{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_alpha(chi_mf,  alpha1=.01, A=1):\n",
    "    alpha1 = alpha1\n",
    "    A = A\n",
    "    B = np.log((alpha1 ** -1) * A - 1)\n",
    "    return A / (1 + np.exp(B * chi_mf))\n",
    "\n",
    "\n",
    "def get_beta(chi_mb,  beta1=.1, A=.5):\n",
    "    beta1 = beta1\n",
    "    A = A\n",
    "    B = np.log((beta1 ** -1) * A - 1)\n",
    "    return A / (1 + np.exp(B * chi_mb))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_alpha(alpha1, A):\n",
    "    plt.plot(np.arange(0, 1, .01), get_alpha(np.arange(0, 1, .01), alpha1, A))\n",
    "    plt.ylim([0,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7c04ffc32b44fadba739a969a13c940",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.197, description='alpha1', max=0.4, min=0.01, step=0.011), FloatSlid…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_alpha(alpha1, A)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(plot_alpha, alpha1=(.01, .4, .011), A=(1,5,.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a93f2eb981794aefacfe64a729740876",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.197, description='beta1', max=0.4, min=0.01, step=0.011), FloatSlide…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_beta(beta1, A)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_beta(beta1, A):\n",
    "    plt.plot(np.arange(0, 1, .01), get_beta(np.arange(0, 1, .01), beta1, A))\n",
    "    plt.ylim([0,2])\n",
    "\n",
    "interact(plot_beta, beta1=(.01, .4, .011), A=(1,5,.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fixed_point(alpha, beta):\n",
    "    return alpha / (alpha + beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_size = .02\n",
    "\n",
    "r_mb, r_mf = np.meshgrid(np.arange(0,1, bin_size), np.arange(0,1, bin_size))\n",
    "\n",
    "r_mf = np.flipud(r_mf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = get_alpha(r_mf)\n",
    "betas = get_beta(r_mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = get_fixed_point(alphas, betas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114c6cac8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fp(A_alpha, A_beta, alpha1, beta1):\n",
    "    alphas = get_alpha(r_mf, A=A_alpha, alpha1=alpha1)\n",
    "    betas = get_beta(r_mb, A=A_beta, beta1=beta1)\n",
    "    \n",
    "    fp = get_fixed_point(alphas, betas)\n",
    "    \n",
    "    plt.imshow(fp)\n",
    "    plt.colorbar()\n",
    "    plt.xlabel('reliability SR')\n",
    "    plt.ylabel('reliability MF')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9603f5f04d9e4f2db8664e5b154b34bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=14.95, description='A_alpha', max=30.0, min=0.1, step=0.11), FloatSlid…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_fp(A_alpha, A_beta, alpha1, beta1)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(plot_fp, A_alpha=(.1, 30, .11), A_beta=(.1,10,.01), alpha1=(.01, .4, .011), beta1=(.01, .4, .011))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded reward traces from file.\n",
      "Loaded reward traces from file.\n"
     ]
    }
   ],
   "source": [
    "# Try running 2 agents\n",
    "from hippocampus.experiments.twostep import CombinedAgent\n",
    "from hippocampus.environments import TwoStepTask\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbd5567113374fbcae6d467f928ca09b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded reward traces from file.\n",
      "Loaded reward traces from file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "a1s = [.01, .2]\n",
    "\n",
    "amplitudes = [.5, 10]\n",
    "slopes = [.01, .2]\n",
    "\n",
    "A_beta = [2, .5]\n",
    "beta1 = [.3, .1]\n",
    "\n",
    "\n",
    "\n",
    "for i, a in tqdm(enumerate([.5,2])):\n",
    "    df = pd.DataFrame({})\n",
    "    e = TwoStepTask()\n",
    "    ag = CombinedAgent(e, A_alpha=amplitudes[i], alpha1=slopes[i], \n",
    "                       A_beta=A_beta[i], beta1=beta1[i],\n",
    "                       lesion_dls=False)\n",
    "\n",
    "    for ep in range(e.n_trials):\n",
    "        results = ag.one_episode()\n",
    "        results['Trial'] = ep\n",
    "        results['Agent'] = i\n",
    "        df = df.append(results, ignore_index=True)\n",
    "    data.append(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hippocampus.analysis.mb_allocentric_analysis import get_model_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.517823\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0930263855503506, 1.9486924956080711)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_model_weights(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.625247\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.0820905498514133, 1.6582280766035324)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_model_weights(data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the spatial one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hippocampus.agents import CombinedAgent as SpatialAgent\n",
    "from hippocampus.environments import HexWaterMaze\n",
    "from hippocampus.analysis.mb_allocentric_analysis import get_allo_index, get_first_trial_info, get_surrounding_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_watermaze():\n",
    "    \n",
    "    dfs = [] \n",
    "    amplitudes = [.5, 10]\n",
    "    slopes = [.01, .2]\n",
    "    \n",
    "    A_beta = [2, .5]\n",
    "    beta1 = [.3, .1]\n",
    "    num_agents = len(amplitudes)\n",
    "    \n",
    "    g = HexWaterMaze(6)\n",
    "\n",
    "    for i_a in range(num_agents):\n",
    "        #possible_platform_states = np.array([192, 185, 181, 174, 216, 210, 203, 197])  # for the r = 10 case\n",
    "\n",
    "        possible_platform_states = np.array([48, 45, 42, 39, 60, 57, 54, 51])\n",
    "        platform_sequence = determine_platform_seq(possible_platform_states, g)\n",
    "\n",
    "        # intialise agent\n",
    "        agent = SpatialAgent(g, init_sr='rw', A_alpha=amplitudes[i_a], alpha1=slopes[i_a], \n",
    "                             A_beta=A_beta[i_a], beta1=beta1[i_a])\n",
    "        agent_results = []\n",
    "        session = 0\n",
    "\n",
    "        total_trial_count = 0\n",
    "\n",
    "        for ses in tqdm(range(11)):\n",
    "            for trial in tqdm(range(4), leave=False):\n",
    "                # every first trial of a session, change the platform location\n",
    "                if trial == 0:\n",
    "                    g.set_platform_state(platform_sequence[ses])\n",
    "\n",
    "                res = agent.one_episode(random_policy=False)\n",
    "                res['trial'] = trial\n",
    "                res['escape time'] = res.time.max()\n",
    "                res['session'] = ses\n",
    "                res['total trial'] = total_trial_count\n",
    "                agent_results.append(res)\n",
    "                total_trial_count += 1\n",
    "\n",
    "\n",
    "        agent_df = pd.concat(agent_results)\n",
    "        agent_df['total time'] = np.arange(len(agent_df))\n",
    "\n",
    "        dfs.append(agent_df)\n",
    "    return dfs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_platform_seq(platform_states, env):\n",
    "    indices = np.arange(len(platform_states))\n",
    "    usage = np.zeros(len(platform_states))\n",
    "\n",
    "    plat_seq = [np.random.choice(platform_states)]\n",
    "    for sess in range(1, 11):\n",
    "        distances = np.array([env.grid.distance(plat_seq[sess - 1], s) for s in platform_states])\n",
    "        candidates = indices[np.logical_and(usage < 2, distances > env.grid.radius)]\n",
    "        platform_idx = np.random.choice(candidates)\n",
    "        plat_seq.append(platform_states[platform_idx])\n",
    "        usage[platform_idx] += 1.\n",
    "\n",
    "    return plat_seq\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a47103c471447c58012cc3449c4b769",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=11), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3807c027d29d47ba93d6c51aba01169c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=11), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data = run_watermaze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = HexWaterMaze(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DLS reliability</th>\n",
       "      <th>HPC reliability</th>\n",
       "      <th>P(SR)</th>\n",
       "      <th>RPE</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>escape time</th>\n",
       "      <th>platform</th>\n",
       "      <th>previous platform</th>\n",
       "      <th>reward</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "      <th>total time</th>\n",
       "      <th>trial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011779</td>\n",
       "      <td>0.776085</td>\n",
       "      <td>0.371749</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.244271</td>\n",
       "      <td>0.989785</td>\n",
       "      <td>285.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>146.297203</td>\n",
       "      <td>142.5</td>\n",
       "      <td>2485.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.046637</td>\n",
       "      <td>0.658424</td>\n",
       "      <td>0.320115</td>\n",
       "      <td>0.045338</td>\n",
       "      <td>0.227375</td>\n",
       "      <td>0.959574</td>\n",
       "      <td>54.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>158.909091</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.123621</td>\n",
       "      <td>0.602345</td>\n",
       "      <td>0.268750</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>0.190995</td>\n",
       "      <td>0.893192</td>\n",
       "      <td>123.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>121.024194</td>\n",
       "      <td>61.5</td>\n",
       "      <td>3715.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.168176</td>\n",
       "      <td>0.496329</td>\n",
       "      <td>0.223521</td>\n",
       "      <td>0.019473</td>\n",
       "      <td>0.170987</td>\n",
       "      <td>0.855167</td>\n",
       "      <td>118.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>179.596639</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4130.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.207541</td>\n",
       "      <td>0.478697</td>\n",
       "      <td>0.202583</td>\n",
       "      <td>0.005679</td>\n",
       "      <td>0.154189</td>\n",
       "      <td>0.821919</td>\n",
       "      <td>257.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.003876</td>\n",
       "      <td>132.186047</td>\n",
       "      <td>128.5</td>\n",
       "      <td>4776.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.244880</td>\n",
       "      <td>0.466913</td>\n",
       "      <td>0.184352</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>0.139138</td>\n",
       "      <td>0.790753</td>\n",
       "      <td>254.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>129.827451</td>\n",
       "      <td>127.0</td>\n",
       "      <td>6195.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.295309</td>\n",
       "      <td>0.435760</td>\n",
       "      <td>0.158472</td>\n",
       "      <td>0.004068</td>\n",
       "      <td>0.120309</td>\n",
       "      <td>0.749335</td>\n",
       "      <td>208.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>160.765550</td>\n",
       "      <td>104.0</td>\n",
       "      <td>6770.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.340893</td>\n",
       "      <td>0.429987</td>\n",
       "      <td>0.140156</td>\n",
       "      <td>0.004648</td>\n",
       "      <td>0.104854</td>\n",
       "      <td>0.712668</td>\n",
       "      <td>337.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>195.082840</td>\n",
       "      <td>168.5</td>\n",
       "      <td>7283.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.398786</td>\n",
       "      <td>0.403911</td>\n",
       "      <td>0.116505</td>\n",
       "      <td>0.007825</td>\n",
       "      <td>0.087399</td>\n",
       "      <td>0.667295</td>\n",
       "      <td>135.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>195.169118</td>\n",
       "      <td>67.5</td>\n",
       "      <td>8039.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.452342</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.098251</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>0.073368</td>\n",
       "      <td>0.626645</td>\n",
       "      <td>213.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>147.696262</td>\n",
       "      <td>106.5</td>\n",
       "      <td>8700.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DLS reliability  HPC reliability     P(SR)       RPE     alpha  \\\n",
       "session                                                                   \n",
       "1               0.011779         0.776085  0.371749  0.006282  0.244271   \n",
       "2               0.046637         0.658424  0.320115  0.045338  0.227375   \n",
       "3               0.123621         0.602345  0.268750  0.011416  0.190995   \n",
       "4               0.168176         0.496329  0.223521  0.019473  0.170987   \n",
       "5               0.207541         0.478697  0.202583  0.005679  0.154189   \n",
       "6               0.244880         0.466913  0.184352  0.007599  0.139138   \n",
       "7               0.295309         0.435760  0.158472  0.004068  0.120309   \n",
       "8               0.340893         0.429987  0.140156  0.004648  0.104854   \n",
       "9               0.398786         0.403911  0.116505  0.007825  0.087399   \n",
       "10              0.452342         0.390282  0.098251  0.006776  0.073368   \n",
       "\n",
       "             beta  escape time  platform  previous platform    reward  \\\n",
       "session                                                                 \n",
       "1        0.989785        285.0     192.0              210.0  0.003497   \n",
       "2        0.959574         54.0     210.0              192.0  0.018182   \n",
       "3        0.893192        123.0     192.0              210.0  0.008065   \n",
       "4        0.855167        118.0     174.0              192.0  0.008403   \n",
       "5        0.821919        257.0     210.0              174.0  0.003876   \n",
       "6        0.790753        254.0     185.0              210.0  0.003922   \n",
       "7        0.749335        208.0     216.0              185.0  0.004785   \n",
       "8        0.712668        337.0     185.0              216.0  0.002959   \n",
       "9        0.667295        135.0     197.0              185.0  0.007353   \n",
       "10       0.626645        213.0     216.0              197.0  0.004673   \n",
       "\n",
       "              state   time  total time  trial  \n",
       "session                                        \n",
       "1        146.297203  142.5      2485.5      0  \n",
       "2        158.909091   27.0      3142.0      0  \n",
       "3        121.024194   61.5      3715.5      0  \n",
       "4        179.596639   59.0      4130.0      0  \n",
       "5        132.186047  128.5      4776.5      0  \n",
       "6        129.827451  127.0      6195.0      0  \n",
       "7        160.765550  104.0      6770.0      0  \n",
       "8        195.082840  168.5      7283.5      0  \n",
       "9        195.169118   67.5      8039.5      0  \n",
       "10       147.696262  106.5      8700.5      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_first_trial_info(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_surrounding_states(state, env, rec_depth=5):\n",
    "    surrounding_states = [state]\n",
    "    for i in range(rec_depth):\n",
    "        added_states = []\n",
    "        for s in surrounding_states:\n",
    "            neighbours = np.flatnonzero(env.adjacency_graph[s])\n",
    "            for n in neighbours:\n",
    "                if n not in surrounding_states and n not in added_states:\n",
    "                    added_states.append(n)\n",
    "        surrounding_states += added_states\n",
    "    return surrounding_states\n",
    "\n",
    "\n",
    "#def get_surrounding_states(state, env):\n",
    "#    x, y = env.get_state_location(216)\n",
    "#    if y >= 0:\n",
    "#        states = [s for s in env.state_indices if env.grid.cart_coords[s][1] >=0]\n",
    "#    else:\n",
    "#        states = [s for s in env.state_indices if env.grid.cart_coords[s][1] <0]\n",
    "#    return states\n",
    "\n",
    "def get_allo_index(agent_data, env):\n",
    "    first_trials = get_first_trial_info(agent_data)\n",
    "    prop_times = []\n",
    "    for ses in range(1,5):\n",
    "        states = np.sort(agent_data[(agent_data.session == ses) & (agent_data.trial == 0)]['state'])\n",
    "        previous_platform = first_trials['previous platform'][ses]\n",
    "        surrounding_states = np.sort(np.array(get_surrounding_states(int(previous_platform), env)))\n",
    "        \n",
    "        #indices = np.searchsorted(states, surrounding_states)\n",
    "        times = np.isin(states, surrounding_states).sum()\n",
    "        #prop_times.append( len(indices) / len(states))\n",
    "        prop_times.append(times)\n",
    "\n",
    "    return np.mean(prop_times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ses = 3\n",
    "agent_data=data[0]\n",
    "first_trials = get_first_trial_info(agent_data)\n",
    "states = np.sort(agent_data[(agent_data.session == ses) & (agent_data.trial == 0)]['state'])\n",
    "previous_platform = first_trials['previous platform'][ses]\n",
    "surrounding_states = np.sort(np.array(get_surrounding_states(int(previous_platform), env)))\n",
    "indices = np.searchsorted(surrounding_states, states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# searchsorted is not the way \n",
    "np.isin(states, surrounding_states).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0.,   1.,   2.,   3.,   5.,   5.,   5.,   5.,   5.,   5.,\n",
       "         6.,   6.,   6.,   6.,  12.,  12.,  13.,  15.,  15.,  16.,  16.,\n",
       "        16.,  17.,  17.,  17.,  17.,  18.,  26.,  32.,  32.,  34.,  34.,\n",
       "        35.,  35.,  35.,  47.,  48.,  57.,  58.,  58.,  75.,  86.,  86.,\n",
       "        87.,  87.,  88.,  92., 109., 120., 120., 121., 121., 121., 121.,\n",
       "       121., 122., 122., 122., 122., 122., 123., 124., 125., 128., 128.,\n",
       "       148., 162., 162., 163., 163., 163., 163., 163., 164., 164., 164.,\n",
       "       165., 165., 166., 166., 167., 167., 167., 167., 167., 169., 170.,\n",
       "       170., 171., 192., 193., 210., 210., 210., 210., 211., 211., 211.,\n",
       "       212., 213., 214., 215., 215., 216., 218., 218., 218., 218., 219.,\n",
       "       219., 219., 219., 219., 219., 219., 219., 219., 220., 243., 244.,\n",
       "       244., 264., 265.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 33,  34,  35,  55,  56,  57,  58,  59,  83,  84,  85,  86,  87,\n",
       "        88,  89, 117, 118, 119, 120, 121, 122, 123, 124, 125, 158, 159,\n",
       "       160, 161, 162, 163, 164, 165, 166, 167, 205, 206, 207, 208, 209,\n",
       "       210, 211, 212, 213, 214, 215, 258, 259, 260, 261, 262, 263, 264,\n",
       "       265, 266, 267, 268])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surrounding_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.25"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_allo_index(data[0], env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.25"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_allo_index(data[1], env) ### returns the same number??? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1caef588>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[0].plot(x='total trial', y='escape time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108a95c88>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[1].plot(x='total trial', y='escape time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = 1\n",
    "ag = 1\n",
    "\n",
    "s2t0 = data[ag][np.logical_and(data[ag].trial == 0, data[ag].session == sess)]\n",
    "\n",
    "previous_platform = data[ag][data[ag].session == sess - 1].platform.iloc[0]\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessegeerts/miniconda3/envs/models/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c122240>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2t0['previous platform'] = previous_platform\n",
    "env.plot_occupancy_on_grid(s2t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DLS reliability</th>\n",
       "      <th>HPC reliability</th>\n",
       "      <th>P(SR)</th>\n",
       "      <th>RPE</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>escape time</th>\n",
       "      <th>platform</th>\n",
       "      <th>reward</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "      <th>total time</th>\n",
       "      <th>total trial</th>\n",
       "      <th>trial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.385670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>122.526474</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011779</td>\n",
       "      <td>0.776085</td>\n",
       "      <td>0.371749</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.244271</td>\n",
       "      <td>0.989785</td>\n",
       "      <td>285.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>146.297203</td>\n",
       "      <td>142.5</td>\n",
       "      <td>2485.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.046637</td>\n",
       "      <td>0.658424</td>\n",
       "      <td>0.320115</td>\n",
       "      <td>0.045338</td>\n",
       "      <td>0.227375</td>\n",
       "      <td>0.959574</td>\n",
       "      <td>54.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>158.909091</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.123621</td>\n",
       "      <td>0.602345</td>\n",
       "      <td>0.268750</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>0.190995</td>\n",
       "      <td>0.893192</td>\n",
       "      <td>123.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>121.024194</td>\n",
       "      <td>61.5</td>\n",
       "      <td>3715.5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.168176</td>\n",
       "      <td>0.496329</td>\n",
       "      <td>0.223521</td>\n",
       "      <td>0.019473</td>\n",
       "      <td>0.170987</td>\n",
       "      <td>0.855167</td>\n",
       "      <td>118.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>179.596639</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4130.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.207541</td>\n",
       "      <td>0.478697</td>\n",
       "      <td>0.202583</td>\n",
       "      <td>0.005679</td>\n",
       "      <td>0.154189</td>\n",
       "      <td>0.821919</td>\n",
       "      <td>257.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.003876</td>\n",
       "      <td>132.186047</td>\n",
       "      <td>128.5</td>\n",
       "      <td>4776.5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.244880</td>\n",
       "      <td>0.466913</td>\n",
       "      <td>0.184352</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>0.139138</td>\n",
       "      <td>0.790753</td>\n",
       "      <td>254.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>129.827451</td>\n",
       "      <td>127.0</td>\n",
       "      <td>6195.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.295309</td>\n",
       "      <td>0.435760</td>\n",
       "      <td>0.158472</td>\n",
       "      <td>0.004068</td>\n",
       "      <td>0.120309</td>\n",
       "      <td>0.749335</td>\n",
       "      <td>208.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>160.765550</td>\n",
       "      <td>104.0</td>\n",
       "      <td>6770.0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.340893</td>\n",
       "      <td>0.429987</td>\n",
       "      <td>0.140156</td>\n",
       "      <td>0.004648</td>\n",
       "      <td>0.104854</td>\n",
       "      <td>0.712668</td>\n",
       "      <td>337.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>195.082840</td>\n",
       "      <td>168.5</td>\n",
       "      <td>7283.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.398786</td>\n",
       "      <td>0.403911</td>\n",
       "      <td>0.116505</td>\n",
       "      <td>0.007825</td>\n",
       "      <td>0.087399</td>\n",
       "      <td>0.667295</td>\n",
       "      <td>135.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>195.169118</td>\n",
       "      <td>67.5</td>\n",
       "      <td>8039.5</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.452342</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.098251</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>0.073368</td>\n",
       "      <td>0.626645</td>\n",
       "      <td>213.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>147.696262</td>\n",
       "      <td>106.5</td>\n",
       "      <td>8700.5</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DLS reliability  HPC reliability     P(SR)       RPE     alpha  \\\n",
       "session                                                                   \n",
       "0               0.000000         0.800000  0.385670  0.000000  0.250000   \n",
       "1               0.011779         0.776085  0.371749  0.006282  0.244271   \n",
       "2               0.046637         0.658424  0.320115  0.045338  0.227375   \n",
       "3               0.123621         0.602345  0.268750  0.011416  0.190995   \n",
       "4               0.168176         0.496329  0.223521  0.019473  0.170987   \n",
       "5               0.207541         0.478697  0.202583  0.005679  0.154189   \n",
       "6               0.244880         0.466913  0.184352  0.007599  0.139138   \n",
       "7               0.295309         0.435760  0.158472  0.004068  0.120309   \n",
       "8               0.340893         0.429987  0.140156  0.004648  0.104854   \n",
       "9               0.398786         0.403911  0.116505  0.007825  0.087399   \n",
       "10              0.452342         0.390282  0.098251  0.006776  0.073368   \n",
       "\n",
       "             beta  escape time  platform    reward       state   time  \\\n",
       "session                                                                 \n",
       "0        1.000000       1000.0     210.0  0.000000  122.526474  500.0   \n",
       "1        0.989785        285.0     192.0  0.003497  146.297203  142.5   \n",
       "2        0.959574         54.0     210.0  0.018182  158.909091   27.0   \n",
       "3        0.893192        123.0     192.0  0.008065  121.024194   61.5   \n",
       "4        0.855167        118.0     174.0  0.008403  179.596639   59.0   \n",
       "5        0.821919        257.0     210.0  0.003876  132.186047  128.5   \n",
       "6        0.790753        254.0     185.0  0.003922  129.827451  127.0   \n",
       "7        0.749335        208.0     216.0  0.004785  160.765550  104.0   \n",
       "8        0.712668        337.0     185.0  0.002959  195.082840  168.5   \n",
       "9        0.667295        135.0     197.0  0.007353  195.169118   67.5   \n",
       "10       0.626645        213.0     216.0  0.004673  147.696262  106.5   \n",
       "\n",
       "         total time  total trial  trial  \n",
       "session                                  \n",
       "0             500.0            0      0  \n",
       "1            2485.5            4      0  \n",
       "2            3142.0            8      0  \n",
       "3            3715.5           12      0  \n",
       "4            4130.0           16      0  \n",
       "5            4776.5           20      0  \n",
       "6            6195.0           24      0  \n",
       "7            6770.0           28      0  \n",
       "8            7283.5           32      0  \n",
       "9            8039.5           36      0  \n",
       "10           8700.5           40      0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0][data[0].trial==0].pivot_table(index='session')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DLS reliability</th>\n",
       "      <th>HPC reliability</th>\n",
       "      <th>P(SR)</th>\n",
       "      <th>RPE</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>escape time</th>\n",
       "      <th>platform</th>\n",
       "      <th>reward</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "      <th>total time</th>\n",
       "      <th>total trial</th>\n",
       "      <th>trial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.799986</td>\n",
       "      <td>0.939509</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>986.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>120.459980</td>\n",
       "      <td>493.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023181</td>\n",
       "      <td>0.736964</td>\n",
       "      <td>0.935511</td>\n",
       "      <td>0.066481</td>\n",
       "      <td>4.774611</td>\n",
       "      <td>0.245983</td>\n",
       "      <td>276.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>155.129964</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.091301</td>\n",
       "      <td>0.671128</td>\n",
       "      <td>0.930192</td>\n",
       "      <td>0.013035</td>\n",
       "      <td>4.120918</td>\n",
       "      <td>0.234200</td>\n",
       "      <td>292.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.003413</td>\n",
       "      <td>101.675768</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2474.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.150995</td>\n",
       "      <td>0.630409</td>\n",
       "      <td>0.927322</td>\n",
       "      <td>0.040144</td>\n",
       "      <td>3.571755</td>\n",
       "      <td>0.223930</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.293706</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3585.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.199267</td>\n",
       "      <td>0.622950</td>\n",
       "      <td>0.926505</td>\n",
       "      <td>0.078175</td>\n",
       "      <td>3.152890</td>\n",
       "      <td>0.215688</td>\n",
       "      <td>557.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>140.741935</td>\n",
       "      <td>278.5</td>\n",
       "      <td>5190.5</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.279718</td>\n",
       "      <td>0.584445</td>\n",
       "      <td>0.923381</td>\n",
       "      <td>0.063639</td>\n",
       "      <td>2.518800</td>\n",
       "      <td>0.202127</td>\n",
       "      <td>683.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>124.328947</td>\n",
       "      <td>341.5</td>\n",
       "      <td>6093.5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.344488</td>\n",
       "      <td>0.529076</td>\n",
       "      <td>0.919011</td>\n",
       "      <td>0.029036</td>\n",
       "      <td>2.073978</td>\n",
       "      <td>0.191414</td>\n",
       "      <td>735.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>164.543478</td>\n",
       "      <td>367.5</td>\n",
       "      <td>7415.5</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.401282</td>\n",
       "      <td>0.499301</td>\n",
       "      <td>0.912159</td>\n",
       "      <td>0.147805</td>\n",
       "      <td>1.734003</td>\n",
       "      <td>0.182203</td>\n",
       "      <td>417.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>108.416268</td>\n",
       "      <td>208.5</td>\n",
       "      <td>8512.5</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.407370</td>\n",
       "      <td>0.454750</td>\n",
       "      <td>0.907308</td>\n",
       "      <td>0.108709</td>\n",
       "      <td>1.700305</td>\n",
       "      <td>0.181226</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>139.327672</td>\n",
       "      <td>500.0</td>\n",
       "      <td>9498.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.407370</td>\n",
       "      <td>0.454750</td>\n",
       "      <td>0.907307</td>\n",
       "      <td>0.019760</td>\n",
       "      <td>1.700305</td>\n",
       "      <td>0.181226</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.388611</td>\n",
       "      <td>500.0</td>\n",
       "      <td>13502.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.239113</td>\n",
       "      <td>0.431998</td>\n",
       "      <td>0.911853</td>\n",
       "      <td>0.121699</td>\n",
       "      <td>2.828073</td>\n",
       "      <td>0.208940</td>\n",
       "      <td>927.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>121.739224</td>\n",
       "      <td>463.5</td>\n",
       "      <td>15768.5</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DLS reliability  HPC reliability     P(SR)       RPE     alpha  \\\n",
       "session                                                                   \n",
       "0               0.000000         0.799986  0.939509  0.001013  5.000000   \n",
       "1               0.023181         0.736964  0.935511  0.066481  4.774611   \n",
       "2               0.091301         0.671128  0.930192  0.013035  4.120918   \n",
       "3               0.150995         0.630409  0.927322  0.040144  3.571755   \n",
       "4               0.199267         0.622950  0.926505  0.078175  3.152890   \n",
       "5               0.279718         0.584445  0.923381  0.063639  2.518800   \n",
       "6               0.344488         0.529076  0.919011  0.029036  2.073978   \n",
       "7               0.401282         0.499301  0.912159  0.147805  1.734003   \n",
       "8               0.407370         0.454750  0.907308  0.108709  1.700305   \n",
       "9               0.407370         0.454750  0.907307  0.019760  1.700305   \n",
       "10              0.239113         0.431998  0.911853  0.121699  2.828073   \n",
       "\n",
       "             beta  escape time  platform    reward       state   time  \\\n",
       "session                                                                 \n",
       "0        0.250000        986.0     192.0  0.001013  120.459980  493.0   \n",
       "1        0.245983        276.0     203.0  0.003610  155.129964  138.0   \n",
       "2        0.234200        292.0     181.0  0.003413  101.675768  146.0   \n",
       "3        0.223930       1000.0     216.0  0.000000  127.293706  500.0   \n",
       "4        0.215688        557.0     185.0  0.001792  140.741935  278.5   \n",
       "5        0.202127        683.0     203.0  0.001462  124.328947  341.5   \n",
       "6        0.191414        735.0     185.0  0.001359  164.543478  367.5   \n",
       "7        0.182203        417.0     174.0  0.002392  108.416268  208.5   \n",
       "8        0.181226       1000.0     210.0  0.000000  139.327672  500.0   \n",
       "9        0.181226       1000.0     174.0  0.000000  116.388611  500.0   \n",
       "10       0.208940        927.0     197.0  0.001078  121.739224  463.5   \n",
       "\n",
       "         total time  total trial  trial  \n",
       "session                                  \n",
       "0             493.0            0      0  \n",
       "1            1577.0            4      0  \n",
       "2            2474.0            8      0  \n",
       "3            3585.0           12      0  \n",
       "4            5190.5           16      0  \n",
       "5            6093.5           20      0  \n",
       "6            7415.5           24      0  \n",
       "7            8512.5           28      0  \n",
       "8            9498.0           32      0  \n",
       "9           13502.0           36      0  \n",
       "10          15768.5           40      0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1][data[1].trial==0].pivot_table(index='session')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, -9.237604307034012)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.get_state_location(216)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}