2_raw-results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# aSPEM Experiment : defining a protocol\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WELCOME\n",
      "\n",
      "2020-03-02T15:57:43+01:00\n",
      "\n",
      "CPython 3.5.2\n",
      "IPython 5.3.0\n",
      "\n",
      "numpy 1.16.3\n",
      "matplotlib 3.0.3\n",
      "torch 1.1.0\n",
      "\n",
      "compiler   : GCC 5.4.0 20160609\n",
      "system     : Linux\n",
      "release    : 4.4.0-83-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 2\n",
      "interpreter: 64bit\n",
      "host name  : INV-OPE-HP01\n",
      "Git hash   : f6b7949acbee171bdffab290b62f0a7fc5595c32\n",
      "Git repo   : https://github.com/chloepasturel/AnticipatorySPEM.git\n",
      "Git branch : master\n"
     ]
    }
   ],
   "source": [
    "%run 0_parameters.ipynb"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%load_ext version_information\n",
    "%version_information numpy, matplotlib"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('figures/Experiment'):\n",
    "    os.makedirs('figures/Experiment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T14:51:29.113058Z",
     "start_time": "2018-07-03T14:51:23.783700Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "msfigpath = '../PasturelMontagniniPerrinet2019/figures'\n",
    "#exts = ['.svg'] #'.png', '.svg', '.pdf']\n",
    "from  aSPEM import Analysis\n",
    "e = Analysis()\n",
    "\n",
    "from aSPEM import binomial_motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_block = 0\n",
    "\n",
    "color=[['k', 'k'], [color_true_p, color_true_p], ['k','none']]\n",
    "alpha = [[.35, .15],[.35,.15], [0,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T10:53:24.678229Z",
     "start_time": "2018-07-03T10:53:24.671271Z"
    }
   },
   "outputs": [],
   "source": [
    "figname = '1_A_Experiment_randomblock'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T14:51:56.474416Z",
     "start_time": "2018-07-03T14:51:38.013681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_trials = 200\n",
    "trials, p = binomial_motion(N_trials=N_trials, N_blocks=3, tau=40, seed=51, Jeffreys=True, N_layer=3)\n",
    "\n",
    "t_label_f = t_label/3\n",
    "fig_width_f = fig_width/6\n",
    "\n",
    "    \n",
    "fig, axs = plt.subplots(3, 1, figsize=(fig_width_f, fig_width_f))\n",
    "fig, axs = e.plot_experiment_old(num_block=[num_block], p=p, fig=fig, axs=axs[::-1], color=color, alpha=alpha,\n",
    "                             t_label=t_label_f,\n",
    "                             titre=None, mode='planter les choux', pause=0, TD=True, ec = 0.15)\n",
    "\n",
    "\n",
    "\n",
    "#axs[0].fill_between(range(N_trials), p[:, 0, 0], lw=.5, alpha=alpha[0][0], facecolor=color[0][0], step='pre')\n",
    "\n",
    "mask = np.ones(N_trials)\n",
    "mask[p[:, num_block, -1]==0] *= np.nan\n",
    "#axs[-1].plot(np.arange(N_trials)-.5, mask-.02, '^')\n",
    "#axs[-1].cla()\n",
    "axs[-1].plot(np.arange(N_trials)-1., mask, 'r*')\n",
    "\n",
    "axs[-1].vlines(np.arange(N_trials)-0.5, 0, p[:, num_block, -1] + (mask-1))\n",
    "\n",
    "\n",
    "mask = np.zeros(N_trials)\n",
    "mask[p[:, num_block, -1]==1] *= np.nan\n",
    "axs[-1].plot(np.arange(N_trials)-1., mask, 'r.', ms=1)\n",
    "\n",
    "#axs[1].vlines(np.arange(N_trials)-.5, 0, p[:, num_block, 1], lw=.1, color='b')\n",
    "#axs[1].vlines(np.arange(N_trials)-.5, 1, p[:, num_block, 1], lw=.1, color='b')\n",
    "axs[1].plot(np.arange(N_trials)-.5, p[:, num_block, 1], 'b.', ms=1)\n",
    "axs[0].plot(np.arange(N_trials)-.5, p[:, num_block, 0], 'k.', ms=2)\n",
    "\n",
    "\n",
    "axs[0].set_xticks(axs[-1].get_xticks()+.5)\n",
    "axs[0].set_xticklabels(axs[-1].get_xticklabels())\n",
    "\n",
    "axs[-1].set_xticks(axs[1].get_xticks())\n",
    "axs[-1].set_xticklabels(axs[1].get_xticklabels())\n",
    "axs[-1].set_xlabel('')\n",
    "\n",
    "#axs[0].set_xlabel(axs[-1].get_xlabel())\n",
    "axs[0].set_xlabel('Trial #', fontsize=t_label_f/1.2)\n",
    "axs[1].set_ylabel('Pr(TD is right)', fontsize=t_label_f/1.2)\n",
    "axs[0].tick_params('y', right=True, labelright=True, left=False, labelleft=False)\n",
    "offset = .06\n",
    "\n",
    "for ax in axs: \n",
    "    #ax.set_xlim(0, 100)\n",
    "    ax.set_ylim(-offset, 1+offset)\n",
    "    ax.tick_params(labelsize=t_label_f/1.8)\n",
    "    #ax.axis('off')\n",
    "    for card in ['left', 'top', 'bottom']:\n",
    "        ax.spines[card].set_visible(False)\n",
    "        ax.spines[card].set_bounds(0, 1)\n",
    "    ax.spines['right'].set_bounds(0, 1)\n",
    "    ax.spines['right'].set_position(('axes', 1.01))\n",
    "#axs[-1].set_ylim(0, 1.1)\n",
    "for ext in exts: fig.savefig('{msfigpath}/{figname}'.format(msfigpath=msfigpath, figname=figname) + ext, dpi=400, bbox_inches='tight', pad_inches=0)\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "63px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
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}