distance_matrices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/decision_point/all_runs_dis.csv',index_col=0)\n",
    "#dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/decision_point/all_exploration.csv',index_col=0)\n",
    "# dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/decision_point/all_runs_exploration.csv',index_col=0)\n",
    "# dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/path_direction/all_runs_path.csv',index_col=0)\n",
    "#dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/path_direction/exploration_path.csv',index_col=0)\n",
    "#dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/path_direction/all_runs_exploration_path.csv',index_col=0)\n",
    "dat = pd.read_csv('~/Desktop/MLINDIV/beta_series/summary_path_decision_directions.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dat = dat[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>4_directions</th>\n",
       "      <th>E&amp;N</th>\n",
       "      <th>E&amp;W</th>\n",
       "      <th>E&amp;S</th>\n",
       "      <th>S&amp;W</th>\n",
       "      <th>S&amp;N</th>\n",
       "      <th>W&amp;N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>decision_explore</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>decision_run</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>decision_all</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>path_explore</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>path_run</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>path_all</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  4_directions   E&N   E&W   E&S   S&W   S&N   W&N\n",
       "decision_explore          0.06  0.57  0.55  0.56  0.51  0.51  0.51\n",
       "decision_run              0.14  0.56  0.53  0.55  0.52  0.50  0.52\n",
       "decision_all              0.10  0.55  0.54  0.56  0.52  0.50  0.52\n",
       "path_explore              0.33  0.58  0.59  0.66  0.58  0.59  0.52\n",
       "path_run                  0.31  0.57  0.54  0.58  0.55  0.53  0.53\n",
       "path_all                  0.31  0.58  0.56  0.61  0.56  0.55  0.51"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.zeros_like(dat)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    #ax = sns.heatmap(dat, mask=mask, vmax=.7,vmin=.5, square=True,  cmap=\"YlGnBu\", annot=True)\n",
    "    ax = sns.heatmap(dat, vmax=.7,vmin=.5, square=True,  cmap=\"YlGnBu\", annot=True)\n",
    "    ax.set_ylim(len(dat)+0.5, 0)\n",
    "    plt.savefig('summary_path_decision_directions.png')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}