{
 "metadata": {
  "name": "",
  "signature": "sha256:af72a1ab5f964d2b02e237b1cb5f5c3ae543cd32081cf52a95948b56063c6312"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Experimental design figure"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import itertools\n",
      "import numpy as np\n",
      "import seaborn as sns\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.set(style=\"white\")\n",
      "mpl.rc(\"savefig\", dpi=130)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(6.85, 2.25))\n",
      "ratio = 6.85 / 2.25\n",
      "\n",
      "# Parameters to control screen axes location\n",
      "left = .015\n",
      "top = .92\n",
      "w, h = .1625, .35\n",
      "w_step, h_step = .115, .07\n",
      "\n",
      "# Durations of the component trial parts\n",
      "durations = [\"4.0\", \"6.5\", \"1.5\", \"0.5\", \"1.0\", \"0.5\", \"2.0\", \"6.5\"]\n",
      "\n",
      "# Make an Axes for each \"screen\"\n",
      "axes = []\n",
      "poly_axes = []\n",
      "for i in range(8):\n",
      "\n",
      "    ax = fig.add_axes([left, top - h, w, h], axis_bgcolor=\"k\")\n",
      "    sns.despine(ax=ax, left=True, bottom=True)\n",
      "    ax.plot([0, 0], [0, 1], \"w\", zorder=3, aa=False)\n",
      "    ax.plot([0, 1], [1, 1], \"w\", zorder=3, aa=False)\n",
      "    ax.set(xticks=[], yticks=[])\n",
      "    axes.append(ax)\n",
      "\n",
      "    # Add separate square axes to facilitate drawing the polygons\n",
      "    if i in [3, 5]:\n",
      "        ax = fig.add_axes([left + (w - (h / ratio)) / 2, top - h, h / ratio, h])\n",
      "        ax.set_axis_off()\n",
      "        poly_axes.append(ax)\n",
      "\n",
      "    fig.text(left + w, top, durations[i] + \" s\", size=8, ha=\"right\", va=\"bottom\")\n",
      "        \n",
      "    left += w_step\n",
      "    top -= h_step\n",
      "\n",
      "# ============================================================================\n",
      "\n",
      "# Add in the cue instructions\n",
      "axes[0].text(.5, .5, \"COLORS\\nSAME\\n[y n]\", color=\"white\",\n",
      "             size=8, ha=\"center\", va=\"center\")\n",
      "cross_kws = dict(size=20, ha=\"center\", va=\"center\")\n",
      "\n",
      "# Add in the fixation crosses\n",
      "axes[1].text(.5, .48, \"+\", color=\"w\", **cross_kws)\n",
      "axes[2].text(.5, .48, \"+\", color=\"r\", **cross_kws)\n",
      "axes[4].text(.5, .48, \"+\", color=\"w\", **cross_kws)\n",
      "axes[6].text(.5, .48, \"+\", color=\"g\", **cross_kws)\n",
      "axes[7].text(.5, .48, \"+\", color=\"w\", **cross_kws)\n",
      "\n",
      "# ============================================================================\n",
      "# Draw the first stimulus polygon\n",
      "\n",
      "ax = poly_axes[0]\n",
      "poly = mpl.patches.CirclePolygon((.5, .5), .3, 7,\n",
      "                                 facecolor=\"#8FB46B\",\n",
      "                                 edgecolor=\"white\")\n",
      "\n",
      "kws = dict(linewidth=1, color=\"#112801\")\n",
      "ax.plot([.40, .60], [.75, .75], **kws)\n",
      "ax.plot([.32, .68], [.71, .71], **kws)\n",
      "\n",
      "ax.plot([.23, .77], [.52, .52], **kws)\n",
      "ax.plot([.22, .78], [.48, .48], **kws)\n",
      "\n",
      "ax.plot([.30, .70], [.32, .32], **kws)\n",
      "ax.plot([.33, .67], [.28, .28], **kws)\n",
      "\n",
      "verts = poly.get_verts()\n",
      "for (xa, ya), (xb, yb) in zip(verts[:-1], verts[1:]):\n",
      "    ax.plot([xa, xb], [ya, yb], \"white\", lw=1)\n",
      "ax.add_artist(poly)\n",
      "ax.set(xlim=(0, 1), ylim=(0, 1))\n",
      "\n",
      "# ============================================================================\n",
      "# Draw the second stimulus polygon\n",
      "\n",
      "ax = poly_axes[1]\n",
      "poly = mpl.patches.CirclePolygon((.5, .5), .3, 5,\n",
      "                                 facecolor=\"#6AF46B\",\n",
      "                                 edgecolor=\"white\")\n",
      "\n",
      "kws = dict(linewidth=1, color=\"#296006\")\n",
      "ax.plot([.39, .61], [.72, .72], **kws)\n",
      "\n",
      "ax.plot([.23, .77], [.60, .60], **kws)\n",
      "\n",
      "ax.plot([.25, .75], [.48, .48], **kws)\n",
      "\n",
      "ax.plot([.29, .71], [.36, .36], **kws)\n",
      "\n",
      "verts = poly.get_verts()\n",
      "for (xa, ya), (xb, yb) in zip(verts[:-1], verts[1:]):\n",
      "    ax.plot([xa, xb], [ya, yb], \"white\", lw=1)\n",
      "ax.add_artist(poly)\n",
      "ax.set(xlim=(0, 1), ylim=(0, 1))\n",
      "\n",
      "# ============================================================================\n",
      "# Draw an arrow to show the looping structure\n",
      "\n",
      "ax = fig.add_axes([0, 0, 1, 1])\n",
      "ax.set_axis_off()\n",
      "arrow = mpl.patches.FancyArrowPatch((.82, .07), (.3, .4),\n",
      "                                    color=\".1\",\n",
      "                                    linewidth=2,\n",
      "                                    arrowstyle=\"->,head_width=10,head_length=15\",\n",
      "                                    connectionstyle=\"arc3,rad=-.2\")\n",
      "ax.add_artist(arrow)\n",
      "ax.text(.56, .1, \"2x\", size=10, color=\".1\")\n",
      "\n",
      "# Save the figure\n",
      "fig.savefig(\"figures/figure_1.pdf\")\n",
      "fig.savefig(\"figures/figure_1.tiff\", dpi=300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cOZOcnNDJdGnFW7tIROjiERGJoLj9Jatk2L10A+lOapfQlAxHnsfjoX///tx7\n7720b98egG9XLeHJFybw6eKPynTujm26Mubq2zn37L4AbN68mfHjx/PKK6+QnZ19wueNh3aRiNPF\nIyISQXH7S1bJsHvpBtKd1C6hKRmOnISEBC6//HLuueceTj/9dAB+2fMDX//wHr/s+SGs71W3WiO6\nndaX5vXa4fF48Pl83H///bzwwgtkZmYe9/kqcrtIudHFIyISQXH7S1bJsHvpBtKd1C6hKRkOv+Tk\nZIYOHcq4ceNo0qQJAFt2ruXrH95jx/4tEX3vmifVo2vzP3B6w84keBLYs2cPDzzwAJMnTyYjI6PU\n56mI7SLlThdPCYwxycAjwBUEJvN/GbjVWpsVon4KkEHRif+7W2uXRjJWEXGnuP0lq2TYvXQD6U7x\n3i7+Y1c5plOBrWE4T2EVqW3S0tK45ppruOOusTRs0IicnBzenT+HSS8+zLofvi/XWE7xnsp1V/6V\nP/YbSmpKKvsP7OPRRx7jqaee4sCBA8c8viK1i0SNLp4SGGMeBgYBQ4EU4CVgprX2thD1WwMrCfw6\nzj/cY6+19sSfiRCRmBW3v2SVDLuXbiDdKd7bpazJsAM0RclwKCeddBLXXXcdd9xxB7Vq1cLv9zP7\n/dd45qXH+PEnG9XY6tVtwF+G3MSVl40iNSWNQ4cOMXHiRB5//HF27doV8riK0C4Sdbp4QjDGpAG7\ngaHW2reCZQOA6UBta22RZxuMMYOBB621Tcs1WBFxrbj9Jatk2L10A+lO8d4uzxBIaAvzANfmez25\nhHP8E9gXzqCCYrlt6tWrx0033cSYMWOoWrUq/hw/a7Z+xbIfPyb98J5oh1dApZST6ND0/+hw6jkk\nJ6WSmZnJ1KlTeeKJJ9i4cWOR+rHcLuIaunhCMMZ0BxYTSHz3BctqAnuAM621S4o5ZjzQ1Vrbt5Tv\n0RKYBHQh0JP8LjDGWpsenk8hItEWt79klQy7l24g3UntEpqeGT5xe/fupWbNmmT7s1j100K+3TSf\njMxjD0GOptSkSrQ7tRedmp1HanIlsrOzSU1NLbIcUyy3i7iGLp4QjDGXAq9aa9MKlR8BhlhrZxVz\nzCygPnAUaAGsBcZaa78J8R7LgSXABKAO8ArwgbX2lnB+FhGJnoRoByAiIvGpWrVq1KhRg0OZB5k6\n/598sW626xNhgMzswyzd+CFT59/FrgO/kJSURJs2baIdlki8qQwcKaY8E0gNccwZQA3gPqAfsAn4\n1BjTPEThM3nsAAAgAElEQVT9ZgSGYv9srV1G4Pnk58oStIi4i5JhERGJim7duuHxePhh+3ccySr9\nLM1ukeU/ytptgZGYZ511VpSjEYk7hyk+6U0FDoU4pj3Q0Vq7wFr7rbV2FIGEeGSI+uOAscAuY8zr\nQFtgQ9nCFhE3UTIsIiJR0aNHDwB8+zZFOZIT59sbiL1nz55RjkQk7vwCpBpjqucWGGNqEZhVeltx\nB1hrjxYzsdZ6oGGI+hOBJsCdBJLsF4G3yhy5iLiGkmEREYmKc845B4DtMZwM70r/Bb8/m969e0c7\nFJF4s5LAmsG98pX1DJatLFzZGFPFGLPXGPPHfGWJBHqL1xVTv4YxZhKAtXaKtXYQMAK4KLi+sYhU\nANGY60VEpELSTDell5CQQNeuXTmU+Rvph/dGO5wTluP42Zm+Fa+3KQ0aNGD79u3RDkkkLlhrDxtj\nngeeNMbsJ/Ar+AlgkrX2KAQSYKCqtXaHtTbDGPMB8KAxZiewE7iNwDPExS0EcAA4H2hsjLkDyAEG\nAyuttVmR/nwiUj7UMywiEgYOxS+9JMVr06YNlSpVwre36JJEseaXPT8Aem5YJArGAh8AbwOzCAxh\nvivf/tsAX77XowgsjzQdWAY0As7JXZopP2utAwwgMOx6EbCUQMI9MOyfQkSiJm47MrS0kntpORJ3\nUru4Vyy2zejRo3nmmWf4fM1MvtvyWbTDKZOmJ7fm4i7X8dhjj/H3v/89rzwW20VcRxePiEgEqWdY\nRETKXe6EU759m6McSdltD36GPn36RDkSEREROR5KhkVEpNz17t2bbH8Wu9K3RjuUMjuSlcGBQ3to\n3aY1aWlp0Q5HRERESiluJ9DyaNyam+nRS5EKrEGDBni9Xrbv20yOkxPtcMLilz2WVo3PpEuXLixc\nuDDa4cgx5A5hjwExE2g80T2kSMWhnmERESlXuRNN5U48VRHkrpWsSbRERERiR9z2DIuISHTkJoy+\nGF5fuLDcz9KrVy8mTJgQ5WiktNTBJyIS39QzLCIi5Sp3oqntFWDyrFz7fttJZtZhevToEe1QRERE\npJSUDIuISLlJS0ujdZvWHDi0myNZGdEOJ4wctu/bTPXq1THGRDsYERERKQUlwyIiUm66dOlCUmJS\nhXpeONe2vRsB1DssIiISI/TMsIiIlJvc54W37f0xypGEX+5zwz169GDatGlRjkZEJHqMMScDjwC/\nJ5BvfAbcbK3dVsIxI4G7gJOBBcB1JdUXCQclwyIiUm56n3s2AA/c/xg//mSjHE14paVWYuCnN9Dr\nnLOjHYqISLS9QWAE6gVANoHE+B1jTGdrrb9wZWNMP2AiMAJYCzwMzAG6llvEEpc0jaK4jhNcAFKz\nfLpL7rqcahf3iaW22btvD3ig3fmnRDuUiHh72ue0b9WJWrVqsXfvXiA22iXexNK/GXElXTglMIGJ\nE9YDLay1NljmBX4BOllrvyvmmAXAKmvtLcHXtYAdwLnWWi3eLhGjnmERESkXxhhq1qjFlp1r+du4\n4dEOJyKcyukAnHnmmVGOREQkan4FLgSKmxyiRuECY0wCgR7gp3LLrLV7jTFrgR5AkWTYGNMSmAR0\nATKBd4Ex1tr0cHwAiR+aQEtERMpF7sRSv+yteJNn5fLt/d9zwyIi8chae8Ba+4G11slX/FdgP7Ck\nmENqApUBX6HyHUCjEG/zCrABaAP0BboB48sSt8Qn9QyLiEi56HVuIEF84vFnWPrdoihHExn16n7I\nRe9eQ+8+PaMdioiIKxhjhgF/A6611h4qpkrl4NcjhcozgZQQp21GoDf4Z2vtFmPMIDR8XU6AeoZF\nRKRc9Ojek6zsLFatXR7tUCLm113b+cX3Ex3bd452KCIiUWeMGQ1MAx601k4NUe1w8GtqofJUoLjk\nGWAcMBbYZYx5HWhLoKdY5LioZ1hERCKuVq1anNbcsCv9F64fOzja4URUBrtplNYk2mGIiESVMeZf\nwD3AOGvtfSVU3UsgIW5QqNwLzC/uAGvtRGPMm0B/AsOkXwSGBl+LlJp6hkVEJOK6d+8OwNbdFWs5\npeL4KuAayiIix8MY8zcCifBfj5EIY63NAb4CeuU7vjbQAlhczLlrGGMmBY+dYq0dRGBJpouMMclh\n+xASF9QzLCIiEderV+Aex7dvU5Qjibx4+IwiIqEEl1Z6EJgCvG6MqZ9v915r7VFjTBWgqrV2R7D8\nSWCGMWYFsILAusTfWmuLm2DiAHA+0NgYcweQAwwGVlprsyLzqaSiUs+wiIhE3LBhwwA4cjQjypFE\n3qHMg3nr2IqIxKFBBDrcrgW2E5glOnfrG6xzG/lmj7bWvg3cCvyHQG/wkeB5igjOUj2AwORai4Cl\nBCbPGhj+jyIVnWZdE9dxgneRHo8uTzfJvblXu7hPLLTNQw89xG233Ub6oT28uvBBMrMPH/ugGJTg\nSeCPZ/6VBjWb5pW5uV3iVSz8mxFX04UjUkGoZ1hERCLu9ttv5/nnn6da5dpc2HEEFfVeslfLS2lQ\nsykLFiyIdigiIiJyDEqGRUSkXNx4440sW7aMJnXP4ExzUbTDCbuWjbrR/tTe/PLLL1x22WXRDkfk\nuNWrV49169bx3nvvkZISanlXEZGKo2L+aV5Ewk7D190rloZ8NmjQgNWrV1G7dh3mLXuOH39dFe2Q\nwuLk6qcw+Ky/kZ3lp2vXrqxatSqm2iXeqG2K6tu3L69Of5VaNWsBsGbNGi699FI2bCi/pVv1rL2U\nlUf/qOU4qWdYRETKzfbt2xkw4GL8fj992w+n5kn1oh1SmVVKOYmLO19LYkISw4cPZ9WqipHgR5Lf\n78/bJLpSUlJ47LHHeP/996lRowYfHf2YVdmraNWqFStWruDqq6+OdogiIhGjv56ISKmoZ9i9YrGX\n67rrruPZZ5/lQMZuXv3yQY5mH4l2SCckwZPAZd1vxlurOY8++ii33npr3r5YbJfykpsEO45DUlL5\nr/Kotgk47bTTmDlzJm3btiU9J53/Zr7AxpyNAPRI6sEfUy4j2ZPMm2++yciRI0lPT49oPGoXCQNd\nPHJcdMGISKkoGXavWL2BfOGFF7j66qvZvHMNc7+ZDMTeEMneLS+lQ9P/44svvqBPnz4FejpjtV3K\ng5Lh6LvyyiuZMmUKaWlprM5ezcuZr5BBwaXP6nvqMyp1JPUT67Nt2zYuu+wyvv7664jFpHaRMNDF\nI8dFF4yIlIqSYfeK1RvI1NRUFi9eTMeOHfnKvseSH96LdkjHpUXDLvRtfxU+n4+2bduyZ8+eAvtj\ntV3Kg5Lh6KlatSrPPfccf/rTn8h2spl5dCYLs78MWT+ZZC5JGUSv5F74/X7uvvtuJkyYQE5OTthj\ni+d2kbDRxSPHRReMiJSKkmH3iuUbyIYNG7Jq1Spq1arF299MYdPO1dEOqVTqVmvEn866Fb8/h27d\nurFixYoidWK5XSJNyXB0dOnShVmzZtG4cWN+9f/K1Mz/4nN8pTq2bWIbrky5kkoJlVi4cCF/+tOf\n8PlKd2xpxWu7SFjp4pHjogm0REQkarZt28bAgQPx+/1c0GE4Nau4f0KttOQqXNxlNImJSYwYMaLY\nRFjETTweD7fffjtfffUVjRs35susL3ngyIOlToQBVvlXc/+Rf/OjfxM9e/Zk7dq19OvXL4JRi4hE\nnv56IiKlop5h96oIvSk33HADkyZNYuv2zfzhirP4LeNgtEMqVmJiIq88OZceXc5h4sSJ3HLLLSHr\nVoR2iRT1DJef+vXr88a8V+nZuQ/ph/Zzz2tjWLBq3gmfL8GTwIjz/8Z1fceSlJjEf9+cxA3DbiUz\nM7PMscZTu0jE6OIpgTEmEbgXuBo4CfgUGG2t3R6ifgqQASQW2tXdWrs0krGWF/UMi4hI1D399NO8\n/PLLNG7QlMfGPefam+GxN4ynR5dzWPjV5wVmjpai8i+fVHjL5fF4SqzXuHHjKH6C2HfBBRewbt06\nenbuw/KNi7n8obPLlAgD5Dg5TP3oEUY+dRHb927lmj/eyHfffUeLFi3CFLWIRNB/gFHAVUAPoA4w\nvYT6hkC+2ASon29bHtkwy4877zZExHXUM+xeFaU3JS0tja+//pp27dqxeMM8lm78MNohFXC6txMX\ndLiaHTt20LZtW3bt2lVi/YrSLieqrGsIO45D06ZN2bp1a5giKnhuqLhtk5KSwoQJE7jlllvIcXJ4\nP+sDPsj6gBzCO+lVJSoxNHUI7ZPak5mZyQ033MB///vfEz5fRW8XKRe6eEIwxlQDfgWGWmtnBcs6\nADOBjtbaA8UcMxh40FrbtFyDLUflPzZJRESkGEeOHKF///6sWrWKM00/dqb/wpada6IdFgB1qjbk\n/LZDycrKol+/fsdMhAWmTJmSl9zk5/F4uPbaa/NeT548OeQ5fvvtt4jEVpEZY5g5cyZt2rThQM4B\n/pv5Aj/m/BiR9zrMYZ7PnMpZ/jO5POVypk6dygUXXMA111zDgQNF7qtFJLp6Br++nVtgrf0OaF7C\nMa2ADaV9A2NMS2AS0AXIBN4FxlhrI7tIeRnoryciUirqGXavitabcs455/DJJ59wJPMQFww7my1b\nI3MjX1o1qtfi49e+pl4dL8OHD+ell14q1XEVrV3CSc8MR8bw4cN5dvKzpKWmsSp7FS9nvsIhDpXL\ne9fz1OMvqaOon1gfn8/HZZddxldffXVc56io7SLlShdPCMaYm4EbgVuBcUA94DPgZmvt7hDHzCIw\nLPoo0AJYC4y11n4Tov5yYAkwgcAQ7FeAD6y1oSfYiDI9MywiIq7y2Wef8fe//50qlasy9eHXqVL5\npKjFkpiYyNP3v0i9Ol6efvrpUifCIuWpWrVqvP7660ybNo2klCRey3yNKZnPlVsiDPCr8ysPHHmQ\nz7I+x+v1snDhQu666y4SEnSrKeISVQkkwPcAtwGXA82AecaYUH9EOAOoAdwH9AM2AZ8aY0L1JjcD\ndgM/W2uXAYOA58L1ASJBfz0RkVJRz7B7VdTelFdffZUrrriCH3esYt630fm/9OwWF9O5+fl89dVX\n9OrVi+zs7FIfW1HbJRzUMxw+3bp1Y+bMmTRq1Ihf/b/yfObzbHd2RDWm1omtuSrlKionVGLRokUM\nHjyYbdu2HfO4itQuEjW6eEIwxvwDeIDA88ErgmXNgI3AmdbaJcUckwJ4rLWZ+cpWAe9aa+8opv7N\nwCNAOvAxMAeYaa0t2yQSEaQ/14mIiCuNHDmS1atX07x+W7o0/325v/9pDTrQufn57Ny5k4EDBx5X\nIiwSaR6Ph7Fjx7Jo0SIaNWrEF1kLeeDIg1FPhAG+93/P/UfuZ6N/Iz169GDt2rUMGDAg2mGJxLvc\nhcXX5hZYazcRGAJ9SnEHWGuP5k+Eg9YDDUPUn0hg5uk7gVTgReCtMkUdYUqGRUTElQ4fPkz//v05\ncOAAZ53enyZ1zyi3965d1csf2l2ZN2HWzp07y+29RY6lQYMGfPrppzzwwAMc9WQx5chzzDg6gyyy\noh1angPOAZ44MpG3j77NSVVPYu7cuTz99NOkpaVFOzSReLUo+LVrboEx5ndACoHhzwUYY6oYY/Ya\nY/6YrywRaA+sK6Z+DWPMJABr7RRr7SBgBHCRMSY5rJ8kjDSUQERKRcOk3auiDy3s06cPH3/8Mdn+\no7z65YMcOFTsPB9hk5pUiSG97qBapVqMGDGCadOmndB5Knq7lEX+ZZcSExPL/f1juW0uuugiXnnl\nFWrUqMEm/yb+m/kC+5390Q6rRE0TmnJN6jXUTKjB+vXrueSSS1i3rsi9dEy3i7iGLp4SGGNmAG2B\nkUAG8DSAtbZHcH8VoKq1dkfw9XSgG4GkdieBZ437Aadba/cVOreHQK/xeuAOIAd4EGhire0Q8Q93\ngnTBiEipKBl2r3i4gfzb3/7Go48+SnZ2NtnZke39SkxMJDk5hcmTJzN69OgTPk88tMuJ8vv9eT8f\nPTNcOikpKTz88MPcdNNN5Dg5vJv1Hh9mfYhD0eWr3CiNNIakDqFjUgcyj2Yy5sYxPP/88wXqxGK7\niOvo4imBMSYNeBj4M5AMvAfcaK3dE9x/D/Ava21C8HUVAs8ZX0pgIq1FwC3W2rVFzw7GmNOBJ4Du\nQCLwKXCTtfanCH6sMtEFIyKlomTYveLlBvKdd96hc+fO5OTkRPR9PB4PK1asoH///mV6Tjhe2iUW\nxVrbnH766cyaNYtWrVpxIOcAUzP/y6acIqMaY8KZSd0ZnDKYZE8yc+bMYcSIEezfH+jZjrV2EVfS\nxSPHRReMiJSKkmH30g2kO6ld3CuW2mbEiBE8/czTpKWmsSJ7Ba9kvsphDkc7rDKp56nHyNSReBMb\nsGPHDi677DIWLVoUU+0irqWLR46LLhgRKRUlw+6lG0h3Uru4Vyy0TUpKCkuWLKF9+/YcOXqIh2aP\nZfZX/y/aYYVNcmIKtwy4lyHnjCYnJ4fp06czdOhQwN3tIq6ni0eOiy4YEZEYpz9UuFMsJFzxKhba\nplevXnz++edkHP2NYY+cy6ZfN0Q7pIjo1aovT4yaDg4kJAQWOXFzu8Sj3H8vIifK4+J/1OU/a4WI\niIiIlGjhwoXs2bOH6rWq0+TmHjSgS7RDiohTE5qR4EngnXffoV+/ftEOR0TijNYZFhEREXEZx3GY\nPn06SZ4kWiW2jHY4EdM+KbDiyssvvxzlSORYPB6PNm0ntLmZkmERERERF5o5cyYAHZM6RjmSyOmc\n2ImsrCzee++9aIciInFIybCIiIiIC3355Zfs27eP1omtSaqAT7Y1SWhCtYRqfPTRR/z222/RDkdE\n4pCSYREREREXysnJ4Y033iDFk8IZiWdEO5ywa5/YHoDXX389ypGISLxSMiwiIiLiUnlDpRM7RDmS\n8OuS1Jns7GzmzZsX7VBEJE4pGRYRERFxqc8++4yDBw/SNrEtiSRGO5ywaZjQkJoJNfnss884cOBA\ntMMRkTilZFhERETEpbKzs5k9ezZpCWmYRBPtcMJGQ6RFxA2UDIuIiIi42JtvvglUrKHSXZI6k5OT\nw9y5c6MdiojEsYo3NaGISCl5vd5GwMtAfWALkEngj4QJgCff98W9Lq7sB+BOn8+3pRw/hohUcB9/\n/DGHDh2ifVp7XuN1csiJdkhlUs9Tj7oJdfnyyy/ZvXt3tMMRkTimZFhE4tk7QLvg9y3CcL7OwEnA\ngDCcS0QEgKNHj/LBl3O55Pd/5vALm1m2cWG0QyqTkb+/FS6C6dOnRzsUEYlzGiYtIvGsVgTOqd+r\nIhJ27yyYBcB57fpHOZKyO6/dAHJycpgzZ060QxGROOeJdgAiItHi9XpvAx4KsTsDSAdygpuT7/tQ\nry3wD5/PtzGykRfkOI4D4PHoV7qbBJtF7eJCsdg2lSpVYu++vWQnZzP28B04ONEO6YTU9tRmfOV7\nWbp0Kd26dSuwLxbbJR6oXSQMXHvxaJi0iMSzJ4HbgLrF7KsC/ASM9Pl8X5VrVCIihRw+fJgP3v+A\ngQMHcmrCqWzO2RztkE5Ih6TALNKvvfZalCMREdFwPhGJYz6fLxOYUkKVlsAir9f7lNfrrVpOYYmI\nFGvGjBkAdEyK3VmlOyd2AWD27NlRjkRERMmwiMhkwJ/v9W4gO99rD3AjsMbr9V5UnoGJiOT37rvv\nkpWVRafETtEO5YTU8NSgcWIjVq5cyc8//xztcERElAyLSHzz+XzbgPxdFHWA0cDSQlUbA+94vd7p\nXq/35PKKT0Qk18GDB/n444+pnlCdUxJOiXY4x619YmCItGaRFhG3UDIsIgJPFXrdDzgL+CtwqNC+\nPwPrvF7vlV6v17UTQohIxZQ7VDo3sYwlnZMCPdqzZs2KciQiIgFKhkVE4EtgZb7X/YHGPp/vCaAV\n8FGh+rWAl4BPvV5v6/IJUUQE3n77bfx+P12SOkc7lONSzVOVUxNOZf369fz444/RDkdEBFAyLCKC\nz+dzgEn5ihKA64P7tgB9gSuBvYUO7Q2s8Hq9T3i93urlEGqxPEEEnm/W5p5NJOz279/P559/Tq2E\nWng93miHU2rtEtvh8Xg0RFpEXEXJsIhIwHRgX77XI71eb2UIJMs+n+9l4IxgvfwSgZsB6/V6r/J6\nvfq9KiIR9frrrwP/W6YoFnTSEOmyGZdvE5Gw0U2biAjg8/kOAVPzFdUk8Hxw/jo7fT7fEOD3wIZC\npzgZeBFY6PV6Y3fdExFxvbfeeoucnBw6x8hQ6SpUoXlCczZt2sTatWujHU5sc6IdgEjFkhTtAERE\nXOQZ4Fb+N8R1jNfrfSE4jDqPz+f72Ov1tiXQIzwOqJJv91nAMq/XOxm42+fzFR5aLSJSJrt27WLJ\nyi85s0Mvfnnia37auTHaIZXo4m5DSbgiQUOkRcR11DMsIhIUfD54Xr6idkCPEHWP+ny+h4HTgdcK\n7c595niD1+sdqaHTIhJu8+bPBOC8dgOiHMmx5caoIdIi4jaa4ENEJB+v13se8HG+ojd8Pt/gUhzX\nm8AkXMXNLv0NcIPP5/smPFFKLHAcxwEIzG0mbhJsmphuG6/Xy7Zt2/Dl+Pj34f9EO5yQ0kjjocoT\n2O7bTuNGjUusWxHaJWJynxV2gPHl+9ZqFwkD11486q0QESloPrAu3+tLvV5vw2Md5PP5Pgc6ArcA\n6YV2dwGWeL3e571eb52wRSoiccvn87Fs2TK8CV5qe2pHO5yQ2iS2IdGTyGvTCw+gERGJPiXDIiL5\nFLPMUiJwXSmPzfL5fBMBQ2Ad4vw8wEgCs06P9nq9ieGIV0Ti12uvBRLM9onunVW6U1JHQEOkRcSd\nXNtlLSISLV6vtyqwDagaLPra5/OdeQLnOQt4GijuTnUFgaHTi084UHE1DZN2r4oy7LNJkyZs2bKF\nn/0/M+HIQ9EOp4hUUnio8kPs2bmHBg0a5P3cQ6ko7XLCwrFs0hPAgTCcJ5+4bxcJB9dePOoZFhEp\nxOfzHQQezVe05gTPsxjoDNwA7C+0uz2wyOv1vuj1euudUKAiEtd++uknVq9ezSmJp1DDUyPa4RTR\nMrEVSZ4kZsyYccxEWMJAP2KR46ZkWESkePcBQwgMkb75RE/i8/n8Pp/vGQJDp5+n6O3KVQSGTt/s\n9Xq13J2IHJfc5YraJbaLciRFdUwKLLk+c+bMKEcSI5aVsJWm3rfA0fIKVqRicG2XtYhIReT1ersS\neCa5SzG7vwduDE7GJTFOw6TdqyIN+zzttNOw1rLJv4lHjzwW7XDyJJPMQ5UnkLE/gzp16pCTk3PM\nYypSu4SdZpOW2Obai0c9wyIi5cjn8y0FugOjgD2FdrcGPvN6vdO9Xq+33IMTkZjzww8/YK2laUJT\nquZNcxB9ZyS2IMWTwhtvvFGqRFhEJBqUDIuIlDOfz5fj8/mmEhg6/QxQ+E7xz8AGr9d7m9frTSn3\nAEUkprz66qt4PB7aJbWNdih5OiQGZpF+8803oxyJiEhoru2yFhGJF16vtwOBodNnFbN7PXAr8F5w\n2SeJERom7V4Vbdhnq1at+P777/Hn+Dnqd8dDo2lJaWRlZVGlShWys7NLdUxFa5ew0jBpiW2uvXg0\nWYuISJT5fL7vvF7v2cAw4CEg/+zSLYB3gIVer/cfPp/vq2jEKCLutWbNGubPn0+7du3IysqKdjgA\npKWl8fnnn5c6ERYRiQbXZukiIvHI6/VWB+4BxgCJxVR5C7jT5/OtK8+45PipZ9i91NPlTmqXEuRf\ng/je8n1rtYuEgWsvHj0zLCLiIj6f74DP5/srgXWI5xdTZSDwvdfr/a/X621cvtGJiEjUOGgtYZEw\nc22WLiIS77xerwc4H3gQ6FBMlUzgSeBBn8+3tzxjk2NTz7B7qafLndQu7qR2kTBw7cXj2sBERCTA\n6/UmAJcD9wPNi6myH5gAPOnz+Q6VZ2wSmpJh99LNvTupXdxJ7SJh4NqLx7WBiYhIQcFllkYB/wJO\nLqaKj8DzxtN8Pp9mrYkyJcPupZt7d1K7uJPaRcLAtRePawMTEZHieb3ek4C/ArcBVYupsgG4C5it\n5ZhEitIfKtxJSZc7qV1C8/v9ed8nJhY356UEufbicW1gIiJSMq/XWxe4E7geSCmmylJgrM/n+7Rc\nAxNxOSXD7qSky53ULqHlJsOO45CUpBVrS+Dai8e1gYmISOl4vd5TgfHAUIr/vf4BcIfP51tRnnGJ\nuJWSYXdS0uVOapfQlAyXmmsvHtcGJiIix8fr9bYF/gNcFKLKdOBun8+3qfyiEnEfJcPupKTLndQu\noSkZLjXXXjyuDUxERE6M1+vtSWB26TOL2Z0FTAbu9/l8O8s1MBGXUDLsTkq63EntEpqS4VJz7cXj\n2sBEROTEBdcoHgA8AJxRTJXfgEeBR30+38HyjE0k2pQMu5OSLndSu4SmZLjUXHvxuDYwEREpu//f\n3r0Hy13Wdxx/kxAgCZdgudgvlwYQuUgJbbEz6ADCWGCkxWjbqVpEHXUQ0XbqVLQ1M53+UcqI4/SC\nXLRYa6XYWop0GBXbahWsMNw6gw5ElDBSviiXQBBIQkjSP549ns3J7mZz8tvfec7u+zWzs3tynj3n\nA/sE9rPP7/d7ImJ34ALgz4FDewx5grJ/8TWZubHNbNJcsQzXydJVJ1+X/izDQ6t28lQbTJLUnIhY\nDFxMufr0/j2GPAz8GfBP7lGscWcZrpOlq06T/rp0b580W8uXL+eRRx5pIM28Ve3kWTDXASRJo5eZ\n6zPzE8CRwGXA+hlDlgP/APwgIi6MiL1ajihJ0tiZ+jBBdaq2pUuSRicigrIS/G5gYY8hj1HOKb4m\nM59rM5s0aq4M12nSVyBrNemvy5VXXtmz0O62225ceOGFP//66quv7vszVq1axdNPPz2SfPNEtZOn\n2mCSpNGLiGMo5wz/Tp8ha4G/Bq7IzLWtBZNGyDJcp0kvXbXydenPc4aHVu3kqTaYJKk9EXEC8FHg\nrfQ+heY54Crgk5n5kzazSU2zDNfJ0lUnX5f+LMNDq3byVBtMktS+iDgKuAR4J7BHjyEbgWuByzPz\n4ZqPklgAAA4FSURBVPaSSc2xDNfJ0lUnX5f+LMNDq3byVBtMkjR3IuIQ4EPA+4AlPYZsBq4DLsvM\n+9vMJu0qy3CdLF118nXpzzI8tGonT7XBJElzLyIOAP4A+CCwrMeQrcC/AX+ZmXe3mU2aLctwnSxd\ndfJ16c8yPLRqJ0+1wSRJ9YiIfYGLKKvFB/UZdgvwF5l5a2vBpFmwDNfJ0lUnX5f+uvcgXriw18YM\n6qh28lQbTJJUn4hYTNmO6RLgsD7DbgMuBb6WmW6wqOpYhutk6aqTr0t/mzdv/vm/H1eGB6p28lQb\nTJJUr4jYA3gb8CfAK/sMu5dSim/MzM19xkitswzXydJVJ18XNaDayVNtMElS/SJiIfBm4E+Bk/oM\nWw1cBlyXmZvayib1Yxmuk6WrTr4uakC1k6faYJKk+SMidgPOAT4GvLbPsB8DHwc+m5nr28omzWQZ\nrpOlq06+LmpAtZOn2mCSpPmnU4pPpawUn91n2OPA3wKfycyftpVNUt38kKJOlmE1oNrJU20wSdL8\nFhEnU84pfnOfIZuAfwE+BdzuxbakyWYZrpNlWA2odvJUG0ySNB4i4njgo5QLbvXbe+Ie4Argix5C\nLU0my3CdLMNqQLWTp9pgkqTxEhFHAB8G3gEs6TNsLfB3wFWZ+XBL0SRVwDJcJ8uwGlDt5Kk2mCRp\nPEXEMuCdwMXAK/oM2wrcTFkt/s/M3NJOOklzxTJcJ8uwGlDt5Kk2mCRpvEXEAuAsSik+l/7/T3qQ\ncl7x5zJzXUvxJLXMMlwny7AaUO3kqTaYJGlyRMSRwEXAu4H9+wx7HvhH4FOZ+b22sklqh2W4TpZh\nNaDayVNtMEnS5ImIJcBbgA8CJw0Y+i3KIdQ3ZeamNrJJGi3LcJ0sw2pAtZOn2mCSpMnV2a/4FMoh\n1L8LLOozNIGrKXsW/6SleJJGwDJcJ8uwGlDt5Kk2mCRJABHxcuC9wPuA6DNsE/AlyrnF33XPYmn+\nsQzXyTKsBlQ7eaoNJklSt4hYBLwR+ABw+oCh91IOob7ePYul+cMyXCfLsBpQ7eSpNpgkSf1ExAmU\nQ6gvoP+exU8D1wKfzswH28omaXYsw3WyDKsB1U6eaoNJkrQjnT2L30EpxkcPGPpdypWo/zkz17aR\nTdLOsQzXyTKsBlQ7eaoNJknSsDp7Fr+ecgj1b9L//2+bgJuBzwNfycwX20koaUcsw3WyDKsB1U6e\naoNJkjQbEXEE5WJb7wFeNmDoWuCLlBXjO7zoljS3LMN1sgyrAdVOnmqDSZK0KyJiMXAe5bzis4GF\nA4Y/SCnFX8jMNS3EkzSDZbhOlmE1oNrJU20wSZKaEhEHA28F3g786g6G30o5jPpfM/OZUWeTVFiG\n62QZVgOqnTzVBpMkaRQi4lWUUnw+cMiAoRuBmygrxrdk5qYW4kkTyzJcJ8uwGlDt5Kk2mCRJoxQR\nC4HXUQ6j/m1g6YDhTwDXU1aM7/H8Yql5luE6WYbVgGonT7XBJElqS0QsBd5EWTF+PbBgwPD7KaX4\nusx8pIV40kSwDNfJMqwGVDt5qg0mSdJciIgA3kbZv/iEAUO3At+kHEZ9Q2b+rIV4ktQqP6RQA6qd\nPNUGkyRpLkXEbsAKymrx7wMHDxi+HrgR+ALwX+5fPHsRcTawinKhsy3A7cCqzLxjToNJE8oyrAZU\nO3mqDSZJUi0iYnfK4dMXACuBxQOG/wz4CvBl4KuZuW70CcdDRJxOWW2/D/gssAh4PxDAqZl55xzG\nkyaSZVgNqHbyVBtMkqQaRcS+lAtuvR04YwfDN1HK3U3Av2fm/4043rwWEfcCy4DjMnND588Oopyn\nfXdmnjWX+aRJZBlWA6qdPNUGkySpdhFxOOUQ6guAY4d4yl2UFeObgO97VeppEbE/8CTwicz8yIzv\n3Qj8RmbuPSfhpAlmGVYDqp081QaTJGm+6JxffBLlEOqVwIlDPO1HTBfj/8nMzaNLWL+IWAAcAbyQ\nmY/N+N6tlIuZvQX4KvClzPy9ru9/GngPcE5mfr291NL4swyrAdVOnmqDSZI0X0XEEcAbO7fTGLxV\nE5R9jG+mlOP/yMz1o004f0TEicD/Us6/Pjci/p5ype9zMvPrEXEW8DXgqsy8eC6zSuPIMqwGVDt5\nqg0mSdI4iIgDgHMpxfgcBl98C8qVqW+hFOObM/Op0SasV0TsDdxGWRU+IzNvjYhlwPeB54FTgHuA\nDcBJfoggNc8yrAZUO3mqDSZJ0riJiMWUq1KvBH4LOHAHT9kC3Eo5lPqmzHxotAnrERFLKKvlrwMu\nzcxVXd87j/JhwRrgMOC0zLx9LnJK484yrAZUO3mqDSZJ0jiLiIWUlc2p84yPGuJp91FK4JeBe8f1\nAlyd1d+bgdcA12bme3uMuQF4E3BlZn6g5YjSxLAMqwHVTp5qg0mSNCk6F+A6nnIo9Urg1UM87RHK\nfsbfAP47Mx8fXcL2dLZSugVYAVyTmRf1GLOU8sHAcuAh4MTMfKHNnNKksAyrAdVOnmqDSZI0qSLi\nEOA8Sjk+E1g0xNPuoxTjbwDfzsxnRpdwNCJiH+DblCL8ycz84z7j/ga4GPgwcDlwRWb+YWtBpQli\nGVYDqp081QaTJEkQEftRLry1EngDsO8QT9sC3M10Of5OZj4/spANiYjPA+cDf5WZH+oz5rWUwnxN\nZr4/Ij4DvAs4PTO/015aaTJYhtWAaidPtcEkSdK2ImIPygWlVlKuUH34kE/dBNzOdDm+IzM3jiLj\nbEXEcZSrRD8D/BEwc9/lrcANlG2W9gWOzcxnI+JlwGrgKWBFbf9c0nxnGVYDqp081QaTJEn9dc4z\nPhI4g3Io9ZnAwUM+fT1ly6KpcnxPZr40ipzDiogLgasopbfX+5OtwMeBS4DzM/P6rue+C7gWuDwz\nP9JCXGliWIbVgGonT7XBJEnS8Drl+Dimi/EZwLIhn/4s8C2my/H3MnPLKHJKml8sw2pAtZOn2mCS\nJGn2Ols3rWC6HJ8GLB3y6U8C32S6HD84rts4SRrMMqwGVDt5qg0mSZKaExGLKFs2TZXj1wB7Dvn0\nRyml+DbgTsrK8aZR5JRUF8uwGlDt5Kk2mCRJGp2IWAycwnQ5/nVg4ZBP30i5kNWdndtdwOrMnHnR\nK0nznGVYDah28lQbTJIktaezx++pTJfjk9i59wnPUbZzuovpkrzGw6ul+c0yrAZUO3mqDSZJkuZO\nRPwCcDrT5fi4WfyYtUyX47uAOzPz0cZCSho5y7AaUO3kqTaYJEmqR0QcBJxMOe946n7YrZy6Pca2\nq8d3ZeaTTeWUJGlYlmFJkrTTOls5Hcq25fhkht/OqdvDdK0eA3dn5rPNJJU0jM7f6cOA5cAPMzPn\nNpE0epZhSZLUiM6b6aMoxXiqHP8asGQWP2415RzkB7puD2bmhmbSSpMpInan/D09nnL6w9TtWKa3\nX1sPnJmZt89JSKkllmFJkjQynf2Oj2PbFeQVwB6z+HFbKavID1DK8gNdj3/qxbqkaZ0rxh/D9qX3\naGDRED/i0sz82OgSSnPPMixJkloVEXsAv8y2K8ivYvitnXpZR++S/MPMfHGXAksV6hyJcSBwRNdt\neef+FZ3Hs32vvxE4PTPv2OWgUsUsw5Ikac5FxBLgV5hePX415Q39gl380ZuBh9i+JD/ghbtUu4hY\nxvZFt/vr2ZyCMNNWYA1wf9ftFq/8rklgGZYkSVWKiD0phfjYzu2Yrsf7NPArnmL7krwGeBR42sOu\nNWoRsZTpkjt13112Z3NBun42AT9g29J7P7A6M9c3+HukecMyLEmS5pXO4aEvp3dJPpxm3t+sB5JS\njGfepv48PQRbg3ROCfglti+6U2X3oBH82icpH+o8wLal90eZ+dIIfp80b1mGJUnS2Ogcbn0025fk\nY2jmkNKZnqB3Ye6+uco8JjofxOxHOVf3QEqZ7fV46utfpPn3289Syu4aygXl1nR/nZnPNfz7pLFl\nGZYkSWMvIhYAh9C7JB864l+/gf6rzI9SDtdeRyk5L1ic27MT5bb78TBXYt4VG+hddKf+zA9XpIZY\nhiVJ0kSLiH2AV3Zuh1FKc3Tupx7v3lKczZRSvG5X7ifh8O3OfrlLOrfFAx7vDRxA/xXcUZfbmV4C\nfkzvorsGtwmTWmMZliRJGqCzqnwg0+W4363Jix3tqg0MLszrKcV7S9dt804+3tlxW4E92XF5HfS4\n+8/a+oBiZ7xEOWf3caYPoZ+5yvtoZm6eq4CSplmGJUmSGtA5X3nmqvLMW5urzNp1L1FK7dTt8R08\nfsZVXWn+sAxLkiS1pMcqc1BWlPelnLva734/2j+cdxxNldvuIjuo5FpupTFmGZYkSZoHImIvdlya\nh7mv/f3fi8ALlEO5u+/7PR7m+09RSu46y62kKbX/x1CSJEkN6axMLwX2AhZ0bgv7PB7F9zaygwLr\n+bSSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSpC7/D0AaRT3pFZQiAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109451790>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}