{ "cells": [ { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 720x72 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import os.path as op\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "from hippocampus.plotting import tsplot_boot\n", "from definitions import RESULTS_FOLDER\n", "import matplotlib\n", "\n", "sns.palplot(sns.color_palette())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "results_dir = op.join(RESULTS_FOLDER, 'pearce', 'lesion_DLS')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "ETs = []\n", "for a in range(100):\n", " df = pd.read_csv(op.join(results_dir, 'agent{}.csv'.format(a)))\n", " escape_time = df.pivot_table(values='escape time', index=['session', 'trial'])\n", " escape_time['agent'] = a\n", " ETs.append(escape_time)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ETs)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "df=pd.concat(ETs).reset_index()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "df = df.set_index(['agent', 'session', 'trial']).reset_index()\n", "df = df[np.logical_or(df.trial==0,df.trial==3)]\n", "df.session = df.session + 1" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pal = [sns.color_palette()[2], sns.color_palette()[2]]\n", "\n", "font = {'size' : 18}\n", "\n", "matplotlib.rc('font', **font)\n", "\n", "fig, ax = plt.subplots(figsize=(6,5))\n", "sns.lineplot(x='session', y='escape time', data=df, ax=ax, \n", " ci=None, linewidth=3, palette=pal, style='trial')\n", "plt.legend(['DLS lesion - trial 1','DLS lesion - trial 2'])\n", "\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "plt.ylabel('Escape time')\n", "plt.xlabel('Session')\n", "\n", "plt.xticks(range(1,12))\n", "plt.tight_layout()\n", "plt.savefig(op.join(RESULTS_FOLDER, 'figures', 'DLS_lesion_watermaze.pdf'))" ] }, { "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 }