{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/simple_example.ipynb)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple RatInABox demo\n",
    "Initialise a 2D Environment. Initialise an Agent in the environment. Initialise some PlaceCells. Simulate for 60 seconds. Print table of times, position and firing rates and spikes. Plot trajectory and rate timeseries'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ratinabox in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (1.3.3)\n",
      "Requirement already satisfied: scipy~=1.9.3 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from ratinabox) (1.9.3)\n",
      "Requirement already satisfied: matplotlib~=3.7.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from ratinabox) (3.7.1)\n",
      "Requirement already satisfied: numpy~=1.24.2 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from ratinabox) (1.24.2)\n",
      "Requirement already satisfied: shapely~=2.0.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from ratinabox) (2.0.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (9.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (4.38.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from matplotlib~=3.7.1->ratinabox) (1.0.7)\n",
      "Requirement already satisfied: six>=1.5 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib~=3.7.1->ratinabox) (1.16.0)\n",
      "Requirement already satisfied: pandas in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (2.0.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from pandas) (1.24.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/tomgeorge/miniconda3/envs/riab/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# Install dependencies \n",
    "!pip install ratinabox\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import ratinabox \n",
    "import ratinabox\n",
    "from ratinabox.Environment import Environment\n",
    "from ratinabox.Agent import Agent\n",
    "from ratinabox.Neurons import *\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a very simple simulation \n",
    "Env = Environment()\n",
    "Ag = Agent(Env)\n",
    "PCs = PlaceCells(Ag)\n",
    "for i in range(int(60 / Ag.dt)):\n",
    "    Ag.update()\n",
    "    PCs.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>pos</th>\n",
       "      <th>vel</th>\n",
       "      <th>rot_vel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>[0.7149792560205227, 0.43050616998020885]</td>\n",
       "      <td>[0.048888166267990446, -0.05442913744195902]</td>\n",
       "      <td>-0.490511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>[0.7154135133118109, 0.43001622026441333]</td>\n",
       "      <td>[0.04342572912882092, -0.04899497157955235]</td>\n",
       "      <td>-0.660761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>[0.7158284321082329, 0.42955056648087875]</td>\n",
       "      <td>[0.04149187964219703, -0.04656537835345809]</td>\n",
       "      <td>0.263463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>[0.7162103493627244, 0.4291176745962729]</td>\n",
       "      <td>[0.038191725449154834, -0.043289188460582384]</td>\n",
       "      <td>-0.492651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>[0.7165781627424781, 0.4287014881615713]</td>\n",
       "      <td>[0.03678133797536676, -0.04161864347016486]</td>\n",
       "      <td>0.085656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>59.96</td>\n",
       "      <td>[0.08190916125337111, 0.06766882211303965]</td>\n",
       "      <td>[-0.018734975593387437, -0.008228372483735924]</td>\n",
       "      <td>2.559365</td>\n",
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       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>59.97</td>\n",
       "      <td>[0.0816941787580427, 0.06756026624305612]</td>\n",
       "      <td>[-0.021498249532840574, -0.010855586998352595]</td>\n",
       "      <td>2.140784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>59.98</td>\n",
       "      <td>[0.08147210263202699, 0.06744186872061435]</td>\n",
       "      <td>[-0.022207612601571336, -0.01183975224417766]</td>\n",
       "      <td>1.479985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>59.99</td>\n",
       "      <td>[0.08125954020737682, 0.06732646623580935]</td>\n",
       "      <td>[-0.02125624246501756, -0.011540248480500226]</td>\n",
       "      <td>1.399114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>60.00</td>\n",
       "      <td>[0.08100283159978669, 0.06717251120796199]</td>\n",
       "      <td>[-0.025670860759012537, -0.01539550278473606]</td>\n",
       "      <td>1.180786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          t                                         pos   \n",
       "0      0.01   [0.7149792560205227, 0.43050616998020885]  \\\n",
       "1      0.02   [0.7154135133118109, 0.43001622026441333]   \n",
       "2      0.03   [0.7158284321082329, 0.42955056648087875]   \n",
       "3      0.04    [0.7162103493627244, 0.4291176745962729]   \n",
       "4      0.05    [0.7165781627424781, 0.4287014881615713]   \n",
       "...     ...                                         ...   \n",
       "5995  59.96  [0.08190916125337111, 0.06766882211303965]   \n",
       "5996  59.97   [0.0816941787580427, 0.06756026624305612]   \n",
       "5997  59.98  [0.08147210263202699, 0.06744186872061435]   \n",
       "5998  59.99  [0.08125954020737682, 0.06732646623580935]   \n",
       "5999  60.00  [0.08100283159978669, 0.06717251120796199]   \n",
       "\n",
       "                                                 vel   rot_vel  \n",
       "0       [0.048888166267990446, -0.05442913744195902] -0.490511  \n",
       "1        [0.04342572912882092, -0.04899497157955235] -0.660761  \n",
       "2        [0.04149187964219703, -0.04656537835345809]  0.263463  \n",
       "3      [0.038191725449154834, -0.043289188460582384] -0.492651  \n",
       "4        [0.03678133797536676, -0.04161864347016486]  0.085656  \n",
       "...                                              ...       ...  \n",
       "5995  [-0.018734975593387437, -0.008228372483735924]  2.559365  \n",
       "5996  [-0.021498249532840574, -0.010855586998352595]  2.140784  \n",
       "5997   [-0.022207612601571336, -0.01183975224417766]  1.479985  \n",
       "5998   [-0.02125624246501756, -0.011540248480500226]  1.399114  \n",
       "5999   [-0.025670860759012537, -0.01539550278473606]  1.180786  \n",
       "\n",
       "[6000 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export data into a dataframe \n",
    "pd.DataFrame(Ag.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: This figure has not been saved.\n",
      "    • To AUTOMATICALLY save all plots (recommended), set  `ratinabox.autosave_plots = True`\n",
      "    • To MANUALLY save plots, call                        `ratinabox.utils.save_figure(figure_object, save_title).\n",
      "      This warning will not be shown again\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x120 with 1 Axes>,\n",
       " <Axes: xlabel='Time / min', ylabel='Neurons'>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data \n",
    "Ag.plot_trajectory()\n",
    "PCs.plot_rate_timeseries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f3cdbb439150f468ef73eca921e462ba1cf0894f575003150b1b7f3566c8fd4a"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}