJOPvKibhxZSku/v5m3Ly6HEXusjLAXthnGeAJ1OvksXBxMVaX2xFJEmmauCcJwB64sgzwhOl2evHrX3wem2vdaG92790J+0HbygHwcGIZ4Anz0W+ux/Lt9eh2i2hvdKPIiyjLUiwD7IFYBnhC5HkRn164ER99eO3eGuVer4wo+/cjKcvS2juAXRLLAE+AbiePD/71Uly/sv3DfEnSD+W7/wvA7rjIADDh8ryID391OVYWN6Pb6W17LEktvQDYD7EMMOGuX1qKpdvr0Wn3otft39a6LPqXkdOtWE4Su2EA7IVlGAATbHVpM357/kqsLm1GWUb0Ov0P80VERBKRZWmkaRJF4QN+AHshlgEm1JXP7sSF9xdiZXHzy28mEff2iisj8l4RZWyFsjXLALtmGQbABFq4eCc++fB65N3tt7FOs52n9SSS/tZx1i8D7JpYBpgwm+ud+OTD6xGxc4eLNE3iQYuTk/hy7TIAj08sA0yYhYuL9yL5Qe2b1R58ar/7oT8AHp9YBpggeV7EtcuL976u1bMdz0nTpDqYbbYMsCs+4AcwQdZW2tFt59He7EaRl1uf2Su3bjzy5WXmNEsjkmTbNnIA7J5YBpgQG2ud+MNvr8edm2vb1ioXeRl5r4gkTSLNkkjT/lXlNE0iTbMoizKKougfY8kywK6IZYAJsHhrPX773uXYWOvu+FBfkiaRpEmURRl5UUaZlVvLMJJ7j2dpf7lGmSR2kAPYBWuWAcbc6tJmfPiry9HrFpHV0h0Xh5Okv0b57tZwd680A7B/YhlgzH384bV78ZumSdSbO38peDeY70ZzkZc7PsyXZUk0pvxCEWA3nDUBxtjq0mYs39nc9r2pVi067d6O5yZJEkmWRJr1t4mr1dJoTtUjSSKyWhbHnpqOmxu3ot0Z1vQAk8+VZYAxdvXzxR3fqzdq0XjA1eX7JWkSRVHG1HQ9pqYb0ZjK4uVvnjqgKQGeXGIZYIytLG088PuzR5tRb+zcY/l+ZdnflzlJI17/7jMxf2LmIEYEeKJZhgEwxu7/oF5+357JSZbEkbmp2FzvxuZGN4qKvZRn56bitW+fibnj00OZF+BJI5YBxliaptHe7Adxr7t9h4t6PY1mqx5zJ1rRbRfR7fT6MZ1EZFl/vfI33ngmjsy1RjQ9wOQTywBjam2lHbe/WI3V5fYDH+92i+h225FlSRyZb0Vzamrb41ktjemZ5jBGBXhiWbMMMIbWVtrx63cvbt1c5OHyvIzlOxtRFNuvPD/93NxjHQ9ANWdRgDFTlmX89r0r0esWUatlUa8//IN8ERFFUcbK0vYt5s48P39AEwIcHmIZYMzcvrEWG2tfboY8faQZyVdv2/cAvW4RvW4eERHPf/1EzByxBANgv8QywJhZuHhn29e1WhpH51uRPkYxb25047mXj8eLr548qPEADhWxDDBGiryIOzfXdny/Vs/i6PFWTLXqlVeZ6/UsWrP1ePkbbj4CMCh2wwAYI71eEfHgLZMjy9KYOdKM6dlGdDZ7kRdlRFlGkibRaNT6H+ZL+muek8dZtwHAI4llgDGSpI+O3CRJotmqVzwWQhlggCzDABgjtVoatfreT81TrcYApwFALAOMkSRJ4unn5qLbzWNjrRPrq+3YWOtEp92LyvUZ9zl9du7ghwQ4RCzDABgTRVHGtc8X44uF5Vi+s7Hj8TRNYmq6vvUhv51LLdI0EcsAAyaWAcZAr5fHb9+7Eos31yMiojlVi/Zmb9tziqKM9dVOdDZ7cWR+KtJ0+y8Hn33pWNQbTusAg2QZBsCIFUUZvzv3ZShHRMwcaUa98eA79/V6RawsbkZZfrks49SzR+PF1+ytDDBoYhlgxK5fXoo7X6xv+16SJHFkbipa0w/eV7nXK2JjvRO1ehovvvpUvPadM3bBADgAfl8HMGJfvWPfXUmSxPRsM1rTjWhv9qLT7kZR9K8mp2kajWYt/uNfvBL1+oOvQAOwf2IZYISW72zE2nL7oc9J7n6wb3rn3sp3vliLU88cPajxAA49yzAARmh5ceeuF7uxss/jAXg4sQwwQnmvGOnxADycWAYYoay2v9Pwfo8H4OGcZQFGaPbo1P6On9vf8QA8nFgGGKH5E9MxfaSxp2PrjSxOnj4y4IkAuJ9YBhixZ144FhERZZTR6+XR7fSi182jKB6+Hvn02blIM6dxgINk6ziAEZt/aibKKGPx5lrc38dJRDSaWTSn61Gvbz9dT8824uwrJ4Y7KMAhJJYBRujapcX46INrURYRWZZuu5pcRkS7nUe7nUejmcXs0amtG5U04lt/fDZqbkYCcODEMsCIXP18MT76zbWIiEjTJI7Mt2JzvRvtzW7kebntuZ12Hutr7Xj9O8/E2VdOCGWAIRHLACOwttKOjz+4tu17SZJEa6YRU9P16Hby6HXzKMuIJInIalk0mlkkaSKUAYZILAOMwMLFO1GWD34sSZJoNGvRaO48RV/9fDGe/9pTkabJAU8IQITdMACGrtfL48aVpT0d223ncfPayoAnAqCKWAYYsqVb65H3Ki4rP4Zb18UywLCIZYAh63Yfvn/yo/T2eTwAj08sAwzZftcbW68MMDxiGWDIplr1/R0/vb/jAXh8YhlgyI4ea8XMkcaejz99dn5wwwDwULaOAxiBMy8ci48/uH7v626nF+3NXhR5GWWUkaZfbh+XJF8uu5g73oqZI81RjAxwKIllgBF4+tm5WPjsTizeWov11c6OO/ZF9O/al6btaLbqMT3TiCRN4oVXnxrBtACHl2UYACOQ1dI4dnKmMpTvKoqIjbVurCxtxtf/6HTMn5gZ4pQAiGWAEbh2eSmufHonjh5r9W9j/ZDnZrU0plr1WLy1PrT5AOizDANgyIqijE8v3IiIiDRN48hcK/K8iPZGN7rdPMoiIkkisiyNZqse9UYWERE3rizHcy8dj9m5qVGOD3CoiGWAIbt5bSW67Xzb97IsjenZR39wb+HzO/Hqt84c1GgAfIVlGABDdu3zxT0f+8XCcuQ9d/ADGBaxDDBkG+udPR+b98rotHsDnAaAhxHLAENWPGT3i8eR564sAwyLWAYYslp9f6feej0b0CQAPIpYBhiyYydn93zs9JFGNFv1AU4DwMOIZYAhO/P8/J6PfeaFY4MbBIBHEssAQzZzpBnzT03v+rhaPY1Tzx49gIkAqCKWAUbgte+ciWbr8be6T9KIb7zxbNRq1isDDJNYBhiB5lQ9vv0nz0drpvHI52a1JP7D956LYydnhjAZAPdzBz+AEWlNN+LNP38xblxZjqsX78Tqcnvb442pWpw+Oxdnnp+P5pQP9QGMglgGGKEsS+PM8/Nx8syRuPLZndhYa0dZ9kP6uZePR802cQAjJZYBRmhtpR0Ln92JGwtLkfe236zk6ueL8fTZuXjmhWMxZbs4gJEQywAj8sXV5fj3969GUTz4jn7dTh6XP7kd1z5fjG9+77mYP7H7HTQA2B8f8AMYgVvXV+J35xcqQ/l+vW4RH/7qUqwsbgxhMgDuJ5YBhizvFfHvv74a8ehOvu+YMi68fzXKchcHAbBvYhlgyG4sLEWvW+z6uI21TizeWj+AiQCoIpYBhmzh4uI+jr0zuEEAeCSxDDBEvW4ea1/ZT3k3lm67sgwwTGIZYIh6vd0vv7hfvoflGwDsnVgGGKIsS/Z1fJo5bQMMk7MuwBDV6lk0W3vf4n7maHOA0wDwKGIZYIiSJInTZ+f3fPyZ5/d+LAC7J5YBhuz02blI9nD2rTeyOHnm6OAHAqCSWAYYsuZUPV74+lO7OyiJeOWbpyJN97fmGYDdEcsAI/D8156K514+/nhPTiJe+ebTcerZuYMdCoAd9v4pEwD25eVvnIrZo824/IfbsVqx9/Lc8VY8/7Wn4tjJmSFPB0CEWAYYqVPPzsWpZ+di+c5GfHF1OTrtXiRJEo2pWjz97FzMHLH7BcAoiWWAMXD0WCuOHmuNegwAvsKaZQAAqCCWAQCgglgGAIAKYhkAACqIZQAAqCCWAQCgglgGAIAKYhkAACqIZQAAqCCWAQCgglgGAIAKYhkAACqIZQAAqCCWAQCgglgGAIAKSVmW5aiHeJR//Md/jC+++GLPx6dpGjMzMwOc6OCtra1FURR7Pn4Sf2YYpsP6HjusPzcMw2F9f+335z558mT87d/+7QAnGqzaqAcYhqIoYmVlZdRjDNVh/JlhmA7re+yw/twwDN5f48kyDAAAqCCWAQCgglgGAIAKExHL3W531CMAAHAAxr3zJiKWO53OqEcAAOAAjHvnTUQs93q9UY8AAMABGPfOm4hY3s/efQAAjK9x77yJiOVWqzXqEQAAOADj3nkTEctzc3OjHgEAgAMwPz8/6hEeaiJi+c033xz1CAAAHIA33nhj1CM81ETE8htvvBHT09OjHgMAgAGamZkRy4Py4x//eNQjAAAwQD/60Y9GPcIjTUwsv/766/H222+PegwAAAbg7bffjtdff33UYzxSUpZlOeohduPChQvxs5/9LNbW1kY9CgAAuzQzMxM/+tGPJiKUIyYwlu86f/58nD9/PhYXF2NjYyPSNI1arRaNRiPq9fqoxzsQ3W43Op1O9Hq9KIriUPzMMEyH9T12WH9uGIbD+v766s/darVifn4+3njjjbFfo/xVExvLAABw0CZmzTIAAAybWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKohlAACoIJYBAKCCWAYAgApiGQAAKvx/n42XKFY3ncgAAAAASUVORK5CYII=",
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]
},
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- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
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+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
"Env1 = Environment()\n",
"Ag1 = Agent(Env1)\n",
- "Ag1.pos = np.array([0.5,0.5])\n",
+ "Ag1.pos = np.array([0.5, 0.5])\n",
"\n",
"\n",
- "Env2 = Environment(params={'aspect':2,\n",
- " 'scale':0.5}) \n",
- "Env2.add_wall([[0.5,0],[0.5,0.4]])\n",
- "Env2.add_wall([[0,0.4],[0.2,0.4]])\n",
- "Env2.add_wall([[0.3,0.4],[0.7,0.4]])\n",
- "Env2.add_wall([[0.8,0.4],[1,0.4]])\n",
+ "Env2 = Environment(params={\"aspect\": 2, \"scale\": 0.5})\n",
+ "Env2.add_wall([[0.5, 0], [0.5, 0.4]])\n",
+ "Env2.add_wall([[0, 0.4], [0.2, 0.4]])\n",
+ "Env2.add_wall([[0.3, 0.4], [0.7, 0.4]])\n",
+ "Env2.add_wall([[0.8, 0.4], [1, 0.4]])\n",
"Ag2 = Agent(Env2)\n",
- "Ag2.pos = np.array([0.22,0.35])\n",
- "Ag2.velocity = 0.3*np.array([0.5,1])\n",
+ "Ag2.pos = np.array([0.22, 0.35])\n",
+ "Ag2.velocity = 0.3 * np.array([0.5, 1])\n",
"\n",
"Env3 = Environment()\n",
- "Env3.add_wall([[0,0.5],[0.2,0.5]])\n",
- "Env3.add_wall([[0.3,0.5],[0.7,0.5]])\n",
- "Env3.add_wall([[0.8,0.5],[1,0.5]])\n",
- "Env3.add_wall([[0.5,0],[0.5,0.2]])\n",
- "Env3.add_wall([[0.5,0.3],[0.5,0.7]])\n",
- "Env3.add_wall([[0.5,0.8],[0.5,1]])\n",
+ "Env3.add_wall([[0, 0.5], [0.2, 0.5]])\n",
+ "Env3.add_wall([[0.3, 0.5], [0.7, 0.5]])\n",
+ "Env3.add_wall([[0.8, 0.5], [1, 0.5]])\n",
+ "Env3.add_wall([[0.5, 0], [0.5, 0.2]])\n",
+ "Env3.add_wall([[0.5, 0.3], [0.5, 0.7]])\n",
+ "Env3.add_wall([[0.5, 0.8], [0.5, 1]])\n",
"Ag3 = Agent(Env3)\n",
- "Ag3.pos = np.array([0.4,0.25])\n",
- "Ag3.velocity = 0.3*np.array([1,0])\n",
+ "Ag3.pos = np.array([0.4, 0.25])\n",
+ "Ag3.velocity = 0.3 * np.array([1, 0])\n",
"\n",
"\n",
"Env4 = Environment()\n",
- "Env4.add_wall([[0.2,0],[0.2,0.8]])\n",
- "Env4.add_wall([[0.4,1],[0.4,0.2]])\n",
- "Env4.add_wall([[0.6,0],[0.6,0.8]])\n",
- "Env4.add_wall([[0.8,1],[0.8,0.2]])\n",
+ "Env4.add_wall([[0.2, 0], [0.2, 0.8]])\n",
+ "Env4.add_wall([[0.4, 1], [0.4, 0.2]])\n",
+ "Env4.add_wall([[0.6, 0], [0.6, 0.8]])\n",
+ "Env4.add_wall([[0.8, 1], [0.8, 0.2]])\n",
"Ag4 = Agent(Env4)\n",
- "Ag4.pos = np.array([0.1,0.1])\n",
- "Ag4.velocity = 0.3*np.array([0,1])\n",
+ "Ag4.pos = np.array([0.1, 0.1])\n",
+ "Ag4.velocity = 0.3 * np.array([0, 1])\n",
"\n",
"\n",
"Env5 = Environment()\n",
- "Env5.add_wall([[0.2,0.5],[0.8,0.5]])\n",
+ "Env5.add_wall([[0.2, 0.5], [0.8, 0.5]])\n",
"Ag5 = Agent(Env5)\n",
- "Ag5.pos = np.array([0.5,0.35])\n",
- "Ag5.velocity = 0.3*np.array([0,1])\n",
- "\n",
- "Env6 = Environment(params={'aspect':2,\n",
- " 'scale':0.5})\n",
- "Env6.add_wall([[0.45,0],[0.45,0.4]])\n",
- "Env6.add_wall([[0.45,0.4],[0,0.4]])\n",
- "Env6.add_wall([[0.55,0],[0.55,0.4]])\n",
- "Env6.add_wall([[0.55,0.4],[1,0.4]])\n",
+ "Ag5.pos = np.array([0.5, 0.35])\n",
+ "Ag5.velocity = 0.3 * np.array([0, 1])\n",
+ "\n",
+ "Env6 = Environment(\n",
+ " params={\n",
+ " \"aspect\": 2,\n",
+ " \"scale\": 0.5,\n",
+ " \"boundary\": [\n",
+ " [0.45, 0],\n",
+ " [0.45, 0.4],\n",
+ " [0, 0.4],\n",
+ " [0, 0.5],\n",
+ " [1, 0.5],\n",
+ " [1, 0.4],\n",
+ " [0.55, 0.4],\n",
+ " [0.55, 0],\n",
+ " ],\n",
+ " }\n",
+ ")\n",
"Ag6 = Agent(Env6)\n",
- "Ag6.pos = np.array([0.5,0.05])\n",
- "Ag6.velocity = 0.3*np.array([0,1])\n",
- "\n",
- "Env7 = Environment(params={'aspect':2,\n",
- " 'scale':0.5})\n",
- "Env7.add_wall([[0.45,0],[0.45,0.4]])\n",
- "Env7.add_wall([[0.45,0.4],[0.1,0.4]])\n",
- "Env7.add_wall([[0.1,0.4],[0.1,0]])\n",
- "\n",
- "Env7.add_wall([[0.55,0],[0.55,0.4]])\n",
- "Env7.add_wall([[0.55,0.4],[0.9,0.4]])\n",
- "Env7.add_wall([[0.9,0.4],[0.9,0]])\n",
+ "Ag6.pos = np.array([0.5, 0.05])\n",
+ "Ag6.velocity = 0.3 * np.array([0, 1])\n",
+ "\n",
+ "Env7 = Environment(params={\"aspect\": 2, \"scale\": 0.5})\n",
+ "Env7.add_wall([[0.45, 0], [0.45, 0.4]])\n",
+ "Env7.add_wall([[0.45, 0.4], [0.1, 0.4]])\n",
+ "Env7.add_wall([[0.1, 0.4], [0.1, 0]])\n",
+ "\n",
+ "Env7.add_wall([[0.55, 0], [0.55, 0.4]])\n",
+ "Env7.add_wall([[0.55, 0.4], [0.9, 0.4]])\n",
+ "Env7.add_wall([[0.9, 0.4], [0.9, 0]])\n",
"Ag7 = Agent(Env7)\n",
- "Ag7.pos = np.array([0.5,0.05])\n",
- "Ag7.velocity = 0.3*np.array([0,1])\n",
+ "Ag7.pos = np.array([0.5, 0.05])\n",
+ "Ag7.velocity = 0.3 * np.array([0, 1])\n",
"\n",
- "Env8 = Environment(params={'aspect':2,\n",
- " 'scale':0.5})\n",
- "Env8.add_wall([[0.1,0.25],[0.5,0.45]])\n",
- "Env8.add_wall([[0.4,0.3],[0.65,0.05]])\n",
- "Env8.add_wall([[0.6,0.25],[0.9,0.3]])\n",
+ "Env8 = Environment(params={\"aspect\": 2, \"scale\": 0.5})\n",
+ "Env8.add_wall([[0.1, 0.25], [0.5, 0.45]])\n",
+ "Env8.add_wall([[0.4, 0.3], [0.65, 0.05]])\n",
+ "Env8.add_wall([[0.6, 0.25], [0.9, 0.3]])\n",
"\n",
"Ag8 = Agent(Env8)\n",
- "Ag8.pos = np.array([0.5,0.05])\n",
- "Ag8.velocity = 0.3*np.array([0,1])\n",
+ "Ag8.pos = np.array([0.5, 0.05])\n",
+ "Ag8.velocity = 0.3 * np.array([0, 1])\n",
"\n",
"\n",
"train_time = 5\n",
- "for i in tqdm(range(int(train_time/Ag1.dt))): \n",
+ "for i in tqdm(range(int(train_time / Ag1.dt))):\n",
" Ag1.update()\n",
" Ag2.update()\n",
" Ag3.update()\n",
@@ -406,29 +404,37 @@
" Ag8.update()\n",
"\n",
"\n",
- "fig1,ax1=Ag1.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig1,'oneroom')\n",
+ "fig1, ax1 = Ag1.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig1, \"oneroom\")\n",
"\n",
- "fig2,ax2=Ag2.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig2,'tworoom')\n",
+ "fig2, ax2 = Ag2.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig2, \"tworoom\")\n",
"\n",
- "fig3,ax3=Ag3.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig3,'fourroom')\n",
+ "fig3, ax3 = Ag3.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig3, \"fourroom\")\n",
"\n",
- "fig4,ax4=Ag4.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig4,'hairpin')\n",
+ "fig4, ax4 = Ag4.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig4, \"hairpin\")\n",
"\n",
- "fig5,ax5=Ag5.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig5,'barrier')\n",
+ "fig5, ax5 = Ag5.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig5, \"barrier\")\n",
"\n",
- "fig6,ax6=Ag6.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig6,'tmaze')\n",
+ "fig6, ax6 = Ag6.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig6, \"tmaze\")\n",
"\n",
- "fig7,ax7=Ag7.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig7,'wmaze')\n",
+ "fig7, ax7 = Ag7.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig7, \"wmaze\")\n",
"\n",
- "fig8,ax8=Ag8.plot_trajectory(t_end=5)\n",
- "if save_plots == True: tpl.saveFigure(fig8,'dunno')"
+ "fig8, ax8 = Ag8.plot_trajectory(t_end=5)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig8, \"dunno\")"
]
},
{
@@ -482,39 +488,49 @@
"Env_s = Environment()\n",
"Ag_s = Agent(Env_s)\n",
"Ag_s.speed_mean = 0.3\n",
- "PC_s = PlaceCells(Ag_s,params={\n",
- " 'description':'gaussian_threshold',\n",
- " 'n':1,\n",
- " 'widths':0.3,\n",
- " 'place_cell_centres':np.array([[0.85,0.8]])})\n",
+ "PC_s = PlaceCells(\n",
+ " Ag_s,\n",
+ " params={\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"n\": 1,\n",
+ " \"widths\": 0.3,\n",
+ " \"place_cell_centres\": np.array([[0.85, 0.8]]),\n",
+ " },\n",
+ ")\n",
"train_time = 3\n",
- "Ag_s.pos = np.array([0.8,0.8])\n",
- "Ag_s.velocity = Ag_s.speed_mean*np.array([1,0])\n",
- "for i in tqdm(range(int(train_time/Ag_s.dt))): \n",
+ "Ag_s.pos = np.array([0.8, 0.8])\n",
+ "Ag_s.velocity = Ag_s.speed_mean * np.array([1, 0])\n",
+ "for i in tqdm(range(int(train_time / Ag_s.dt))):\n",
" Ag_s.update()\n",
" PC_s.update()\n",
- "fig, ax = PC_s.plot_rate_map(chosen_neurons='all')\n",
+ "fig, ax = PC_s.plot_rate_map(chosen_neurons=\"all\")\n",
"fig, ax = Ag_s.plot_trajectory(fig=fig, ax=ax[0])\n",
- "if save_plots == True: tpl.saveFigure(fig,'solid')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"solid\")\n",
"\n",
- "Env_p = Environment(params={'boundary_conditions':'periodic'})\n",
+ "Env_p = Environment(params={\"boundary_conditions\": \"periodic\"})\n",
"Ag_p = Agent(Env_p)\n",
"Ag_p.speed_mean = 0.3\n",
- "PC_p = PlaceCells(Ag_p,params={\n",
- " 'widths':0.3,\n",
- " 'description':'gaussian_threshold',\n",
- " 'n':1,\n",
- " 'wall_geometry':'euclidean',\n",
- " 'place_cell_centres':np.array([[0.85,0.8]])})\n",
+ "PC_p = PlaceCells(\n",
+ " Ag_p,\n",
+ " params={\n",
+ " \"widths\": 0.3,\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"n\": 1,\n",
+ " \"wall_geometry\": \"euclidean\",\n",
+ " \"place_cell_centres\": np.array([[0.85, 0.8]]),\n",
+ " },\n",
+ ")\n",
"train_time = 3\n",
- "Ag_p.pos = np.array([0.8,0.8])\n",
- "Ag_p.velocity = Ag_s.speed_mean*np.array([1,0])\n",
- "for i in tqdm(range(int(train_time/Ag_p.dt))): \n",
+ "Ag_p.pos = np.array([0.8, 0.8])\n",
+ "Ag_p.velocity = Ag_s.speed_mean * np.array([1, 0])\n",
+ "for i in tqdm(range(int(train_time / Ag_p.dt))):\n",
" Ag_p.update()\n",
" PC_p.update()\n",
- "fig, ax = PC_p.plot_rate_map(chosen_neurons='all')\n",
+ "fig, ax = PC_p.plot_rate_map(chosen_neurons=\"all\")\n",
"fig, ax = Ag_p.plot_trajectory(fig=fig, ax=ax[0])\n",
- "if save_plots == True: tpl.saveFigure(fig,'periodic')"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"periodic\")"
]
},
{
@@ -574,27 +590,23 @@
}
],
"source": [
- "Env = Environment(\n",
- " params = {'dimensionality':'1D',\n",
- " 'boundary_conditions':'periodic'})\n",
- "Ag = Agent(Env,\n",
- " params = {'speed_mean':0.05,\n",
- " 'speed_std':0.15})\n",
- "PCs = PlaceCells(Ag,\n",
- " params = {'cell_class':'place_cell',\n",
- " 'widths':0.1}\n",
- ")\n",
- "for i in tqdm(range(int(60/Ag.dt))): \n",
+ "Env = Environment(params={\"dimensionality\": \"1D\", \"boundary_conditions\": \"periodic\"})\n",
+ "Ag = Agent(Env, params={\"speed_mean\": 0.05, \"speed_std\": 0.15})\n",
+ "PCs = PlaceCells(Ag, params={\"cell_class\": \"place_cell\", \"widths\": 0.1})\n",
+ "for i in tqdm(range(int(60 / Ag.dt))):\n",
" Ag.update()\n",
" PCs.update()\n",
"\n",
"fig, ax = Ag.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,'1dtraj')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1dtraj\")\n",
"fig, ax = PCs.plot_rate_map(spikes=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,'1drms')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1drms\")\n",
"# PCs.plot_rate_map(plot_spikes=False,by_history=True)\n",
"fig, ax = PCs.plot_rate_timeseries(spikes=True)\n",
- "if save_plots == True: tpl.saveFigure(fig,'1dtimeseries')\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1dtimeseries\")"
]
},
{
@@ -655,37 +667,51 @@
],
"source": [
"Env = Environment()\n",
- "Ag1 = Agent(Env,\n",
- " params={\"dt\":0.05,\n",
- " })\n",
- "Ag1.pos = np.array([0.5,0.5])\n",
+ "Ag1 = Agent(\n",
+ " Env,\n",
+ " params={\n",
+ " \"dt\": 0.05,\n",
+ " },\n",
+ ")\n",
+ "Ag1.pos = np.array([0.5, 0.5])\n",
"Ag1.walls_repel = False\n",
"\n",
"\n",
- "Ag2 = Agent(Env,\n",
- " params={\"rotational_velocity_std\":360*np.pi/180, \n",
- " \"dt\":0.05,})\n",
- "Ag2.pos = np.array([0.5,0.5])\n",
+ "Ag2 = Agent(\n",
+ " Env,\n",
+ " params={\n",
+ " \"rotational_velocity_std\": 360 * np.pi / 180,\n",
+ " \"dt\": 0.05,\n",
+ " },\n",
+ ")\n",
+ "Ag2.pos = np.array([0.5, 0.5])\n",
"Ag2.walls_repel = False\n",
"\n",
- "Ag3 = Agent(Env,\n",
- " params={\"rotational_velocity_std\":60*np.pi/180, \n",
- " \"dt\":0.05,})\n",
- "Ag3.pos = np.array([0.5,0.5])\n",
+ "Ag3 = Agent(\n",
+ " Env,\n",
+ " params={\n",
+ " \"rotational_velocity_std\": 60 * np.pi / 180,\n",
+ " \"dt\": 0.05,\n",
+ " },\n",
+ ")\n",
+ "Ag3.pos = np.array([0.5, 0.5])\n",
"Ag3.walls_repel = False\n",
"\n",
- "for i in tqdm(range(int(30/Ag1.dt))): \n",
+ "for i in tqdm(range(int(30 / Ag1.dt))):\n",
" Ag1.update()\n",
" Ag2.update()\n",
" Ag3.update()\n",
"\n",
"# Ag.plot_trajectory()\n",
"fig, ax = Ag1.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,\"ag1\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"ag1\")\n",
"fig, ax = Ag2.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,\"ag2\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"ag2\")\n",
"fig, ax = Ag3.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,\"ag3\")"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"ag3\")"
]
},
{
@@ -724,13 +750,14 @@
"Env = Environment()\n",
"Ag = Agent(Env)\n",
"\n",
- "for i in tqdm(range(int(1.5*60/Ag.dt))): \n",
- " drift = utils.rotate(Ag.pos-Env.centre,np.pi/2)\n",
- " drift = 0.2*drift/np.linalg.norm(drift)\n",
- " Ag.update(drift_velocity=drift,drift_to_random_strength_ratio=1)\n",
+ "for i in tqdm(range(int(1.5 * 60 / Ag.dt))):\n",
+ " drift = utils.rotate(Ag.pos - Env.centre, np.pi / 2)\n",
+ " drift = 0.2 * drift / np.linalg.norm(drift)\n",
+ " Ag.update(drift_velocity=drift, drift_to_random_strength_ratio=1)\n",
"\n",
- "fig, ax= Ag.plot_trajectory()\n",
- "if save_plots == True: tpl.saveFigure(fig,\"policycontrol\")"
+ "fig, ax = Ag.plot_trajectory()\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"policycontrol\")"
]
},
{
@@ -739,9 +766,9 @@
"metadata": {},
"outputs": [],
"source": [
- "if save_plots == True: \n",
+ "if save_plots == True:\n",
" anim = Ag.animate_trajectory(speed_up=3)\n",
- " anim.save(\"../figures/animations/circular_motion.mp4\",dpi=250)"
+ " anim.save(\"../figures/animations/circular_motion.mp4\", dpi=250)"
]
},
{
@@ -841,27 +868,36 @@
"Env = Environment()\n",
"Ag = Agent(Env)\n",
"locs = Env.sample_positions(n=9)\n",
- "PC_g = PlaceCells(Ag,params={\"n\":9,\"description\":\"gaussian\",\"place_cell_centres\":locs})\n",
- "PC_gt = PlaceCells(Ag,params={\"n\":9,\"description\":\"gaussian_threshold\",\"place_cell_centres\":locs})\n",
- "PC_dog = PlaceCells(Ag,params={\"n\":9,\"description\":\"diff_of_gaussians\",\"place_cell_centres\":locs})\n",
- "PC_th = PlaceCells(Ag,params={\"n\":9,\"description\":\"top_hat\",\"place_cell_centres\":locs})\n",
- "PC_oh = PlaceCells(Ag,params={\"n\":9,\"description\":\"one_hot\",\"place_cell_centres\":locs})\n",
+ "PC_g = PlaceCells(\n",
+ " Ag, params={\"n\": 9, \"description\": \"gaussian\", \"place_cell_centres\": locs}\n",
+ ")\n",
+ "PC_gt = PlaceCells(\n",
+ " Ag, params={\"n\": 9, \"description\": \"gaussian_threshold\", \"place_cell_centres\": locs}\n",
+ ")\n",
+ "PC_dog = PlaceCells(\n",
+ " Ag, params={\"n\": 9, \"description\": \"diff_of_gaussians\", \"place_cell_centres\": locs}\n",
+ ")\n",
+ "PC_th = PlaceCells(\n",
+ " Ag, params={\"n\": 9, \"description\": \"top_hat\", \"place_cell_centres\": locs}\n",
+ ")\n",
+ "PC_oh = PlaceCells(\n",
+ " Ag, params={\"n\": 9, \"description\": \"one_hot\", \"place_cell_centres\": locs}\n",
+ ")\n",
"\n",
"fig, ax = PC_g.plot_place_cell_locations()\n",
- "fig0, ax0 = PC_g.plot_rate_map(shape=(3,3))\n",
- "fig1, ax1 = PC_gt.plot_rate_map(shape=(3,3))\n",
- "fig2, ax2 = PC_dog.plot_rate_map(shape=(3,3))\n",
- "fig3, ax3 = PC_th.plot_rate_map(shape=(3,3))\n",
- "fig4, ax4 = PC_oh.plot_rate_map(shape=(3,3))\n",
- "\n",
- "if save_plots == True: \n",
- " tpl.saveFigure(fig,\"placecelllocations\")\n",
- " tpl.saveFigure(fig0,\"PC_g\")\n",
- " tpl.saveFigure(fig1,\"PC_gt\")\n",
- " tpl.saveFigure(fig2,\"PC_dog\")\n",
- " tpl.saveFigure(fig3,\"PC_th\")\n",
- " tpl.saveFigure(fig4,\"PC_oh\")\n",
- "\n"
+ "fig0, ax0 = PC_g.plot_rate_map(shape=(3, 3))\n",
+ "fig1, ax1 = PC_gt.plot_rate_map(shape=(3, 3))\n",
+ "fig2, ax2 = PC_dog.plot_rate_map(shape=(3, 3))\n",
+ "fig3, ax3 = PC_th.plot_rate_map(shape=(3, 3))\n",
+ "fig4, ax4 = PC_oh.plot_rate_map(shape=(3, 3))\n",
+ "\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"placecelllocations\")\n",
+ " tpl.saveFigure(fig0, \"PC_g\")\n",
+ " tpl.saveFigure(fig1, \"PC_gt\")\n",
+ " tpl.saveFigure(fig2, \"PC_dog\")\n",
+ " tpl.saveFigure(fig3, \"PC_th\")\n",
+ " tpl.saveFigure(fig4, \"PC_oh\")"
]
},
{
@@ -915,40 +951,51 @@
],
"source": [
"Env = Environment()\n",
- "Env.add_wall([[0.5,0],[0.5,0.5]])\n",
+ "Env.add_wall([[0.5, 0], [0.5, 0.5]])\n",
"Ag = Agent(Env)\n",
- "N1 = PlaceCells(Ag,\n",
- " params={'place_cell_centres':np.array([[0.55,0.45]]),\n",
- " 'wall_geometry':'euclidean',\n",
- " 'description':'gaussian_threshold',\n",
- " 'widths':0.3}\n",
+ "N1 = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"place_cell_centres\": np.array([[0.55, 0.45]]),\n",
+ " \"wall_geometry\": \"euclidean\",\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"widths\": 0.3,\n",
+ " },\n",
")\n",
"\n",
- "N2 = PlaceCells(Ag,\n",
- " params={'place_cell_centres':np.array([[0.55,0.45]]),\n",
- " 'wall_geometry':'line_of_sight',\n",
- " 'description':'gaussian_threshold',\n",
- " 'widths':0.3}\n",
+ "N2 = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"place_cell_centres\": np.array([[0.55, 0.45]]),\n",
+ " \"wall_geometry\": \"line_of_sight\",\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"widths\": 0.3,\n",
+ " },\n",
")\n",
"\n",
- "N3 = PlaceCells(Ag,\n",
- " params={'place_cell_centres':np.array([[0.55,0.45]]),\n",
- " 'wall_geometry':'geodesic',\n",
- " 'description':'gaussian_threshold',\n",
- " 'widths':0.3}\n",
+ "N3 = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"place_cell_centres\": np.array([[0.55, 0.45]]),\n",
+ " \"wall_geometry\": \"geodesic\",\n",
+ " \"description\": \"gaussian_threshold\",\n",
+ " \"widths\": 0.3,\n",
+ " },\n",
")\n",
"fig, ax = N1.plot_place_cell_locations()\n",
- "fig, ax = N1.plot_rate_map(fig=fig,ax=ax)\n",
- "if save_plots == True: tpl.saveFigure(fig,'euc')\n",
+ "fig, ax = N1.plot_rate_map(fig=fig, ax=ax)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"euc\")\n",
"\n",
"fig, ax = N2.plot_place_cell_locations()\n",
- "fig, ax = N2.plot_rate_map(fig=fig,ax=ax)\n",
- "if save_plots == True: tpl.saveFigure(fig,'los')\n",
+ "fig, ax = N2.plot_rate_map(fig=fig, ax=ax)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"los\")\n",
"\n",
"fig, ax = N3.plot_place_cell_locations()\n",
- "fig, ax = N3.plot_rate_map(fig=fig,ax=ax)\n",
- "if save_plots == True: tpl.saveFigure(fig,'geo')\n",
- "\n"
+ "fig, ax = N3.plot_rate_map(fig=fig, ax=ax)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"geo\")"
]
},
{
@@ -980,8 +1027,7 @@
"Env = Environment()\n",
"Ag = Agent(Env)\n",
"Ag.dt = 100e-3\n",
- "GCs = GridCells(Ag,\n",
- " params={'n':1})\n",
+ "GCs = GridCells(Ag, params={\"n\": 1})\n",
"fig, ax = GCs.plot_rate_map()"
]
},
@@ -1062,40 +1108,37 @@
}
],
"source": [
- "for i in tqdm(range(int(1*60/Ag.dt))): \n",
+ "for i in tqdm(range(int(1 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" GCs.update()\n",
- "fig, ax = GCs.plot_rate_map(\n",
- " method=\"neither\",\n",
- " spikes=True)\n",
- "if save_plots == True: tpl.saveFigure(fig,'1min')\n",
+ "fig, ax = GCs.plot_rate_map(method=\"neither\", spikes=True)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1min\")\n",
"\n",
- "for i in tqdm(range(int(4*60/Ag.dt))): \n",
+ "for i in tqdm(range(int(4 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" GCs.update()\n",
- "fig, ax = GCs.plot_rate_map(\n",
- " method=None,\n",
- " spikes=True)\n",
- "if save_plots == True: tpl.saveFigure(fig,'5min')\n",
+ "fig, ax = GCs.plot_rate_map(method=None, spikes=True)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"5min\")\n",
"\n",
- "for i in tqdm(range(int(15*60/Ag.dt))): \n",
+ "for i in tqdm(range(int(15 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" GCs.update()\n",
- "fig, ax = GCs.plot_rate_map(\n",
- " method=None,\n",
- " spikes=True)\n",
- "if save_plots == True: tpl.saveFigure(fig,'20min')\n",
+ "fig, ax = GCs.plot_rate_map(method=None, spikes=True)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"20min\")\n",
"\n",
- "for i in tqdm(range(int(40*60/Ag.dt))): \n",
+ "for i in tqdm(range(int(40 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" GCs.update()\n",
- "fig, ax = GCs.plot_rate_map(\n",
- " method=None,\n",
- " spikes=True)\n",
- "if save_plots == True: tpl.saveFigure(fig,'60min')\n",
+ "fig, ax = GCs.plot_rate_map(method=None, spikes=True)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"60min\")\n",
"\n",
- "fig, ax = GCs.plot_rate_map(method='groundtruth',spikes=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,'rfgc')"
+ "fig, ax = GCs.plot_rate_map(method=\"groundtruth\", spikes=False)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"rfgc\")"
]
},
{
@@ -1152,26 +1195,38 @@
"Ag = Agent(Env)\n",
"Ag.dt = 100e-3\n",
"\n",
- "PC = PlaceCells(Ag,\n",
+ "PC = PlaceCells(\n",
+ " Ag,\n",
" params={\n",
- " 'n':1,\n",
- " 'place_cell_centres':np.array([[0.5,0.5]]),\n",
- " 'widths':0.15,\n",
- " }\n",
+ " \"n\": 1,\n",
+ " \"place_cell_centres\": np.array([[0.5, 0.5]]),\n",
+ " \"widths\": 0.15,\n",
+ " },\n",
")\n",
"\n",
- "GC = GridCells(Ag,\n",
- " params={'n':1,})\n",
- "BVC = BoundaryVectorCells(Ag,\n",
- " params={'n':1,})\n",
+ "GC = GridCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 1,\n",
+ " },\n",
+ ")\n",
+ "BVC = BoundaryVectorCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 1,\n",
+ " },\n",
+ ")\n",
"VC = VelocityCells(Ag)\n",
"\n",
"fig, ax = PC.plot_rate_map(by_history=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,'truepc')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"truepc\")\n",
"fig, ax = GC.plot_rate_map(by_history=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,'truegc')\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"truegc\")\n",
"fig, ax = BVC.plot_rate_map(by_history=False)\n",
- "if save_plots == True: tpl.saveFigure(fig,'truebvc')\n"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"truebvc\")"
]
},
{
@@ -1334,50 +1389,62 @@
}
],
"source": [
- "for i in tqdm(range(int(1*60/Ag.dt))): \n",
+ "for i in tqdm(range(int(1 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" PC.update()\n",
" GC.update()\n",
" BVC.update()\n",
" VC.update()\n",
- "fig, ax = PC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'1minpc')\n",
- "fig, ax = GC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'1mingc')\n",
- "fig, ax = BVC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'1minbvc')\n",
- "fig, ax = VC.plot_rate_map(method='history',chosen_neurons=[0])\n",
- "if save_plots == True: tpl.saveFigure(fig,'1minvc')\n",
- "\n",
- "for i in tqdm(range(int(4*60/Ag.dt))): \n",
+ "fig, ax = PC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1minpc\")\n",
+ "fig, ax = GC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1mingc\")\n",
+ "fig, ax = BVC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1minbvc\")\n",
+ "fig, ax = VC.plot_rate_map(method=\"history\", chosen_neurons=[0])\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"1minvc\")\n",
+ "\n",
+ "for i in tqdm(range(int(4 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" PC.update()\n",
" GC.update()\n",
" BVC.update()\n",
" VC.update()\n",
- "fig, ax = PC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'4minpc')\n",
- "fig, ax = GC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'4mingc')\n",
- "fig, ax = BVC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'4minbvc')\n",
- "fig, ax = VC.plot_rate_map(method='history',chosen_neurons=[0])\n",
- "if save_plots == True: tpl.saveFigure(fig,'4minvc')\n",
- "\n",
- "for i in tqdm(range(int(15*60/Ag.dt))): \n",
+ "fig, ax = PC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"4minpc\")\n",
+ "fig, ax = GC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"4mingc\")\n",
+ "fig, ax = BVC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"4minbvc\")\n",
+ "fig, ax = VC.plot_rate_map(method=\"history\", chosen_neurons=[0])\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"4minvc\")\n",
+ "\n",
+ "for i in tqdm(range(int(15 * 60 / Ag.dt))):\n",
" Ag.update()\n",
" PC.update()\n",
" GC.update()\n",
" BVC.update()\n",
" VC.update()\n",
- "fig, ax = PC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'20minpc')\n",
- "fig, ax = GC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'20mingc')\n",
- "fig, ax = BVC.plot_rate_map(method='history')\n",
- "if save_plots == True: tpl.saveFigure(fig,'20minbvc')\n",
- "fig, ax = VC.plot_rate_map(method='history',chosen_neurons=[0])\n",
- "if save_plots == True: tpl.saveFigure(fig,'20minvc')"
+ "fig, ax = PC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"20minpc\")\n",
+ "fig, ax = GC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"20mingc\")\n",
+ "fig, ax = BVC.plot_rate_map(method=\"history\")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"20minbvc\")\n",
+ "fig, ax = VC.plot_rate_map(method=\"history\", chosen_neurons=[0])\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"20minvc\")"
]
},
{
@@ -1439,7 +1506,7 @@
],
"source": [
"t_start = 0\n",
- "t_test = 120 \n",
+ "t_test = 120\n",
"\n",
"Env_riab = Environment()\n",
"Ag_riab = Agent(Env_riab)\n",
@@ -1449,18 +1516,19 @@
"\n",
"Env_sargolini = Environment()\n",
"Ag_sargolini = Agent(Env_sargolini)\n",
- "Ag_sargolini.import_trajectory(dataset='sargolini')\n",
+ "Ag_sargolini.import_trajectory(dataset=\"sargolini\")\n",
"Ag_sargolini.t = t_start\n",
"\n",
- "for _ in tqdm(range(int(t_test/Ag_riab.dt))):\n",
+ "for _ in tqdm(range(int(t_test / Ag_riab.dt))):\n",
" Ag_riab.update()\n",
" Ag_sargolini.update()\n",
"\n",
- "fig, ax = Ag_riab.plot_trajectory(t_start=t_start,t_end=t_start+t_test)\n",
- "if save_plots == True: tpl.saveFigure(fig,'riab_traj')\n",
- "fig, ax = Ag_sargolini.plot_trajectory(t_start=t_start,t_end=t_start+t_test)\n",
- "if save_plots == True: tpl.saveFigure(fig,'sargolini_traj')\n",
- "\n"
+ "fig, ax = Ag_riab.plot_trajectory(t_start=t_start, t_end=t_start + t_test)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"riab_traj\")\n",
+ "fig, ax = Ag_sargolini.plot_trajectory(t_start=t_start, t_end=t_start + t_test)\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"sargolini_traj\")"
]
},
{
@@ -1478,15 +1546,14 @@
}
],
"source": [
- "if save_plots == True: \n",
- " anim = Ag_riab.animate_trajectory(t_end=120,speed_up=2)\n",
- " anim.save(\"../figures/animations/riab_trajectory.mp4\",dpi=250)\n",
+ "if save_plots == True:\n",
+ " anim = Ag_riab.animate_trajectory(t_end=120, speed_up=2)\n",
+ " anim.save(\"../figures/animations/riab_trajectory.mp4\", dpi=250)\n",
" print(\"RiaB animation saved\")\n",
"\n",
- " anim = Ag_sargolini.animate_trajectory(t_end=120,speed_up=2)\n",
- " anim.save(\"../figures/animations/sargolini_trajectory.mp4\",dpi=250)\n",
- " print(\"Sargolini animation saved\")\n",
- "\n"
+ " anim = Ag_sargolini.animate_trajectory(t_end=120, speed_up=2)\n",
+ " anim.save(\"../figures/animations/sargolini_trajectory.mp4\", dpi=250)\n",
+ " print(\"Sargolini animation saved\")"
]
},
{
@@ -1524,12 +1591,12 @@
],
"source": [
"Env = Environment()\n",
- "Ag = Agent(Env,params={'dt':0.1})\n",
- "PCs = PlaceCells(Ag,params={'place_cell_centres':'uniform'})\n",
- "PCs_noisy = PlaceCells(Ag,params={'place_cell_centres':'uniform',\n",
- " 'noise_std':0.1})\n",
- "PCs_verynoisy = PlaceCells(Ag,params={'place_cell_centres':'uniform',\n",
- " 'noise_std':0.2})\n",
+ "Ag = Agent(Env, params={\"dt\": 0.1})\n",
+ "PCs = PlaceCells(Ag, params={\"place_cell_centres\": \"uniform\"})\n",
+ "PCs_noisy = PlaceCells(Ag, params={\"place_cell_centres\": \"uniform\", \"noise_std\": 0.1})\n",
+ "PCs_verynoisy = PlaceCells(\n",
+ " Ag, params={\"place_cell_centres\": \"uniform\", \"noise_std\": 0.2}\n",
+ ")\n",
"\n",
"while Ag.t < 60:\n",
" Ag.update()\n",
@@ -1537,35 +1604,157 @@
" PCs_noisy.update()\n",
" PCs_verynoisy.update()\n",
"\n",
- "fig, ax = plt.subplots(1,3,figsize=(10,2))\n",
- "PCs.plot_rate_timeseries(fig=fig,ax=ax[0],spikes=False)\n",
- "PCs_noisy.plot_rate_timeseries(fig=fig,ax=ax[1],spikes=False)\n",
- "PCs_verynoisy.plot_rate_timeseries(fig=fig,ax=ax[2],spikes=False)\n",
+ "fig, ax = plt.subplots(1, 3, figsize=(10, 2))\n",
+ "PCs.plot_rate_timeseries(fig=fig, ax=ax[0], spikes=False)\n",
+ "PCs_noisy.plot_rate_timeseries(fig=fig, ax=ax[1], spikes=False)\n",
+ "PCs_verynoisy.plot_rate_timeseries(fig=fig, ax=ax[2], spikes=False)\n",
"ax[0].set_title(\"PlaceCells.noise_std = 0\")\n",
"ax[1].set_title(\"PlaceCells.noise_std = 0.1\")\n",
"ax[2].set_title(\"PlaceCells.noise_std = 0.2\")\n",
"\n",
- "tpl.saveFigure(fig,\"noise\")"
+ "tpl.saveFigure(fig, \"noise\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Polygon shaped environments and Environments with holes"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
+ "image/png": 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",
"text/plain": [
- "['pdf', 'svg', 'png']"
+ ""
]
},
- "execution_count": 3,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "Env = Environment(params={\"boundary\": [[0, -0.2], [0, 0.2], [1.5, 0.5], [1.5, -0.5]]})\n",
+ "Ag = Agent(Env)\n",
+ "\n",
+ "while Ag.t < 100:\n",
+ " Ag.update()\n",
+ "fig, ax = Ag.plot_trajectory(t_end=45)\n",
+ "anim = Ag.animate_trajectory(t_start=10, speed_up=3)\n",
+ "if save_plots == True:\n",
+ " # anim.save(\"../figures/animations/trapezium.mp4\")\n",
+ " tpl.saveFigure(fig, \"trap_trajectory\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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EEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBm2PQA8SOPxuF544YW2xwBgB4zH47ZHIIC1AkAuawWmieDH1PGPPACwHmsFAKDrXNILAAAAAEEEPwAAAAAIIvgBAAAAQBDBj05pmqbtEQAI5RiTwecIwE5xjKFLBD86xT+wAOwUx5gMPkcAdopjDF0i+AEAAABAEMEPAAAAAIIIfgAAAAAQRPCjUwaDQdsjABBqOBy2PQLbwFoBgJ1irUCXCH50ylNPPdX2CACEOnz4cNsjsA2sFQDYKdYKdIngR6ecOnWq7REACOUYk8HnCMBOcYyhSwQ/OufYsWNtjwBAGMeWLD5PALabYwtdI/jROSdOnKgjR460PQYAIY4cOVInTpxoewy2kbUCANvJWoEuEvzopFOnTvkNCwD37dixYy7PCWWtAMB2sFagqzxihs46ceJEnThxos6fP1+XL1+utbW1j23T72vaANNsMpl87GvD4bAOHz5s8T4FrBUA2Ii1AqkEPzrv1KlT/iEGAO7KWgEAmDZ+pQkAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIM2x4AAAAAOqlpauby5Zr92c9q9o03qr+8XFVVk337anV+vlaOHKm1xx9veUhgGgl+AAAAsAW9paXa9+1v1/5//dcaXrmy7rar8/P1we//fl1/9tlqZmYe0ITAtBP8AAAAYJN2v/pqPfLXf13999/f1Pazly7Vw3/1V3Xg3Lm6+md/VitHj+7whADu4QcAAAAbm0zq4b/8y3rs61/fdOy72fCtt+rxr32tDvzDP+zAcAC3coYfAAAArKdp6tFvfKP2/Pu/3/d+Dv7d31V/NKprf/zH2zMbwB04ww8AAADW8dDf//39x76bHPjHf6y93/3utu0P4HaCHwAAANzF7KVL9dA//dO27/fQ3/5tDd57b9v3C1Al+AEAAMBdPfw3f1M1mWz7fvvLy3XwpZe2fb8AVYIfAAAA3NHswkLN/vSnO7b/vf/xH9VfXNyx/QPTS/ADAACAO9j3rW/t7BuMx7XvO9/Z2fcAppLgBwAAAHew64c/vO99NE1TK8ujev/aci2+s1SL7yzVB+/dqNHKWlXTbMt7ANxu2PYAAAAA8EnTW1qq4ZUr976Dpqnl66t1Y3lUze23ABxNavXGWvUHvZr5/o/va06AO3GGHwAAANxm5u237/3FTVPvv3ejlq/fIfbdZDJuauXn79SlV3927+8FcAeCHwAAANymNxrd82uvv79So5Xxprd/8wdv1ZsL797z+wHcTvADAACA2zTDe7sD1mRtUis31rb0mvFgUAs/uFKTSXNP7wlwO/fwAwAAgNusPfbYPb3uxvKoagvdbnVuV63N7a5aHdfb/7NYu/fM1s8vXavl66vVNE0NZwb1yOP76lNPPlSDoXN2gM0R/AAAAOA2k717a+2RR2p49ermX9Q0tXJja5cCLz7yRFVVjVbX6tV/W6hde2Y/ts07b12v//7+2/XE/MH6lWOPVb/f29J7ANPHrwcAAADgDlY++9ktbd80te5DOu7kncfna+XGqN6/dqOWl+4eC9dGk3rjJ+/Uhe9eqvF4i28CTB3BDwAAAO7g/dOnt7R902ztHnyTfr9+cuTzdX1xZdNXAV+7ulTf/883t/Q+wPRxSS8AAADcwerRo7U6P1+zly5tavtev1fVq03fw+9/n/5cvTuZraY+PGOv19vcpbpXf/5BXbtyvZaXVuvalaUajcY1GPRrz/65+vT8wdq1Z2ZzAwCxBD8AAAC4i3f+5E/qib/4iw+v191Ar9ermZlBjVbHG267Njtb//Wbf1Brq/9/ee7s3GDD1zVNU0vXV+s7//yj2rNv7pbvXf3fD+rSj6/Ww4/trc8886nau3/uLnsB0rmkFwAAAO5i9TOfqcUvfnHT289t8uy61059sd4b7L71tbvXf23TNLV4bbluLI3qxtKomskdImTz4UM+Xv32Qi2+u7zpuYEsgh8AAACs49pzz9XyF76wqW1nZwfVH6x/ae5/H3+2Ln3uN2o8/v9gNzs3qMFgvR/Rm3r/2o1aG00++lPV5E7B7yNro0ldOH+plq+vbmpuIIvgBwAAAOvp9+vtP//zuv7ssxtv2+vV/oO7q3eHn7abXtWPvvB79drvfHTG4EeXCQ8Gvdq7f9e6u11dGddodOulws0GNwtcW53UpR9f3XhmII7gBwAAABsZDuvqV75SV77ylZrs2bPupoNhvw4c2n3LmX7L+w7Ud7/0p/WD3/qDX36t1+/VcNivA4f2VL+//lmBN5ZHH/tafxMP+XjrzcVaG218T0Egi4d2AAAAwCYtPfts3Xjmmdr38su1/1vfqsG1a3fcbjAc1EOP7KnF/Q/XD5/+9frhk8/UeGb2w2/2qg49ureeePpg/c/Cuxs+nXcymXzsQSDDYb/6614C/NFrx029dXmxnvyVQ5v6+wEZBD8AAADYgsn+/bX4R39Ui1/6Us3+9Kc1d+lSzbzxRg2Wlqrp9Wqyb1+tzs/XytNP12h+vj5VVQeWVmt1dVy9XtXc3EzN7hrWeDypKz9fvOVJvXdy873+fmHXBg/4uNnykvv4wbQR/AAAAOBe9Pu1evRorR49uuGmu/bM1q7brgQeDPr16acP1aUfbXCfvebW4Nfv92p21+Z/nF/v4R5AJvfwAwAAgJYc+bVH69Bje9fdpnfT/f16var9D+3a8DLgm83MDu55PqCbBD8AAABoSb/fq8//9uH61JMH7rrNcNivfr9X/X6vDhzcXcOZrQW8Rx/ff79jAh3jkl4AAABoUX/Qr2dOPllPHX243lx4t956c7EmN923b//B3XXosX31zlsfbOnMvg9fu6v2PbRru0cGPuEEPwAAAPgE2PfQrvrcFz5dv/r5x2u0slaTSVPDmUHNzg1rdWWtvvcvP6nx2voP+LjdU0cf3qFpgU8yl/QCAADAJ8hg0K9de2Zrz765mp378Dyd2blhHf/tw9Xbwk/xhz9zqB779N0vFQZyCX4AAADQAYce3Vu//ux8DWfX/1G+16s68rlH61ePP/6AJgM+aVzSCwAAAB1x8NG99bt/+Nl6+83FenPh3frgvZVffm9mblBPzB+sJ58+WHO7Z1qcEmib4AcAAAAdMhj064n5g/XE/MEarY5rbW1cg36/ZuYGW36oB5BpaoLfZDKpF198se0xAAAAAGjJZLK1B9901dQEv6rp+VABAAAAmF4e2gEAAAAAQQQ/AAAAAAgi+AEAAABAkM4Ev6Zp2h4BAAAAgCnWlT7VmeDngRsAAAAAtKkrfaozwQ8AAAAA2JjgBwAAAABBOhP8er1e2yMAAAAAMMW60qc6E/xmZmbaHgEAAACAKdaVPtWZ4Hf8+PG2RwAAAABginWlT3Um+D3//PNtjwAAAADAFOtKn+pM8KuqOn36dNsjAAAAADCFutSlOhX8zpw5UydPnmx7DAAAAACmyMmTJ+vMmTNtj7FpvaZpmraH2Kpz587Vyy+/3PYYAAAAAIQ7ffp0p2JfVUeD3y+cPXu2Ll68WKPRqG7+a/R6ver3O3XyIgAAAAAtmEwmH+tKMzMzdfz48c7cs+92nQ5+AAAAAMCtnAYHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIIjgBwAAAABBBD8AAAAACCL4AQAAAEAQwQ8AAAAAggh+AAAAABBE8AMAAACAIIIfAAAAAAQR/AAAAAAgiOAHAAAAAEEEPwAAAAAIIvgBAAAAQBDBDwAAAACCCH4AAAAAEETwAwAAAIAggh8AAAAABBH8AAAAACCI4AcAAAAAQQQ/AAAAAAgi+AEAAABAEMEPAAAAAIIIfgAAAAAQRPADAAAAgCCCHwAAAAAEEfwAAAAAIMj/AXYtupcLRfazAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "tpl.saveTypes"
+ "Env = Environment(\n",
+ " params={\n",
+ " \"aspect\": 1.8,\n",
+ " \"holes\": [\n",
+ " [[0.2, 0.2], [0.8, 0.2], [0.8, 0.8], [0.2, 0.8]],\n",
+ " [[1, 0.2], [1.6, 0.2], [1.6, 0.8], [1, 0.8]],\n",
+ " ],\n",
+ " }\n",
+ ")\n",
+ "Ag = Agent(Env)\n",
+ "while Ag.t < 150:\n",
+ " if Ag.pos[0] < 0.9: # anticlockwise on left\n",
+ " drift = utils.rotate(Ag.pos - np.array([0.5, 0.5]), np.pi / 2)\n",
+ " else: # clockwise of right\n",
+ " drift = utils.rotate(Ag.pos - np.array([1.3, 0.5]), -np.pi / 2)\n",
+ " drift = 0.2 * drift / np.linalg.norm(drift)\n",
+ " Ag.update(drift_velocity=drift, drift_to_random_strength_ratio=1)\n",
+ "\n",
+ "anim = Ag.animate_trajectory(t_start=10, speed_up=5)\n",
+ "# fig, ax = Env.plot_environment()\n",
+ "fig1, ax1 = Ag.plot_trajectory(t_start=0, t_end=40)\n",
+ "if save_plots == True:\n",
+ " # anim.save(\"../figures/animations/room_with_hole.mp4\")\n",
+ " # tpl.saveFigure(fig,\"figure_of_eight\")\n",
+ " tpl.saveFigure(fig1, \"figeight_trajectory\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `ThetaSequenceAgent()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ratinabox.contribs.ThetaSequenceAgent import ThetaSequenceAgent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Env = Environment()\n",
+ "# Env.add_wall([[0.5,0.2],[0.5,0.8]])\n",
+ "Ag = ThetaSequenceAgent(Env, params={\"v_sequence\": 10})\n",
+ "\n",
+ "while Ag.t < 10:\n",
+ " Ag.update()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = Ag.plot_trajectory()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "anim = Ag.animate_trajectory(t_start=3, speed_up=0.2)\n",
+ "if save_plots:\n",
+ " tpl.saveFigure(fig, \"theta_sequences\")\n",
+ " anim.save(\"../figures/animations/theta_sequences.mp4\")"
]
},
{
diff --git a/demos/reinforcement_learning_example.ipynb b/demos/reinforcement_learning_example.ipynb
index cc4dbc6e..fcd9cb51 100644
--- a/demos/reinforcement_learning_example.ipynb
+++ b/demos/reinforcement_learning_example.ipynb
@@ -72,21 +72,22 @@
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
- "import numpy as np \n",
+ "import numpy as np\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "#Leave this as False.\n",
- " \n",
- "#For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save. \n",
- "if False: \n",
+ "# Leave this as False.\n",
+ "\n",
+ "# For paper/readme production I use a plotting library (tomplotlib) to format and save figures. Without this they will still show but not save.\n",
+ "if False:\n",
" import tomplotlib.tomplotlib as tpl\n",
+ "\n",
" tpl.figureDirectory = \"../figures/\"\n",
" tpl.setColorscheme(colorscheme=2)\n",
" save_plots = True\n",
@@ -103,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -111,59 +112,68 @@
" def __init__(self, Agent, params={}):\n",
" default_params = {\n",
" \"input_layer\": None, # the features it is using as inputs\n",
- " \"reward_layer\": None, # the layer which is the reward \n",
- " \"tau\":10, #discount time horizon\n",
- " \"tau_e\":5, #eligibility trace timescale\n",
- " \"eta\":0.0001, #learning rate\n",
+ " \"reward_layer\": None, # the layer which is the reward\n",
+ " \"tau\": 10, # discount time horizon\n",
+ " \"tau_e\": 5, # eligibility trace timescale\n",
+ " \"eta\": 0.0001, # learning rate\n",
" }\n",
"\n",
" default_params.update(params)\n",
" self.params = default_params\n",
- " self.params['activation_params']={'activation':'linear'} #we use linear func approx\n",
- " self.params['n']=1 #one value neuron \n",
- " self.params['input_layers'] = [self.params['input_layer']]\n",
- " super().__init__(Agent, self.params) #initialise parent class\n",
- "\n",
- " self.et = np.zeros(params['input_layer'].n) #initialise eligibility trace\n",
- " self.firingrate = np.zeros(1) #initialise firing rate\n",
- " self.firingrate_deriv = np.zeros(1) #initialise firing rate derivative\n",
- " self.max_fr = 1 #will update this with each episode later \n",
- "\n",
+ " self.params[\"activation_params\"] = {\n",
+ " \"activation\": \"linear\"\n",
+ " } # we use linear func approx\n",
+ " self.params[\"n\"] = 1 # one value neuron\n",
+ " self.params[\"input_layers\"] = [self.params[\"input_layer\"]]\n",
+ " super().__init__(Agent, self.params) # initialise parent class\n",
+ "\n",
+ " self.et = np.zeros(params[\"input_layer\"].n) # initialise eligibility trace\n",
+ " self.firingrate = np.zeros(1) # initialise firing rate\n",
+ " self.firingrate_deriv = np.zeros(1) # initialise firing rate derivative\n",
+ " self.max_fr = 1 # will update this with each episode later\n",
"\n",
" def update_firingrate(self):\n",
- " \"\"\"Updates firing rate as weighted linear sum of feature inputs\n",
- " \"\"\" \n",
- " firingrate_last = self.firingrate \n",
- " #update the firing rate\n",
- " self.update() #FeedForwardLayer builtin function. this sums the inouts from the input features over the weight matrix and saves the firingrate. \n",
- " #calculate temporal derivative of the firing rate \n",
- " self.firingrate_deriv = (self.firingrate - firingrate_last)/self.Agent.dt\n",
- " # update eligibility trace \n",
- " self.et = ((self.Agent.dt/self.tau_e) * self.input_layer.firingrate + \n",
- " (1 - self.Agent.dt/self.tau_e) * self.et)\n",
+ " \"\"\"Updates firing rate as weighted linear sum of feature inputs\"\"\"\n",
+ " firingrate_last = self.firingrate\n",
+ " # update the firing rate\n",
+ " self.update() # FeedForwardLayer builtin function. this sums the inouts from the input features over the weight matrix and saves the firingrate.\n",
+ " # calculate temporal derivative of the firing rate\n",
+ " self.firingrate_deriv = (self.firingrate - firingrate_last) / self.Agent.dt\n",
+ " # update eligibility trace\n",
+ " self.et = (self.Agent.dt / self.tau_e) * self.input_layer.firingrate + (\n",
+ " 1 - self.Agent.dt / self.tau_e\n",
+ " ) * self.et\n",
" return\n",
"\n",
" def update_weights(self):\n",
" \"\"\"Trains the weights by implementing the TD learnign rule\"\"\"\n",
- " w = self.inputs[self.input_layer.name]['w'] #weights\n",
- " R = self.reward_layer.firingrate #current reward\n",
- " V = self.firingrate #current value estimate\n",
- " dVdt = self.firingrate_deriv #currrent value derivative estimate\n",
+ " w = self.inputs[self.input_layer.name][\"w\"] # weights\n",
+ " R = self.reward_layer.firingrate # current reward\n",
+ " V = self.firingrate # current value estimate\n",
+ " dVdt = self.firingrate_deriv # currrent value derivative estimate\n",
" td_error = R + self.tau * dVdt - V\n",
- " dw = self.Agent.dt*self.eta*(np.outer(td_error,self.et)) - 0.0001*w #note L2 regularisation\n",
- " self.inputs[self.input_layer.name]['w'] += dw\n",
+ " dw = (\n",
+ " self.Agent.dt * self.eta * (np.outer(td_error, self.et)) - 0.0001 * w\n",
+ " ) # note L2 regularisation\n",
+ " self.inputs[self.input_layer.name][\"w\"] += dw\n",
" return\n",
- " \n",
- " def get_steep_ascent(self,pos):\n",
- " \"\"\"This function will be used for policy improvement. Calculates direction steepest ascent (gradient) of the value function and returns a drift velocity in this direction.\"\"\" \n",
- " V = self.get_state(evaluate_at=None,pos = pos)[0][0]\n",
- " if V < 0.05*self.max_fr: #<--remeber to set max_fr on each training loop\n",
- " return None #if the value function is too low it is unreliabe, return None\n",
- " else: #calculate gradient locally \n",
- " V_plusdx = self.get_state(evaluate_at=None,pos = pos+np.array([1e-3,0]))[0][0]\n",
- " V_plusdy = self.get_state(evaluate_at=None,pos = pos+np.array([0,1e-3]))[0][0]\n",
- " gradV = np.array([V_plusdx - V,V_plusdy-V])\n",
- " greedy_drift_velocity = 3*self.Agent.speed_mean*gradV / np.linalg.norm(gradV)\n",
+ "\n",
+ " def get_steep_ascent(self, pos):\n",
+ " \"\"\"This function will be used for policy improvement. Calculates direction steepest ascent (gradient) of the value function and returns a drift velocity in this direction.\"\"\"\n",
+ " V = self.get_state(evaluate_at=None, pos=pos)[0][0]\n",
+ " if V < 0.05 * self.max_fr: # <--remeber to set max_fr on each training loop\n",
+ " return None # if the value function is too low it is unreliabe, return None\n",
+ " else: # calculate gradient locally\n",
+ " V_plusdx = self.get_state(evaluate_at=None, pos=pos + np.array([1e-3, 0]))[\n",
+ " 0\n",
+ " ][0]\n",
+ " V_plusdy = self.get_state(evaluate_at=None, pos=pos + np.array([0, 1e-3]))[\n",
+ " 0\n",
+ " ][0]\n",
+ " gradV = np.array([V_plusdx - V, V_plusdy - V])\n",
+ " greedy_drift_velocity = (\n",
+ " 3 * self.Agent.speed_mean * gradV / np.linalg.norm(gradV)\n",
+ " )\n",
" return greedy_drift_velocity"
]
},
@@ -176,40 +186,56 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"Env = Environment()\n",
- "Env.add_wall([[0.8,0.0],[0.8,0.8]])\n",
+ "Env.add_wall([[0.8, 0.0], [0.8, 0.8]])\n",
"\n",
"Ag = Agent(Env)\n",
- "Ag.dt = 50e-3 #set discretisation time, large is fine\n",
- "Ag.episode_data = {'start_time':[],\n",
- " 'end_time':[],\n",
- " 'start_pos':[],\n",
- " 'end_pos':[],\n",
- " 'success_or_failure':[]} #a dictionary we will use later\n",
- "Ag.exploit_explore_ratio = 0.3 #a parameter we will use later\n",
+ "Ag.dt = 50e-3 # set discretisation time, large is fine\n",
+ "Ag.episode_data = {\n",
+ " \"start_time\": [],\n",
+ " \"end_time\": [],\n",
+ " \"start_pos\": [],\n",
+ " \"end_pos\": [],\n",
+ " \"success_or_failure\": [],\n",
+ "} # a dictionary we will use later\n",
+ "Ag.exploit_explore_ratio = 0.3 # a parameter we will use later\n",
"\n",
"n_pc = 400\n",
- "Inputs = PlaceCells(Ag, params={'n':n_pc,\n",
- " 'widths':np.random.uniform(0.04,0.4,size=(n_pc)),\n",
- " 'color':'C1'})\n",
- "#just manually setting the last four cell's widths and locations for some plots I wish to make later, this is not critical\n",
+ "Inputs = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": n_pc,\n",
+ " \"widths\": np.random.uniform(0.04, 0.4, size=(n_pc)),\n",
+ " \"color\": \"C1\",\n",
+ " },\n",
+ ")\n",
+ "# just manually setting the last four cell's widths and locations for some plots I wish to make later, this is not critical\n",
"Inputs.place_cell_widths[-4:] = 0.2\n",
- "Inputs.place_cell_centres[-4:] = np.array([[0.15,0.55],[0.55,0.25],[0.65,0.7],[0.9,0.7]])\n",
- "\n",
- "\n",
- "Reward = PlaceCells(Ag, params={'n':1,\n",
- " 'place_cell_centres':np.array([[0.9,0.05]]),\n",
- " 'description':'top_hat',\n",
- " 'widths':0.2,\n",
- " 'max_fr':1,\n",
- " 'color':'C5'})\n",
- "Reward.episode_end_time = 3 #a param we will use later\n",
- "\n",
- "ValNeur = ValueNeuron(Ag, params={'input_layer':Inputs,'reward_layer':Reward,'color':'C2'})"
+ "Inputs.place_cell_centres[-4:] = np.array(\n",
+ " [[0.15, 0.55], [0.55, 0.25], [0.65, 0.7], [0.9, 0.7]]\n",
+ ")\n",
+ "\n",
+ "\n",
+ "Reward = PlaceCells(\n",
+ " Ag,\n",
+ " params={\n",
+ " \"n\": 1,\n",
+ " \"place_cell_centres\": np.array([[0.9, 0.05]]),\n",
+ " \"description\": \"top_hat\",\n",
+ " \"widths\": 0.2,\n",
+ " \"max_fr\": 1,\n",
+ " \"color\": \"C5\",\n",
+ " },\n",
+ ")\n",
+ "Reward.episode_end_time = 3 # a param we will use later\n",
+ "\n",
+ "ValNeur = ValueNeuron(\n",
+ " Ag, params={\"input_layer\": Inputs, \"reward_layer\": Reward, \"color\": \"C2\"}\n",
+ ")"
]
},
{
@@ -221,14 +247,14 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -236,9 +262,9 @@
},
{
"data": {
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",
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"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -246,9 +272,9 @@
},
{
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",
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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -256,14 +282,14 @@
}
],
"source": [
- "fig, ax = Inputs.plot_rate_map(chosen_neurons=[-4,-3,-2,-1]) \n",
- "fig1, ax1 = Reward.plot_rate_map(chosen_neurons='1')\n",
- "fig2, ax2 = ValNeur.plot_rate_map(chosen_neurons='1')\n",
- "\n",
- "if save_plots == True: \n",
- " tpl.saveFigure(fig,'RLfeatures')\n",
- " tpl.saveFigure(fig1,'RLreward') \n",
- " tpl.saveFigure(fig2,'RLvalue0')"
+ "fig, ax = Inputs.plot_rate_map(chosen_neurons=[-4, -3, -2, -1])\n",
+ "fig1, ax1 = Reward.plot_rate_map(chosen_neurons=\"1\")\n",
+ "fig2, ax2 = ValNeur.plot_rate_map(chosen_neurons=\"1\")\n",
+ "\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"RLfeatures\")\n",
+ " tpl.saveFigure(fig1, \"RLreward\")\n",
+ " tpl.saveFigure(fig2, \"RLvalue0\")"
]
},
{
@@ -286,66 +312,65 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
- "from time import time \n",
- "from copy import copy \n",
- "\n",
- "def do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=True,\n",
- " max_t=60):\n",
- " Ag.episode_data['start_time'].append(Ag.t)\n",
- " Ag.episode_data['start_pos'].append(Ag.pos)\n",
+ "from time import time\n",
+ "from copy import copy\n",
+ "\n",
+ "\n",
+ "def do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=True, max_t=60):\n",
+ " Ag.episode_data[\"start_time\"].append(Ag.t)\n",
+ " Ag.episode_data[\"start_pos\"].append(Ag.pos)\n",
"\n",
" ValNeur.et = np.zeros_like(ValNeur.et)\n",
- " while True: \n",
+ " while True:\n",
" drift_velocity = ref_ValNeur.get_steep_ascent(Ag.pos)\n",
- " #you can ignore this (force agent to travel towards reward when v nearby) helps stability. \n",
- " if (Ag.pos[0] > 0.8) and (Ag.pos[1] < 0.4):\n",
- " dir_to_reward = Reward.place_cell_centres[0]-Ag.pos\n",
- " drift_velocity = 3*Ag.speed_mean*(dir_to_reward/np.linalg.norm(dir_to_reward))\n",
- " \n",
- " #move the agent\n",
- " Ag.update(drift_velocity=drift_velocity,\n",
- " drift_to_random_strength_ratio = Ag.exploit_explore_ratio)\n",
- " #update inputs and train weights \n",
+ " # you can ignore this (force agent to travel towards reward when v nearby) helps stability.\n",
+ " if (Ag.pos[0] > 0.8) and (Ag.pos[1] < 0.4):\n",
+ " dir_to_reward = Reward.place_cell_centres[0] - Ag.pos\n",
+ " drift_velocity = (\n",
+ " 3 * Ag.speed_mean * (dir_to_reward / np.linalg.norm(dir_to_reward))\n",
+ " )\n",
+ "\n",
+ " # move the agent\n",
+ " Ag.update(\n",
+ " drift_velocity=drift_velocity,\n",
+ " drift_to_random_strength_ratio=Ag.exploit_explore_ratio,\n",
+ " )\n",
+ " # update inputs and train weights\n",
" Inputs.update()\n",
" Reward.update()\n",
" ValNeur.update_firingrate()\n",
- " #train the weights\n",
- " if train == True: \n",
+ " # train the weights\n",
+ " if train == True:\n",
" ValNeur.update_weights()\n",
- " #end episode when at some random moment when reward is high OR after timeout \n",
- " if np.random.uniform() < Ag.dt * Reward.firingrate/Reward.episode_end_time:\n",
- " Ag.exploit_explore_ratio *= 1.1 #policy gets greedier if it was successful\n",
- " Ag.episode_data['success_or_failure'].append(1)\n",
+ " # end episode when at some random moment when reward is high OR after timeout\n",
+ " if np.random.uniform() < Ag.dt * Reward.firingrate / Reward.episode_end_time:\n",
+ " Ag.exploit_explore_ratio *= 1.1 # policy gets greedier if it was successful\n",
+ " Ag.episode_data[\"success_or_failure\"].append(1)\n",
" break\n",
- " if (Ag.t - Ag.episode_data['start_time'][-1]) > max_t: #timeout\n",
- " Ag.episode_data['success_or_failure'].append(0)\n",
+ " if (Ag.t - Ag.episode_data[\"start_time\"][-1]) > max_t: # timeout\n",
+ " Ag.episode_data[\"success_or_failure\"].append(0)\n",
" break\n",
- " Ag.episode_data['end_time'].append(Ag.t)\n",
- " Ag.episode_data['end_pos'].append(Ag.pos)\n",
- " Ag.exploit_explore_ratio = max(0.1,min(1,Ag.exploit_explore_ratio))\n",
- " Ag.velocity = np.random.uniform(-0.1,0.1,size=(2,))\n",
- " return \n"
+ " Ag.episode_data[\"end_time\"].append(Ag.t)\n",
+ " Ag.episode_data[\"end_pos\"].append(Ag.pos)\n",
+ " Ag.exploit_explore_ratio = max(0.1, min(1, Ag.exploit_explore_ratio))\n",
+ " Ag.velocity = np.random.uniform(-0.1, 0.1, size=(2,))\n",
+ " return"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -353,19 +378,21 @@
}
],
"source": [
- "#Lets do an initial test (without learning)\n",
- "Ag.pos = np.array([0.4,0.2])\n",
+ "# Lets do an initial test (without learning)\n",
+ "Ag.pos = np.array([0.4, 0.2])\n",
"Ag.exploit_explore_ratio = 1\n",
- "do_episode(ref_ValNeur=ValNeur,\n",
- " ValNeur=ValNeur,\n",
- " Ag=Ag,\n",
- " Inputs=Inputs,\n",
- " Reward=Reward,\n",
- " train=False) \n",
+ "do_episode(\n",
+ " ref_ValNeur=ValNeur,\n",
+ " ValNeur=ValNeur,\n",
+ " Ag=Ag,\n",
+ " Inputs=Inputs,\n",
+ " Reward=Reward,\n",
+ " train=False,\n",
+ ")\n",
"fig, ax = ValNeur.plot_rate_map()\n",
"fig, ax = Ag.plot_trajectory(fig=fig, ax=ax[0])\n",
- "if save_plots==True: \n",
- " tpl.saveFigure(fig,\"RL_notraining\")"
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"RL_notraining\")"
]
},
{
@@ -377,45 +404,47 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Batch 1/10: 8 timeout(s), 0 success(es), average episode time 60.01s\n",
- "Batch 2/10: 7 timeout(s), 1 success(es), average episode time 59.69s\n",
- "Batch 3/10: 7 timeout(s), 1 success(es), average episode time 53.22s\n",
- "Batch 4/10: 6 timeout(s), 2 success(es), average episode time 46.86s\n",
- "Batch 5/10: 7 timeout(s), 1 success(es), average episode time 55.58s\n",
- "Batch 6/10: 2 timeout(s), 6 success(es), average episode time 28.43s\n",
- "Batch 7/10: 0 timeout(s), 8 success(es), average episode time 27.10s\n",
- "Batch 8/10: 0 timeout(s), 8 success(es), average episode time 7.71s\n",
- "Batch 9/10: 0 timeout(s), 8 success(es), average episode time 19.14s\n",
- "Batch 10/10: 0 timeout(s), 8 success(es), average episode time 12.38s\n"
+ "Batch 1/10: 7 timeout(s), 1 success(es), average episode time 58.29s\n",
+ "Batch 2/10: 7 timeout(s), 1 success(es), average episode time 52.77s\n",
+ "Batch 3/10: 6 timeout(s), 2 success(es), average episode time 48.99s\n",
+ "Batch 4/10: 6 timeout(s), 2 success(es), average episode time 45.16s\n",
+ "Batch 5/10: 7 timeout(s), 1 success(es), average episode time 53.68s\n",
+ "Batch 6/10: 0 timeout(s), 8 success(es), average episode time 15.74s\n",
+ "Batch 7/10: 0 timeout(s), 8 success(es), average episode time 17.69s\n",
+ "Batch 8/10: 0 timeout(s), 8 success(es), average episode time 19.55s\n",
+ "Batch 9/10: 0 timeout(s), 8 success(es), average episode time 13.64s\n",
+ "Batch 10/10: 0 timeout(s), 8 success(es), average episode time 15.33s\n"
]
}
],
"source": [
- "Ag.exploit_explore_ratio = 0.2 #mostly random exploration to start, this will increase with time\n",
+ "Ag.exploit_explore_ratio = (\n",
+ " 0.2 # mostly random exploration to start, this will increase with time\n",
+ ")\n",
"for i in range(10):\n",
- " #cache copy of the ValueNeuron and use this to dictate policy\n",
+ " # cache copy of the ValueNeuron and use this to dictate policy\n",
" ref_ValNeur = copy(ValNeur)\n",
- " ref_ValNeur.max_fr = np.max(ValNeur.get_state(evaluate_at='all'))\n",
- "\n",
- " for j in range(8): #batches of episodes \n",
- " Ag.pos = Env.sample_positions(n=1)[0] #put agent in random position\n",
- " do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=True)\n",
- "\n",
- " n_success = sum(Ag.episode_data['success_or_failure'][-8:])\n",
- " av_episode_time = np.mean(np.array(Ag.episode_data['end_time'][-8:]) - np.array(Ag.episode_data['start_time'][-8:]))\n",
- " print(f\"Batch {i+1}/{10}: {8-n_success} timeout(s), {n_success} success(es), average episode time {av_episode_time:.2f}s\")\n"
+ " ref_ValNeur.max_fr = np.max(ValNeur.get_state(evaluate_at=\"all\"))\n",
+ "\n",
+ " for j in range(8): # batches of episodes\n",
+ " Ag.pos = Env.sample_positions(n=1)[0] # put agent in random position\n",
+ " do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=True)\n",
+ "\n",
+ " n_success = sum(Ag.episode_data[\"success_or_failure\"][-8:])\n",
+ " av_episode_time = np.mean(\n",
+ " np.array(Ag.episode_data[\"end_time\"][-8:])\n",
+ " - np.array(Ag.episode_data[\"start_time\"][-8:])\n",
+ " )\n",
+ " print(\n",
+ " f\"Batch {i+1}/{10}: {8-n_success} timeout(s), {n_success} success(es), average episode time {av_episode_time:.2f}s\"\n",
+ " )"
]
},
{
@@ -427,21 +456,21 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Batch 10/10: 0 timeouts, 8 successes, average episode time 20.83s\n"
+ "Batch 10/10: 0 timeouts, 8 successes, average episode time 13.61s\n"
]
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -449,39 +478,49 @@
}
],
"source": [
- "test_pos = np.array([[0.1,0.9],\n",
- " [0.1,0.7],\n",
- " [0.1,0.5],\n",
- " [0.1,0.3],\n",
- " [0.1,0.1],\n",
- " [0.3,0.1],\n",
- " [0.5,0.1],\n",
- " [0.7,0.1],])\n",
- "test_pos += np.random.uniform(-0.05,0.05,size=test_pos.shape)\n",
+ "test_pos = np.array(\n",
+ " [\n",
+ " [0.1, 0.9],\n",
+ " [0.1, 0.7],\n",
+ " [0.1, 0.5],\n",
+ " [0.1, 0.3],\n",
+ " [0.1, 0.1],\n",
+ " [0.3, 0.1],\n",
+ " [0.5, 0.1],\n",
+ " [0.7, 0.1],\n",
+ " ]\n",
+ ")\n",
+ "test_pos += np.random.uniform(-0.05, 0.05, size=test_pos.shape)\n",
"np.random.shuffle(test_pos)\n",
- "Env.walls[-1,-1,-1]=0.8\n",
+ "Env.walls[-1, -1, -1] = 0.8\n",
"Reward.episode_end_time = 1\n",
"for j in range(8):\n",
" Ag.pos = test_pos[j]\n",
- " do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=False)\n",
- "n_success = sum(Ag.episode_data['success_or_failure'][-8:])\n",
- "av_episode_time = np.mean(np.array(Ag.episode_data['end_time'][-8:]) - np.array(Ag.episode_data['start_time'][-8:]))\n",
- "print(f\"Batch {i+1}/{10}: {8-n_success} timeouts, {n_success} successes, average episode time {av_episode_time:.2f}s\")\n",
+ " do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=False)\n",
+ "n_success = sum(Ag.episode_data[\"success_or_failure\"][-8:])\n",
+ "av_episode_time = np.mean(\n",
+ " np.array(Ag.episode_data[\"end_time\"][-8:])\n",
+ " - np.array(Ag.episode_data[\"start_time\"][-8:])\n",
+ ")\n",
+ "print(\n",
+ " f\"Batch {i+1}/{10}: {8-n_success} timeouts, {n_success} successes, average episode time {av_episode_time:.2f}s\"\n",
+ ")\n",
"\n",
"Ag.average_measured_speed = 0.15\n",
"fig, ax = ValNeur.plot_rate_map()\n",
- "fig, ax = Ag.plot_trajectory(fig=fig,ax=ax[0],t_start=Ag.episode_data['start_time'][-8]+Ag.dt)\n",
- "start_pos = np.array(Ag.episode_data['start_pos'][-8:])\n",
- "end_pos = np.array(Ag.episode_data['end_pos'][-8:])\n",
- "ax.scatter(start_pos[:,0],start_pos[:,1],s=20,c='C2',zorder=11,alpha=0.8,linewidths=0)\n",
- "ax.scatter(end_pos[-8:,0],end_pos[-8:,1],s=20,c='r',zorder=11,alpha=0.8,linewidths=0)\n",
- "if save_plots == True: \n",
- " tpl.saveFigure(fig,'RL_trainedagent')"
+ "fig, ax = Ag.plot_trajectory(\n",
+ " fig=fig, ax=ax[0], t_start=Ag.episode_data[\"start_time\"][-8] + Ag.dt\n",
+ ")\n",
+ "start_pos = np.array(Ag.episode_data[\"start_pos\"][-8:])\n",
+ "end_pos = np.array(Ag.episode_data[\"end_pos\"][-8:])\n",
+ "ax.scatter(\n",
+ " start_pos[:, 0], start_pos[:, 1], s=20, c=\"C2\", zorder=11, alpha=0.8, linewidths=0\n",
+ ")\n",
+ "ax.scatter(\n",
+ " end_pos[-8:, 0], end_pos[-8:, 1], s=20, c=\"r\", zorder=11, alpha=0.8, linewidths=0\n",
+ ")\n",
+ "if save_plots == True:\n",
+ " tpl.saveFigure(fig, \"RL_trainedagent\")"
]
},
{
@@ -499,14 +538,14 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -514,9 +553,9 @@
},
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -524,47 +563,49 @@
}
],
"source": [
- "#WITHOUT SHORTCUT\n",
- "Env.walls[-1] = np.array([[0.8,0.0],[0.8,0.8]])\n",
+ "# WITHOUT SHORTCUT\n",
+ "Env.walls[-1] = np.array([[0.8, 0.0], [0.8, 0.8]])\n",
"Reward.episode_end_time = 3\n",
- "Ag.pos = np.array([0.4,0.2])\n",
- "do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=False)\n",
+ "Ag.pos = np.array([0.4, 0.2])\n",
+ "do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=False)\n",
"\n",
"Ag.average_measured_speed = 0.15\n",
"fig, ax = ValNeur.plot_rate_map()\n",
- "fig, ax = Ag.plot_trajectory(fig=fig,ax=ax[0],t_start=Ag.episode_data['start_time'][-1]+Ag.dt)\n",
+ "fig, ax = Ag.plot_trajectory(\n",
+ " fig=fig, ax=ax[0], t_start=Ag.episode_data[\"start_time\"][-1] + Ag.dt\n",
+ ")\n",
"\n",
"if save_plots == True:\n",
- " tpl.saveFigure(fig,\"rl_noshortcut\")\n",
- " anim = Ag.animate_trajectory(t_start = Ag.episode_data['start_time'][-1]+Ag.dt, t_end=Ag.history['t'][-1],speed_up=1)\n",
- " anim.save(\"../figures/animations/rl_agent_noshortcut.mp4\",dpi=250)\n",
+ " tpl.saveFigure(fig, \"rl_noshortcut\")\n",
+ " anim = Ag.animate_trajectory(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-1] + Ag.dt,\n",
+ " t_end=Ag.history[\"t\"][-1],\n",
+ " speed_up=1,\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_agent_noshortcut.mp4\", dpi=250)\n",
"\n",
"\n",
- "#WITH SHORTCUT \n",
- "Env.walls[-1] = np.array([[0.8,0.1],[0.8,0.8]])\n",
+ "# WITH SHORTCUT\n",
+ "Env.walls[-1] = np.array([[0.8, 0.1], [0.8, 0.8]])\n",
"Reward.episode_end_time = 3\n",
- "Ag.pos = np.array([0.4,0.2])\n",
- "do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=False)\n",
+ "Ag.pos = np.array([0.4, 0.2])\n",
+ "do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=False)\n",
"Ag.average_measured_speed = 0.15\n",
"fig1, ax1 = ValNeur.plot_rate_map()\n",
- "fig1, ax1 = Ag.plot_trajectory(fig=fig1,ax=ax1[0],t_start=Ag.episode_data['start_time'][-1]+Ag.dt)\n",
+ "fig1, ax1 = Ag.plot_trajectory(\n",
+ " fig=fig1, ax=ax1[0], t_start=Ag.episode_data[\"start_time\"][-1] + Ag.dt\n",
+ ")\n",
"\n",
"if save_plots == True:\n",
- " tpl.saveFigure(fig1,\"rl_shortcut\")\n",
- " anim = Ag.animate_trajectory(t_start = Ag.episode_data['start_time'][-1]+Ag.dt, t_end=Ag.history['t'][-1],speed_up=1)\n",
- " anim.save(\"../figures/animations/rl_agent_shortcut.mp4\",dpi=250)\n",
- "\n",
- "Env.walls[-1] = np.array([[0.8,0.0],[0.8,0.8]])\n"
+ " tpl.saveFigure(fig1, \"rl_shortcut\")\n",
+ " anim = Ag.animate_trajectory(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-1] + Ag.dt,\n",
+ " t_end=Ag.history[\"t\"][-1],\n",
+ " speed_up=1,\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_agent_shortcut.mp4\", dpi=250)\n",
+ "\n",
+ "Env.walls[-1] = np.array([[0.8, 0.0], [0.8, 0.8]])"
]
},
{
@@ -581,22 +622,15 @@
"metadata": {},
"outputs": [],
"source": [
- "test_pos = np.array([[0.1,0.5],\n",
- " [0.75,0.05],\n",
- " [0.1,0.95]])\n",
+ "test_pos = np.array([[0.1, 0.5], [0.75, 0.05], [0.1, 0.95]])\n",
"n_test = len(test_pos)\n",
"\n",
"Reward.episode_end_time = 1\n",
"t_start = Ag.t\n",
"for j in range(n_test):\n",
" Ag.pos = test_pos[j]\n",
- " do_episode(ref_ValNeur,\n",
- " ValNeur,\n",
- " Ag,\n",
- " Inputs,\n",
- " Reward,\n",
- " train=False)\n",
- "t_end = Ag.t "
+ " do_episode(ref_ValNeur, ValNeur, Ag, Inputs, Reward, train=False)\n",
+ "t_end = Ag.t"
]
},
{
@@ -605,30 +639,48 @@
"metadata": {},
"outputs": [],
"source": [
- "if save_plots == True: \n",
- " #make plot of value function, place cells and reward neuron\n",
+ "if save_plots == True:\n",
+ " # make plot of value function, place cells and reward neuron\n",
" fig, ax = ValNeur.plot_rate_map()\n",
- " tpl.saveFigure(fig,'demo_valuefnc')\n",
+ " tpl.saveFigure(fig, \"demo_valuefnc\")\n",
"\n",
- " fig, ax = Inputs.plot_rate_map(chosen_neurons=[-4,-3,-2,-1])\n",
- " tpl.saveFigure(fig,'demo_placecells')\n",
+ " fig, ax = Inputs.plot_rate_map(chosen_neurons=[-4, -3, -2, -1])\n",
+ " tpl.saveFigure(fig, \"demo_placecells\")\n",
"\n",
" fig, ax = Reward.plot_rate_map()\n",
- " tpl.saveFigure(fig,'demo_reward')\n",
- "\n",
- " #make animations \n",
- " anim = Ag.animate_trajectory(t_start=Ag.episode_data['start_time'][-n_test]+Ag.dt,fps=15)\n",
- " anim.save(\"../figures/animations/rl_twotest_trajectory.mp4\",dpi=300)\n",
- "\n",
- " anim = Inputs.animate_rate_timeseries(t_start=Ag.episode_data['start_time'][-n_test]+Ag.dt,chosen_neurons=[-4,-3,-2,-1],fps=15)\n",
- " anim.save(\"../figures/animations/rl_twotest_pcs.mp4\",dpi=300)\n",
- "\n",
- " anim = Reward.animate_rate_timeseries(t_start=Ag.episode_data['start_time'][-n_test]+Ag.dt,norm_by=1.0,fps=15)\n",
- " anim.save(\"../figures/animations/rl_twotest_reward.mp4\",dpi=300)\n",
- "\n",
- " norm = 1.2*max(ValNeur.history['firingrate'][np.argmin(np.abs(np.array(Ag.history['t'])-Ag.episode_data['start_time'][-n_test]))])\n",
- " anim = ValNeur.animate_rate_timeseries(t_start=Ag.episode_data['start_time'][-n_test]+Ag.dt,fps=15,norm_by=norm)\n",
- " anim.save(\"../figures/animations/rl_twotest_value.mp4\",dpi=300)"
+ " tpl.saveFigure(fig, \"demo_reward\")\n",
+ "\n",
+ " # make animations\n",
+ " anim = Ag.animate_trajectory(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-n_test] + Ag.dt, fps=15\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_twotest_trajectory.mp4\", dpi=300)\n",
+ "\n",
+ " anim = Inputs.animate_rate_timeseries(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-n_test] + Ag.dt,\n",
+ " chosen_neurons=[-4, -3, -2, -1],\n",
+ " fps=15,\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_twotest_pcs.mp4\", dpi=300)\n",
+ "\n",
+ " anim = Reward.animate_rate_timeseries(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-n_test] + Ag.dt, norm_by=1.0, fps=15\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_twotest_reward.mp4\", dpi=300)\n",
+ "\n",
+ " norm = 1.2 * max(\n",
+ " ValNeur.history[\"firingrate\"][\n",
+ " np.argmin(\n",
+ " np.abs(\n",
+ " np.array(Ag.history[\"t\"]) - Ag.episode_data[\"start_time\"][-n_test]\n",
+ " )\n",
+ " )\n",
+ " ]\n",
+ " )\n",
+ " anim = ValNeur.animate_rate_timeseries(\n",
+ " t_start=Ag.episode_data[\"start_time\"][-n_test] + Ag.dt, fps=15, norm_by=norm\n",
+ " )\n",
+ " anim.save(\"../figures/animations/rl_twotest_value.mp4\", dpi=300)"
]
},
{
@@ -673,7 +725,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.10.9"
},
"orig_nbformat": 4
},
diff --git a/demos/simple_example.ipynb b/demos/simple_example.ipynb
index bc18ad34..bf513562 100644
--- a/demos/simple_example.ipynb
+++ b/demos/simple_example.ipynb
@@ -28,7 +28,7 @@
"Env = Environment()\n",
"Ag = Agent(Env)\n",
"PCs = PlaceCells(Ag)\n",
- "for i in range(int(60/Ag.dt)):\n",
+ "for i in range(int(60 / Ag.dt)):\n",
" Ag.update()\n",
" PCs.update()"
]
@@ -54,10 +54,10 @@
}
],
"source": [
- "print(\"Timestamps:\",Ag.history['t'][:10],\"\\n\")\n",
- "print(\"Positions:\",Ag.history['pos'][:10],\"\\n\")\n",
- "print(\"Firing rate timeseries:\",PCs.history['firingrate'][:10],\"\\n\")\n",
- "print(\"Spikes:\",PCs.history['spikes'][:10],\"\\n\")"
+ "print(\"Timestamps:\", Ag.history[\"t\"][:10], \"\\n\")\n",
+ "print(\"Positions:\", Ag.history[\"pos\"][:10], \"\\n\")\n",
+ "print(\"Firing rate timeseries:\", PCs.history[\"firingrate\"][:10], \"\\n\")\n",
+ "print(\"Spikes:\", PCs.history[\"spikes\"][:10], \"\\n\")"
]
},
{
diff --git a/demos/testing-jvsc-98fc4c0f-5ef8-4c0a-a84e-151fc396e122.ipynb b/demos/testing-jvsc-98fc4c0f-5ef8-4c0a-a84e-151fc396e122.ipynb
new file mode 100644
index 00000000..363fcab7
--- /dev/null
+++ b/demos/testing-jvsc-98fc4c0f-5ef8-4c0a-a84e-151fc396e122.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ratinabox/Agent.py b/ratinabox/Agent.py
index 43ff808a..9f99064c 100644
--- a/ratinabox/Agent.py
+++ b/ratinabox/Agent.py
@@ -1,4 +1,4 @@
-import ratinabox
+import ratinabox
import numpy as np
import matplotlib
@@ -36,6 +36,10 @@ class Agent:
"rotational_velocity_coherence_time": 0.08,
"rotational_velocity_std": 120 * (np.pi / 180),
"thigmotaxis": 0.5,
+ "wall_repel_distance": 0.1,
+ "walls_repel": True,
+
+
}
"""
@@ -113,22 +117,22 @@ def __init__(self, Environment, params={}):
def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1):
"""Movement policy update.
- In principle this does a very simple thing:
- • updates time by dt
- • updates velocity (speed and direction) according to a movement policy
- • updates position along the velocity direction
- In reality it's a complex function as the policy requires checking for immediate or upcoming collisions with all walls at each step as well as
- handling boundary conditions.
- Specifically the full loop looks like this:
- 1) Update time by dt
- 2) Update velocity for the next time step.
- In 2D this is done by varying the agents heading direction and speed according to ornstein-uhlenbeck processes.
- In 1D, simply the velocity is varied according to ornstein-uhlenbeck. This includes, if turned on, being repelled by the walls.
- 3) Propose a new position (x_new =? x_old + velocity.dt)
- 3.1) Check if this step collides with any walls (and act accordingly)
- 3.2) Check you distance and direction from walls and be repelled by them is necessary
- 4) Check position is still within maze and handle boundary conditions appropriately
- 6) Store new position and time in history data frame
+ In principle this does a very simple thing:
+ • updates time by dt
+ • updates velocity (speed and direction) according to a movement policy
+ • updates position along the velocity direction
+ In reality it's a complex function as the policy requires checking for immediate or upcoming collisions with all walls at each step as well as
+ handling boundary conditions.
+ Specifically the full loop looks like this:
+ 1) Update time by dt
+ 2) Update velocity for the next time step.
+ In 2D this is done by varying the agents heading direction and speed according to ornstein-uhlenbeck processes.
+ In 1D, simply the velocity is varied according to ornstein-uhlenbeck. This includes, if turned on, being repelled by the walls.
+ 3) Propose a new position (x_new =? x_old + velocity.dt)
+ 3.1) Check if this step collides with any walls (and act accordingly)
+ 3.2) Check you distance and direction from walls and be repelled by them is necessary
+ 4) Check position is still within maze and handle boundary conditions appropriately
+ 6) Store new position and time in history data frame
"""
if dt == None:
dt = self.dt
@@ -172,7 +176,8 @@ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1)
x=self.velocity,
drift=drift_velocity,
noise_scale=0,
- coherence_time=self.speed_coherence_time / drift_to_random_strength_ratio, # <--- this controls how "powerful" this signal is
+ coherence_time=self.speed_coherence_time
+ / drift_to_random_strength_ratio, # <--- this controls how "powerful" this signal is
)
# Deterministically drift the velocity away from any nearby walls
@@ -183,7 +188,9 @@ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1)
if len(self.Environment.walls) > 0:
distance_to_walls = np.linalg.norm(vectors_from_walls, axis=-1)
normalised_vectors_from_walls = (
- vectors_from_walls / np.expand_dims(distance_to_walls, axis=-1))
+ vectors_from_walls
+ / np.expand_dims(distance_to_walls, axis=-1)
+ )
x, d, v = (
distance_to_walls,
self.wall_repel_distance,
@@ -206,17 +213,21 @@ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1)
See paper for full details"""
- spring_constant = v ** 2 / d ** 2
+ spring_constant = v**2 / d**2
wall_accelerations = np.piecewise(
x=x,
- condlist=[(x <= d), (x > d),],
+ condlist=[
+ (x <= d),
+ (x > d),
+ ],
funclist=[
lambda x: spring_constant * (d - x),
lambda x: 0,
],
)
wall_acceleration_vecs = (
- np.expand_dims(wall_accelerations, axis=-1) * normalised_vectors_from_walls
+ np.expand_dims(wall_accelerations, axis=-1)
+ * normalised_vectors_from_walls
)
wall_acceleration = wall_acceleration_vecs.sum(axis=0)
dv = wall_acceleration * dt
@@ -229,18 +240,22 @@ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1)
As a result the agent which is walking into the wall will continue to barge hopelessly into the wall causing it the "hug" close to the wall."""
wall_speeds = np.piecewise(
x=x,
- condlist=[(x <= d), (x > d),],
+ condlist=[
+ (x <= d),
+ (x > d),
+ ],
funclist=[
- lambda x: v * (1 - np.sqrt(1 - (d - x) ** 2 / d ** 2)),
+ lambda x: v * (1 - np.sqrt(1 - (d - x) ** 2 / d**2)),
lambda x: 0,
],
)
wall_speed_vecs = (
- np.expand_dims(wall_speeds, axis=-1) * normalised_vectors_from_walls
+ np.expand_dims(wall_speeds, axis=-1)
+ * normalised_vectors_from_walls
)
wall_speed = wall_speed_vecs.sum(axis=0)
dx = wall_speed * dt
- self.pos += 6 * (self.thigmotaxis ** 2) * dx
+ self.pos += 6 * (self.thigmotaxis**2) * dx
# proposed position update
proposed_new_pos = self.pos + self.velocity * dt
@@ -344,7 +359,9 @@ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1)
if len(self.history["pos"]) >= 1:
self.distance_travelled += np.linalg.norm(
- self.pos - self.history["pos"][-1]
+ self.Environment.get_vectors_between___accounting_for_environment(
+ self.pos, np.array(self.history["pos"][-1])
+ )
)
tau_speed = 10
self.average_measured_speed = (
@@ -434,7 +451,10 @@ def import_trajectory(self, times=None, positions=None, dataset=None):
if self.Environment.dimensionality == "2D":
positions = positions.reshape(-1, 2)
if (
- (max(positions[:, 0]) > ex[1]) or (min(positions[:, 0]) < ex[0]) or (max(positions[:, 1]) > ex[3]) or (min(positions[:, 1]) < ex[2])
+ (max(positions[:, 0]) > ex[1])
+ or (min(positions[:, 0]) < ex[0])
+ or (max(positions[:, 1]) > ex[3])
+ or (min(positions[:, 1]) < ex[2])
):
print(
f"""WARNING: the size of the trajectory is significantly larger than the environment you are using.
@@ -468,12 +488,16 @@ def plot_trajectory(
framerate=10,
fig=None,
ax=None,
+ point_size=15,
decay_point_size=False,
+ decay_point_timescale=10,
plot_agent=True,
color=None,
alpha=0.7,
xlim=None,
background_color=None,
+ axis_labels=True,
+ **kwargs,
):
"""Plots the trajectory between t_start (seconds) and t_end (defaulting to the last time available)
@@ -483,12 +507,15 @@ def plot_trajectory(
• framerate: how many scatter points / per second of motion to display
• fig, ax: the fig, ax to plot on top of, optional, if not provided used self.Environment.plot_Environment().
This can be used to plot trajectory on top of receptive fields etc.
+ • point_size: size of scatter points
• decay_point_size: decay trajectory point size over time (recent times = largest)
+ • decay_point_timescale: if decay_point_size is True, this is the timescale over which sizes decay
• plot_agent: dedicated point show agent current position
• color: plot point color
• alpha: plot point opaqness
• xlim: In 1D, forces the xlim to be a certain time (minutes) (useful if animating this function)
• background_color: color of the background if not matplotlib default, only for 1D (probably white)
+ • axis_labels: whether to show axes labels
Returns:
fig, ax
@@ -499,8 +526,8 @@ def plot_trajectory(
t, pos = np.array(self.history["t"]), np.array(self.history["pos"])
if t_end == None:
t_end = t[-1]
- startid = np.argmin(np.abs(t - (t_start)))
- endid = np.argmin(np.abs(t - (t_end)))
+ startid = np.nanargmin(np.abs(t - (t_start)))
+ endid = np.nanargmin(np.abs(t - (t_end)))
if self.Environment.dimensionality == "2D":
skiprate = max(1, int((1 / framerate) / dt))
trajectory = pos[startid:endid, :][::skiprate]
@@ -511,20 +538,21 @@ def plot_trajectory(
if self.Environment.dimensionality == "2D":
fig, ax = self.Environment.plot_environment(fig=fig, ax=ax)
- s = 15 * np.ones_like(time)
+ s = point_size * np.ones_like(time)
if decay_point_size == True:
- s = 15 * np.exp((time - time[-1]) / 10)
- s[(time[-1] - time) > 15] *= 0
+ s = point_size * np.exp((time - time[-1]) / decay_point_timescale)
+ s[(time[-1] - time) > (1.5 * decay_point_timescale)] *= 0
c = [color] * len(time)
if plot_agent == True:
s[-1] = 40
c[-1] = "r"
+
ax.scatter(
trajectory[:, 0],
trajectory[:, 1],
s=s,
alpha=alpha,
- zorder=2,
+ zorder=0,
c=c,
linewidth=0,
)
@@ -533,8 +561,9 @@ def plot_trajectory(
fig, ax = plt.subplots(figsize=(3, 1.5))
ax.scatter(time / 60, trajectory, alpha=alpha, linewidth=0, c=color, s=5)
ax.spines["left"].set_position(("data", t_start / 60))
- ax.set_xlabel("Time / min")
- ax.set_ylabel("Position / m")
+ if axis_labels == True:
+ ax.set_xlabel("Time / min")
+ ax.set_ylabel("Position / m")
ax.set_xlim([t_start / 60, t_end / 60])
if xlim is not None:
ax.set_xlim(right=xlim)
@@ -562,6 +591,7 @@ def animate_trajectory(
t_end (_type_, optional): _description_. Defaults to None.
fps: frames per second of end video
speed_up: #times real speed animation should come out at
+ kwargs: passed to trajectory plotting function (chuck anything you wish in here)
Returns:
animation
@@ -589,9 +619,12 @@ def animate_(i, fig, ax, t_start, t_max, speed_up, dt):
plt.close()
return
- fig, ax = self.plot_trajectory(0, 10 * self.dt, xlim=t_end / 60, **kwargs)
+ fig, ax = self.plot_trajectory(
+ t_start=0, t_end=10 * self.dt, xlim=t_end / 60, **kwargs
+ )
from matplotlib import animation
+
anim = matplotlib.animation.FuncAnimation(
fig,
animate_,
@@ -616,7 +649,9 @@ def plot_position_heatmap(self, dx=None, weights=None, fig=None, ax=None):
ex = self.Environment.extent
if fig is None and ax is None:
fig, ax = self.Environment.plot_environment(height=1)
- heatmap, centres = utils.bin_data_for_histogramming(data=pos, extent=ex, dx=dx)
+ heatmap, centres = utils.bin_data_for_histogramming(
+ data=pos, extent=ex, dx=dx
+ )
# maybe do smoothing?
ax.plot(centres, heatmap)
ax.fill_between(centres, 0, heatmap, alpha=0.3)
@@ -635,7 +670,14 @@ def plot_position_heatmap(self, dx=None, weights=None, fig=None, ax=None):
_, _ = self.Environment.plot_environment(fig=fig, ax=ax)
vmin = 0
vmax = np.max(heatmap)
- ax.imshow(heatmap, extent=ex, interpolation="bicubic", vmin=vmin, vmax=vmax)
+ ax.imshow(
+ heatmap,
+ extent=ex,
+ interpolation="bicubic",
+ vmin=vmin,
+ vmax=vmax,
+ zorder=0,
+ )
return fig, ax
def plot_histogram_of_speeds(
diff --git a/ratinabox/Environment.py b/ratinabox/Environment.py
index 40e0e366..2c94e2b4 100644
--- a/ratinabox/Environment.py
+++ b/ratinabox/Environment.py
@@ -1,8 +1,11 @@
-import ratinabox
+import ratinabox
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
+import shapely
+
+import warnings
from ratinabox import utils
@@ -49,8 +52,10 @@ def __init__(self, params={}):
"dimensionality": "2D", # 1D or 2D environment
"boundary_conditions": "solid", # solid vs periodic
"scale": 1, # scale of environment (in metres)
- "aspect": 1, # x/y aspect ratio for the (rectangular) 2D environment
+ "aspect": 1, # x/y aspect ratio for the (rectangular) 2D environment (how wide this is relative to tall)
"dx": 0.01, # discretises the environment (for plotting purposes only)
+ "boundary": None, # coordinates [[x0,y0],[x1,y1],...] of the corners of a 2D polygon bounding the Env (if None, Env defaults to rectangular). Corners must be ordered clockwise or anticlockwise, and the polygon must be a 'simple polygon' (no holes, doesn't self-intersect).
+ "holes": [], # coordinates [[[x0,y0],[x1,y1],...],...] of corners of any holes inside the Env. These must be entirely inside the environment and not intersect one another. Corners must be ordered clockwise or anticlockwise. holes has 1-dimension more than boundary since there can be multiple holes
}
default_params.update(params)
@@ -58,27 +63,75 @@ def __init__(self, params={}):
utils.update_class_params(self, self.params)
if self.dimensionality == "1D":
+ self.D = 1
self.extent = np.array([0, self.scale])
self.centre = np.array([self.scale / 2, self.scale / 2])
- self.walls = np.array([])
- if self.dimensionality == "2D":
- if self.boundary_conditions != "periodic":
+ elif self.dimensionality == "2D":
+ self.D = 2
+ self.is_rectangular = False
+ if (
+ self.boundary is None
+ ): # Not passing coordinates of a boundary, fall back to default rectangular env
+ self.is_rectangular = True
+ self.boundary = [
+ [0, 0],
+ [self.aspect * self.scale, 0],
+ [self.aspect * self.scale, self.scale],
+ [0, self.scale],
+ ]
+ else: # self.boundary coordinates passed in the input params
+ self.is_rectangular = False
+ b = self.boundary
+
+ # make the arena walls
+ self.walls = np.array([])
+ if (self.boundary_conditions == "periodic") and (
+ self.is_rectangular == False
+ ):
+ warnings.warn(
+ "Periodic boundary conditions are only allowed in rectangual environments. Changing boundary conditions to 'solid'."
+ )
+ self.params["boundary_conditions"] = "solid"
+ elif self.boundary_conditions == "solid":
self.walls = np.array(
[
- [[0, 0], [0, self.scale]],
- [[0, self.scale], [self.aspect * self.scale, self.scale]],
- [
- [self.aspect * self.scale, self.scale],
- [self.aspect * self.scale, 0],
- ],
- [[self.aspect * self.scale, 0], [0, 0]],
+ [b[(i + 1) if (i + 1) < len(b) else 0], b[i]]
+ for i in range(len(b))
]
- )
- self.centre = np.array([self.aspect * self.scale / 2, self.scale / 2])
- self.extent = np.array([0, self.aspect * self.scale, 0, self.scale])
- self.params["extent"] = self.extent
- self.params["centre"] = self.centre
+ ) # constructs walls from points on polygon
+
+ # make the hole walls (if there are any)
+ self.holes_polygons = []
+ self.has_holes = False
+ if len(self.holes) > 0:
+ assert (
+ np.array(self.holes).ndim == 3
+ ), "Incorrect dimensionality for holes list. It must be a list of lists of coordinates"
+
+ self.has_holes = True
+ for h in self.holes:
+ hole_walls = np.array(
+ [
+ [h[(i + 1) if (i + 1) < len(h) else 0], h[i]]
+ for i in range(len(h))
+ ]
+ )
+ self.walls = np.append(
+ self.walls.reshape(-1, 2, 2), hole_walls, axis=0
+ )
+ self.holes_polygons.append(shapely.Polygon(h))
+ self.boundary_polygon = shapely.Polygon(self.boundary)
+
+ # make some other attributes
+ left = min([c[0] for c in b])
+ right = max([c[0] for c in b])
+ bottom = min([c[1] for c in b])
+ top = max([c[1] for c in b])
+ self.centre = np.array([(left + right) / 2, (top + bottom) / 2])
+ self.extent = np.array(
+ [left, right, bottom, top]
+ ) # [left,right,bottom,top] ]the "extent" which will be plotted, always a rectilinear rectangle which will be the extent of all matplotlib plots
# save some prediscretised coords (useful for plotting rate maps later)
self.discrete_coords = self.discretise_environment(dx=self.dx)
@@ -136,23 +189,60 @@ def plot_environment(self, fig=None, ax=None, height=1):
fig, ax = plt.subplots(
figsize=(3 * (extent[1] - extent[0]), 3 * (extent[3] - extent[2]))
)
- background = matplotlib.patches.Rectangle(
- (extent[0], extent[2]),
- extent[1],
- extent[3],
- facecolor="lightgrey",
- zorder=-1,
+ # plot background/arena
+ background = matplotlib.patches.Polygon(
+ xy=np.array(self.boundary), facecolor="lightgrey", zorder=-1
)
- setattr(background, 'name', 'background')
+ setattr(background, "name", "background")
ax.add_patch(background)
+
+ # plot holes
+ for hole in self.holes:
+ hole_ = matplotlib.patches.Polygon(
+ xy=np.array(hole),
+ facecolor="white",
+ linewidth=1.0,
+ edgecolor="white",
+ zorder=1,
+ )
+ setattr(background, "name", "hole")
+ ax.add_patch(hole_)
+
+ # plot anti-arena (difference between area and the full extent shown)
+ if self.is_rectangular is False:
+ # size = self.extent[1]-self.extent[0]
+ extent_corners = np.array(
+ [
+ [self.extent[0], self.extent[2]],
+ [self.extent[1], self.extent[2]],
+ [self.extent[1], self.extent[3]],
+ [self.extent[0], self.extent[3]],
+ ]
+ )
+ extent_poly = shapely.Polygon(extent_corners)
+ arena_poly = shapely.Polygon(np.array(self.boundary))
+ anti_arena_multipoly = extent_poly.difference(arena_poly)
+ for poly in anti_arena_multipoly.geoms:
+ (x, y) = poly.exterior.coords.xy
+ coords = np.stack((list(x), list(y)), axis=1)
+ anti_arena_segment = matplotlib.patches.Polygon(
+ xy=np.array(coords),
+ facecolor="white",
+ linewidth=1.0,
+ edgecolor="white",
+ zorder=1,
+ )
+ setattr(background, "name", "hole")
+ ax.add_patch(anti_arena_segment)
+
for wall in walls:
ax.plot(
[wall[0][0], wall[1][0]],
[wall[0][1], wall[1][1]],
color="grey",
- linewidth=4,
- solid_capstyle='round',
- zorder=1.1,
+ linewidth=4.0,
+ solid_capstyle="round",
+ zorder=2,
)
ax.set_aspect("equal")
ax.grid(False)
@@ -186,6 +276,7 @@ def sample_positions(self, n=10, method="uniform_jitter"):
return positions
elif self.dimensionality == "2D":
+
if method == "random":
positions = np.random.uniform(size=(n, 2))
positions[:, 0] *= self.extent[1] - self.extent[0]
@@ -208,6 +299,15 @@ def sample_positions(self, n=10, method="uniform_jitter"):
n=n_remaining, method="random"
)
positions = np.vstack((positions, positions_remaining))
+
+ if (self.is_rectangular) or (self.has_holes is True):
+ # in this case, the positions you have sampled within the extent of the environment may not actually fall within it's legal area (i.e. they could be outside the polygon boundary or inside a hole). Brute for this by randomly resampling these oints until all fall within the env.
+ for (i, pos) in enumerate(positions):
+ if self.check_if_position_is_in_environment(pos) == False:
+ pos = self.sample_positions(n=1, method="random").reshape(
+ -1
+ ) # this recursive call must pass eventually, assuming the env is sufficiently large. this is why we don't need a while loop
+ positions[i] = pos
return positions
def discretise_environment(self, dx=None):
@@ -242,7 +342,9 @@ def get_vectors_between___accounting_for_environment(
Returns:
N x M x dimensionality array of pairwise vectors
"""
- vectors = utils.get_vectors_between(pos1=pos1, pos2=pos2, line_segments=line_segments)
+ vectors = utils.get_vectors_between(
+ pos1=pos1, pos2=pos2, line_segments=line_segments
+ )
if self.boundary_conditions == "periodic":
flip = np.abs(vectors) > (self.scale / 2)
vectors[flip] = -np.sign(vectors[flip]) * (
@@ -306,8 +408,12 @@ def get_distances_between___accounting_for_environment(
distances[wall_obstructs_view_of_cell == True] = 1000
if wall_geometry == "geodesic":
- assert (boundary_conditions == "solid"), "geodesic geometry is not available for periodic boundary conditions"
- assert (len(walls) <= 5), """unfortunately geodesic geomtry is only defined in closed rooms with one additional wall
+ assert (
+ boundary_conditions == "solid"
+ ), "geodesic geometry is not available for periodic boundary conditions"
+ assert (
+ len(walls) <= 5
+ ), """unfortunately geodesic geomtry is only defined in closed rooms with one additional wall
(efficient geometry calculations with more than 1 wall are super hard I have discovered!)"""
distances = utils.get_distances_between(vectors=vectors)
if len(walls) == 4:
@@ -335,8 +441,8 @@ def get_distances_between___accounting_for_environment(
line_segments.shape[:2]
)
flattened_distances = distances.reshape(-1)
- flattened_wall_obstructs_view_of_cell = wall_obstructs_view_of_cell.reshape(
- -1
+ flattened_wall_obstructs_view_of_cell = (
+ wall_obstructs_view_of_cell.reshape(-1)
)
flattened_distances[
flattened_wall_obstructs_view_of_cell
@@ -359,22 +465,36 @@ def check_if_position_is_in_environment(self, pos):
bool: True if pos is inside environment.
"""
pos = np.array(pos).reshape(-1)
- if self.dimensionality == "2D":
- if all([
- (pos[0] > self.extent[0]),
- (pos[0] < self.extent[1]),
- (pos[1] > self.extent[2]),
- (pos[1] < self.extent[3]),
- ]):
- return True
- else:
- return False
- elif self.dimensionality == "1D":
+ if self.dimensionality == "1D":
if (pos[0] > self.extent[0]) and (pos[0] < self.extent[1]):
return True
else:
return False
+ if self.dimensionality == "2D":
+ if (
+ self.is_rectangular == True and self.holes is None
+ ): # fast way (don't use shapely)
+ return all(
+ [
+ (pos[0] > self.extent[0]),
+ (pos[0] < self.extent[1]),
+ (pos[1] > self.extent[2]),
+ (pos[1] < self.extent[3]),
+ ]
+ )
+ else: # the slow way (polygon check for environment boundaries and each hole within env)
+ is_in = True
+ is_in *= self.boundary_polygon.contains(
+ shapely.Point(pos)
+ ) # assert inside area
+ if self.has_holes is True:
+ for hole_poly in self.holes_polygons:
+ is_in *= not hole_poly.contains(
+ shapely.Point(pos)
+ ) # assert inside area, "not" because if it's in the hole it isn't in the environment
+ return bool(is_in)
+
def check_wall_collisions(self, proposed_step):
"""Given proposed step [current_pos, next_pos] it returns two lists
1. a list of all the walls in the environment #shape=(N_walls,2,2)
@@ -405,7 +525,9 @@ def vectors_from_walls(self, pos):
Returns:
vector array: np.array(shape=(N_walls,2))
"""
- walls_to_pos_vectors = utils.shortest_vectors_from_points_to_lines(pos, self.walls)[0]
+ walls_to_pos_vectors = utils.shortest_vectors_from_points_to_lines(
+ pos, self.walls
+ )[0]
return walls_to_pos_vectors
def apply_boundary_conditions(self, pos):
@@ -421,16 +543,28 @@ def apply_boundary_conditions(self, pos):
if self.boundary_conditions == "solid":
pos = min(max(pos, self.extent[0] + 0.01), self.extent[1] - 0.01)
pos = np.reshape(pos, (-1))
- if self.dimensionality == "2D":
- if self.boundary_conditions == "periodic":
- pos[0] = pos[0] % self.extent[1]
- pos[1] = pos[1] % self.extent[3]
- if self.boundary_conditions == "solid":
- # in theory this wont be used as wall bouncing catches it earlier on
- pos[0] = min(
- max(pos[0], self.extent[0] + 0.01), self.extent[1] - 0.01
- )
- pos[1] = min(
- max(pos[1], self.extent[2] + 0.01), self.extent[3] - 0.01
- )
+ elif self.dimensionality == "2D":
+ if self.is_rectangular == True:
+ if not (
+ matplotlib.path.Path(self.boundary).contains_point(
+ pos, radius=-1e-10
+ )
+ ): # outside the bounding environment (i.e. not just in a hole), apply BCs
+ if self.boundary_conditions == "periodic":
+ pos[0] = pos[0] % self.extent[1]
+ pos[1] = pos[1] % self.extent[3]
+ if self.boundary_conditions == "solid":
+ # in theory this wont be used as wall bouncing catches it earlier on
+ pos[0] = min(
+ max(pos[0], self.extent[0] + 0.01),
+ self.extent[1] - 0.01,
+ )
+ pos[1] = min(
+ max(pos[1], self.extent[2] + 0.01),
+ self.extent[3] - 0.01,
+ )
+ else: # in this case, must just be in a hole. sample new position (there should be a better way to do this but, in theory, this isn't used)
+ pos = self.sample_positions(n=1, method="random").reshape(-1)
+ else: # polygon shaped env, just resample random position
+ pos = self.sample_positions(n=1, method="random").reshape(-1)
return pos
diff --git a/ratinabox/Neurons.py b/ratinabox/Neurons.py
index b8352122..8dbb22c2 100644
--- a/ratinabox/Neurons.py
+++ b/ratinabox/Neurons.py
@@ -1,4 +1,4 @@
-import ratinabox
+import ratinabox
import numpy as np
import matplotlib
@@ -87,9 +87,9 @@ def __init__(self, Agent, params={}):
"n": 10,
"name": "Neurons",
"color": None, # just for plotting
-
- "noise_std":0, #0 means no noise, std of the noise you want to add (Hz)
- "noise_coherence_time":0.5,
+ "noise_std": 0, # 0 means no noise, std of the noise you want to add (Hz)
+ "noise_coherence_time": 0.5,
+ "save_history": True, # whether to save history (set to False if you don't intend to acess Neuron.history for data after)
}
self.Agent = Agent
default_params.update(params)
@@ -104,26 +104,31 @@ def __init__(self, Agent, params={}):
self.history["firingrate"] = []
self.history["spikes"] = []
-
if ratinabox.verbose is True:
print(
f"\nA Neurons() class has been initialised with parameters f{self.params}. Use Neurons.update() to update the firing rate of the Neurons to correspond with the Agent.Firing rates and spikes are saved into the Agent.history dictionary. Plot a timeseries of the rate using Neurons.plot_rate_timeseries(). Plot a rate map of the Neurons using Neurons.plot_rate_map()."
)
def update(self):
- #update noise vector
- dnoise = utils.ornstein_uhlenbeck(dt=self.Agent.dt,
- x = self.noise,
- drift=0,
- noise_scale = self.noise_std,
- coherence_time = self.noise_coherence_time)
- self.noise = self.noise + dnoise
-
- #update firing rate
- firingrate = self.get_state()
+ # update noise vector
+ dnoise = utils.ornstein_uhlenbeck(
+ dt=self.Agent.dt,
+ x=self.noise,
+ drift=0,
+ noise_scale=self.noise_std,
+ coherence_time=self.noise_coherence_time,
+ )
+ self.noise = self.noise + dnoise
+
+ # update firing rate
+ if np.isnan(self.Agent.pos[0]):
+ firingrate = np.zeros(self.n) # returns zero if Agent position is nan
+ else:
+ firingrate = self.get_state()
self.firingrate = firingrate.reshape(-1)
- self.firingrate = self.firingrate + self.noise
- self.save_to_history()
+ self.firingrate = self.firingrate + self.noise
+ if self.save_history == True:
+ self.save_to_history()
return
def plot_rate_timeseries(
@@ -171,8 +176,8 @@ def plot_rate_timeseries(
# neurons to plot
chosen_neurons = self.return_list_of_neurons(chosen_neurons)
- spike_data = spike_data[startid:endid,chosen_neurons]
- rate_timeseries = rate_timeseries[:,chosen_neurons]
+ spike_data = spike_data[startid:endid, chosen_neurons]
+ rate_timeseries = rate_timeseries[:, chosen_neurons]
if imshow == False:
firingrates = rate_timeseries.T
@@ -356,7 +361,7 @@ def plot_rate_map(
rate_map = rate_maps[chosen_neurons[i], :].reshape(
self.Agent.Environment.discrete_coords.shape[:2]
)
- im = ax_.imshow(rate_map, extent=ex)
+ im = ax_.imshow(rate_map, extent=ex, zorder=0)
elif method == "history":
rate_timeseries_ = rate_timeseries[chosen_neurons[i], :]
rate_map = utils.bin_data_for_histogramming(
@@ -364,8 +369,9 @@ def plot_rate_map(
)
im = ax_.imshow(
rate_map,
- extent=self.Agent.Environment.extent,
+ extent=ex,
interpolation="bicubic",
+ zorder=1,
)
ims.append(im)
vmin, vmax = (
@@ -505,6 +511,7 @@ def animate_(i, fig, ax, chosen_neurons, t_start, t_max, dt, speed_up):
)
from matplotlib import animation
+
anim = matplotlib.animation.FuncAnimation(
fig,
animate_,
@@ -607,7 +614,7 @@ def __init__(self, Agent, params={}):
self.place_cell_centres = self.Agent.Environment.sample_positions(
n=self.n, method="uniform_jitter"
)
- elif type(self.place_cell_centres) is str:
+ elif type(self.place_cell_centres) is str:
if self.place_cell_centres in ["random", "uniform", "uniform_jitter"]:
self.place_cell_centres = self.Agent.Environment.sample_positions(
n=self.n, method=self.place_cell_centres
@@ -618,11 +625,16 @@ def __init__(self, Agent, params={}):
# Assertions (some combinations of boundary condition and wall geometries aren't allowed)
if self.Agent.Environment.dimensionality == "2D":
- if all([
- ((self.wall_geometry == "line_of_sight") or ((self.wall_geometry == "geodesic"))),
- (self.Agent.Environment.boundary_conditions == "periodic"),
- (self.Agent.Environment.dimensionality == "2D")
- ]):
+ if all(
+ [
+ (
+ (self.wall_geometry == "line_of_sight")
+ or ((self.wall_geometry == "geodesic"))
+ ),
+ (self.Agent.Environment.boundary_conditions == "periodic"),
+ (self.Agent.Environment.dimensionality == "2D"),
+ ]
+ ):
print(
f"{self.wall_geometry} wall geometry only possible in 2D when the boundary conditions are solid. Using 'euclidean' instead."
)
@@ -657,23 +669,26 @@ def get_state(self, evaluate_at="agent", **kwargs):
pos = np.array(pos)
# place cell fr's depend only on how far the agent is from cell centres (and their widths)
- dist = self.Agent.Environment.get_distances_between___accounting_for_environment(
- self.place_cell_centres, pos, wall_geometry=self.wall_geometry
+ dist = (
+ self.Agent.Environment.get_distances_between___accounting_for_environment(
+ self.place_cell_centres, pos, wall_geometry=self.wall_geometry
+ )
) # distances to place cell centres
widths = np.expand_dims(self.place_cell_widths, axis=-1)
if self.description == "gaussian":
- firingrate = np.exp(-(dist ** 2) / (2 * (widths ** 2)))
+ firingrate = np.exp(-(dist**2) / (2 * (widths**2)))
if self.description == "gaussian_threshold":
firingrate = np.maximum(
- np.exp(-(dist ** 2) / (2 * (widths ** 2))) - np.exp(-1 / 2), 0,
+ np.exp(-(dist**2) / (2 * (widths**2))) - np.exp(-1 / 2),
+ 0,
) / (1 - np.exp(-1 / 2))
if self.description == "diff_of_gaussians":
ratio = 1.5
- firingrate = np.exp(-(dist ** 2) / (2 * (widths ** 2))) - (
- 1 / ratio ** 2
- ) * np.exp(-(dist ** 2) / (2 * ((ratio * widths) ** 2)))
- firingrate *= ratio ** 2 / (ratio ** 2 - 1)
+ firingrate = np.exp(-(dist**2) / (2 * (widths**2))) - (
+ 1 / ratio**2
+ ) * np.exp(-(dist**2) / (2 * ((ratio * widths) ** 2)))
+ firingrate *= ratio**2 / (ratio**2 - 1)
if self.description == "one_hot":
closest_centres = np.argmin(np.abs(dist), axis=0)
firingrate = np.eye(self.n)[closest_centres].T
@@ -828,8 +843,8 @@ def set_phase_offsets(self):
dy = self.gridscale / n_y
grid = np.mgrid[
- (0 + dx / 2): (self.gridscale - dx / 2): (n_x * 1j),
- (0 + dy / 2): (self.gridscale - dy / 2): (n_y * 1j),
+ (0 + dx / 2) : (self.gridscale - dx / 2) : (n_x * 1j),
+ (0 + dy / 2) : (self.gridscale - dy / 2) : (n_y * 1j),
]
grid = grid.reshape(2, -1).T
remaining = np.random.uniform(0, self.gridscale, size=(n_remaining, 2))
@@ -915,7 +930,8 @@ def __init__(self, Agent, params={}):
)
self.tuning_angles = np.random.uniform(0, 2 * np.pi, size=self.n)
self.tuning_distances = np.random.rayleigh(
- scale=self.pref_wall_dist, size=self.n,
+ scale=self.pref_wall_dist,
+ size=self.n,
)
self.sigma_distances = self.tuning_distances / beta + xi
@@ -998,10 +1014,10 @@ def get_state(self, evaluate_at="agent", **kwargs):
if self.reference_frame == "egocentric":
if evaluate_at == "agent":
vel = self.Agent.pos
- elif 'vel' in kwargs.keys():
+ elif "vel" in kwargs.keys():
vel = kwargs["vel"]
- else:
- vel = np.array([1,0])
+ else:
+ vel = np.array([1, 0])
vel = np.array(vel)
head_direction_angle = utils.get_angle(vel)
test_angles = test_angles - head_direction_angle
@@ -1012,7 +1028,8 @@ def get_state(self, evaluate_at="agent", **kwargs):
) # (N_cell,N_pos,N_test)
sigma_angles = np.tile(
np.expand_dims(
- np.expand_dims(np.array(self.sigma_angles), axis=-1), axis=-1,
+ np.expand_dims(np.array(self.sigma_angles), axis=-1),
+ axis=-1,
),
reps=(1, N_pos, N_test),
) # (N_cell,N_pos,N_test)
@@ -1053,8 +1070,18 @@ def boundary_vector_preference_function(self, x):
assert x.shape[-1] == 2
pref = np.piecewise(
x=x,
- condlist=(x[..., 0] > 0, x[..., 0] < 0, x[..., 1] < 0, x[..., 1] > 1,),
- funclist=(1 / x[x[..., 0] > 0], -1, -1, -1,),
+ condlist=(
+ x[..., 0] > 0,
+ x[..., 0] < 0,
+ x[..., 1] < 0,
+ x[..., 1] > 1,
+ ),
+ funclist=(
+ 1 / x[x[..., 0] > 0],
+ -1,
+ -1,
+ -1,
+ ),
)
return pref[..., 0]
@@ -1115,24 +1142,25 @@ def bvc_rf(theta, r, mu_r=0.5, sigma_r=0.2, mu_theta=0.5, sigma_theta=0.1):
class ObjectVectorCells(Neurons):
"""Initialises ObjectVectorCells(), takes as input a parameter dictionary. Any values not provided by the params dictionary are taken from a default dictionary below.
- Each object vector cell has a preferred tuning_distance and tuning_angle. Only when the angle is (with gaussian spread) close to this distance and angle away from the OVC wll the cell fire.
+ Each object vector cell has a preferred tuning_distance and tuning_angle. Only when the angle is (with gaussian spread) close to this distance and angle away from the OVC wll the cell fire.
- It is possible for these cells to be "field_of_view" in which case the cell fires iff the agent is looking towards it. Essentially this is an egocentric OVC with tuning angle set to zero (head on).
+ It is possible for these cells to be "field_of_view" in which case the cell fires iff the agent is looking towards it. Essentially this is an egocentric OVC with tuning angle set to zero (head on).
- default_params = {
- "n": 10,
- "min_fr": 0,
- "max_fr": 1,
- "name": "ObjectVectorCell",
- "walls_occlude":True, #whether walls occuled OVC firing
- "field_of_view":False, #set to true for "field of view" OVC
- "object_locations":None, #otherwise random across Env, the length of this will overwrite "n"
- "angle_spread_degrees":15, #can be an array, one for each object, spread of von Mises angular preferrence functinon for each OVC
- "pref_object_dist": 0.25, #can be an array, one for each object, otherwise randomly drawn from a Rayleigh with this sigma. How far away from OVC the OVC fires.
- "xi": 0.08, #parameters determining the distance preferrence function std given the preferred distance. See BoundaryVectorCells or de cothi and barry 2020
- "beta": 12,
- }
+ default_params = {
+ "n": 10,
+ "min_fr": 0,
+ "max_fr": 1,
+ "name": "ObjectVectorCell",
+ "walls_occlude":True, #whether walls occuled OVC firing
+ "field_of_view":False, #set to true for "field of view" OVC
+ "object_locations":None, #otherwise random across Env, the length of this will overwrite "n"
+ "angle_spread_degrees":15, #can be an array, one for each object, spread of von Mises angular preferrence functinon for each OVC
+ "pref_object_dist": 0.25, #can be an array, one for each object, otherwise randomly drawn from a Rayleigh with this sigma. How far away from OVC the OVC fires.
+ "xi": 0.08, #parameters determining the distance preferrence function std given the preferred distance. See BoundaryVectorCells or de cothi and barry 2020
+ "beta": 12,
+ }
"""
+
def __init__(self, Agent, params={}):
default_params = {
@@ -1140,11 +1168,11 @@ def __init__(self, Agent, params={}):
"min_fr": 0,
"max_fr": 1,
"name": "ObjectVectorCell",
- "walls_occlude":True,
- "field_of_view":False,
- "object_locations":None,
- "angle_spread_degrees":15,
- "pref_object_dist":0.25,
+ "walls_occlude": True,
+ "field_of_view": False,
+ "object_locations": None,
+ "angle_spread_degrees": 15,
+ "pref_object_dist": 0.25,
"xi": 0.08,
"beta": 12,
}
@@ -1153,29 +1181,41 @@ def __init__(self, Agent, params={}):
default_params.update(params)
self.params = default_params
- assert (self.Agent.Environment.dimensionality == "2D"), "object vector cells only possible in 2D"
-
- if self.params['object_locations'] is None:
- self.object_locations = self.Agent.Environment.sample_positions(self.n)
- print(f"No object locations passed so {self.n} object locations have been randomly sampled across the environment")
- else: self.params['n'] = len(params['object_locations'])
+ assert (
+ self.Agent.Environment.dimensionality == "2D"
+ ), "object vector cells only possible in 2D"
super().__init__(Agent, self.params)
-
- #preferred distance and angle to objects and their tuning widths (set these yourself if needed)
+ if self.params["object_locations"] is None:
+ self.object_locations = self.Agent.Environment.sample_positions(
+ n=int(self.params["n"])
+ )
+ print(
+ f"No object locations passed so {self.params['n']} object locations have been randomly sampled across the environment"
+ )
+ else:
+ self.n = len(params["object_locations"])
+
+ # preferred distance and angle to objects and their tuning widths (set these yourself if needed)
self.tuning_angles = np.random.uniform(0, 2 * np.pi, size=self.n)
- self.tuning_distances = np.random.rayleigh(scale=self.pref_object_dist, size=self.n)
+ self.tuning_distances = np.random.rayleigh(
+ scale=self.pref_object_dist, size=self.n
+ )
self.sigma_distances = self.tuning_distances / self.beta + self.xi
- self.sigma_angles = np.array([(self.angle_spread_degrees / 360) * 2 * np.pi] * self.n)
+ self.sigma_angles = np.array(
+ [(self.angle_spread_degrees / 360) * 2 * np.pi] * self.n
+ )
if self.field_of_view == True:
self.tuning_angles = np.zeros(self.n)
- if self.walls_occlude == True: self.wall_geometry = 'line_of_sight'
- else: self.wall_geometry = 'euclidean'
+ if self.walls_occlude == True:
+ self.wall_geometry = "line_of_sight"
+ else:
+ self.wall_geometry = "euclidean"
- # normalises activity over the environment
+ # normalises activity over the environment
locs = self.Agent.Environment.discretise_environment(dx=0.04)
locs = locs.reshape(-1, locs.shape[-1])
@@ -1189,10 +1229,10 @@ def get_state(self, evaluate_at="agent", **kwargs):
"""Returns the firing rate of the ObjectVectorCells.
The way we do this is a little complex. We will describe how it works from a single position to a single OVC (but remember this can be called in a vectorised manner from an array of positons in parallel and there are in principle multiple OVCs)
- 1. A vector from the position to the OVC is calculated.
+ 1. A vector from the position to the OVC is calculated.
2. The bearing of this vector is calculated and its length. Note if self.field_of_view == True then the bearing is relative to the heading direction of the agent (along its current velocity), not true-north.
- 3. Since the distance to the OVC is calculated taking the environment into account if there is a wall occluding the agent from the obvject this object will not fire.
- 4. It is now simple to calculate the firing rate of the cell. Each OVC has a preferred distance and angle away from it which cause it to fire. Its a multiple of a gaussian (distance) and von mises (for angle) which creates teh eventual firing rate.
+ 3. Since the distance to the OVC is calculated taking the environment into account if there is a wall occluding the agent from the obvject this object will not fire.
+ 4. It is now simple to calculate the firing rate of the cell. Each OVC has a preferred distance and angle away from it which cause it to fire. Its a multiple of a gaussian (distance) and von mises (for angle) which creates teh eventual firing rate.
By default position is taken from the Agent and used to calculate firing rates. This can also by passed directly (evaluate_at=None, pos=pass_array_of_positions) or you can use all the positions in the environment (evaluate_at="all").
@@ -1206,41 +1246,74 @@ def get_state(self, evaluate_at="agent", **kwargs):
else:
pos = kwargs["pos"]
pos = np.array(pos)
- pos = pos.reshape(-1, pos.shape[-1]) #(N_pos, 2)
+ pos = pos.reshape(-1, pos.shape[-1]) # (N_pos, 2)
N_pos = pos.shape[0]
N_cells = self.n
-
- (distances_to_OVCs, vectors_to_OVCs) = self.Agent.Environment.get_distances_between___accounting_for_environment(pos,self.object_locations,return_vectors=True,wall_geometry=self.wall_geometry,) #(N_pos,N_cells) (N_pos,N_cells,2)
- flattened_vectors_to_OVCs = vectors_to_OVCs.reshape(-1,2) #(N_pos x N_cells, 2)
- bearings_to_OVCs = utils.get_angle(flattened_vectors_to_OVCs,is_array=True).reshape(N_pos,N_cells) #(N_cells,N_pos)
- if self.field_of_view == True:
+ (
+ distances_to_OVCs,
+ vectors_to_OVCs,
+ ) = self.Agent.Environment.get_distances_between___accounting_for_environment(
+ pos,
+ self.object_locations,
+ return_vectors=True,
+ wall_geometry=self.wall_geometry,
+ ) # (N_pos,N_cells) (N_pos,N_cells,2)
+ flattened_vectors_to_OVCs = vectors_to_OVCs.reshape(
+ -1, 2
+ ) # (N_pos x N_cells, 2)
+ bearings_to_OVCs = utils.get_angle(
+ flattened_vectors_to_OVCs, is_array=True
+ ).reshape(
+ N_pos, N_cells
+ ) # (N_cells,N_pos)
+ if self.field_of_view == True:
if evaluate_at == "agent":
vel = self.Agent.velocity
- elif 'vel' in kwargs.keys():
+ elif "vel" in kwargs.keys():
vel = kwargs["vel"]
else:
- vel = np.array([1,0])
- print("Field of view OVCs require a velocity vector but none was passed. Using [1,0]")
+ vel = np.array([1, 0])
+ print(
+ "Field of view OVCs require a velocity vector but none was passed. Using [1,0]"
+ )
head_bearing = utils.get_angle(vel)
bearings_to_OVCs -= head_bearing
- tuning_distances = np.tile(np.expand_dims(self.tuning_distances,axis=0),reps=(N_pos,1)) #(N_pos,N_cell)
- sigma_distances = np.tile(np.expand_dims(self.sigma_distances,axis=0),reps=(N_pos,1)) #(N_pos,N_cell)
- tuning_angles = np.tile(np.expand_dims(self.tuning_angles,axis=0),reps=(N_pos,1)) #(N_pos,N_cell)
- sigma_angles = np.tile(np.expand_dims(self.sigma_angles,axis=0),reps=(N_pos,1)) #(N_pos,N_cell)
+ tuning_distances = np.tile(
+ np.expand_dims(self.tuning_distances, axis=0), reps=(N_pos, 1)
+ ) # (N_pos,N_cell)
+ sigma_distances = np.tile(
+ np.expand_dims(self.sigma_distances, axis=0), reps=(N_pos, 1)
+ ) # (N_pos,N_cell)
+ tuning_angles = np.tile(
+ np.expand_dims(self.tuning_angles, axis=0), reps=(N_pos, 1)
+ ) # (N_pos,N_cell)
+ sigma_angles = np.tile(
+ np.expand_dims(self.sigma_angles, axis=0), reps=(N_pos, 1)
+ ) # (N_pos,N_cell)
- firingrate = (utils.gaussian(
- distances_to_OVCs, tuning_distances, sigma_distances, norm=1
- ) * utils.von_mises(
- bearings_to_OVCs, tuning_angles, sigma_angles, norm=1
- )).T #(N_cell,N_pos)
+ firingrate = (
+ utils.gaussian(distances_to_OVCs, tuning_distances, sigma_distances, norm=1)
+ * utils.von_mises(bearings_to_OVCs, tuning_angles, sigma_angles, norm=1)
+ ).T # (N_cell,N_pos)
firingrate = (
firingrate * (self.max_fr - self.min_fr) + self.min_fr
) # scales from being between [0,1] to [min_fr, max_fr]
return firingrate
-
+ def plot_rate_map(self, chosen_neurons="all", **kwargs):
+ """Plots the rate maps, takes identical kwargs as the parent Neurons class function plot_rate_map, just also plots location of the object in question
+ Returns:
+ fig, ax
+ """
+ chosen_neurons = self.return_list_of_neurons(chosen_neurons=chosen_neurons)
+ fig, ax = super().plot_rate_map(chosen_neurons, **kwargs)
+ locations = self.object_locations[chosen_neurons]
+ for (i, ax) in enumerate(ax):
+ loc = locations[i]
+ ax.scatter(loc[0], loc[1], color="w", s=10)
+ return fig, ax
class HeadDirectionCells(Neurons):
@@ -1271,8 +1344,8 @@ def __init__(self, Agent, params={}):
default_params = {
"min_fr": 0,
"max_fr": 1,
- "n":4,
- "angular_spread_degrees":30, #width of HDC preference function (degrees)
+ "n": 4,
+ "angular_spread_degrees": 30, # width of HDC preference function (degrees)
"name": "HeadDirectionCells",
}
self.Agent = Agent
@@ -1281,10 +1354,12 @@ def __init__(self, Agent, params={}):
self.params = default_params
if self.Agent.Environment.dimensionality == "2D":
- self.n = self.params['n']
- self.preferred_angles = np.linspace(0,2*np.pi,self.n+1)[:-1]
+ self.n = self.params["n"]
+ self.preferred_angles = np.linspace(0, 2 * np.pi, self.n + 1)[:-1]
# self.preferred_directions = np.array([np.cos(angles),np.sin(angles)]).T #n HDCs even spaced on unit circle
- self.angular_tunings = np.array([self.params['angular_spread_degrees']*np.pi/180]*self.n)
+ self.angular_tunings = np.array(
+ [self.params["angular_spread_degrees"] * np.pi / 180] * self.n
+ )
if self.Agent.Environment.dimensionality == "1D":
self.n = 2 # one left, one right
self.params["n"] = self.n
@@ -1299,41 +1374,45 @@ def get_state(self, evaluate_at="agent", **kwargs):
if evaluate_at == "agent":
vel = self.Agent.history["vel"][-1]
- elif 'vel' in kwargs.keys():
+ elif "vel" in kwargs.keys():
vel = np.array(kwargs["vel"])
- else:
+ else:
print("HeadDirection cells need a velocity but not was given, taking...")
if self.Agent.Environment.dimensionality == "2D":
- vel = np.array([1,0])
+ vel = np.array([1, 0])
print("...[1,0] as default")
if self.Agent.Environment.dimensionality == "1D":
vel = np.array([1])
print("...[1] as default")
-
+
if self.Agent.Environment.dimensionality == "1D":
hdleft_fr = max(0, np.sign(vel[0]))
hdright_fr = max(0, -np.sign(vel[0]))
firingrate = np.array([hdleft_fr, hdright_fr])
if self.Agent.Environment.dimensionality == "2D":
current_angle = utils.get_angle(vel)
- firingrate = utils.von_mises(current_angle,self.preferred_angles,self.angular_tunings,norm=1)
+ firingrate = utils.von_mises(
+ current_angle, self.preferred_angles, self.angular_tunings, norm=1
+ )
firingrate = (
firingrate * (self.max_fr - self.min_fr) + self.min_fr
) # scales from being between [0,1] to [min_fr, max_fr]
return firingrate
-
- def plot_HDC_receptive_field(self,):
- return
+
+ def plot_HDC_receptive_field(
+ self,
+ ):
+ return
class VelocityCells(HeadDirectionCells):
- """The VelocityCells class defines a population of Velocity cells. This basically takes the output from a population of HeadDirectionCells and scales it proportional to the speed (dependence on speed and direction --> velocity).
+ """The VelocityCells class defines a population of Velocity cells. This basically takes the output from a population of HeadDirectionCells and scales it proportional to the speed (dependence on speed and direction --> velocity).
- Must be initialised with an Agent and a 'params' dictionary. Initalise tehse cells as if they are HeadDirectionCells
+ Must be initialised with an Agent and a 'params' dictionary. Initalise tehse cells as if they are HeadDirectionCells
- VelocityCells defines a set of 'dim x 2' velocity cells. Encoding the East, West (and North and South) velocities in 1D (2D). The firing rates are scaled according to the multiple current_speed / expected_speed where expected_speed = Agent.speed_mean + self.Agent.speed_std is just some measure of speed approximately equal to a likely ``rough`` maximum for the Agent.
+ VelocityCells defines a set of 'dim x 2' velocity cells. Encoding the East, West (and North and South) velocities in 1D (2D). The firing rates are scaled according to the multiple current_speed / expected_speed where expected_speed = Agent.speed_mean + self.Agent.speed_std is just some measure of speed approximately equal to a likely ``rough`` maximum for the Agent.
List of functions:
@@ -1392,7 +1471,7 @@ class SpeedCell(Neurons):
"max_fr": 1,
"name": "SpeedCell",
}
- """
+ """
def __init__(self, Agent, params={}):
"""Initialise SpeedCell(), takes as input a parameter dictionary, 'params'. Any values not provided by the params dictionary are taken from a default dictionary below.
@@ -1469,7 +1548,7 @@ class FeedForwardLayer(Neurons):
},
"name": "FeedForwardLayer",
}
- """
+ """
def __init__(self, Agent, params={}):
default_params = {
diff --git a/ratinabox/__init__.py b/ratinabox/__init__.py
index 2bab4c01..5061d2c4 100644
--- a/ratinabox/__init__.py
+++ b/ratinabox/__init__.py
@@ -1,7 +1,7 @@
-from .Environment import *
-from .Agent import *
-from .Neurons import *
+from .Environment import *
+from .Agent import *
+from .Neurons import *
from . import contribs
-verbose = False
\ No newline at end of file
+verbose = False
diff --git a/ratinabox/contribs/PhasePrecessingPlaceCells.py b/ratinabox/contribs/PhasePrecessingPlaceCells.py
index c7380fc4..421ce2c9 100644
--- a/ratinabox/contribs/PhasePrecessingPlaceCells.py
+++ b/ratinabox/contribs/PhasePrecessingPlaceCells.py
@@ -116,8 +116,7 @@ def theta_modulation_factors(self):
if __name__ == "__main__":
- """Example of use
- """
+ """Example of use"""
from ratinabox.contribs.PhasePrecessingPlaceCells import PhasePrecessingPlaceCells
Env = Environment()
diff --git a/ratinabox/contribs/PlaneWaveNeurons.py b/ratinabox/contribs/PlaneWaveNeurons.py
index 9183fbea..aa92bc4b 100644
--- a/ratinabox/contribs/PlaneWaveNeurons.py
+++ b/ratinabox/contribs/PlaneWaveNeurons.py
@@ -3,7 +3,8 @@
from ratinabox.Neurons import *
from ratinabox.utils import *
-import numpy as np
+import numpy as np
+
class PlaneWaveNeurons(Neurons):
"""
@@ -87,8 +88,7 @@ def get_state(self, evaluate_at="agent", **kwargs):
if __name__ == "__main__":
- """Example of use
- """
+ """Example of use"""
from ratinabox.contribs.PlaneWaveNeurons import PlaneWaveNeurons
Env = Environment()
diff --git a/ratinabox/contribs/ThetaSequenceAgent.py b/ratinabox/contribs/ThetaSequenceAgent.py
new file mode 100644
index 00000000..3fe4ceef
--- /dev/null
+++ b/ratinabox/contribs/ThetaSequenceAgent.py
@@ -0,0 +1,278 @@
+import ratinabox
+from ratinabox.Agent import Agent
+from scipy.interpolate import interp1d
+import numpy as np
+
+
+class ThetaSequenceAgent(Agent):
+ """ThetaSequneceAgent is a type of Agent who's position is NOT the true position but instead a "theta sequence" over the position. This starts from behind the "true" position and rapidly moves to infront of the true position (default sequence speed = 5ms-1) once every "theta cycle" (default 10Hz). Each theta sequence is split into the following phases (marked as fraction of the theta cycle):
+
+ |.......A.........|................B..............|.................C.............|........A'.......|
+ 0 1/2-β/2 1/2 1/2+β/2 1
+
+ • A and A': within these segments the position is [nan], the sequence hasn't started yet or has finished.
+ • B, "Look behind": The sequence starts behind the agents current position and moves along the historic trajectory until it meets the agent half way through the theta cycle.
+ • C, "Look ahead": A new "random" trajectory into the future is sampled starting from the agents current position and velocity.
+
+ The velocity of the sequence, v_sequence, is constant. This is the velocity of the sequence in the reference frame of the TrueAgent (i.e. ground truth see below) so the "apparent" velocity of the sequence will be v_sequence + the speed of the TrueAgent.
+
+ ThetaSequenceAgent has within it two other Agent classes:
+ • self.TrueAgent (riab.Agent) is the real Agent moving in the Environment
+ • self.ForwardSequenceAgent (riab.Agent) is a sham Agent only used to access riab's stochastic motion model and generate the forward sequences.
+
+ The default params (beyond the standard Agent params) are:
+ default_params = {
+ "dt" : 0.001, #this MUST be at least 10x smaller than the theta time period
+ "theta_freq" : 10.0, #theta frequency
+ "v_sequence" : 5.0, #sequence speed in reference frame of Agent, ms-1
+ "theta_frac" : 0.5, #fraction of theta cycle over which}
+ """
+
+ def __init__(self, Environment, params={}):
+
+ default_params = {
+ "v_sequence": 5.0, # sequence speed in reference frame of Agent, ms-1
+ "theta_freq": 10.0, # theta frequency
+ "theta_frac": 0.5, # fraction of theta cycle over which
+ "dt": 0.001,
+ }
+
+ self.Environment = Environment
+ default_params.update(params)
+ self.params = default_params
+ super().__init__(Environment, self.params)
+
+ # ground truth Agent
+ self.TrueAgent = Agent(self.Environment, self.params)
+ self.TrueAgent.history[
+ "distance_travelled"
+ ] = [] # history of distance travelled
+ # a sham Agent we're initialising just in order to do a forward sequence
+ self.ForwardSequenceAgent = Agent(self.Environment, self.params)
+
+ # some variables/constants
+ self.T_theta = 1 / self.theta_freq
+ self.d_half = (
+ (self.theta_frac / 2) * self.T_theta * self.v_sequence
+ ) # how far agent will travel in half a sequence
+ self.last_theta_phase = 0
+ self.d_half = (
+ (self.theta_frac / 2) * self.T_theta * self.v_sequence
+ ) # how far agent will travel in half a sequence
+
+ # its very time consuming to continually convert position data into arrays so we preallocate a memory location
+ self.n_half = int(
+ 2 * self.d_half / (self.TrueAgent.speed_mean * self.dt)
+ ) # approx how many steps for the agent to travel d_half in real time
+ self.keep_count = (
+ 20 * self.n_half
+ ) # how many data points to save in preallocated memory
+ self.recent_data_stash = {} # its time consuming
+ self.recent_data_stash["distance"] = np.zeros(
+ (self.keep_count)
+ ) # its time consuming
+ self.recent_data_stash["position"] = np.zeros(
+ (self.keep_count, self.Environment.D)
+ ) # its time consuming
+ self.recent_data_stash["distance"][0] = self.TrueAgent.distance_travelled
+ self.recent_data_stash["position"][0, :] = self.TrueAgent.pos
+ self.counter = 1
+
+ assert (
+ self.dt < self.T_theta / 10
+ ), f"params['dt'] is too large. It must be < 10% of theta time period., i.e. smaller than {self.T_theta/10:.5f}"
+
+ def update(self, dt=None, drift_velocity=None, drift_to_random_strength_ratio=1):
+ """
+ Updates and saves the position of the Agent along the theta sequence.
+
+ None that this is quite a complicated function! Some complexities which may help you to understand this code include:
+
+ • Achilles and the tortoise: When behind the Agent we can interpolate along historic data but on each step the true agent moves forwards a little, so we must recollect this new data. The ThetaSequenceAgent position is Achilles, the TrueAgent is the tortoise.
+ • Interpolation expense: We must interpolate smoothly over historic data but this is expensive since it requires converting the list of past positions into an array then running scipy.interpolate.interp1d. So we want to take the least possible historic data which guarantees we'll have enough to do the behind sequence.
+ • Reference frame: In the current model the speed of the sequence is constant (in the reference frame of the TrueAgent) but the speed of the TrueAgent may not be. Therefore it is not enough to just interpolate over the past trajectory (indexed by time), we mmust transform coordinates to "distance travelled" (which is linear wrt the sequence).
+ • Boundary conditions
+ """
+
+ # update True position of Agent (ground truth) in normal fashion
+ self.TrueAgent.update(
+ dt=None, drift_velocity=None, drift_to_random_strength_ratio=1
+ )
+ self.TrueAgent.history["distance_travelled"].append(
+ self.TrueAgent.distance_travelled
+ )
+
+ # append TrueAgent position and distance data into our preallocated arrays:
+ if self.counter == self.keep_count:
+ self.counter = 10 * self.n_half
+ self.recent_data_stash["distance"][: self.counter] = self.recent_data_stash[
+ "distance"
+ ][-self.counter :]
+ self.recent_data_stash["position"][
+ : self.counter, :
+ ] = self.recent_data_stash["position"][-self.counter :, :]
+ self.recent_data_stash["distance"][
+ self.counter
+ ] = self.TrueAgent.distance_travelled
+ self.recent_data_stash["position"][self.counter, :] = self.TrueAgent.pos
+
+ self.t = self.TrueAgent.t
+ theta_phase = (self.t % (1 / self.theta_freq)) / ((1 / self.theta_freq))
+ self.d_half = (
+ (self.theta_frac / 2) * self.T_theta * self.v_sequence
+ ) # how far agent will travel in half a sequence
+
+ # PRE SWEEP (returns nan's)
+ if theta_phase < (0.5 - (self.theta_frac / 2)):
+ # No position
+ pos = np.full(shape=(self.Environment.D,), fill_value=np.nan)
+
+ # LOOK BEHIND (EARLY SWEEP, from behind to current position, taken from historical data)
+ if (theta_phase >= (0.5 - self.theta_frac / 2)) and (theta_phase < 0.5):
+ true_distances = self.TrueAgent.history["distance_travelled"]
+ # Backwards sequence
+ if true_distances[-1] < self.d_half:
+ # handle case where not enough data has been collected yet
+ # just dont do a backwards sequence and take current positions
+ pos = self.TrueAgent.pos
+ else:
+ # get just enough past data
+ lookback = int(
+ 5 * self.d_half / (self.dt * self.TrueAgent.average_measured_speed)
+ ) # so argmin will never grow arbitrarily large, 3 to be safe
+ lookback = min(lookback, self.counter)
+ true_positions = self.recent_data_stash["position"][
+ self.counter - lookback + 1 : self.counter + 1, :
+ ]
+ true_distances = self.recent_data_stash["distance"][
+ self.counter - lookback + 1 : self.counter + 1
+ ]
+ # interpolate it
+ a = np.argmin(true_positions)
+ # calculate how far back the current sequence should be look (sequence closing in on Agent at speed v_sequence so net speed of sequence = v_sequence + v_agent)
+ # converts current theta phase to how far back along the current trajectory to take position from
+ c = self.d_half / self.theta_frac
+ m = -2 * c
+ distance_back = (
+ m * theta_phase + c
+ ) # how far behind the agents current position the sequence should be at
+ interp_distance = (
+ true_distances[-1] - distance_back
+ ) # and the TrueAgent's actual distance travelled at this point
+ idx = np.argmin(np.abs(true_distances - interp_distance))
+ self.pos_interp = interp1d(
+ true_distances[idx - 3 : idx + 3],
+ true_positions[idx - 3 : idx + 3],
+ axis=0,
+ )
+ pos = self.pos_interp(interp_distance)
+
+ # LOOK AHEAD (LATE SWEEP, from current position to infront, stochastically generated)
+ if (theta_phase >= 0.5) and (theta_phase < 0.5 + self.theta_frac / 2):
+ # Forward sequence
+ if (
+ theta_phase >= 0.5 and self.last_theta_phase < 0.5
+ ): # catch on first time each loop
+ self.ForwardSequenceAgent.pos = self.TrueAgent.pos
+ self.ForwardSequenceAgent.history["pos"].append(self.TrueAgent.pos)
+ self.ForwardSequenceAgent.velocity = self.TrueAgent.velocity
+ self.ForwardSequenceAgent.history["vel"].append(self.TrueAgent.velocity)
+ if self.Environment.dimensionality == "2D":
+ self.ForwardSequenceAgent.rotational_velocity = (
+ self.TrueAgent.rotational_velocity
+ )
+ self.ForwardSequenceAgent.history["rot_vel"].append(
+ self.TrueAgent.rotational_velocity
+ )
+ self.ForwardSequenceAgent.distance_travelled = (
+ self.TrueAgent.distance_travelled
+ )
+ recent_speed = self.TrueAgent.average_measured_speed
+ forward_distance_to_simulate = (
+ self.d_half
+ + 100 * recent_speed * (self.theta_frac / 2) * self.T_theta
+ )
+ future_positions = [self.ForwardSequenceAgent.pos]
+ future_distances = [self.ForwardSequenceAgent.distance_travelled]
+ while (
+ self.ForwardSequenceAgent.distance_travelled
+ < self.TrueAgent.distance_travelled + forward_distance_to_simulate
+ ):
+ self.ForwardSequenceAgent.update(
+ dt=self.dt
+ * self.v_sequence
+ / self.TrueAgent.average_measured_speed
+ )
+ future_positions.append(self.ForwardSequenceAgent.pos)
+ future_distances.append(
+ self.ForwardSequenceAgent.distance_travelled
+ )
+ future_positions, future_distances = np.array(
+ future_positions
+ ), np.array(future_distances)
+ self.pos_interp = interp1d(future_distances, future_positions, axis=0)
+ # calculate how far forward the current sequence should be look (sequence moving away fromAgent at speed v_sequence so net speed of sequence = v_sequence + v_agent)
+ # converts current theta phase to how far forward along the current trajectory to take position from
+ c = -self.d_half / self.theta_frac
+ m = -2 * c
+ distance_ahead = (
+ m * theta_phase + c
+ ) # how far ahead of the agents current position the sequence should be at
+ interp_distance = (
+ self.TrueAgent.distance_travelled + distance_ahead
+ ) # and the ForwardSequenceAgent's actual distance travelled at this point
+ pos = self.pos_interp(interp_distance)
+
+ # POST SWEEP (returns nan's)
+ if theta_phase >= (0.5 + (self.theta_frac / 2)):
+ # No position
+ pos = np.full(shape=(self.Environment.D,), fill_value=np.nan)
+
+ # handle periodic boundaries by just testing if the distance between current and true position of the Agent is over d_half this can only be because the interpolation has crossed a boundary, in which case just set the position to nan (minimally damaging for small dt)
+ dist = self.Environment.get_distances_between___accounting_for_environment(
+ pos.reshape(1, -1), self.TrueAgent.pos.reshape(1, -1)
+ )
+ if np.isnan(dist):
+ pass
+ elif dist > self.d_half:
+ # pos = np.array(self.history['pos'][-1])
+ pos = np.full(shape=(self.Environment.D,), fill_value=np.nan)
+
+ self.last_theta_phase = theta_phase
+ self.counter += 1
+ self.pos = np.array(pos)
+ self.history["t"].append(self.t)
+ self.history["pos"].append(list(pos))
+
+ return
+
+ def plot_trajectory(self, sequences_ontop=False, **kwargs):
+ """A bespoke plotting function taking the same arguments as Agent.plot_trajectory() except now it will jointly plot the True trajectory and the the ThetaSequenceTrajectory() below that.
+
+ • sequences_ontop (bool, default False): determines whether sequences get plotted ontop of or below the true trajectory.
+ """
+
+ kwargs_ = kwargs.copy()
+ kwargs_["decay_point_timescale"] = (
+ self.T_theta / 2
+ ) # decays sequences fast if animated
+ kwargs_["framerate"] = (
+ self.v_sequence / 0.02
+ ) # 2cm point seperation for sequences
+ kwargs_["color"] = "C1"
+ kwargs_["alpha"] = 0.4
+ kwargs_["plot_agent"] = False
+
+ if sequences_ontop == False:
+ fig, ax = super(ThetaSequenceAgent, self).plot_trajectory(**kwargs_)
+ kwargs["fig"] = fig
+ kwargs["ax"] = ax
+ kwargs["alpha"] = 0.4
+ fig, ax = self.TrueAgent.plot_trajectory(**kwargs)
+ else:
+ fig, ax = self.TrueAgent.plot_trajectory(**kwargs)
+ kwargs_["fig"] = fig
+ kwargs_["ax"] = ax
+ fig, ax = super(ThetaSequenceAgent, self).plot_trajectory(**kwargs_)
+
+ return fig, ax
diff --git a/ratinabox/contribs/ValueNeuron.py b/ratinabox/contribs/ValueNeuron.py
index 6903658a..8a9a1758 100644
--- a/ratinabox/contribs/ValueNeuron.py
+++ b/ratinabox/contribs/ValueNeuron.py
@@ -3,7 +3,8 @@
from ratinabox.Neurons import *
from ratinabox.utils import *
-import numpy as np
+import numpy as np
+
class ValueNeuron(FeedForwardLayer):
"""
@@ -12,6 +13,12 @@ class ValueNeuron(FeedForwardLayer):
The ValueNeuron class defines a neuron which learns the "value" of a policy using temporally continuous TD learning . This class is a subclass of FeedForwardLayer() which is a subclass of Neurons() and inherits it properties/plotting functions from both of these.
+ Ιt learn the value function defined by:
+
+ \begin{equation}
+ V(x) = \int_{t}^{\infty}e^{-\frac{t^{\prime}-t}{\tau}}R(x(t^{\prime}))) dt^{\prime} | x(t) = x
+ \end{equation}
+
It takes as input a layer of neurons (these are the "features" over which value is calculated). You could pass in any ratinabox Neurons class here (a set of PlaceCells, BoundaryVectorCells, GridCells etc...or more complex things)
It linearly sums these inputs to calculate its firing rate (this summation is all handled by the FeedForwardLayer class).
@@ -29,6 +36,7 @@ def __init__(self, Agent, params={}):
"tau": 10, # discount time horizon
"tau_e": 1, # eligibility trace timescale
"eta": 0.001, # learning rate
+ "L2": 0.001, # L2 regularisation
}
default_params.update(params)
@@ -46,17 +54,20 @@ def __init__(self, Agent, params={}):
self.max_fr = 1 # will update this with each episode later
def update(self):
- """Updates firing rate as weighted linear sum of feature inputs
- """
+ """Updates firing rate as weighted linear sum of feature inputs"""
firingrate_last = self.firingrate
# update the firing rate
- super().update() # FeedForwardLayer builtin function. this sums the inouts from the input features over the weight matrix and saves the firingrate.
+ super().update() # FeedForwardLayer builtin function. this sums the inputs from the input features over the weight matrix and saves the firingrate.
# calculate temporal derivative of the firing rate
self.firingrate_deriv = (self.firingrate - firingrate_last) / self.Agent.dt
# update eligibility trace
- self.et = (self.Agent.dt / self.tau_e) * self.input_layer.firingrate + (
- 1 - self.Agent.dt / self.tau_e
- ) * self.et
+ if self.tau_e == 0:
+ self.et = self.input_layer.firingrate
+ else:
+ self.et = (
+ self.Agent.dt * self.input_layer.firingrate
+ + (1 - self.Agent.dt / self.tau_e) * self.et
+ )
return
def update_weights(self, reward):
@@ -65,11 +76,12 @@ def update_weights(self, reward):
w = self.inputs[self.input_layer.name]["w"] # weights
V = self.firingrate # current value estimate
dVdt = self.firingrate_deriv # currrent value derivative estimate
- td_error = (
- reward + self.tau * dVdt - V
+ self.td_error = (
+ reward + dVdt - V / self.tau
) # this is the continuous analog of the TD error
dw = (
- self.Agent.dt * self.eta * (np.outer(td_error, self.et)) - 0.01 * w
+ self.Agent.dt * self.eta * (np.outer(self.td_error, self.et))
+ - self.eta * self.Agent.dt * self.L2 * w
) # note L2 regularisation
self.inputs[self.input_layer.name]["w"] += dw
return
@@ -82,20 +94,26 @@ def update_weights(self, reward):
from ratinabox.contribs.ValueNeuron import ValueNeuron
from tqdm import tqdm
- #initialise
+ # initialise
Env = Environment()
Ag = Agent(Env, params={"speed_mean": 0.2})
PCs = PlaceCells(Ag, params={"n": 100})
Reward = PlaceCells(
Ag, params={"n": 1, "place_cell_centres": np.array([[0.5, 0.5]])}
)
- VN = ValueNeuron(Ag, params={"input_layer": PCs, "tau": 1,})
+ VN = ValueNeuron(
+ Ag,
+ params={
+ "input_layer": PCs,
+ "tau": 1,
+ },
+ )
fig, ax = plt.subplots(1, 4, figsize=(16, 4))
Reward.plot_place_cell_locations(fig=fig, ax=ax[0])
VN.plot_rate_map(fig=fig, ax=ax[1])
- #explore/learn for 300 seconds
+ # explore/learn for 300 seconds
for i in tqdm(range(int(300 / Ag.dt))):
Ag.update()
Reward.update()
diff --git a/ratinabox/contribs/__init__.py b/ratinabox/contribs/__init__.py
index 14f75c54..a3b10102 100644
--- a/ratinabox/contribs/__init__.py
+++ b/ratinabox/contribs/__init__.py
@@ -1,4 +1,6 @@
from .PhasePrecessingPlaceCells import *
from .PlaneWaveNeurons import *
-from .ValueNeuron import *
-# from .STDPFeedForwardLayer import * #not ready yet
+from .ValueNeuron import *
+from .ThetaSequenceAgent import *
+
+# from .STDPFeedForwardLayer import * #not ready yet
diff --git a/ratinabox/utils.py b/ratinabox/utils.py
index 249841ba..4c51fbcf 100644
--- a/ratinabox/utils.py
+++ b/ratinabox/utils.py
@@ -92,7 +92,12 @@ def vector_intercepts(vector_list_a, vector_list_b, return_collisions=False):
intercepts = np.stack((l_a, l_b), axis=-1)
if return_collisions == True:
- direct_collision = ((intercepts[:, :, 0] > 0) * (intercepts[:, :, 0] < 1) * (intercepts[:, :, 1] > 0) * (intercepts[:, :, 1] < 1))
+ direct_collision = (
+ (intercepts[:, :, 0] > 0)
+ * (intercepts[:, :, 0] < 1)
+ * (intercepts[:, :, 1] > 0)
+ * (intercepts[:, :, 1] < 1)
+ )
return direct_collision
else:
return intercepts
@@ -208,7 +213,7 @@ def get_distances_between(pos1=None, pos2=None, vectors=None):
return distances
-def get_angle(segment,is_array=False):
+def get_angle(segment, is_array=False):
"""Given a 'segment' (either 2x2 start and end positions or 2x1 direction bearing)
returns the 'angle' of this segment modulo 2pi
Args:
@@ -218,31 +223,41 @@ def get_angle(segment,is_array=False):
float: angle of segment
"""
segment = np.array(segment)
- #decide if we are dealing with vectors or segments
- is_vec = True #whether we're dealing with vectors (2,) or segments (2,2,)
+ # decide if we are dealing with vectors or segments
+ is_vec = True # whether we're dealing with vectors (2,) or segments (2,2,)
a_segment = segment
- if is_array == True:
+ if is_array == True:
a_segment = segment[0]
N = segment.shape[0]
- if a_segment.shape == (2,2,): is_vec = False
+ if a_segment.shape == (
+ 2,
+ 2,
+ ):
+ is_vec = False
# reshape so segments have shape (N,2,2,) and vectors have shape (N,2,)
- if (not is_array and is_vec): segment = segment.reshape(1,2)
- if (not is_array and not is_vec): segment = segment.reshape(1,2,2)
+ if not is_array and is_vec:
+ segment = segment.reshape(1, 2)
+ if not is_array and not is_vec:
+ segment = segment.reshape(1, 2, 2)
eps = 1e-6
- if is_vec: angs = np.mod(np.arctan2(segment[:,1], (segment[:,0] + eps)), 2 * np.pi)
+ if is_vec:
+ angs = np.mod(np.arctan2(segment[:, 1], (segment[:, 0] + eps)), 2 * np.pi)
elif not is_vec:
angs = np.mod(
np.arctan2(
- (segment[:,1,1] - segment[:,0,1]), (segment[:,1,0] - segment[:,0,0] + eps)
+ (segment[:, 1, 1] - segment[:, 0, 1]),
+ (segment[:, 1, 0] - segment[:, 0, 0] + eps),
),
2 * np.pi,
)
-
- if not is_array: angs = angs[0]
+
+ if not is_array:
+ angs = angs[0]
return angs
+
def rotate(vector, theta):
"""rotates a vector shape (2,) by angle theta.
Args:
@@ -315,7 +330,7 @@ def ornstein_uhlenbeck(dt, x, drift=0.0, noise_scale=0.2, coherence_time=5.0):
drift = drift * np.ones_like(x)
noise_scale = noise_scale * np.ones_like(x)
coherence_time = coherence_time * np.ones_like(x)
- sigma = np.sqrt((2 * noise_scale ** 2) / (coherence_time * dt))
+ sigma = np.sqrt((2 * noise_scale**2) / (coherence_time * dt))
theta = 1 / coherence_time
dx = theta * (drift - x) * dt + sigma * np.random.normal(size=x.shape, scale=dt)
return dx
@@ -346,17 +361,15 @@ def interpolate_and_smooth(x, y, sigma=None):
def normal_to_rayleigh(x, sigma=1):
- """Converts a normally distributed variable (mean 0, var 1) to a rayleigh distributed variable (sigma)
- """
+ """Converts a normally distributed variable (mean 0, var 1) to a rayleigh distributed variable (sigma)"""
x = stats.norm.cdf(x) # norm to uniform)
x = sigma * np.sqrt(-2 * np.log(1 - x)) # uniform to rayleigh
return x
def rayleigh_to_normal(x, sigma=1):
- """Converts a rayleigh distributed variable (sigma) to a normally distributed variable (mean 0, var 1)
- """
- x = 1 - np.exp(-(x ** 2) / (2 * sigma ** 2)) # rayleigh to uniform
+ """Converts a rayleigh distributed variable (sigma) to a normally distributed variable (mean 0, var 1)"""
+ x = 1 - np.exp(-(x**2) / (2 * sigma**2)) # rayleigh to uniform
x = min(max(1e-6, x), 1 - 1e-6)
x = stats.norm.ppf(x) # uniform to normal
return x
@@ -372,9 +385,9 @@ def gaussian(x, mu, sigma, norm=None):
Returns gaussian(x;mu,sigma)
"""
g = -((x - mu) ** 2)
- g = g / (2 * sigma ** 2)
+ g = g / (2 * sigma**2)
g = np.exp(g)
- norm = norm or (1 / (np.sqrt(2 * np.pi * sigma ** 2)))
+ norm = norm or (1 / (np.sqrt(2 * np.pi * sigma**2)))
g = g * norm
return g
@@ -390,7 +403,7 @@ def von_mises(theta, mu, sigma, norm=None):
norm: if provided the maximum (i.e. in the centre) value will be the norm
Returns von_mises(x;mu,sigma)
"""
- kappa = 1 / (sigma ** 2)
+ kappa = 1 / (sigma**2)
v = np.exp(kappa * np.cos(theta - mu))
norm = norm or (np.exp(kappa) / (2 * np.pi * scipy.special.i0(kappa)))
norm = norm / np.exp(kappa)
@@ -485,13 +498,15 @@ def mountain_plot(
for i in range(len(NbyX)):
ax.plot(X, NbyX[i] + i + 1, c=c, zorder=zorder)
zorder -= 0.01
- ax.fill_between(X, NbyX[i] + i + 1, i + 1, color=fc, zorder=zorder, alpha=0.9)
+ ax.fill_between(
+ X, NbyX[i] + i + 1, i + 1, color=fc, zorder=zorder, alpha=0.9, linewidth=0
+ )
zorder -= 0.01
ax.spines["left"].set_bounds(1, len(NbyX))
ax.spines["bottom"].set_position(("outward", 1))
ax.spines["left"].set_position(("outward", 1))
ax.set_yticks([1, len(NbyX)])
- ax.set_ylim(1 - 0.5, len(NbyX) + 1.1*overlap)
+ ax.set_ylim(1 - 0.5, len(NbyX) + 1.1 * overlap)
ax.set_xticks(np.arange(max(X + 0.1)))
ax.spines["left"].set_color(None)
ax.spines["right"].set_color(None)
@@ -613,4 +628,7 @@ def activate(x, activation="sigmoid", deriv=False, other_args={}):
)
elif deriv == True:
return (
- other_args["gain"] * (1 - np.tanh(x) ** 2) * ((x - other_args["threshold"]) > 0))
+ other_args["gain"]
+ * (1 - np.tanh(x) ** 2)
+ * ((x - other_args["threshold"]) > 0)
+ )
diff --git a/setup.cfg b/setup.cfg
index 218bb215..d327afeb 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
[metadata]
name = ratinabox
-version = 1.1.0
+version = 1.2.0
author = Tom George
author_email = tomgeorge1@btinternet.com
project_urls =
@@ -24,7 +24,8 @@ install_requires =
numpy ~= 1.23
matplotlib ~= 3.5.3
scipy ~= 1.9.3
-python_reuires = >=3.7
+ shapely ~= 2.0.1
+python_requires = >=3.7
include_package_data = False
[options.extras_require]