data-science-ipython-notebooks/matplotlib/matplotlib.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Setting Global Parameters\n",
      "* Bar Plots, Histograms, subplot2grid\n",
      "* Normalized Plots\n",
      "* Scatter Plots, subplots\n",
      "* Kernel Density Estimation Plots\n",
      "* Basic Plots\n",
      "* Histograms\n",
      "* Two Histograms on the Same Plot\n",
      "* Scatter Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as plt\n",
      "import seaborn\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "seaborn.set()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare data to plot:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train = pd.read_csv('../data/titanic/train.csv')\n",
      "\n",
      "def clean_data(df):\n",
      "    \n",
      "    # Get the unique values of Sex\n",
      "    sexes = sort(df['Sex'].unique())\n",
      "    \n",
      "    # Generate a mapping of Sex from a string to a number representation    \n",
      "    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
      "\n",
      "    # Transform Sex from a string to a number representation\n",
      "    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
      "    \n",
      "    # Get the unique values of Embarked\n",
      "    embarked_locs = sort(df['Embarked'].unique())\n",
      "\n",
      "    # Generate a mapping of Embarked from a string to a number representation        \n",
      "    embarked_locs_mapping = dict(zip(embarked_locs, \n",
      "                                     range(0, len(embarked_locs) + 1)))\n",
      "    \n",
      "    # Transform Embarked from a string to dummy variables\n",
      "    df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\n",
      "    \n",
      "    # Fill in missing values of Embarked\n",
      "    # Since the vast majority of passengers embarked in 'S': 3, \n",
      "    # we assign the missing values in Embarked to 'S':\n",
      "    if len(df[df['Embarked'].isnull()] > 0):\n",
      "        df.replace({'Embarked_Val' : \n",
      "                       { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
      "                       }\n",
      "                   }, \n",
      "                   inplace=True)\n",
      "    \n",
      "    # Fill in missing values of Fare with the average Fare\n",
      "    if len(df[df['Fare'].isnull()] > 0):\n",
      "        avg_fare = df['Fare'].mean()\n",
      "        df.replace({ None: avg_fare }, inplace=True)\n",
      "    \n",
      "    # To keep Age in tact, make a copy of it called AgeFill \n",
      "    # that we will use to fill in the missing ages:\n",
      "    df['AgeFill'] = df['Age']\n",
      "\n",
      "    # Determine the Age typical for each passenger class by Sex_Val.  \n",
      "    # We'll use the median instead of the mean because the Age \n",
      "    # histogram seems to be right skewed.\n",
      "    df['AgeFill'] = df['AgeFill'] \\\n",
      "                        .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
      "                        .apply(lambda x: x.fillna(x.median()))\n",
      "            \n",
      "    # Define a new feature FamilySize that is the sum of \n",
      "    # Parch (number of parents or children on board) and \n",
      "    # SibSp (number of siblings or spouses):\n",
      "    df['FamilySize'] = df['SibSp'] + df['Parch']\n",
      "    \n",
      "    return df\n",
      "\n",
      "df_train = clean_data(df_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Setting Global Parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set the global default size of matplotlib figures\n",
      "plt.rc('figure', figsize=(10, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bar Plots, Histograms, subplot2grid"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Size of matplotlib figures that contain subplots\n",
      "figsize_with_subplots = (10, 10)\n",
      "\n",
      "# Set up a grid of plots\n",
      "fig = plt.figure(figsize=figsize_with_subplots) \n",
      "fig_dims = (3, 2)\n",
      "\n",
      "# Plot death and survival counts\n",
      "plt.subplot2grid(fig_dims, (0, 0))\n",
      "df_train['Survived'].value_counts().plot(kind='bar', \n",
      "                                         title='Death and Survival Counts',\n",
      "                                         color='r',\n",
      "                                         align='center')\n",
      "\n",
      "# Plot Pclass counts\n",
      "plt.subplot2grid(fig_dims, (0, 1))\n",
      "df_train['Pclass'].value_counts().plot(kind='bar', \n",
      "                                       title='Passenger Class Counts')\n",
      "\n",
      "# Plot Sex counts\n",
      "plt.subplot2grid(fig_dims, (1, 0))\n",
      "df_train['Sex'].value_counts().plot(kind='bar', \n",
      "                                    title='Gender Counts')\n",
      "plt.xticks(rotation=0)\n",
      "\n",
      "# Plot Embarked counts\n",
      "plt.subplot2grid(fig_dims, (1, 1))\n",
      "df_train['Embarked'].value_counts().plot(kind='bar', \n",
      "                                         title='Ports of Embarkation Counts')\n",
      "\n",
      "# Plot the Age histogram\n",
      "plt.subplot2grid(fig_dims, (2, 0))\n",
      "df_train['Age'].hist()\n",
      "plt.title('Age Histogram')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0x10bf0fed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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RnqlbgNsAMvPWiPgDsFfD6/OBu4A1QF/D9j5gcKxCFy6cy4wZ07e6wq0wODivI8edLBYt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\nkiRV8P8AUrk88f84hHIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bbc2b50>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Embarked and its maximum\n",
      "family_sizes = sort(df_train['FamilySize'].unique())\n",
      "family_size_max = max(family_sizes)\n",
      "\n",
      "df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
      "df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
      "plt.hist([df1, df2], \n",
      "         bins=family_size_max + 1, \n",
      "         range=(0, family_size_max), \n",
      "         stacked=True)\n",
      "plt.legend(('Died', 'Survived'), loc='best')\n",
      "plt.title('Survivors by Family Size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x10c679150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1FZxS7Sq0I4bKz1d7ezuLFy/auQXtoGOPPc7hzho1XH6+cvdgvQeoj4hjUkp/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bbbb090>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Normalized Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\n",
      "\n",
      "# Normalize the cross tab to sum to 1:\n",
      "pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
      "\n",
      "pclass_xt_pct.plot(kind='bar', \n",
      "                   stacked=True, \n",
      "                   title='Survival Rate by Passenger Classes')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "\n",
      "# Plot survival rate by Sex\n",
      "females_df = df_train[df_train['Sex'] == 'female']\n",
      "females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\n",
      "females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
      "females_xt_pct.plot(kind='bar', \n",
      "                    stacked=True, \n",
      "                    title='Female Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "\n",
      "# Plot survival rate by Pclass\n",
      "males_df = df_train[df_train['Sex'] == 'male']\n",
      "males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\n",
      "males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
      "males_xt_pct.plot(kind='bar', \n",
      "                  stacked=True, \n",
      "                  title='Male Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10ca22f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VMnxJkiRVyPAlSZJUIcOXJElShQxfkiRJFTJ8SZIkVcjwJUmSVCHDlyRJUoUM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\nJEmSKvT/AdrOw+EZX3rtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8a9f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JkqQKGb4kSZIqZPiSJEmqkOFLkiSpQoYvSZKkChm+JEmSKmT4kiRJqpDhS5Ik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\nn6BBhs62AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c928450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c9c2690>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Scatter Plots, subplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig, axes = plt.subplots(2, 1, figsize=figsize_with_subplots)\n",
      "\n",
      "# Histogram of AgeFill segmented by Survived\n",
      "df1 = df_train[df_train['Survived'] == 0]['Age']\n",
      "df2 = df_train[df_train['Survived'] == 1]['Age']\n",
      "max_age = max(df_train['AgeFill'])\n",
      "\n",
      "axes[1].hist([df1, df2], \n",
      "             bins=max_age / 10, \n",
      "             range=(1, max_age), \n",
      "             stacked=True)\n",
      "axes[1].legend(('Died', 'Survived'), loc='best')\n",
      "axes[1].set_title('Survivors by Age Groups Histogram')\n",
      "axes[1].set_xlabel('Age')\n",
      "axes[1].set_ylabel('Count')\n",
      "\n",
      "# Scatter plot Survived and AgeFill\n",
      "axes[0].scatter(df_train['Survived'], df_train['AgeFill'])\n",
      "axes[0].set_title('Survivors by Age Plot')\n",
      "axes[0].set_xlabel('Survived')\n",
      "axes[0].set_ylabel('Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10bfbe890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xqn29qhlJkoqrvn4R2dSY3fPbrXlMlVDxAi0iFkXE6ymlJrJi7ewuebwODKt0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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8a98d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Kernel Density Estimation Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Pclass:\n",
      "passenger_classes = sort(df_train['Pclass'].unique())\n",
      "\n",
      "for pclass in passenger_classes:\n",
      "    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\n",
      "plt.title('Age Density Plot by Passenger Class')\n",
      "plt.xlabel('Age')\n",
      "plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x10d0a7750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2FBUR6S0FbCJJrrm8EldKCu7MzISU780NB2xa2kNEpPcUsIkkueaKCnz58V+D\nLap1pqgCNhGRXlPAJpLEgnv2EKivT9iEAwBvXjRgU5eoiEhvKWATSWKByIK2iViDLcqXp90ORET6\nSgGbSBKLro+WqAkHAJ7sbPB41CUqItIHCthEklg0w5aIbamiXG43vtw8/JolKiLSawrYRJJYf2TY\nALx5eQR31+IEAgl9jojIQKWATSSJRTdl9xUkLsMGkV0UHIdATU1CnyMiMlApYBNJYoGqKlxeL57s\nnIQ+RxMPRET6RgGbSBLzV1WSWlCAy53YHwWfLe2hgE1EpDcUsIkkqZC/heDu3aQWJWYP0bZ8eZHd\nDqoUsImI9IYCNpEkFd3bsz8CNi2eKyLSNwrYRJJUdMJBamF/BGzR/UQVsImI9IYCNpEkFQ3YBvVD\nhs2TloY7PV1j2EREekkBm0iSCkTGk/VHhg3Am5uHv6oKx3H65XkiIgOJAjaRJNXaJdoPGTYIL87r\nNDcR2tPYL88TERlIFLCJJKlAVRW4XKQkeJeDqNaJB9qiSkSkxxSwiSQpf1Ul3txc3F5vvzyvdWmP\nGo1jExHpKQVsIknICQQI1NTgS+Cm7+1585VhExHpLQVsIkkosKsGHKe1m7I/eHOjS3sowyYi0lMJ\n6wsxxriB3wEzgWbgEmvthjbnzwRuBgLAn6y1f2xzrghYApxkrV2bqDqKJCt/Zf9s+t6WL1/bU4mI\n9FYiM2xnAynW2qOBHwK/iJ4wxviAXwKnAMcDl0WCtOi5PwANCaybSFKLbhHl7acJBwDenMHgcmm3\nAxGRXkhkwHYMsBDAWrsYOKLNuSnAemttrbXWDywC5kXO3Q3cD+xMYN1Eklo0y9WfY9hcXi/ewbnq\nEhUR6YVEBmzZwO42nwcj3aTRc7VtztUBOcaYbwIV1tpXI8ddCayfSNKKrsHm68cMG4S3qArU1OCE\nQv36XBGRg10i5/PvBrLafO621kZ/Ste2O5cF7AKuBRxjzMnALOARY8wXrLVlXT2osDCrq9NJS+3S\nObULlO3eBUDxpNFA/7VJ9dAhNG1YT44nQGpB/waLvaHvlc6pXTqndulIbRI/iQzY3gXOBJ4wxswB\nVrQ5twaYaIzJJTxWbR5wt7X2X9ELjDFvApd3F6wBVFTUxbXiA0FhYZbapRNql7DGnWV4srOp3t1C\nYWFqv7VJMCMbgLJ1W0hzUvrlmb2l75XOqV06p3bpSG3Sud4GsYkM2J4GTjHGvBv5/CJjzFeBTGvt\ng8aY64AdUsHYAAAgAElEQVRXCHfLPmSt1Zg1kX7ghEIEaqpJHTmq35/duhabJh6IiPRIwgI2a60D\nXNHu8No2518AXuji/hMSVDWRpBbcXYsTCPTrDNEoX2TdN008EBHpGS2cK5JkWtdg2w8BmzeyPZXW\nYhMR6RkFbCJJJroGW38u6RH1WYZNXaIiIj2hgE0kyQQiS3p490PA5s7IwJWSQqBKGTYRkZ5QwCaS\nZD7LsPV/l6jL5cKXl4+/Rhk2EZGeUMAmkmT8+zHDFn5uPqH6ekLNzfvl+SIiByMFbCJJJlBVhTs9\nHU9a2n55vjdXEw9ERHpKAZtIEnEcB39V5X6ZcBAV7YrVxAMRkdgpYBNJIqH6epyWlv2yBltU69Ie\nmnggIhIzBWwiSWR/LukR1bq0hyYeiIjETAGbSBLxV1UA+2eGaJQ3ErApwyYiEjsFbCJJJBok7a8Z\nogDevFxA21OJiPSEAjaRJHIgdIm6fSl4srK1AbyISA8oYBNJItE12PZnl6jjOIQGZ9FSVcnCTf/m\nw9Kl1Lc07Lf6iIgcDLz7uwIi0n8CVZW4UlNxZ2bul+dv3r2Vx+2zTKeSCcEgr69+mT2D3HhdHo4b\nPpczxi1gkDd1v9RNRORApoBNJIn4q6rw5efjcrn6/dlvblvEU+tfIOSEOCK/ELaVcPHwz7N9sItF\n29/nzZJFfFptuXrWJeQNyu33+omIHMjUJSqSJIKNjYQaG/Hm9f/4tVc3v8mT654jw5fONbMu5dBJ\nxwIwMpDFKaPn8+M51zN/xDGUNVbwyyX3U7lHExJERNpSwCaSJKIzRH0F/RuwvbfjA57d+DK5qYO5\n4fCrmZw38bOlPSIzRX1uL+dP+gJnjvscNc27+P2Kh2kKNPVrPUVEDmQK2ESSxP6YcLB1dwn/XPsM\nGd50rj30MvLTwrscfBaw7T1T9HNjTuT4Ecews6GMv61+Asdx+q2uIiIHMgVsIkkiuu5Zf21L1Rxs\n4aFVfyMYCvKNaV+lKP2zzJ4vP2+vOrV17oQzGJ8zlqUVK/mobFm/1FVE5ECngE0kSQQqoxm2/ukS\nfWHjK1Q2VXPSqHlMyzd7nfNkZYPH0+labB63h69P/RIpbh9PrH2Wupb6fqmviMiBTAGbSJL4rEs0\n8QHb1t0lvLltEYVp+Xx+7Kkdzrvcbnx5efvc7aAgLZ+zxp9GQ6CR5zcuTHR1RUQOeArYRJKEv6oK\nl9eLJzs7oc9xHId/rX8eB4evmHNI8fg6vc6bl0+wtpaQ39/p+XnD51KcMYT3dnzI9vqdiayyiMgB\nTwGbSJIIVFXizc/H5U7sP/uVlZ+yftcmpudPYXLexH1e54tOPNhV0+l5j9vDORPOwMHh6fUvJqSu\nIiIHCwVsIkkg1NxMsK4OX4LXYAuGgjyz4WVcuDh7wuldXuuNTDyILjfSmWn5hkmDx7O6ei2bd2+N\na11FRA4mCthEkkCgn2aIflC2lLLGco4edhRDM4Z0ea03t/OlPdo7bexJACzc/EZ8KikichBSwCaS\nBFonHCRw0dyQE+K1LW/idrk5bcxJ3V7f1dIebU0cPJ5xOaNZWfkpJXU74lJXEZGDTbcBmzHm+8aY\n4v6ojIgkhj+6y0ECM2zLKlZR1ljB7OLDyR00uNvr97V4bnsul4vPRQLAV7e82feKiogchGLZ/D0N\neMsYswH4M/CMtbbzaV0ickCKjhPzJmhJD8dxeHXzG7hwccro+THd48uLLcMGMDXPMDxzKEsrVrKr\nuZbBqTl9qa6IyEGn2wybtfZWYDJwJ3ACsNwYc58xZlaiKyci8eFP8KK5tmY92+p3cGjRDIakF8Z0\nj3tQGu709NbxdV1xuVwcP/xoQk6Id7a/39fqiogcdGIdw5YGjAXGAyGgGrjHGPO/iaqYiMSPv6oS\n3G68g7vvquyNt0reA+CkUfN6dJ83Lx9/VXVMe4YeWXwoad403t2+GH8o0Kt6iogcrGIZw/YosBGY\nD9xmrZ1urb0FOBW4LLHVE5F4CFRX4c3NxeXxxL3sqj3VrKz8lNFZIxmTPapH9/ry8nCamwg1NnZ7\nbYonhaOHHkmdv56l5St6W10RkYNSLBm2fwMTrLUXW2sXARhjUqy1zcC0hNZORPrMCQQI7NqVsO7Q\nd7a/j4PD8SOO7vG90WVGorNYuzNvxNG4cKlbVESSTiwB26XW2tbdl40xHmAJgLVW+8WIHOD81dXg\nOAkJ2FqCft7b+QGZvgwOK5rZ4/t9BeHxbv6KipiuL0jLw+ROYGPtZsoaY7tHRGQg2OcsUWPMm8Dx\nkY9DbU4FgWcTXC8RiZNAJHuViEVzl5Qvp8HfyKmjT8C3jz1Du+IrLALAXxl78DV36BGsqVnH+zs/\n4gvjT+vxM0VEDkb7DNistScAGGPusdZ+u/+qJCLx1LoGWwIWzX13+2JcuDh22Jxe3Z8SDdhizLAB\nzCycTpp3EIt3LuHMcQtwu7T+t4gMfF1l2M6w1r4AfGyM+Xr789bavyS0ZiISF627HMS5S7S0oZxN\nu7cwJW8S+Wm5vSrDVxiuk7+iPOZ7Ujw+Dh8yi0Xb32d19Vqm5U/u1bNFRA4mXf1qemTk7xP28UdE\nDgKfdYnGN2B7f+dHAMwZekSvy3APSsOTld2jDBuEu0UB/hOpg4jIQNdVl+hPIn9/M3rMGJMDjLTW\nrkp81UQkHvxVVeByte4sEA/BUJDFpUtI86ZxSEHfJov7Cgtp2rIZJxTC5Y6te3N01kiGZgxhZcUn\nNPgbyfCld3tPKOTw8doK3v+0jC2lu2kJhMjPHsT0cfnMnzWMvOxBfXoPEZFE6nZrKmPMJcDRwA+B\nj4F6Y8y/rLU3dXOfG/gdMBNoBi6x1m5oc/5M4GYgAPzJWvvHyAzUB4FJgAP8t7X2k169mYgA4S5R\nT04OLm8sO9HFZnX1Wna31HHc8Lm9mmzQlq+wkKaNGwhUV7XOGu2Oy+XiqOLDeHbDyyyrWMkxw2Z3\nef3Wsjr+9OJqtpaHJ7znZKSQPshHSUU9m0vreOWDrZwxdzSfnzsGt9vVp/cREUmEWH6CXwmcDPwX\n4dmh3wYWA10GbMDZQIq19mhjzGzgF5FjGGN8wC+BI4BG4F1jzHOEA8OQtfZYY8zxwO3Re0Sk55xQ\niEBNDYPGjI1rudGuyLl96A6N8rWZeBBrwAZweNEsnt3wMh+VLe8yYFtiK3jw+U9oCYQ4enoxp80Z\nzfCCDACaW4IsXl3Gs4s28fQ7m1hXUstV58wg1Rf/BYZFRPoipv4Ha201cDrwkrU2AMTSd3AMsDBy\n/2LCwVnUFGC9tbY2spH8ImCetfYZ4PLINWOAmljqJyKdC+yqgWAwrhMO6lsaWFn5KUMzhjAqa0Sf\ny/MVhoO0lh5MPADIT8tlXM5o1tVsoLZ5d6fXvLtyJ797eiUul4trz53JJWdMbQ3WAFJTPMw7ZBi3\nXnwUM8bls2pTNb96fDl7mrX1lYgcWGIJ2D4xxrxAeB/R14wxjwMfxnBfNtD2p2gw0k0aPVfb5lwd\nkANgrQ0aYx4G7gX+HsNzRGQfooP547mkx0dlywg6QeYMPQKXq+/dh75eLO0RdXjRLBwcPu5kq6qV\nG6t4+OU1pA/y8oMLDmXWxH23QWaaj2vOncERppC123bxh+c+IRjqfn9TEZH+EkuX6MXAXGCVtbbF\nGPMI8EoM9+0Gstp87rbWRhfgrW13Los22TRr7TeNMT8AFhtjplhr93T1oMLCrK5OJy21S+eSqV3K\nVtQBkDt2ZJfv3ZM2WbZsOS6Xi9OmHsfgtL63ZbN7LCWAe3dNj782p2TO5cn1z7G8eiVfOuyzRXRL\nyuv4/bOrcLtd3PytOUwbF9uiwT/+1hx++tBiPrbl/OXFT7noTO2+15lk+jfUE2qXjtQm8RNLwJZJ\neOLAfGNM9Nfpw4GfdnPfu8CZwBPGmDlA21+B1wATjTG5QAMwD7jbGHMhMMJaeyewBwhF/nSpoqIu\nhtdILoWFWWqXTiRbu1Rv3AZA86B9v3dP2qRyTxXrqjczOXci/no3FfV9b0sn5MXl81FfsqMXXxs3\nkwaPx1atZ/XWLRSk5eEPBLn9kSXsaQ5y+VnTKMpK6VG5F59m2FFRz1P/bz0j8tO7zMwlo2T7NxQr\ntUtHapPO9TaIjaVL9AlgfrtrY+kHeRpoMsa8S3jCwXeNMV81xlwaGbd2HeFM3XvAQ5F9SZ8EZhlj\n3iI8/u3bkU3mRaQXols+9WQwf1c+KlsOwOFDZsWlPACX242voLBXXaIAR0Tq8nGkbo+/uYGSinqO\nnzWM2VOH9Li89EE+rvzidHxeNw+/vJrdDS29qpeISDzFkmEbYq09uacFW2sd4Ip2h9e2Of8C8EK7\ne/YAX+7ps0Skc/7KSnC78cZpDbYlZcvwujzMKpwel/KifIWFtOzcQbChAU9GRvc3tDGrcDqP2af5\nqHwZ47yH8caSEobmp/PVkyb2uj4jCjP5+ulTeei5Vfz1FctV58zodVkiIvEQS4ZtqTHmkITXRETi\nzl9RgS8vH5en78tU7KgvZUdDKVPzJ5PuS4tD7T7Tl4kH6b50puRNYnv9Tv707w9xgG98bjIpfVya\n46zjxjFpRA5L1lawYkNln8oSEemrWAK2GYT3E91pjNkU+bMx0RUTkb4JtbQQrN2FN04zRD8qWwbA\nEUPi//ubb0i467KlrLRX9x9aFM6AVbKJ+bOGMWnk4D7Xye128V8LDG6Xi0dfW0uLP9jnMkVEeiuW\nLtEvRv52iG3smogcAPyVkU3fC/s+fs1xHJaULSPFk8KMgql9Lq+9lCHFALSU7uzV/SNSxuM4LlIK\nyjlv/vi41WtEYSanHDmCVz7Yxkvvb+Hs48bFrWwRkZ7oNsNmrd1MeBHcy4BKwgvcbk5stUSkr+I5\n4WDz7m1UNlUzs2AqKZ6UPpfXXkrxUAD8vcywvfTeDkK783DSatnjxHdW2lnHjGVwZgovvb+Vqtqm\nuJYtIhKrbgM2Y8xdhHc5OAfwARcZY36Z6IqJSN+0BmxxyLAtae0Ojd/s0La8ubm4UlJoKe15wLZp\n527e/6SMwYExACyrWBXXuqWlejn3+PEEgiGeWaTRICKyf8Qyhm0BcCHQZK2tAU4BTuv6FhHZ3wIV\n8cmwhZwQH5cvJ92bxpS8SfGoWgcutxtf0RBaykpxnNh3GHAch3/+ex0AXzrsGFy4WFaxMu71mzut\nmOGFGby3qpSSivq4ly8i0p1YArb2I21TOzkmIgeYljh1iW6q3UptSx2HFE7H645l2GvvpBQX4zQ3\nE6iJfQvhFRuqWFtSy6wJBRw2fgQTBo9lY+0WdjXXdn9zD7jdLs47fjyOA0+9pSybiPS/WBfOfQzI\nM8Z8F3gH+EdCayUifRaorMCVmoonq29bwyytCG9SEu+119pLKQ5PPIh1HJvjODy7aBMA58wLTwaY\nVRieLbq84pO412/m+Hwmjchh2fpK1m7bFffyRUS6EkvA9iLwPFABHAvcYq29PaG1EpE+cRwnvAZb\nQWGfNmh3HIdl5asY5BmEyev9QrSxSBkSnngQ6zi25Ruq2FxaxxGTixhRlAnArKJwULmsPP7doi6X\ni/NOmADAv97a0KOuWxGRvtpnwGaMKTLGvA28BVxNuBv0ROBKY0zfFzkSkYQJNTQQamrq84SDrXUl\n1DTvYkbBVHwJ7A4F8EWX9ijrfmmPaHbNBZx1zJjW44NTcxibPYp1uzZS1xL/sWYThucwa0IB60pq\nWb0l9q5bEZG+6irDdh+wiPDWVLOttbOBIcBy4Nf9UTkR6R1/RTkAvj4umrs0kqk6tCix3aHwWZdo\nLBm2Zesr2RLNrhVm7nVuVtEMHBxWVn6akHqedewYAJ5btElZNhHpN10FbDOttT+KbNQOgLW2BbgJ\nOCzhNRORXmtdNLegqNdlOI7DsoqVpHhSmJJn4lW1ffKkp+PJzsbfTcC2V3bt2LEdzkfHsS1NwGxR\ngDHF2RwyPp+1JbWsUZZNRPpJVwHbns4OWmtDaJaoyAEtHhm27fU7qdhTxbT8yaR4fPGqWpdSiofi\nr6ok5G/Z5zXL1lWytayeI6cUMbyg40bxBWl5jMwchq1eT6O/0x9jfRYNFJ99d7OybCLSL2KZdCAi\nB5nPtqXqfYYtup7ZoZGMVX9IKS4Gx8FfXt7p+b3HrnXMrkXNKppB0Amyqmp1Quo5dmg2M8fns3bb\nLtZs1YxREUm8rkYRTzPGbNrHuWGJqIyIxIe/ddHc3mfYllaswuf2Mi1/cryq1a3oFlUtO3eQOnxE\nh/Mfr61ka3k9s6cOYVgn2bWoWYUzeH7jKywrX8lRxYkZwfGFY8eyYkMVzy3axJTRuQl5hohIVFcB\nW2KWNBeRhGspL8WTMxh3amqv7i9tKKO0oYyZBdMY5O1dGb2REgnSmrdvJ+uIvc+Fotk1194zQztT\nnFFEccYQPq22NAWaE/IOY4dmM2NcPis3VrFmSw2TFbSJSALtM2DTBu8iB6eQ30+gupq0ib3/nWtp\neXg/zkQvltte6vDhALRsL+lYp7UVlFTUM2faEIbm7zu7FjWrcDoLN/+bT6sthxXNjHtdITxjdOXG\nKp5dtEkBm4gklMawiQwwgcoKcBx8RX0bv+ZxeZhRMDWONeueJ2cw7owMmtsFbG2za2cePSamsqKz\nRROxiG7U+GE5zBiXj922SzNGRSShFLCJDDAtZWUApBQN6dX9lXuqKKnfgcmbQLovLZ5V65bL5SJ1\n+Aj85eWEWj6bKfqxraCkooE5U2PLrgGMyBxKwaA8VlWtxh/0d39DL30hMmP0Ga3LJiIJpIBNZICJ\nzrD09TJga10stx9nh7aVMnwEOA4tO3YAkezau5HsWhczQ9tzuVzMKppBc7CF1dVrE1Vdxg1rM2NU\nWTYRSRAFbCIDTEt5JMM2pJcBW8VK3C43MwumxbNaMYuOY4t2i360ppztFQ3MnVZMcV56j8pq7Rat\nWBXfSrajLJuIJJoCNpEBxh8J2HqzBltN0y627N7GhMHjyEyJresx3lKHjwTCEw9CofDYNbfL1e3M\n0M6Mzh7B4NQcVlR+SjCUuPW+xw4N736wrqSWT5VlE5EEUMAmMsD4y8vx5OTgHjSox/dGM1GH9vPs\n0LZShoeXeWzeXsLiT8vYWdXIsTOLKcrtWXYNwO1yM6twOnsCe1hbsyHeVd3LF46L7H7wjrJsIhJ/\nCthEBhAnEMBfVdnrCQfLIwHbzML90x0K4EnPwJuXR/P2Ep59dxMet4sz5o7pdXmJ3ls0akxxNrMm\nFLB+ey2fbKpO6LNEJPkoYBMZQPytS3r0PGCra6ln/a5NjM0exeDUnATULnYpw0YQ3LWL3ZW7OO6Q\nYRQM7v1s1fGDx5Dly2RFxSeEnFAca9mRxrKJSKIoYBMZQFqX9OjFhIOVlatxcDhkP3aHRvmGhSce\nFAd2ccbc0X0qy+1yc0jhNOr89WzYta/d9uJjdHEWh04sYOOO3azcqCybiMSPAjaRAaR1wkEvFs1d\nHukyPBACts1OJgBzC0LkZfd8LF57s4qi3aKJnS0Kn2XZnl20UVk2EYkbBWwiA0hLL9dgawo0saZ6\nHcMyiilK7/2G8fHQ7A+ycFs40JnkqYtLmZMGjyfdm8byilUJ7xYdNSSLw00hm3bWsXRdZUKfJSLJ\nQwGbyAASzbCl9DDD9knVGgJO8IDIrr364Ta2+NMI+lJwdmyNS5ked3ibrV3NtWzZvS0uZXblnHnj\ncLtcPPH/NhAIJjZAFJHkoIBNZADxl5fhyc7GPahng/SXV3wC9P9m7+3tbmjh5fe3kJmeQvqYMbTs\n3EmoqSkuZR9a1D+zRQGG5mcwb9YwyqobeWf5joQ/T0QGPgVsIgOEEwjgr6wkZUhxj+5rCfpZVbWa\n/EF5DM8cmqDaxea5dzfR1BLkC8eOJX3MWHAcmrZuiUvZk3MnkupJYVn5qn4ZW/aFY8eS6vPw7KJN\n7GkOJPx5IjKwKWATGSD8lZXhJT16uMPBqrI1NAdbmFU4HZfLlaDada+0upG3lu1gSG4ax88aRuqY\n8OD95i2b41K+z+Njev4UqpqqKanfGZcyu5KTkcJps0exu9HPKx/Ep2tXRJKXAjaRAaKlvBQAXw+X\n9PigZBmwf2eHOo7D319bSzDkcN788Xg9bgaNGQNA0+bNcXtOdLbosvIVcSuzK6ceNZKcjBQWfrCV\nXfXN/fJMERmYFLCJDBAtO8NZo5Ti2Ls1g6EgH+5YQXZKFmNzRiWqat1aYitYtamaaWNyOWxSIRDe\nC9WdlkbTlvitnTYtfzIpbh9Lypf3S7fooBQvZx83lhZ/iH/9v8RujSUiA5sCNpEBojVgGxp7wLax\ndjN1zfXMLJiK27V/fhw0tQT4x7/X4fW4uOBU09ot63K7SR09Bn9ZGcE9e+LyrFRPCjMKplKxp4qt\ndSVxKbM7x80cxqiiTN5dVcr6ktp+eaaIDDwK2EQGiJbSneB292gMW3Sz9+h+m/vD8+9upqaumc/N\nHkVx3t4bvA8aPRoch+Y4TTwAOGLILAA+KlsWtzK74na7+K9TDQB/e9USCmkxXRHpOQVsIgNES+lO\nfIWFuH2+mK53HIflFZ+Q7ktjYu64BNeuc9vK63n1w23kZw/i851s8D5oTLheTRs3xu2ZU/MN6d40\nlpQtT/giulETRuRwzPRitpbX8+bS7f3yTBEZWBSwiQwAwbo6QvX1PRq/trWuhJrmXRw+bAZetzeB\ntetcIBjioRc+JRhyuHDBJFJ9ng7XDBo/AYA9G9bF7blet5dZhTOobdnN+gTvLdrWeSdMIC3Vy9Nv\nb6S2oaXfnisiA4MCNpEBoKU0On5tWMz3RBfLPWrErITUqTsv/mcLW8vrOXbGUGaO73w7LF9eHt6C\nAvasX4cTil82rL+7RSG8zMc588bR2Bzg76+t7bfnisjAkLBfq40xbuB3wEygGbjEWruhzfkzgZuB\nAPAna+0fjTE+4E/AaCAV+Jm19vlE1VFkoGjeGV5NvycZtmUVq/C5fRxSPJW6mv7N+GwpreOF9zaT\nm5XKV06a2OW1aRMmUvf+f2gpLSV1WOwBaVcm5o4jJyWLZeUr+dKkL/RbhvGEQ4ez+NMyPlxTzuy1\nFa0zYkVEupPIDNvZQIq19mjgh8AvoicigdkvgVOA44HLjDFFwAVAhbV2HvA54L4E1k9kwPD3cIZo\naUM5ZY3lTM2bxCBvaiKr1sGe5gD3P7OKYMjh4tOnkD6o62ApbeKk8H3r45eVcrvcHFZ0CA2BRtZU\nx6+7tdvnul1887TJeD0u/vqKpaHJ32/PFpGDWyIDtmOAhQDW2sXAEW3OTQHWW2trrbV+YBEwD3gC\nuKVN3bSfi0gMWrtEY9yWanlkdmh/L5brOA6PLFxD+a49nD5nNNPG5nV7T9qEcMDWtC6+gdURxeFu\n0Q/Llsa13O4MK8jgrGPGUtvQwj/fWN+vzxaRg1ciA7ZsYHebz4ORbtLoubYLEtUBOdbaBmttvTEm\ni3DwdlMC6ycyYLSU7sSTlY0nMzOm65dVrMLtcjOjYEqCa7a3N5du54PV5UwYnsPZx42N6Z6UoUNx\np2fENcMGMDprJAVp+ayo/JSmQP/uQvC52aMYVZTJohU7Wbquol+fLSIHp0QO3NgNZLX53G2tjY4a\nrm13LguoATDGjASeAn5rrX0slgcVFmZ1f1ESUrt0bqC1S6ilhbWVlWRPmxrTu1U2VLO1roSZQ6Yw\nelh4G6v+aJPl6yr4++vryM5I4UcXzaYwNy3meyunTabmwyVku/2k5neflYvV/HGzefKTl9jQtI75\nY+d2OJ/Idvn+N47ku796i0cWWo6cPozc7EEJe1a8DbR/Q/GidulIbRI/iQzY3gXOBJ4wxswB2m7e\ntwaYaIzJBRoId4febYwZArwKXGmtfTPWB1VU1MWv1gNEYWGW2qUTA7FdmrdtA8fBlV8Y07u9uW0x\nAFNyJlNRUdcvbVJW08idj3yEC7jy7OkQCPTomZ5R4+DDJWxfvJSsI4+KW71mZM/kSV7itbWLmJa5\nd/dwotsl3ePivPnj+cfr6/j5Xz/ku+cf0rrLw4FsIP4bige1S0dqk871NohNZJfo00CTMeZdwhMO\nvmuM+aox5tLIuLXrgFeA94CHrLU7gR8BOcAtxpg3I38Onl87RfaDlh7OEF1esQoXLg4pnJbIarWq\nbWjh148vp6EpwNcXGCaNHNzjMtImhXcKaFyzOq51K0jLY+LgcazbtZHKPVVxLTsWJx8+gulj81i1\nsZo3PtaCuiKybwnLsFlrHeCKdofXtjn/AvBCu3u+DXw7UXUSGYiat4f3xEwZPqLba+ta6lm/axNj\nskeRk5qd6KrR0OTnF48to6xmD5+fO5rjDundshyDxozFnZZG46efxLmGMHfokazbtZH3dy7hjHGn\nxr38rrhcLi7+/BRueegD/vnGeszIwYwoim0coogkFy2cK3KQiwZsqTEEbMsqVuHgcGhR4vcObWoJ\n8OsnllNSUc8Jhw7nnHm93/7K5fGQNnkK/opyWirK41hLmFU0g0GeVBaXLum3raraGpyZykWnTyYQ\nDPG7Z1axp1mT40WkIwVsIge5lu0leLKy8ObkdHvt0vLwUNJEB2x7mgP8+vHlbNi+mznThnDBqZP6\nPD4rY2p4jFm8s2ypnhQOK5pJdVMNa2s2dH9DAhw6sZAFR42ktLqRh19eg+Nog3gR2ZsCNpGDWKip\nCX9FRczdoWtrNjAmexR5g3ITVqf6PX7u/sdS1pbUcuTkIi4+fQruOAymT58aHnOXiG7ROUOPBOD9\nnR/FvexYnXv8eCaMyOHDNeUazyYiHShgEzmINe8I/8ceS3foiopPEt4dWlPXzF1//5jNpXUcO3Mo\nl581Da8nPj9mfEVFeAsKaFz9aVz3FQUYlzOaovQCllWspMHfGNeyY+X1uPnvs6aRmebjsX+vY+OO\n3d3fJCJJQwGbyEGspST28WsfR7tDC2cmpC5bSuu47ZEP2V7RwMlHjOCbp03G7Y7fMhUul4uMqdMJ\nNR5mlmQAACAASURBVDbStHlT3MqNln3MsNn4QwEW78csW172IC4/axqhkMP9z6ykrrF/93gVkQOX\nAjaRg1jrDNERXQds9S0NrN21gdFZI8lPi3936NK1Fdz56BJq61v48okT+OpJE+PSDdpea7foqpVx\nL3vO0CPwub28s/39/TL5IGra2DzOOnYsVbub+f2znxCMczZRRA5OCthEDmKtM0SHDe/yuuWVqwg5\nobh3hzqOw8LFW7nvqXAAdfU5M1hw1KiELQCbPnUaeDzUL4v//p+ZvgwOL5pF+Z5KbM3+3ePzzGPG\ncOjEAlZvqeHxN/bPRAgRObAoYBM5SDmOQ0tJCb7CQtyDul5feml5OKA6tCh+3aGBYIhHFloef3M9\nOZkp3HjB4Rw6qTBu5XfGk55O+uQpNG/dgr8q/gvdHjdiDgDvlPwn7mX3hNvl4pIzpjI0P53XPtrG\nuyt37tf6iMj+p4BN5CAV3F1LsL6u2xmi9f4GbM16RmWNoCAtPvtwNjb5+dXjy3l7+Q5GDcnk5m8c\nyeji/tkzMHPWYQDUL/s47mWP/v/t3Xd4HNXZ+P3vbNU29VW1ZLmOezeuGIzBQOjdlCSQXkggpPPk\neV6SNz0kISSEEEwNARJa6AZiXHDFXXIbW5bVe1tJu9o68/tjZVuy1k3WalfS+VyXLkk75RzfnrO6\n98yccxx55DtyKWzcT6Onud/Pfy4sZgPfvmkaVrOB51YpYhCCIAxzImEThEHKd5YDDgob9qFqKrP6\nqXetvrWTX/xjBwfKWpgxNp0f3TmLFIe5X859NmwzZgLgjsJtUUmSuDB3IRoaHxxe1+/nP1eZqVa+\net1kQqrKY28U4erwxbpKgiDEiEjYBGGQ8lVWAGAekXfa/U7cDj3/59eKq1z84vnt1DR5WD43j3tv\nnEqCKWor3EVkTEkhYdRoPMpBQm53v59/buYMHEY7Hx35BG/Q2+/nP1dTR6dx80VjaGn38dh/9hIM\niUEIgjAciYRNEAYpX1kpAOaRBafcxx3wcLDlMHmOXNItaedV3qcH6vjti7twdwb57PLxrFg2rl+n\n7TgX9pmzQFVxF+7u93Mb9UYuGrEIT6CTTTXb+v38fXHFvHzmTcqkuNLFPz86dOYDBEEYckTCJgiD\nlLesFJ3VitF56gf99xy7HXoec69pmsa7m0v525v7MOgl7r9lGktnnXnet2iyzww/x9a+PToJ1YUj\n5mPSG/m4/BNCaigqZZwLSZK4+8oJ5GfYWbe7mtU7KmNdJUEQBphI2ARhEAp5PATq6jDnjzztFBrb\n68LPec3KnN6ncjRN45W1R3htXQmpiWYevGs2U0afX09dfzBl52DOy8e9t4hQe3u/n99utLF01EJa\nfK3sauj/Od/6wmzU862bppFoNfLifw+xt6T/R8kKghC/RMImCIOQr7wMgITT3A51+do41HKE0Ukj\n+zQ6VNU0/vHhIVZtLScz1cqDd81mRIa9r1Xud475CyAUilov21XyMiQkPipbGzeLsaclJfCtm6ah\n1+l4/M29VDX2/zN8giDEJ5GwCcIg5O16fu10CdvO+kI0NOZkzjzn84dUlZXv7GftriryMuz8+M5Z\npCaefq63gea4YD5IEm1bozNnWpbdyayMaVR2VFPYuD8qZfTFmNwkvvCZCXT6Qjz66h6xfJUgDBMi\nYROEQchXFu5hM48cecp9ttXtQifpznk6D1XTePa9g2zZV8eY3ER+cMdMEm2m86pvNBhTUrDIE/AW\nHybQ2BCVMq4cdSkSEu8d/ShuetkA5k/O4pqFBTS0enns9SICQTFyVBCGOpGwCcIg5C0rRWexYHRm\nRNxe72mkrK2CCSnjcJjO/jampmn888NDbNxby6jsRB64dQa2BGN/VbvfJc5fAEDbluj0smXbMpmd\nOT3uetkArrtwFHNkJ4cqXTz/wcG4SigFQeh/ImEThEEm1NlJoK42POBAF7kJ76gLT3cxJ3PGOZ37\n1bVHWLOrihFOO9+5dToW88DOsXau7LPmIJlMtG34BC1Ki6RfWbAsLnvZdJLEF6+eREGWg41Ftaza\nWh7rKgmCEEUiYROEQeZMAw40TWNb3S6MOgPTnZPP+rwf76zk/a3lZKVa+d6KGdgt8duzdozeasUx\nbz6BxgY8+/ZGpYysbr1sO+sLo1JGXx0bOZriMPPq2iPsPBSdW8OCIMSeSNgEYZDxlh4FTj1hbmVH\nNXWeBqamTyLBcHYDBQqPNPLPjw6RaDXynVunx+Uza6eSfNElALSu/ThqZVw96nL0kp43j7xPQA1G\nrZy+SHGY+fZN0zAadfz97X2U1fb/NCeCIMSeSNgEYZDxlhwBwDJmTMTt27rmXjvb0aElVS4ef3Mf\nBr2Ob908DWeypX8qOkASCgowF4zCXbiHQFN05iZzWtO4aMRCmrzNrKvcGJUyzsfILAdfvnoSgYDK\nI6/sobG1M9ZVEgShn4mETRAGEU3T6Cw+jD4pGUNaeq/tITXE9tpdWAwWJqXJZzxfR2eAXzyzFb8/\nxJevnsSYnKRoVDvqki9eCpqGa/3aqJVxRcEyrAYLq0pX0+GPv/nPZssZ3LZsHC63nz++soeOzkCs\nqyQIQj8SCZsgDCLBpkZCLheWsWMjrnBwoPkQLn87czNnYNSdfsCAqmr8/a191Ld0ct3iUcyZEHnE\n6WDgmDsPnc1G65qPUb3RWbDdZrRy5ahL6Qx6eefoh1Ep43wtn5vH8rl51DR5ePS1QvyB2C+rJQhC\n/xAJmyAMIp1HigGwjBkbcfuW2h0AzM+ec8ZzvbXxKHuPNjNnYiZXLyrotzrGgs5sJmXZZageN671\n66JWzpLcBWRZM9hQtYWjrrKolXM+br1kLBdMzKC40sWTb+9HVeNnZKsgCH0nEjZBGEQ6i8MJW0KE\nhM0d8FDUsI9sWyb5jtMvzl5U0sRbG0tJT0rggTtmoTvNeqSDRfIllyKZzbR8tAo1EJ3bgQadgdsn\n3ISGxosHX4uLheFPppMkvnjVJCbkJ7PjUAMvrT4cV9ORCILQNyJhE4RBxHukGMlgwJzfe4WDbXW7\nCGoh5mfPOe2C8G1uP0+9ewCDXuKbN0zFYR08I0JPR2+3k7zkYoItLbRv2RS1csYmj2Jh9gVUu2tZ\nXbE+auWcD6NBx703TiXXaWP1jkre2RyfvYGCIJw9kbAJwiAR8njwVZRjLhiFzth7jrQtNdvRSTou\nyJp1ynNomsaz7x+kze3nxiVjGJnliGaVB1zy8iuQDAaa3n07ar1sADeM/QwOo533jn5ErbsuauWc\nD2uCke/cMp20xATeWF/Ch9sqYl0lQRDOg0jYBGGQ6DykgKZhnTCx17aqjhoq2quYnCaTaDp1ErZu\ndzW7ixuZODKF5RfkRbO6MWFMSSFp6TKCjY20fvzfqJVjNVpZMeFGAmqQZ/a9FHdzsx2TmpjA92+f\nQZLdxMurD7Nud1WsqyQIQh+JhE0QBgnPwQMAERO2zTXbAJifPfeUx9c0uXl59WFsCQa+eNXEIfHc\nWiRpV12Dzmql+d23CXV0RK2cGc4pLMy+gMqOat4uWRW1cs5XRoqV762Yid1i5PlVClv21ca6SoIg\n9IFI2ARhkOhUDiAZjSScNGGuPxRga80OHEY7U9ImRDxWVTVWvnMAf1Dl81dMIDXx7FZAGIz0djup\nV12D6vHQ9O7bUS3rpnHXkGFJZ3X5evY3KVEt63zkptv47m0zSDAbWPnOAXYoYgkrQRhsRMImCINA\nqL0dX0UFlrHj0Bl7DhLYUb8HT7CTBTlzMZxi7rWPtldwtKaN+ZMyB/V8a2cr+ZJlGJ1OWld/hLc8\neg/cJxjM3D35dgySnmf2vUiDJzorLfSHkVkOvnPrdIwGHX97cy/bD9bHukqCIJwDkbAJwiDgUQ4C\nYIlwO3RD1RYkJBbnzIt4bH2LhzfWl2C3GLn90nFRrWe80BlNZNz1eVBV6p57Bi0Uvek3RibmcZt8\nI55gJ08UPYs3GJ2Je/vD2Nyk40nb42/uZbO4PSoIg4ZI2ARhEPDs3wv0fn6tor2K0rZyJqfJpFlS\nex13bFSoP6hyx2XjhswUHmfDNnkKiQsW4SsrpTnKt0YX5szlohGLqHHX8ez+l+NyfrZjxucl890V\nM0gwGVj59n4+2VMd6yoJgnAWRMImCHFO0zQ69uxBb3eQMGp0j22fVG0G4MLcBRGP/aSwhoPlrUwf\nk8a8iZlRr2u8ca64A0NqGk1vv4nnUHSfMbtp7NXIKWMpatzPS8rrcT1Z7ZicJH5w+0ysCQaeef8g\na3aJ0aOCEO9EwiYIcc5XXkbI1Ypt6jQk3Ykm6wl0sq1uN6kJKREXene5/fzr42ISTHo+e7l82sl0\nhyq9zUb2l78GkkTNE38l0By9Z8z0Oj1fmfo58h0j2FyzjdeL34nrpG1kloMf3jELh9XIPz5QeHdz\naVzXVxCGO5GwCUKccxfuAcA2fXqP1zdUb8Ef8rMkdwE6qXdTfnVNMZ2+IDddNGZIjwo9E8u4cThv\nuY2Qy0XVn/5IqLMzamUlGBL45vQvkmnN4OOKT3jjyLtxnQSNyLDzoztnkZpo5rV1Jby8uhg1jusr\nCMOZSNgEIc517NkNej3WSVOOvxZUg6yt2IhZb2JRhMEGxZUuNu6tJT/DztKZuQNZ3biUfOlyki6+\nBH9VJVWP/B7VG72kzW6y8e2ZXybT6mR1+XpePPgaqqZGrbzzlZ1m48G7ZpOTbuOj7RWsfHs/wVD8\n1lcQhquoJ2yyLOtkWf6bLMubZFleI8vymJO2XyPL8qdd27900rZ5siyviXYdBSFeBZqb8ZUexTJu\nPHqr9fjrO+r24PK3sTDnAqxGS49jVFXjhY/Cz2vduXw8Ot3wuxV6MkmSyLj9ThwXzMN7pJjKPzxM\nsK0tauUlm5P4zqyvk+fIZVPNp6ws+kdcjx5NTUzgR3fOYmxuElv21/Hoq4V4/fG5eoMgDFcD0cN2\nPWBSFGUh8CPg98c2yLJsBP4AXAZcBHxFluWMrm0/AJ4EzANQR0GIS+3btgLgmHNiBQNN01hdsR4J\niaUjFvc6Zu3uKsrrOlg4JYtxI5IHrK7xTtLryfriV3DMX4C35Ajlv/gpvorora/pMNm5b+ZXGJ88\nhj2N+3h4x2NxPU+b3WLkuytmMG1MGnuPNvO7l3bR5vHHulqCIHQZiIRtEbAKQFGUrcCcbtsmAsWK\norgURQkAG4AlXduKgRsB0T0gDFvtn24FvR7H7BMJm9JSTFVHDTMzpvaayqPN4+f1dSVYzHpuuXjM\nyacb9o4lbWnX3UCwqYnyX/3/tHz8XzQ1OrcALQYL98740vEpP367/VF21hdGpaz+YDbquffGqSya\nmsXRmnZ+9cJOGlujd/tYEISzNxAJWyLQ/d5DSJZlXbdtrm7b2oEkAEVRXgdEn7wwbPlra/GVlWKd\nOBm948SC7qtKVwNwaf5FvY55fd0RPL4g1y0eTZJddE5HIkkSaddcR/Y3voVkNNLw4gtU/uF3+Kqj\nMx+ZXqfn1vHXceeEWwioQZ7a+wLP7/8XncH4TIQMeh1f+MxErpyfT12zh1+8sIOK+uitySoIwtmJ\nvI5N/2oDHN1+1ymKcuzjrOukbQ6g5VwLcDodZ95pGBJxiWywxKX8v+8BkHvpRcfrvL/+EIdbS5iZ\nPZk5Yyb12P9QeQufFNYwMsvBissnoNef/eexwRKT/uS8/GJGzJ1O8V8fp2XbDsoe+gnZn7mCvBW3\nYuxKkPszLtc5L2HuqEk8uuUZttbuQGk9zOdm3MSi/LlxOeXKN26ZSW5mIivf3MtvXtzJT74wj6lj\n0oHheb2cDRGX3kRM+s9AJGwbgWuAV2RZng90vx9wEBgny3IK4CZ8O/R351pAQ0N7f9RzSHE6HSIu\nEQyWuGihEDUffIQuIQFt7KTjdX5x11sALMtZ2uPfoWoaf/n3LjQNVlwyluZm91mXNVhiEh0G0r9y\nL5Z5u2n498vUvPMedWvWkXbN9Yy95VqaWvq3F8yIjfunf50Py9byQdlqHt3yDO8fXMdN464hzxF/\no3kXTsxAp03iqXcO8H9PbOar107iisVjhvH1cmrDux1FJmISWV+T2IFI2N4ALpNleWPX7/fIsnw7\nYFcU5UlZlh8APiB8e/YpRVFqTjpeTAokDDsdu3cSbGkh+ZJl6BLCo0APtxzhUOsRJqXJjErK77H/\nJ3uqOVrTzrxJmcj5KbGo8qAlSRL2GTOxTp5C6+qPaH73bRpe/ift6z8m9Yabsc2Y1a89YHqdnitH\nLWNu1kxePfwWRY37+fW2PzHTOZWrRi8n2xZfK1LMn5SFw2LiL28U8dc39qLqdMwdlx7ragnCsCPF\n86SOZ0kTGXxv4pNNZIMlLhUP/4bOgwcY+bNfYs7JQdVUHt7+GGXtFXx/zr0UJJ5I2Do6Azz49y0E\nQiq//PJ8Uhzn9uzaYInJQAm2t9H01n9wrVsLqoplvIzz1hUkFIyKSnkHmw/zVskqytoqkJCYnTmd\nZflLyHeMiEp5fVVa28Yf/72Hdk+AaxcVcN3iUXF5KzdWRDvqTcQkMqfT0aeGo3/ooYf6uSoD7iGP\nGHrei81mRsSlt8EQF295GU2vvYJlwkRSL78SgO11u1lXtZHZGdNZmndhj/3/9XExhypauWnJGKaO\nTjvn8gZDTAaSzmzGPm06+ZddTHtVLZ79+3CtX0ewvR3r+PFIBmO/lpduSWNh9gXkJ46g1l3PwZbD\nbKzeSnHrURwmG05LWlwkRsl2M7PGO9lX2swOpQGX28/U0ano4qBu8UC0o95ETCKz2cw/7ctxA3FL\nVBCEc9D0n9cBSL3yKgD8oQBvHnkfg87AdWOu7LFvWW07a3dVkZ1m5dI58dUjM9hZ80aQ+6378RzY\nT/0//4FrzWrce3aT+fl7sE2ecuYTnANJkpiaPokpaRM50HyI1eXrOdhymEMtxWTbMlmWt4Q5WTMx\n6mL7lp2ZYuW3917ITx7fyLrd1bS5/Xz12smYjPqY1ksQhgPRwzZEiU82kcV7XDqPFNP42itYxsuk\nXX8jkiTxfulqipr2syxvCbMypx3fV9U0/vqfIprbfXztuslkplpPc+ZTi/eYxMqxuBidThIvXAIS\nuPcW0b5pI6rfj3W8jKTr35mRJEnCaU1nXvZspqVPxhfyc7i1hD2N+9hU/SkhNUSOLROjvn97+c5F\nWqqNqQUpHK1po6ikGaWildnjMzAahvdKh6Id9SZiEllfe9iGdwsThDiiqSoNr/wL4HiyVt1Ry4dl\na0g2J3FFwSU99t9UVMuRqjbmTMhgUkFqpFMK/URnNJF+/U3kP/i/GDMyaVn1HhW/+zWB5uaolZnn\nyOHuySv42YIfsSx/Cf6Qn7dKVvE/m37JK4fepLEzemWficVs4P5bpjNnQgaHK108/PIuOjoDMauP\nIAwHImEThDjh2rAeb/Fh7LNmYx0vo2oqLx58jZAWYoV8AwmGhOP7erwBXl1bjMmoY8UlY2NY6+El\nYWQB+f/70PE1Sct/8TO8pUejWmZKQjI3jr2any96kBvGXoXVYGFt5UYe2vwbntr7ApXt0Znw90yM\nBh1fu3Yyi6ZmUVrbLpayEoQoE7dEhyjRFR3Z2cZFU1V8FeV4S0rwVpQRamsDvQ69pW+3Hc/EX1tD\n9V//gs5kIufb30FvsfBR2Vq21G5nVsY0rihY1mP/f31czMHyVq6/cBTTx57fFAviWonsVHHRGY3Y\nZ81Bb7HSsWsHbVs2YcrMwpwT3XnUjDojo5MKuHjEIjKsTho7m1FaitlQvYU6dz05tizsJltU6wA9\n4yJJEjPGpdPmCVB4pInCI03MGu8kwTT8Ho8W7ag3EZPIxKADQegH/oZ6Wj5cRfuWzaidvSdNNaSl\nYZ04CcfceVgnTETSn//D1qGODqr/8iiaz0fWV7+BMSWF4tajvH30A5JMidw6/voe+5fVtrNmVxVZ\nqVYuvyD/FGcVokmSJFKWX44xI4OaJ5+g5m+PEbjltuOjeqNJr9NzQdYs5mbOZH+zwjslH7Cjfg87\n6wuZlzWbq0cvJyUhOer1OEYnSXx2+XiMeh0fba/gN//cyfdvn0lqYsKZDxYE4ayJHrYhSnyyiexU\ncdFCIVpWvUfN3x7DW3IEvSMR+5y5OObNxz5jJuaCUeisVgIN9XiLi2nfsgnXujUEmprQ22wYUlL6\nNPVCsL2Nqj/9AX9lBSnLryBl+eW0+zv4y+6VeIM+vj79Cz0mUlU1jcfeKKLlPAcadCeulcjOJi6m\nrGzs06bjLtxNx47taGoIizxxQKbhkCSJDGs6i3LmkevIodpdy8GWw2yo3ooOifzEPPRS/z/1Eiku\nkiQxZVQqIVVj1+FGdh5qYMa4dGwJsRscMdBEO+pNxCQy0cMmCH2ker1U/+0xPHuL0Ccl4bzlNhxz\n50XsPdNUFe+RYto+3UrH9k9xrVmNa81qjM4MHPMXkDhvAaasrLMq13PwALVPryTY3ETi4gtJv/lW\nvEEff93zNK0+F9eOvoKxyT0na91QWENJdRsXTBQDDeKFOS+PvB8+SOXvf0fzO2+jejw4V9zZ7yNI\nT0WSJGY4pzAtfRJbanbw5pH3eLPkfTbXbOOmcdcwJX3igNXjpovGYDLoeOOTo/z6nzv5we0z++VD\nhSAIYqWDIUvMMB3ZyXEJdXZS9cff4S0pwTplKqa7bqU02EB5eyVtvnY8wU6MOiMWQwIZVie59ixG\nJxVgM1rRgkE8B/bTtmUzHbt2oPnDnyTN+SOxTpyIZfwEzHl5GJJTkHQ6NE0j5GrFoxykbeMGPPv3\ngSSRdu31pF59LX41wBOFz6K0FDM/ew53TbilR0/N+a5ocLYxEcLONS7B1lYq//gw/qpKEhcsIvPu\nL/TLLfNz5Ql08u7RD1lftRlVU5mdMZ1bx1/fb8+3nU1c3t9axitrjpBkN/GD22eSnRb9Z+tiTbSj\n3kRMIuvrSgciYRuiREOJrHtctGCQqj/9Ac+B/XROG8eH8xyUuivPeA4JifzEEUxNm8jszOlkWJ2o\nXi8du3bStmUTnoMHIBQ6cYBej85kQgsG0QInpj6wTpxM+o03kTBqNO3+Dv5e9DwlrlKmpk/iy1M+\ni17X84/9c6sOsm53NbcuHcsV8/rv2TVxrUTWl7iEOjqoevQPeEtKcMxbQNYXvzxgPW0nq+qo4aWD\nr3G0rRy70cZt8g3Myph25gPP4Gzj8tH2Cl7672ESrUa+d/tMRjjt5112PBPtqDcRk8hEwib0IBpK\nZN3jUvPMSto3bqA8z8p/FtlAp2NC6jgmp01gZGIeqQnJWA0WAmoQd8BNnaeB8vYqlObDHG0rR9VU\nAEY68piTOZ1ZmdNJNieh+nx4S47gOaQQqKsl0NSE5veBTo8xLQ3zyILwc3G54ZUJiluP8uy+l2jx\ntTI7Yzqfn7SiV7J2pNrFL5/fQXa6jYfumYtB339JgLhWIutrXMK9tg/jLTmCY8FCsu75UsySNlVT\nWVOxgbdLVhFQg8x0TmXFhBuxG/ve43UucVmzs5J/fHgIu8XI91bMID/T0edy451oR72JmEQmEjah\nB9FQIjsWl7KP38X34ivUpRp46/IMFo1czNK8xSSZE8/qPJ3BTgob9rOtbhdKS/Hx5G10UgGzMqYx\nM2Mqyeak056jzl3PB2Vr2Fq7AwmJq0cvZ/nIpehOelA8EFT56bPbqG5088M7ZiLnp/TtH38K4lqJ\n7HziEvJ4qHrkYbwlJSQuXETm3V+MWdIGUO9p4IUDr3DEVUqyOYnPT7qN8Sl9m7/vXOOyfk81z71/\nEGuCge+umEFB1tm1scFGtKPeREwiEwmb0INoKJE5nQ7e+e8r2P/6L1QdFN21iKvn3kaiqe+f/Nv9\nHeyqL2RnfSHFrUfRCLepLFsmoxPzybXnkGh2YNQZ6Ax6qfM0HO+lA8i1Z7NCvpHRSSMjnv8/n5Tw\n1sZSls7M5bOXy32u56mIayWy841LyOMJ97QdLSFx0YVkfv6emCZtqqbyUdla3jn6IZqmsXzkUq4a\ndVmv3twz6UtcNhbV8PR7B0gwGXjgtumMyTn9h5nBSLSj3kRMIhMJm9CDaCi9BUIB3ix9h8wn3iWz\nOUjgzuuYvPSGfi3D5Wtnd0MRexr2crStHH8o8pB2naRjbNIoloxYyHTn5F69asdU1nfw02e3kWgz\n8fMvzcNi7v+B3eJaiaw/4hLyuKn8w8P4So+SuHgJmZ+7O6ZJG8BRVznP7nuRRm8zIxPzuGfSHTit\naWd9fF/jsmVfLSvfOYDJqOP+W6YzPm/g5oobCKId9SZiEplI2IQeREPpyRPo5O9Fz2HdupeLd3Rg\nmjubgq9+K6plhtQQ1e466j0NtPnbCapBzHozGdZ08hy52Iynn+4gpKr84vkdlNa2c/8t05g25vxW\nNDgVca1E1l9xCbndVP7hd/jKSkm8cAmZn4190tYZ9PIv5T9sq9tJgt7M7fKNzMmaeVbHnk9cth2s\n5+9v7cOg13HfzdOYMLJ/b+/HkmhHvYmYRNbXhE3MwyYMea0+F4/tfgpXQxV3F3Wis1oZcfvno16u\nXqcnz5FDniOnT8d/+GkFpbXtLJicGbVkTYg+vc3GiAe+T+Xvf0vbJ+tBI+Y9bRZDAndPXsGktPG8\nrLzOM/tf4mBLMbeMvw6z3hS1cudOyECvk3j8P3t55JU9fOvmaUwW8wkKwlkRi78LQ1qLt5U/7nic\nanctNx1IwOAPkX7TrRgS4/vB57Ladl5fX0KizcSKZeNiXR3hPB1L2swjC2jbsJ66555BU9VYV4sL\nsmbxo7n3kefIZXPNNn6z7VGqOmqiWuas8U7uvXEqqgZ/eqWQopKmqJYnCEOFSNiEIavF28oju56g\n0dvMjaGJpChVOOTxJF24JNZVOy2fP8QTb+0jpGp86aqJOKzR6/EQBo7ebg8nbQWjaNv4CXXPPh0X\nSVuG1cl3Z3+TpXmLqfPU89vtf2Z95Wai+bjM9LHpfPvmqUgS/Pm1QnYfboxaWYIwVIiETRiSp1Hc\nywAAGudJREFUWn0u/rTrCRo7m7gy9yJGf7wfdDrGfOOrMX9+6Exe/vgwtc0eLpuTx5TRZ/8wuBD/\nwj1t3wsnbZs2UPfMU3GRtBl1Bm4edy1fm3Y3Zr2Jfx16g5V7X8AT8EStzCmj0rj/5mnodBKPvVHE\npr3R7dkThMEuvv9yCUIftPpc/GnnEzR0NnH5yEuYv99LoKGBlMuWYysoiHX1Tmv7wXrW7a5mhNPO\nzRePjnV1hCjQW8O3RxNGj6Zt80Zqn34yLpI2gKnpk/jx3PsZmzyK3Q1F/GrbnyhxlUWtvIkFqTxw\n6wzMRj0r3znAu5tLo9qzJwiDmUjYhCGl3d/Bo7uepL6zkeUjl3K5ZSrNq97DkJpK2jXXx7p6p1XV\n0MFT7x7AbNTz1esmYzQM/DqUwsDQW63k3v89EkaPoX3LZmpX/h0tGIx1tQBISUjmvplf5TOjLgs/\nA7rzcT4o/fj45ND9bXxeMj++axapiWZeW1fCPz5QCMVJAisI8UQkbMKQ4Ql4+PPuJ6nz1LMsbwnX\njLqc+heeh1CIjNvvQpeQEOsqnpLHG+QvrxfhC4T4wlUTyU0f+otlD3d6q5Xc73yPhDFjaf90C1V/\neRTV54t1tYDwPIFXjbqM+2Z+hUSTg7dKVoVHWvuiM0VDrtPO/3x2DvkZdtburubPrxXR6YuPBFYQ\n4oVI2IQhwRv08tiep6nqqGFx7nxuGHsVHVs203lIwTZjJvaZs2JdxVMKhlQef3MvdS2dXDkvn7kT\nMmJdJWGA6C0WRjzwfaxTpuHZW0jl739DqKMj1tU6blzKGH48936mpE3kYMthfvXpH9ldsz8qZaU4\nzPzwzllMGZVK4ZEmfv78dmqa3FEpSxAGI5GwCYOePxTgb4XPUtpWzgVZs7ht/PWobjcN/34ZyWQi\n4/Y7Y13FU9I0jWffP8i+o81MG5PGjReJ59aGG53ZTO6938axYCHekhIqfvNLAs3xM9WF3WTja9Pu\n5uZx1+IJdvLL9X/mnwdexRPo7PeyLGYD990yjeVz86hp8vDz57ez63BDv5cjCIORSNiEQS2oBnly\n7/Mcbi1hhnMKd024BZ2ko+HVfxPqaCftmusxpsXnpLOapvHauhI27a1lVHYiX79uCvo4H8EqRIdk\nMJB1z5dIWX4F/ppqKn71c7zl0XvY/1xJksTSvMV8f869jEzKZVPNp/x86+8pbNjX72XpdTpWLBvH\nV66ZRCik8efXinhlTTHBkHiuTRjexF8HYdAKqSGe2fcS+5sUJqXJ3DP5DvQ6PZ6DB2jbsB7TiDxS\nLlse62pGpGkar68v4b0tZWSkWLjv5mmYTWKQwXAm6XQ4b11B+i23EWxtpeLXv6B9x/ZYV6uHPEcu\nv1r+Y64edTnugJsnip5j5d4XaPa29HtZ8ydn8eBnZ5ORYuH9reX84vkd4hapMKzpH3rooVjX4Xw9\n5PFEXmB7OLPZzAzluITUEM/tf5ldDYWMSx7N16bdg1FvRA34qXr0EVSPm5x778OY1nMes3iIi6pp\nvLrmCO9vLSczxcIP75hFst0cs/rEQ0ziUaziYhk7DnNePh27dtC+ZTPodFjGjUeS+rT8YL9z2BPI\nMeUyI2MqFe3VHGg+xIaqrYQ0lYLEPPS6/vvgkWQ3s3haNi63n6KSJjYU1WAxGyjIdsRNPI4R7ag3\nEZPIbDbzT/tynEjYhqih3FCCapCn973IroYixiQV8PXp92A2hBOepnfewr1zB8nLLiN5yUW9jo11\nXPyBEE++vZ/1e6rJSrXygztmkeKIXbIGsY9JvIplXEzZ2dinzcBdVIh710781VVYJ09BZzTGpD7d\nHYuLw2RnfvYc0i2pHHGVsrfpAJ/W7sRhspNty+y3hMqg1zFznJOcdBtFR5rYeaiBfaXNjM5JJNEW\nP6uAiHbUm4hJZCJhE3oYqg0loAZZWfQPihr3Mz55DF+f/gUSDOHpOrylpdQ+sxJDcgo537gXydD7\nj1ss49LY2skjr+5hX2kL40ck8cBtM0iKYc/aMUP1WjlfsY6LISkJx7wFeI+W4NlbRMf2T7GMG48h\nOTlmdYKecZEkiRGOHBbnzEPTNJSWw+xsKGRXQxEOk51Mq7PfErfcdBsLp2bR3OZj79Fm1u+pxhcI\nMTo7EaMh9k/3xPp6iUciJpGJhE3oYSg2lM6gl78XPsf+ZoUJKeP42vS7j/esqT4flY88jNreTvbX\nv4k5JzfiOWIRF03T2Lyvlj+9Wkijy8uiKVl8/fqpWMyGAa3HqQzFa6U/xENcdGYzifMXgqbh3r2L\ntk0b0FutmAtGxeyWYKS4GHQGJqSOY27WLLwhL4dajrCzfg97GvZi1BvJsmWgl84/qUowGZg7IYNR\n2Q4OVbgoPNLEJ4XVmAx68jPt6HSxu00aD9dLvBExiayvCZs0BJYB0RoaojOZ42DmdDoYSnFp8bby\neOEzVHXUMDV9Il+cfBdG/YketLp/PIdr3RqSL7ucjNtuP+V5BjouVY1uXl59mH1HmzGb9Nx12XgW\nTsmKq+dvhtq10l/iLS7uvUXUrvw7oY52rJOnkPm5e3o9ozkQziYu9Z5GVpWuZlvdLlRNxWG0c+GI\nBSzKuYBkc1K/1MMXCPHhtgre21KGzx8iM8XC1QsLmDcpE4N+4Hvc4u16iQciJpE5nY4+/QEQCdsQ\nNZQaSkV7NX8rfIZWn4sLcxdwy7hrezzY3LYpvB6jKXcE+T/5P3TGUz/XMlBxqW50s+rTcjYV1aJq\nGpMKUvjcFRPISLZEvexzNZSulf4Uj3EJtLRQ99zTePYWoUtIIP3WFSQtXoI0gNPBnEtcmr0trKvc\nxMbqrXQGvUhITEwbz/ysOUxLn9TjQ1dftbn9vLnxKOt3VxNSNdISE7hyfj6Lp2ZjMg7cyOt4vF5i\nTcQkMpGwCT0MlYayuWY7/1LeIKAGuH7MZ7g0/6IevVOdJSVU/vaXSEYj+f/z/2HKyjrt+aIZF483\nyM5DDWzZX8v+0vA0B1mpVm5ZOoYZY9Pjqletu6FyrfS3eI2Lpmm0bdxAw79eRO3sJGH0aJwr7sQy\nesyAlN+XuHiDPrbV7WRLzQ5K28oBsBgsTEufxDTnZCamjsesP78BBI2uTj7YWsH6wmoCQRVbgoFF\nU7O5eGYuWanW8zr32YjX6yWWREwiEwmb0MNgbyjeoJdXDr/FlprtWAwJfHbibUx3Tu6xT6ChgfJf\n/4JQm4vc+x7ANmXqGc/bX3EJqSr1LZ1UN3ooqXFxsKyVstp21K72NG5EEpdfkM+Mceno4jRRO2aw\nXyvREu9xCTQ30/Dvl+nY/ikAjvkLSLvuBkzO6C5tdr5xqXXXsaVmB9vqdtHqcwFg1BmYkDqeKWkT\nkFPGkW5J7fMHHJfbz+odFazfXU2bJwDAhPxk5k3KZLacgd0SnZG28X69xIKISWQiYRN6GMwN5UDT\nIf558FVafK3kOXL50pS7SLf0fFYn0NxM5W9/RaCxAeeKO0i59OwmyD1dXDRNw+sP4XL7aXP7afeE\nv7d5ArR5/LR3/exy+2ls7SSknmg7ep1EQbaDaWPSmTcxg4yU6H+i7y+D+VqJpsESF88hhYaXX8RX\nXgY6HY4L5pF6xWcwj8iLSnn9FRdVU6lor2JPwz72NO6j1l13fFtqQgpyyljGp4xhXPJoUhLOfWRs\nMKSy81ADa3ZWoVS0AuF2OnlUKrNlJ1NHp/Xr/IeD5XoZSCImkYmETehhMDaUps5m3ipZxfa63egk\nHcvzL+aKUZdi1PUcTemrrqbqkd8TbG4i7bobSLvmurM6fzCkohn0KEcaqWvppKE1/HUsQXO5/QSC\np1/+RgJsFiPOZAs56VZy0mzkZdgZOyKJBFN8jPo8V4PxWhkIgykumqrSvu1Tmt97B39VJQAJo0aT\nuPhC7DNnY0hM7LeyohWXek8DB5sPo7QUc6jlCJ7gibVKk81JjErMpyApn9FJI8mz557T82+Nrk62\nHahn64E6yus6jr+en2Fnyug0Jo5MYXRO4nmN3B5M18tAETGJTCRsQg+DqaG0+lx8XP4J66o2EVSD\n5DtyuWPCzeQ5ek/N0bF7F7VPr0T1uEm7/kZSr7om4q2TNrefivoOKuo7KK9vp6K+g9omT49esWP0\nOolEm4kkm4nErq8km4lEqwmHzUii9djPJhwWY0ynDoiGwXStDKTBGBdNVXEX7sG1bg3uvUWgaSBJ\nmEcWYJs0GfPIAhLyR2JIS+vzQIVjcdE0DS0YQAuEv5B06ExGJKMJSX9+D/urmkplezVKSzFHXWWU\ntJXR7j+RaOklPSPsOYxw5JDnyCXPkUOOLRvTWSRxtc0e9hQ3srekCaWilWAo/J4gAblOO2NzE8nP\ncjDCaSc33XbWSdxgvF6iTcQkMpGwCT3Ee0PRNI2jbeVsqNrC9rrdhLQQKeZkrh1zBXMyZ6A7ac6m\nkNtN4xuv4Vr7MZLBQObn7iFx4SJUVaOuxUN5XVdiVhdO0lzunnP/mI16RjhtjMxJItFiICPZgjPF\ngjPZgsNijNsBAQMh3q+VWBnscQm0tND+6RbchXvoLD4ModCJjXo9hpQUDEnJ6BISkEwmdEYjWigU\n/gqGIBRCCwZQ/f6upMyP6g8ghYKEfL5wknYqej06iwWDIxG9w9H1lYghKQl9UhKGpOSun5MxJCae\nMcHTNI1mb0tX8lbOUVcZVR01hLQT/yadpCPLmhFO4rqSuWxbFnaj7ZTt2+cPoVS0cLjSRXGli6M1\nbfhP6mVPT0ogJ91GelICzmQL6UkWnMnhn7snc4P9eokGEZPI4i5hk2VZB/wVmAb4gC8pinKk2/Zr\ngP8FgsDTiqKsPNMxpyAStgjisaGE1BBH28o50KSwo34PDZ1NAGRanSzLX8IFmbN63eYIdXTQuvZj\nWv/7EaGOdnBmUb/sFo6G7JTXtVPR0IE/0PMNNjXRTH6GgxEZdvIz7ORl2HGmWNBJUlzGJdZETCIb\nSnEJdXbiLTmCr7wMX3kZgaZGAs3NhFyucC/cqUgSktF4PKGTjCaMFjMhSY/OZApvMxrDq4poKlqg\nK8Hz+1E9HoId7ahu9xnL0Nvt4eStK5k7ltTpLBYkowHJYAyXbzCAXo8k6QhJKk0+F7XeRuq8DdR4\nGqnx1uNXA6g6CU0CVQKLyYrTmoHT7iQzMYuspByyrBkkmRN7fTAMhlQqG8If+qoa3FQ2dFBZ33F8\n8MLJEkx6kuxmUuwmMtJsJBh0JNvNJNtNJNvNx3vsrQmGuB98FA1DqQ31p74mbNF86OZ6wKQoykJZ\nlucBv+96DVmWjcAfgDmAB9goy/JbwGLAHOkYYXAJqSGavC3Uuusob6+krL2SktYyvCEvAEadkbmZ\nM7kgaxYTUsehk3SoqkZLu4/mqno6Dh4gtG8PlvJD6EMBfDoTm9JmsS1xIup2F+BCr5PITrORn9mV\nmGU6yMuwR20UmCAMVnqLBdvkKdgmT+nxevi2ZhDN70cL+MPJkF6PpDeEe730+l69U+f6R1gLhQh1\ndBBqbyPochFyuQi6XARdrYRcrV0/uwg2NuCvrDjnf1tK19eEU+7RBJw4b0APRxJ0dJr1hKxmNLsV\nvcOBKSkFS0oa1hQnY1OczJyZjTVpNDq9Ho83QEOrl0ZXJw2tXhpcnTS2emnt8NHa4aOu2cPB8tZT\n1kAnSdgthuOPVSTaTDgs4UcuHNZur1nDvw/XBE84vWgmbIuAVQCKomyVZXlOt20TgWJFUVwAsixv\nAJYAC4D3T3HMWQmGVDp9weO/9/hcpxHu2vd5I2wL/6aqITq7korjx2igHd9b63aIhhbeeHzbsf00\n7cSuWrfXj5/z2Inp/uGz57nDP5zoPTrxk9bt3N32P34+DUdiAu1t3m5VU08+9ESdjr92okytZ3XQ\nNJWgGiSoBU9814IEQgF8IS8+tZPOoBdvyEtHoI2OYAcaGt3fctJ1NlKkAhL9qSR47IQOBCjs3MFe\n91qMbhcJnjac/mYSgx6OTS/bbHSwO3k6FXlTSMtI5pJUK3lOO/mZDnLSbXGxhqAgDFZSVw8aRiNg\ni04Zen1Xz1nSGUeuqj5fV1LXStDViur1ogWCJ56VCwbRVBVUtdv3UPh7SA2/d6kqhI5tD6GpKmoo\nhC/gxedzE+rowOR2Y3P50TcHgA6g/ngdNKC960uVwJegx28xEbSZCdoSsNqsjLRZGG0yo0+zYsi1\nIJnMGKxWWjwqvhD4/BoeP3QGoNMPHr+G2xfC3a7iag51vbdK4e+SFP6ZY39rJHToSDDrSTDpMRsN\nXd/1JJgMmE06EkwGTEY9Rr0Og15Cb9BhNOjQ6ySMBj0GnQ6DXodeHz69TpLC/9dSuLzjryGhO3n7\nyf9/PX45fRJ58la3x4qrxXPqHc7AoDMcn5+vPx9bkaynvk0eTUaDDvN5TOYczYQtEWjr9ntIlmWd\noihq1zZXt23tQNIZjjkjTdP4yZNbqW/tjLg9IeTla2VvkKCe5tmLOBHpUjqX/2YfcH7TUEZDM90/\n6UbiM1tpzRyHlpOPcdI0cuXRzE61YjQM3IzlgiDEhs5sxpSRARnRnUsOuj5w+3x4WhppaaqmrakG\nd0sDAVcrals7UocbnbsTk8ePpc2LqTny35Xu4m8dk8g0IHTGvc5fx5l3iYk9jrG8n7lwwMs16HU8\ndM9cnE5H347v5/p01wZ0r1X3xMt10jYH0HqGY05F6v6Pf+p/zzQf121n2C4MdX1tLEOZiElkIi6R\nDam45DkZxcRY10IYQIuAb8S6En0QzftJG4HPAMiyPB8o7LbtIDBOluUUWZZNhG+HbjrDMYIgCIIg\nCMNSNEeJSpwY8QlwDzAbsCuK8qQsy1cD/0c4aXxKUZTHIx2jKMqhqFRQEARBEARhkBgK87AJgiAI\ngiAMaWKInSAIgiAIQpwTCZsgCIIgCEKcEwmbIAiCIAhCnIvmtB5RJ8vyBGALkKEoir9rZOkjhJe7\n+lBRlJ/FtIIDTJblJOAFwlOjmIAHFEXZIuLSpyXPhqSuVUaeBkYCZuDnwAHgWcJzM+8FvqkoyrB8\nuFWW5QxgB7CMcDyeZZjHRZblHwPXAEbgL4RH8z/LMI1L1/vJSmA84Rh8mfC0Zs8yfGMyD/i1oihL\nZVkeS4RYyLL8ZeArhP8O/VxRlHdjVuEBcFJMZgCPEr5OfMDnFEWpP9eYDNoeNlmWEwkvXdVtWQIe\nB25XFGUxMK8rSMPJd4CPFEW5GLgbeKzr9b8xvONyfJk04EeEr5vh6k6gQVGUJcAVhK+R3wMPdr0m\nAdfFsH4x05XMPgG4CcfhDwzzuMiyfDGwoKvtXAyMRlwvywFb1/vpz4BfMoxjIsvyD4AnCX8AhAjt\nRpblLOBbwELgcuBXXVN6DUkRYvIIcK+iKEuB14EfyrKcyTnGZFAmbF3TfzwB/Bjo7HotkfA6pEe7\ndvsAuDQ2NYyZPwJ/7/rZCHTKsuwgnKwM57j0WCaN8Bq2w9UrhKfTgXD7DwCzFEVZ3/Xa+wy/6+OY\n3xH+0FfT9buISzg5KZJl+T/A28BbwOxhHpdOIKnr71AS4Gd4x6QYuJETC/REajdzgY2KogQURWnr\nOmZarzMNHSfHZIWiKMfmlTUSvoYu4BxjEve3RGVZ/iJw/0kvlwEvK4pSKMsyhINy8rJW7YQ/DQ5J\np4jL3Yqi7Oj6NPMP4D7CbyjDJi6ncF5Lng0liqK4AboS+VeAnwAPd9ulg/A1M6zIsnw34Z7HD7tu\nAZ5Y4DFsWMYFcAJ5wNWE3zfeRsRlI5BAeAL4NMK3i5d02z6sYqIoyuuyLBd0e6n79dF92clIy1EO\nSSfHRFGUWgBZlhcC3wQuJHyH45xiEvcJm6IoTwFPdX9NluXDwBe7kpYswr1G19BzWatEwstdDUmR\n4gIgy/JU4CXgu4qifNLV8zhs4nIKfVnybMiSZTmPcLf8Y4qivCTL8m+7bT62TNxwcw+gybJ8KTAD\neI5wsnLMcI1LI3BAUZQgcEiWZS+Q2237cIzLDwj3jPyPLMsjgDWEe02OGY4x6a77e+uxvzcnvwc7\ngJaBrFSsybJ8G/Ag8BlFUZpkWT7nmAzKW6KKooxTFGVp1/3gWmC5oijtgF+W5dFdXdXLgfWnPdEQ\nI8vyJMK9JrcrivIBQFdX67COC2LJs+O6npv4EPiBoijPdr28S5bli7p+vpLhd32gKMpFiqJc3PWe\nshv4HLBquMcF2EC4JwBZlnMAK7B6mMfFxoke+xbCHR/Dvg11EykWnwIXyrJs7hocN5HwgIRhQZbl\nuwj3rF2sKEpp18vnHJO472E7C91H4nwN+CegBz5QFGVbbKoUM78kPDr00a5bxa2KotyAiMsbwGWy\nLG/s+v2eWFYmxh4k3O3+f7IsH3uW7T7C14wJ2A+8GqvKxREN+C7w5HCOi6Io78qyvESW5U8Jf8D/\nBlDK8I7L74BnZFn+hHDP2o8JjywezjGBE3+Le7WbrlGijwKfEL6OHlQUxR+jeg4krWtU8Z8IP8r1\netff5rWKovz0XGMilqYSBEEQBEGIc4PylqggCIIgCMJwIhI2QRAEQRCEOCcSNkEQBEEQhDgnEjZB\nEARBEIQ4JxI2QRAEQRCEOCcSNkEQBEEQhDgnEjZBEIYtWZanyLKsyrJ8Y6zrIgiCcDoiYRMEYTi7\nh/Akp1+LdUUEQRBOR0ycKwjCsCTLsgGoJLwQ8yZgnqIoJbIsXww8CgSBLcBERVGWyrI8Fvgr4QW/\nPcC3FEXZHZPKC4Iw7IgeNkEQhqurgFJFUQ4D/wG+2pXEPQ/coSjKLMDPiSV3niO8Buts4KvAyzGo\nsyAIw5RI2ARBGK7u4UTS9W/gbmAmUK8oyrFFmJ8GJFmWbcBcwmtI7iK8Nq9NluWUga2yIAjD1VBY\n/F0QBOGcyLKcAXwGmC3L8n2ABCQDV9Lzg6zU9V0PdCqKMrPbOfIURWkZoCoLgjDMiR42QRCGo7uA\njxRFyVMUZZSiKAXAL4ErgGRZlqd07XcHoCqK0gYclmX5TgBZli8F1g58tQVBGK5ED5sgCMPR3cCP\nT3rtceD7wOXA87Isq4ACeLu23wn8TZblHwA+4NaBqaogCIIYJSoIgnCcLMsS8Gvgp4qieGRZfgDI\nVhTl+zGumiAIw5y4JSoIgtBFURQNaAa2dQ0uWEz4VqkgCEJMiR42QRAEQRCEOCd62ARBEARBEOKc\nSNgEQRAEQRDinEjYBEEQBEEQ4pxI2ARBEARBEOKcSNgEQRAEQRDinEjYBEEQBEEQ4tz/AyNTf0/5\nIrkAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bdc1ed0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Basic Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 2, 10)\n",
      "\n",
      "plt.plot(x, x, 'o-', label='linear')\n",
      "plt.plot(x, x ** 2, 'x-', label='quadratic')\n",
      "\n",
      "plt.legend(loc='best')\n",
      "plt.title('Linear vs Quadratic progression')\n",
      "plt.xlabel('Input')\n",
      "plt.ylabel('Output');\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gWnxcIqnFUYvGmFca2fZPb8oRERGJDL/jZ/GWd7nviz9RVFnMGTlTuXH8TxTK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\n1tpXjTEDG9mVCZQEPS8Dunp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       "text": [
        "<matplotlib.figure.Figure at 0x10c9ff410>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}