0006 Baby Name Diversity with Pandas.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "# turn of data table rendering\n",
      "pd.set_option('display.notebook_repr_html', False)\n",
      "plt.style.use('ggplot')\n",
      "pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'0.15.2'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The data set contains the top 1000 baby names in the US\n",
      "# for each year from 1880 until 2008\n",
      "names_df = pd.read_csv('data/baby-names2.csv')\n",
      "names_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "   year     name      prop  sex soundex\n",
        "0  1880     John  0.081541  boy    J500\n",
        "1  1880  William  0.080511  boy    W450\n",
        "2  1880    James  0.050057  boy    J520\n",
        "3  1880  Charles  0.045167  boy    C642\n",
        "4  1880   George  0.043292  boy    G620"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Explore the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get popular names for 1969\n",
      "names_df[names_df.year == 1969].tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "        year       name      prop   sex soundex\n",
        "218995  1969   Josefina  0.000067  girl    J215\n",
        "218996  1969   Marisela  0.000067  girl    M624\n",
        "218997  1969     Sallie  0.000067  girl    S400\n",
        "218998  1969      Suzan  0.000067  girl    S250\n",
        "218999  1969  Francisca  0.000066  girl    F652"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Separate the boys from the girls\n",
      "boys_df = names_df[names_df.sex == 'boy']\n",
      "girls_df = names_df[names_df.sex == 'girl']\n",
      "girls_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "        year       name      prop   sex soundex\n",
        "129000  1880       Mary  0.072381  girl    M600\n",
        "129001  1880       Anna  0.026678  girl    A500\n",
        "129002  1880       Emma  0.020521  girl    E500\n",
        "129003  1880  Elizabeth  0.019865  girl    E421\n",
        "129004  1880     Minnie  0.017888  girl    M500"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the number of boy baby name rows per year (showing first 5) \n",
      "boys_df.groupby('year').size().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "year\n",
        "1880    1000\n",
        "1881    1000\n",
        "1882    1000\n",
        "1883    1000\n",
        "1884    1000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the hierarchical index of baby name rows per year \n",
      "# grouped by gender for the year 1969\n",
      "names_df.groupby(['year', 'sex']).size().ix[1969]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "sex\n",
        "boy     1000\n",
        "girl    1000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the most popular boy name for the year 1969\n",
      "rowid = boys_df[boys_df.year == 1969].prop.idxmax()\n",
      "boys_df.ix[rowid]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "year           1969\n",
        "name        Michael\n",
        "prop       0.046564\n",
        "sex             boy\n",
        "soundex        M240\n",
        "Name: 89000, dtype: object"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Apply the get_max_proportion to the whole dataframe\n",
      "# to get the most popular name for each year (showing first 5)\n",
      "def get_max_proportion(group):\n",
      "    return group.ix[group.prop.idxmax()]\n",
      "    \n",
      "popular_boys_df = boys_df.groupby('year').apply(get_max_proportion)\n",
      "popular_boys_df.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "      year   name      prop  sex soundex\n",
        "year                                    \n",
        "2004  2004  Jacob  0.013196  boy    J210\n",
        "2005  2005  Jacob  0.012148  boy    J210\n",
        "2006  2006  Jacob  0.011331  boy    J210\n",
        "2007  2007  Jacob  0.010948  boy    J210\n",
        "2008  2008  Jacob  0.010355  boy    J210"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the proportion of the most popular boy name per year\n",
      "# Maybe showing more diversity in chosen boy names over time\n",
      "popular_boys_df.prop.plot(legend=True, title='Proportion most popular boy name', \n",
      "                          kind='area', color='#00A99D', alpha=.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xd4282b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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w8HCWkZHBWltbGWOMtbe3s7lz5zK9Xs9kMhmLiIhgf/zjH1lVVZVr+927d7MJEyYwqVTK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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd428278>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set the index to a name/year combination\n",
      "boys_index_df = boys_df.set_index(['name', 'year'])\n",
      "boys_index_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                  prop  sex soundex\n",
        "name    year                       \n",
        "John    1880  0.081541  boy    J500\n",
        "William 1880  0.080511  boy    W450\n",
        "James   1880  0.050057  boy    J520\n",
        "Charles 1880  0.045167  boy    C642\n",
        "George  1880  0.043292  boy    G620"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Narcissistic query for the popularity of my first name\n",
      "leon_popularity = boys_index_df.ix['Leon']\n",
      "leon_popularity.prop.plot(kind='area', color='#00A99D', alpha=.5, \n",
      "                          legend=True, title='Popularity of the name Leon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xb722780>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QRbOXR6yzo60ZBklBiiRib2sT6oIqxhqMUGK0zLQoilB0/Z+FPJwoAESvB3WM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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb722b38>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# My name was the most popular in the year...\n",
      "boys_index_df.ix['Leon'].prop.idxmax()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "1918"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Highest boy name proportion ever...\n",
      "boys_index_df.prop.idxmax()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "('John', 1880)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the mean proportion for each girl name in the set\n",
      "# Show first 5 in descending order\n",
      "mean_prop = girls_df.groupby('name')['prop'].mean()\n",
      "mean_prop.order(ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "name\n",
        "Mary         0.034976\n",
        "Jennifer     0.011555\n",
        "Elizabeth    0.010791\n",
        "Margaret     0.010550\n",
        "Helen        0.009568\n",
        "Name: prop, dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Retrieve summary statistics for each year in the data set\n",
      "result = girls_df.groupby('year').describe()\n",
      "result.head(24)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                   prop\n",
        "year                   \n",
        "1880 count  1000.000000\n",
        "     mean      0.000935\n",
        "     std       0.003270\n",
        "     min       0.000041\n",
        "     25%       0.000061\n",
        "     50%       0.000123\n",
        "     75%       0.000400\n",
        "     max       0.072381\n",
        "1881 count  1000.000000\n",
        "     mean      0.000933\n",
        "     std       0.003214\n",
        "     min       0.000040\n",
        "     25%       0.000061\n",
        "     50%       0.000121\n",
        "     75%       0.000407\n",
        "     max       0.069986\n",
        "1882 count  1000.000000\n",
        "     mean      0.000931\n",
        "     std       0.003211\n",
        "     min       0.000043\n",
        "     25%       0.000061\n",
        "     50%       0.000121\n",
        "     75%       0.000406\n",
        "     max       0.070431"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Calculating Baby Name Diversity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a single year of girl names and sort by proportion descending\n",
      "girls_df_1972 = girls_df[girls_df.year == 1972]\n",
      "girls_df_1972_sorted = girls_df_1972.sort_index(by='prop', ascending=False)\n",
      "girls_df_1972_sorted.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "        year      name      prop   sex soundex\n",
        "221000  1972  Jennifer  0.039447  girl    J516\n",
        "221001  1972  Michelle  0.018157  girl    M240\n",
        "221002  1972      Lisa  0.017086  girl    L200\n",
        "221003  1972  Kimberly  0.016310  girl    K516\n",
        "221004  1972       Amy  0.016042  girl    A500"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What is the proportion of this top 1000 girl names of the total names?\n",
      "girls_df_1972_sorted.prop.cumsum().max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0.84374700000000091"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Where is the 50% crossover point for cumulative sum\n",
      "girls_df_1972_cumsum = girls_df_1972_sorted.prop.cumsum()\n",
      "crossover = girls_df_1972_cumsum.searchsorted(.5)\n",
      "girls_df_1972_cumsum[(crossover[0]-2):crossover[0]+3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "221088    0.496577\n",
        "221089    0.498685\n",
        "221090    0.500773\n",
        "221091    0.502820\n",
        "221092    0.504862\n",
        "Name: prop, dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The crossover can be seen as a meassure of diversity\n",
      "# It's the number of names accounting for 50% of all the names given\n",
      "# So the higher, the more creative name giving\n",
      "crossover[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "90"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's calculate this crossover point for boys and girls per year\n",
      "def get_crossover(group, quantile):\n",
      "    df = group.sort_index(by='prop', ascending=False)\n",
      "    return df.prop.cumsum().searchsorted(quantile)[0]\n",
      "\n",
      "q = .5\n",
      "\n",
      "girls_crossover = girls_df.groupby('year').apply(get_crossover, quantile=q)\n",
      "girls_crossover.name = 'girls'\n",
      "\n",
      "boys_crossover = boys_df.groupby('year').apply(get_crossover, quantile=q)\n",
      "boys_crossover.name = 'boys'\n",
      "\n",
      "# Plot the crossover counts over time\n",
      "girls_crossover.plot(kind='area', color='#00A99D', alpha=.5, legend=True)\n",
      "boys_crossover.plot(title='Baby name diversity', kind='area', \n",
      "                    color='#F5CA0C', alpha=.5, legend=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xb74e8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hw+LFi7n11lvZv38/8+bN4/3332+xi+Xtt9/mtddeY//+/ZhMpkbrfvnLX3LXXXdx8OBB\nPvvsM771rW8FPV7pihFCiDbs2rULv9/P97//fQCuu+46Jk6c2Oy2mqbx/e9/n0GDBjW73mKxcOzY\nMex2OwkJCVx88cVBj1da7EII0Ybi4uImhXrw4MHNPvu5fl1LnnnmGY4ePcqMGTOYM2cO//rXv4Ia\nK0iLXQgh2pScnExhYeMLuKdOnWLYsGHNbt/aCJjhw4ezZs0aAP7xj39w9913s2/fPiIiIoIWr7TY\nhRCiDRkZGZhMJv7whz/g8/l4//332bNnD0CLrfaW/PWvf6WsrAz4Zg4YXQ9uKZYWuxAipCVbLFyX\n1Hx/dbCO35awsDDWrVvHgw8+yBNPPMHMmTOZNWsWYWFhHR6jvmnTJh577DFqa2sZOnQozz//PFar\ntSspNCFPUOoG/e3pL/0tH+h/OfWXfPpSHnPnzuXOO+9s9pmjwdbS70WeoCSEEF3w+eefU1JSgs/n\nY8OGDRw8eJCZM2f2dljNkq4YIYRohyNHjvDDH/6QmpoazjvvPF544QWSkpJ6O6xmSWEXQoh2mD9/\nPvPnz+/tMAIOVFfR0qBK6YoRQog+xq8U/5t3pMX1UtiFEKKPOeWupdLnbXF9q10xHo+HZcuW4fV6\n8fl8TJ48mdtvvx2n08mqVasoLS0lKSmJpUuXEhUVBUBWVhbZ2dnous6iRYuYMGFCcDMSQohz3K6q\ncsJaGfveamG3WCz86le/wmq14vf7+eUvf8nBgwfZuXMn48eP58Ybb2Tjxo1s3LiR+fPnk5+fz9at\nW1m5ciV2u53ly5ezevXqoA++F0KIc9mOCgeDwsJbXN9mxa0fOO/z+TAMg6ioKHbu3MmMGTMAuPLK\nK9mxY0fdyXbsYNq0aZjNZpKTk0lJSSE3NzcYeQghhAC8hsEJdy0JYWEtbtNmYTcMg5/97Gfcdddd\npKenM3ToUCoqKoiLiwMgNjaWiooKABwOB4mJiYF9ExMTsdvtXc1DCCF6VU8/2q41ea4aUGBu5W7X\nNoc76rrO008/TU1NDY8//jj79u1rtL6tW2mbW5+Tk0NOTk7gdWZmZmDOhP7AYrFIPiGuv+XUX/I5\ne+5yAJMqQzdKu+2chj4Av5bY6jY9/Wi7s5lMpsD7u7+ijBirFcuZ3pQNGzYEtktPTyc9Pb3949gj\nIyOZNGkSR48eJTY2lvLycuLi4nA4HMTGxgKQkJAQmNwGoKysjISEhCbHqj95Q33lNuL26Eu3RbdH\nf8sH+l9O/SWf5j6cdKMUU/UH3XfSqNn4Ta0X9t7m9/sD7+8nJYXEazoetxug2SkNWu2KqayspLq6\nGqgbIfPVV18xfPhwMjIy2LRpEwAff/wxkydPBupmQNuyZQs+n4+SkhKKiooYOXJk0JITQoje8uWX\nXzJz5kzS09P56U9/ivtMYX399deZNm0a6enpLFq0iOLiYgAeeeQRHnvssUbHWLhwIevWrQNgzZo1\nXHLJJYwePZrp06fz6aefthmD2zA45XYRa265fx3a6IopLy9nzZo1GIaBUorp06czbtw4hg8fzqpV\nq8jOzg4MdwQYMmQIU6dOZenSpZhMJhYvXtyrX1+EECIYlFJs3LiRN954g4iICBYuXMjq1au5/PLL\nefLJJ/m///s/LrjgApYvX869997LX//6VzIzM1m8eDGPPvoomqZht9v59NNPWbFiBbm5ufzxj3/k\n3XffJTk5mVOnTuHz+dqM41htNTpgaqOutlrY09LSeOqpp5osj46O5tFHH212n5tuuombbrqpzQCF\nEKKv0DSNhQsXBp6i9OMf/5hHH32UkpISbrvtNi666CIA/t//+39ceOGFnDp1iokTJ2Kz2di8eTPT\np0/n7bff5vLLLycxMZHKyko8Hg9ff/018fHxpKamtiuO3ZUVRDRzHeJsMsBcCCHaoeEUuampqRQV\nFVFcXNxoeWRkJPHx8YGnLd1888289dZbQN0DNr7zne8AdU9R+vWvf83KlSuZOHEi9957b6ALpzV7\nqstJCmt7/ngp7EII0Q6nTp1q9HNKSgoDBw5stLympgaHwxFo2d9000188MEH5OTkcOTIEf7zP/8z\nsO28efPIyspi27ZtaJrG448/3ur5/UpR4HYR00b/OkhhF0KINimleOWVVygsLMThcPC///u/3HDD\nDdx44428+eab5OTk4Ha7efLJJ7n44osDXSuDBw9m/PjxPPDAA8yZMydww+eRI0f49NNPcbvdWCwW\nrFZrs0M9GypyuzDaGL9eT6btFUKENEMfAFGzu/f4bdA0jW9/+9vcfvvtFBcXc+211/LAAw8QHh7O\nz372M5YsWUJ5eTmTJ0/mueeea7TvLbfcwgMPPMDy5csDyzweD08++SSHDx/GbDYzefJkfvOb37Qa\nQ051JRa9fYNR5NF43aC/jCmu19/ygf6XU3/Jp7/k0dC2bdu4//772b59e6ePYbPZ+O+vvqDE42aw\ntW6OGB14euqMZreXrhghhOgmXq+XdevWcfvtt3fpOEopcmudrc4P05AUdiGE6AaHDx/mwgsv5PTp\n09x1111dOpYfRY3fj1VrX8mWPnYhhOgGo0aN4vDhw0E5Vq3fj1nT233Dp7TYhRAixDn9fqLbcWNS\nPSnsQggR4qr9PgaY274xqZ4UdiGECHEepYg2t7/nXPrYhRAhxWazYTKZ8Pv9vR1KUHU2p2q/j7LK\ncvQOTKgohV0IETLqx7D3x/Hsnc1pfeFJPi0vY1RkdLv3ka4YIYQIYbuqykkKs3ZoHynsQggRomr9\nfoo9HmI60L8OUtiFECJk5dY4MWl0qH8dpLALIUTI2l7pIFrv+KVQKexCCBGi9jgrSLa0f/x6PSns\nQggRgqr9Pkq9HqJN0mIXQoh+4VCNE7OmtXt+mIaksAshRAjaXuHo8GiYelLYhRAixHgNg22VDlI6\nOH69nhR2IYQIMbuqyvEqg4hO9K9DG1MKlJaWsmbNGioqKtA0jauvvprrr7+eDRs28NFHHxETEwPA\nbbfdxqRJkwDIysoiOzsbXddZtGgREyZM6FRgQghxrsoqKWBwJ1vr0EZhN5vNLFiwgGHDhuFyuXjo\noYcYP348mqYxd+5c5s6d22j7/Px8tm7dysqVK7Hb7SxfvpzVq1ej6/LFQAgh2qPQ7eKku5aLo2M7\nfYxWK25cXBzDhg0DIDw8nNTUVOx2O1D3DL6z7dixg2nTpmE2m0lOTiYlJYXc3NxOByeEEOeav50u\nJMYU1qnRMPXa3ZQuKSnh+PHjXHDBBQC89957/OxnP+P555+nuroaAIfDQWJiYmCfxMTEwAeBEEKI\n1rkNg0/L7aRZw7t0nHb1zLtcLlauXMnChQsJDw9n9uzZ3HzzzQC8+eabvPrqq9xzzz3N7tvcp05O\nTg45OTmB15mZmdhsts7EH5IsFovkE+L6W06ST+hrT05f2U8TFmbCFhnZ5vHqW+UbNmwILEtPTyc9\nPb3twu7z+VixYgX/8R//wZQpUwCIjf2m7+eqq67iqaeeAiAhIYGysrLAurKyMhISEpocs/7kDfWn\nuZf721zS/S0f6H85ST6hrz05ZReeIkppeNzuNo9XX9gzMzNbXNcspRRr164lNTWVOXPmBJY7HI7A\nz9u3byctLQ2AjIwMtmzZgs/no6SkhKKiIkaOHNlmgEIIca5TSrGvupKksI7PDXO2VlvsX3/9NZs3\nbyYtLY2f//znQN3Qxi1btnD8+HE0TSMpKYklS5YAMGTIEKZOncrSpUsxmUwsXry4SxcAhBDiXOHw\neqj2+4gwmbp8rFYL+5gxY3jzzTebLK8fs96cm266iZtuuqnLgQkhxLlkf00VFk0PSmNYBpgLIUQI\n+LzCTmwn54Y5mxR2IYToZUopDlY7SbJ0/m7ThqSwCyFELyv1uKk1/ITrXe9fBynsQgjR6/bVVGEJ\nUlEHKexCCNHrPq9wkGAOC9rxpLALIUQv8ivFoZoqBgRh/Ho9KexCCNGLDtc48SmFJYiz4EphF0KI\nXvRhWUnQhjnWk8IuhBC9xKcMdlaVk2qJCOpxpbALIUQvOVjtxAhyNwxIYRdCiF7zgb2Y+CCOhqkn\nhV0IIXqBxzD4sqqSwV18qEZzpLALIUQvOFBdiUIR1g3PhJbCLoQQPcynDF4pPMnAsODMDXM2KexC\nCNHD3iouoNTrJSVIk36dTQq7EEL0oGO1NWSdLmRsRFS3PYhICrsQQvQQr2Gw4uRhhlrDsQThSUkt\nkcIuhBA9ZH91JRU+LwO7YSRMQ1LYhRCih/zbcZoEU/DHrZ9NCrsQQvQAn2Gwp6qSlG5urYMUdiGE\n6BGHq6swCP70Ac2Rwi6EED3gX6eLsJmCO4tjS6SwCyFENzOU4nNHKYO7adz62Vr9+CgtLWXNmjVU\nVFSgaRpXX301119/PU6nk1WrVlFaWkpSUhJLly4lKioKgKysLLKzs9F1nUWLFjFhwoQeSUQIIUJV\nnruGWsMgooda7K2exWw2s2DBAoYNG4bL5eKhhx5i/PjxbNq0ifHjx3PjjTeyceNGNm7cyPz588nP\nz2fr1q2sXLkSu93O8uXLWb16NXoP9CkJIUSo2uwoI7obx62frdWKGxcXx7BhwwAIDw8nNTUVu93O\nzp07mTFjBgBXXnklO3bsAGDHjh1MmzYNs9lMcnIyKSkp5Obmdm8GQggRwpRSbKmwkxoe2WPnbHdT\nuqSkhOPHjzNq1CgqKiqIi4sDIDY2loqKCgAcDgeJiYmBfRITE7Hb7UEOWQgh+o4Ct4sKn5foID/+\nrjXtOpPL5WLFihUsXLiQiIjGj3Bqa66D5tbn5OSQk5MTeJ2ZmYnNZmtPKH2CxWKRfEJcf8tJ8gld\nW+0lxFvDMZvNQZ0bpr5VvmHDhsCy9PR00tPT2y7sPp+PFStWMH36dKZMmQLUtdLLy8uJi4vD4XAQ\nGxsLQEJCAmVlZYF9y8rKSEhIaHLM+pM3VFVV1eHEQpXNZpN8Qlx/y0nyCU1KKT4sPsUQSzh+vx+P\n2x20Y9cX9szMzBbXtRjU2rVrSU1NZc6cOYHlGRkZbNq0CYCPP/6YyZMnB5Zv2bIFn89HSUkJRUVF\njBw5MjhZCCFEH3PSXYPT7yOqB7thoI2umK+//prNmzeTlpbGz3/+cwBuv/125s2bx6pVq8jOzg4M\ndwQYMmQIU6dOZenSpZhMJhYvXtxt01IKIUSo+1fZaWJ6YG6Ys2lKKdXjZ21GQUFBb4cQNP3la2S9\n/pYP9L+cJJ/QYyjFkoO7GWaNIMJkxmK1Br0r5umpM1pcJ4QQIsiO1lbj8vt77KakhqSwCyFEN3iv\ntJg4c893w4AUdiGECDqPYbC9qpxUa0TbG3cDKexCCBFku6vKQfXMFL3NkcIuhBBB9vfSIlJ6aCbH\n5khhF0KIICr3ejhSW02SFHYhhOgfPi4vJUI3offiPTxS2IUQIkiUUnxgP82QHniuaWuksAshRJCc\ncrtweL099gi8lkhhF0KIIPm34zQ2k6nXp1KRwi6EEEGglGJLeRmplt7thgEp7EIIERQnXDVU+/09\nPpNjc6SwCyFEEPzLfpqYXu5bryeFXQghushQis8q7KRae2/sekNS2IUQoouO1lbjMnpnJsfmSGEX\nQogu+rCspNdmcmyOFHYhhOgCr2HweaUjJEbD1JPCLoQQXbDXWYmBwmoy9XYoAVLYhRCiC/5WWsjA\nsNC4aFpPCrsQQnRSpdfLoRonyb04k2NzpLALIUQnfVJeSrhuwtTLUwicTQq7EEJ0glKK9+0lDAmh\ni6b1pLALIUQnnHTXUOb1EhMCUwicrc2InnvuOXbv3k1MTAwrVqwAYMOGDXz00UfExMQAcNtttzFp\n0iQAsrKyyM7ORtd1Fi1axIQJE7oxfCGE6B0bik8xICys12dybE6bhX3mzJlcd911/O53vwss0zSN\nuXPnMnfu3Ebb5ufns3XrVlauXIndbmf58uWsXr0avZce6CqEEN3B7nGzq6qCSdGxvR1Ks9qsuGPH\njiUqKqrJcqVUk2U7duxg2rRpmM1mkpOTSUlJITc3NziRCiFEiMg6XUiM2RxyF03rdbpz6L333uOT\nTz5hxIgR3HnnnURFReFwOBg1alRgm8TEROx2e1ACFUKIUFDj95HtKOXCyOjeDqVFnSrss2fP5uab\nbwbgzTflhe8hAAAgAElEQVTf5NVXX+Wee+5pdtvm+p9ycnLIyckJvM7MzMRms3UmlJBksVgknxDX\n33KSfHrOe6dOEGmxYIuM7NB+JpMJSxBnf6zvbtmwYUNgWXp6Ounp6Z0r7LGx3/QrXXXVVTz11FMA\nJCQkUFZWFlhXVlZGQkJCk/3rT95QVVVVZ0IJSTabTfIJcf0tJ8mnZ5R63LyRd5RR4VF43O4O7Wux\nWju8T2vqC3tmZmaL6zrE4XAEft6+fTtpaWkAZGRksGXLFnw+HyUlJRQVFTFy5MjOnEIIIUKKy/Cz\n/NjXJJgtRIbgEMeG2ozu2Wef5cCBA1RWVnLPPfdwyy23sH//fo4fP46maSQlJbFkyRIAhgwZwtSp\nU1m6dCkmk4nFixeH5FAgIYToCEMpVp88Qo3hY0xkaHYRNaSp5oa39IKCgoLeDiFoQvVrZGf1t3yg\n/+Uk+XSvd0oK2FBSwIToGPRONla7oyvm6akzWlwnhBCiBeVeD2+WnGJMZFSni3pPk8IuhBCteKng\nBDGmsJB57F17SGEXQogWHK5xsqOynOERHRva2NuksAshRDMMpXg+/yhDreEhe4dpS6SwCyFEMzY5\nTlPi9TAwxB6i0R5S2IUQ4izVPi+vFOYxKjyqTw7ZlsIuhBBneaM4H4umER3iNyK1RAq7EEI0cMpd\ny0f2UkZGNp3Vtq+Qwi6EEA28dOo4A61WzFrfLY99N3IhhAiyUo+bg9VOUkPwOaaNKSZqH7a4Vgq7\nEEKc8Y+yIqLN5pC/w3QIX3OR9kmL66WwCyEE4DUMsh2lpFlDu7Vuwss0PYtSNaTFbaSwCyEEsLOq\nHMNQIT91wEXaZnTlpZaYFreRwi6EEEBWSQGDQ7y1HkkF47VNnNaGtrqdFHYhxDkv31VDnruWAWGW\n3g6lRTp+ZmpvUKNi8NN6nFLYhRDnNKUULxacYGCYNYTvMlVcpr1NLKexayltbi2FXQhxTvuyqoLD\nNU6GhHA3zGi2MVLbRZE2HGj7w0cKuxDinOUxDNaeOsbw8MiQba0P5BiX6X+ngBGodpZsKexCiHPW\n30uLcCs/CSHatx6Fg1n6q5QwBD/tn2VSCrsQ4pxU6fXyVkkBo8KjezuUZplxc63+MtUqBhcde4C2\nFHYhxDnpLyWniNRNhJtMvR1KExoGM7Q/EabcVGjJHd5fCrsQ4pxT7vXwL8dphodH9HYozZqkfcgg\n7SglWlqn9pfCLoQ457xZnI/NZMYSgq318/iKcdonFNK+ETDNafPe2eeee47du3cTExPDihUrAHA6\nnaxatYrS0lKSkpJYunQpUVF1cxdnZWWRnZ2NrussWrSICRMmdCowIYToDmUeN5+UlzEuquVb8ntL\nPIVM1/9MIcMwCOv0cdpssc+cOZNHHnmk0bKNGzcyfvx4Vq9ezUUXXcTGjRsByM/PZ+vWraxcuZJH\nHnmEdevWYRhGp4MTQohgMpTi96eOE2MKI0wPrQ4LK9Vcq7+Mg2S8RHbpWG1mNnbs2EBrvN7OnTuZ\nMWMGAFdeeSU7duwAYMeOHUybNg2z2UxycjIpKSnk5uZ2KUAhhAiWDcWnOFBTxfkRXSucwabj4xrt\nj3iVhWrig3C8TqioqCAuLg6A2NhYKioqAHA4HCQmJga2S0xMxG63dzlIIUT/YSjFtgo7R2udeA0D\npRQlbhdbysuo9Hq77byfl5fx9ukCLoyIDrGbkRTTtLeIxo5dGxSUI3Z5fsq2fkHNrc/JySEnJyfw\nOjMzE5utY+M0Q5nFYpF8Qlx/yylU81FKUeP3E3XmodCGUvz22CE+Ki3CrOlogFnX8fj9aJpGrDmM\nZ9InBT2f4zVOnis6ybi4RKLDOt933RUmkwmL9aybjJTBpcZfGc4BCrkAU4cex1dXWzds2BBYkp6e\nTnp6eucKe2xsLOXl5cTFxeFwOIiNjQUgISGBsrKywHZlZWUkJCQ02b/+5A1VVVV1JpSQZLPZJJ8Q\n199yCsV8qn1e/jf/KF9WVXBJTBy3DxxCVkkh26scjI+KwaRp+JXCrxQWa11BO+Gq4cd7trN6/BQs\nHk9Q4qj0enkodx+DTGashoHH7Q7KcTvKYrU2OreGwTTtr5yn7SOfkSgMoAPXJFXd7ywzM7PJqk51\nxWRkZLBp0yYAPv74YyZPnhxYvmXLFnw+HyUlJRQVFTFy5MjOnEII0Ud5DYN9zkoeOLyPE7U1XGyL\nJc9Vy88O5/BFg6IOYNI0LA0uYp4XHolV07k/Zye5Nc6gxPI/Jw5h1XSSQ2aSL0USJ7heW8t5Wg4F\njGz3HDDtpSmlVGsbPPvssxw4cIDKykri4uLIzMxk8uTJLQ53fOutt8jOzsZkMrFw4UImTpzYrkAK\nCgq6nk2ICMXWU1f0t3yg/+UUCvkccFbyStFJTrldKGCoJZwBlm+6HpRS7e7bLkdxuLKCzIGpzEsa\n1KlnkDq8Hl4sOM4BZxXpUbZe71e3WK3o7lJmaa+RoBVSwQAqSaSzY9VROnMvW9vsqjYLe0+Rwh66\n+ls+0P9y6u189lRV8OSJQ6RZw0kMswZa5J1lsVqpqqnhQK0Tq64zOiKay2ITuDwusc1jV3g9vFaU\nz9aKMmLMZkaER3U5nmAIt+hc412NlVrKtMF0uqDXa6Wwh/bD/YQQIe+LCgdPn8xldGQUMebgXZi0\nmkxMjI7FZfgp9Lj4Xf5R7F4PNyYPbnGfLyvLWZV3hAhdZ0J0DOYOXYzsToqpxgaiqaBIG0aXi3ob\npLALITrFUIp3Thfyp+J8xkRGYwtiUW8oXDeRao0g0WzhT8WnuCQmjiHhjcehew2D9YUn+cBewqjI\naGK7KZbOSudT0thHnnY+3V3UQQq7EKITKrweVuYd4WhNNeOjYrD2wJwr4SYTg63hrDiZy9OjLgq0\nxovdLp46cQi718tEWxzmEOh2aSieQi7R36eI0UG/SNoSKexCiA457XHziyP7MWsaE6JjevSi5CCL\nla+qq1h5IpfB1nB8SvGhvYRki5WLQuAC6dlMeJmpv045yfg1C+DrkfNKYRdCtFuxx8UvcvcTYzaT\nau35KW81TWNsZBTHXTUcra0GYHREdOAGqFAzSfsAq6rBoQ2iJ+eRDM3fhhAi5JR43DySu59Ycxip\nvTgmPEw3cX5EVNsb9rJUDpKufUY+o3r83FLYhRBtqvH5eOzYwTMt9VC50Sc06fi4RHuPsdrnFDMU\noxfKrBR2IUSrDKVYlZeLxzAYHhmazwcNFVHYmaWtJ4pyTnEBRo92wHxDCrsQokVKKd4oyuNgjZMJ\nIfhgitChGMZertDfwqniKNRG0BPDGlsihV0I0SylFG8Wn+IfpcU9PvqlL9EwuEx7m1HaFxRxHl6t\n9+d6l8IuhGhCKcXrRXn8s6yY8VG2kHvaUKjQ8HOF9lfStP3k9+A49bZIYRdCNOI2DNadOsbWCkeI\n3ZYfWjT8TNfeZIh2mELOD5miDlLYhRAN5LtqePLEYWr9fiZEx4TE5FmhSMfPDO3/GKwdoYARdHIG\n9G4jhV0IgTpzB+cfC08y2BrB8PDe7ycOVTo+ZmqvM5ATIVnUQQq7EOcUv1JNWuHVPi+/zT/GXmcl\nF0ZGE2GSstASE16u1l4jkVO9PvKlNfIOCtGPOX0+cmud7KwsZ6+zglKvh3hzGBdFxRAfZmFXVTmn\n3LXYTGYmRcd06oEW5woTXq7RXiGeIoq04YRqUQcp7EL0K06fjwPVVeysdLCvupJynxeTphFtMpMc\nZmGoNYIav4+c6ko8SpEUZmFCdKz0pbfBjIfZ2h+IobRH5lPvKinsQvQDpW4XL+UfZUt5GWZNx2Yy\nkRxmYXh4ZJPx59HmMKJDbL7yUGbGxbXay0RTTrF2HqFe1EEKuxB9WrHbxZ+K89lZXUU0GuNleGJQ\nhVHLf2ovEUEVJVoafaGogxR2IfqkQreL14vy2FVZTlxYGBfHxWN4vL0dVr9ixs312otYqeG0NpS+\nUtRBCrsQfUqBu5ZXCk+yt6qSxLAwJtrq+sfNmo6nt4PrRzQMZmpvEI6zT7XU60lhF6IPUErxflkJ\nrxSdZECYlYttsTKCpRtN0j5koHaCU4ykrxV1kMLeJ/mUQYHbRYXXy4UhfHegxzA46arhlNsF1P33\nGBIezlBrpMw90k5ewyDfXcv/FeeT46wkXcaZd7vhfMlF2uY+W9Shi4X9vvvuIyIiAl3XMZlMPPHE\nEzidTlatWkVpaSlJSUksXbqUqKjQf9pJsBhKYfd4cHpb/mJ82uPhS2c5e5wVeAwFQJRu4pKYWMZF\nx1Hl87LLWcGRGidjI21MjInDqmt8WVXJzkoH+e5a6nZT2Mxh3J6cykW2WABMmobNZO7yTHy1fj8u\nww+At0E+Dp+HPVWV7Koqx2UYLe7vVQalHg9oYGlwMc9jGGgaDAizYNZ0FJAUZmFyTBzpUTEkW6yN\nYm8YR0NRJjOWFj4c3IaBDn36w8Pu9fBc3lG+rnWiFMSawpgYLa307qTjY7L2LmO0bRQyDIO+O3JI\nU0qpzu5833338dRTTxEd/c3k++vXr8dms3HjjTeyceNGqqurmT9/fpvHKigo6GwYIcGvFJ9X2Hmj\nKA8nCr+v5YfWappGuG4iKcwSKHq1ho8yrxeX4UfXdKJ0E3FmM3aflxq/HzQI13UGmK3EhZkDIx/K\nvV5OuGowqHsbFRrhus7IyGjGREYH2hvDIiIZFRlNVAutvWq/j9waJzvO3MhS5vWinTmmyWwO5FMX\nu06i2UK43vpDBCJNpma/TfiVqssJUCiq/H4cPi8ew8Cia5wfEUWyJZx9Z8XRkFnXuWHAIK5LHAhA\nbo2TXc4K9lSVU+xxo6Ex2BrOxbY4JtliGRYRhbVBobfZbFRVVbUaf2/ZWeFgdf4R4kxhDA2PaNc3\nMovVisft7oHoekZP5xNNGbO09URSSbF2Xrc8IMNkMuP3B/Fh1kpn7mVrm13V5e90Z38u7Ny5k2XL\nlgFw5ZVXsmzZsnYV9r7CZfg5UVvD7qoKdlWV4zxToFyGH68yOM8awZhoW4f/KG2YSbY0feTYAIu1\n1f3iwsKIC4tttMxt+Cn1uPlnjRMApdW1lP1KEWUyNxkO51MG1X5foxtZ0qwRgZZzsP+TmTQNW4OH\nD8c0eIamxzCwez0Uul0knRVHQ7V+H38/XcRfSwqAutvko3QzSWEWBkWHo4BKn5ePHKf5R2kRBjAu\nOoYfDRkesmO4C9wuXi86yReVFVwQGUVsiMbZvyhGsIdp+ltUqXgKQ/yO0vbqUmHXNI3ly5ej6zqz\nZs1i1qxZVFRUEBcXB0BsbCwVFRVBCbQn+ZWiyO1ib1UF+Z5aALyGIrfWSbHHjQmN8DM3gAw+U3h1\nTWvUIuxNVt3EYKuJwWc9m1IphVsZnP0dTdPAqukh8SAFi66T0o5nakaYzKRH2/AaBmZNazb2+DAL\n8WEWoK6L7KirmgcOfcWDaaOYYrMFPfbOynfV8FphHnuddSNdLrbJnaA9wYyHy7W3GKbtC5kHZARL\nlwr78uXLiY+Pp7KykuXLl5OamtpofUuFIicnh5ycnMDrzMxMbD30H63W7yevthr3Wf3DhlLkVjvZ\n5ijleK0Tn1KE6yYiTKZAv2Z8eCTntWM0gslkwmJtvaXdWzoTVSjnY+nAtheGh1PmdvH/5R3mNnxk\npgzt1QJ6tNrJSyePsK+qnGSrlUsHJHc6nlB+jzqju/OJNQq5Sr2Mjo8C0kHTu/3ppLqmQVAvfNf9\nrWzYsCGwJD09nfT09K4V9vj4eABiYmKYMmUKubm5xMbGUl5eTlxcHA6Hg9jY2Cb71Z+8oWD2d9aP\nGtlbVcH+6ir8SqFQlHg9nPa40dFo7v+PRdMZEBbGKEtE8xfelMLnaXu0sPR3hi4bGhdZI/nrqRNs\nKSrgZ+eNIiGsIx8PXaOU4khtNa8W5nG4xslAq5UJEdHomobf46HpZeL26U/vEXRvPqPYzlT9bzhI\nwskgwDjzr5t1Qx871DWMz9bpwu52uzEMg4iICFwuF3v37uXmm28mIyODTZs2MW/ePD7++GMmT57c\n+cDbyWsY5Llr2HNm1EjemVEj4bpOrDks8Ekcq5tJjQ6XkQXnOIvJxKSIeHIry3ng0F5+PGQEk2MT\ngnqOWr+fo7XV2H11DQGPYbDHWcHBaidOv4+BYRYZi94LxrKVKfo/KOB8fJ36/to3dLqwV1RU8PTT\nTwNgGAZXXHEFEyZM4Pzzz2fVqlVkZ2cHhjt2Ra3fj0cZgZ/3V1eyo7Ic+5nhd16lKPG40RqMGkmP\nssl8GaJVmqZxXngk8T4vK/KOcJWzgsyBQ5pcNnP66mZC3FlVToxuJiM2njFRNmLPGlJaN6qomp2V\njjPT43oxaQT+DjUUNpOZNGs44bopJK5nnFsU6XxKhv4epxiJv0OdeH1Pl4Y7BtPv93zR6HW+qzYw\n7ah25r+bAqy6Trw5jGhTXTtcQ2txWF1vka/Foa9hTn6l+LrG2eS6CwAaWDWNOLMFr2FQafjwGgZR\nJjNjoqKJ1Ex8VVOFw+vB3GBUUXQQ7iXobD79QTDy0fExnD2M0PYwQDuFCR9FDOu1ot6nhjsGyzul\nhY1eWzW9xWlHhQgmk6ZxYVT7L94rpXAZfk7W1uBTqu7vtIVhmaLnmfAyiu1M1LPR8VFFAqcZir8P\n33DUUSFT2MdEhs7wMyFao2kaESYzQ+XW/pBixsNotjFBz0YpKGMQXvrPEMaOkL9MIURQ6fiIp4hU\n7RBDOUiMZoez7h5W6JSrJE6qCyllCFB3HSKJPNK0A8T4KyjV6taXU3d3sYFOJQPwnCnWGn5iOM0g\njnKetp8BWj6G0jlNKj6t7Xsh+jMp7CFMw6D+P0Q41QzkBKnaQSy42rV/LTby1RhOk4anmREACp3+\ncJed6F0mvCRQQKr2NUP5mlitFA3wKCtOLZYShjbZR0MRoVUxno/RtW/6nQ0VhlOLoZwUojQHF/MB\nulZ37UNHoWFQSzS1Kpo47TQafrzKSo0WQxHDMTQpaSCFvdfVtTrKiKP4zGvFAC2fIdohbNgD86Qo\nNHzKjFuLxNfOvsJEVcj5+h50/M3Ot+IhnNNqKHlqDG4imqw/TRo1NL0PQQiAcKqYqP2LkdqXgHam\nkMdRwMi6RkMbbQYnCTi1s4aZntnHpJlxM4BKbcBZeynCcBGmeeoKOSZpmzRDCnsP0/ETRzGDySVN\nO0C8VoSOH78KC9wi4cdCFXFUMApoMGyzo3/AbWxvwkO0ZucSPkCdVfh1FCbNy3E1jhzjP6k+609F\nAR4iOhGU6OuiKGeS9iHDta9wqUgKGYGBuYf+FDS8ROBtpiEiviGFvTsoRRR2Ujge6DaxatUM4RCx\n2mk0FN4zX1N7s9Xhx0IlSVRqSc2u1/EzkOOMUCsx9LNb/AofFk6rVE6qCynkfJwknOneEf2NGTeJ\nFDBG+5zztP3UqmgKGImhdfeN+KIzpLB3kYaf88hhnPYxFq1u3G244UbX3fhUGMaZQqfQcWpxFDIC\n1Ue+PhqYKNNSKdfM+Gk6/taElyitgov5gDDNix8z7naOQvBhpkiNIE+NpYoE1Fm/kAicpGqHSOUQ\n4VpNk/1rVRT5jCZfjaGc5LoW4zlCx88w9jBO34yZuuec6n4Tht54QgIfFk6pkeSr0TiJb3IcHxZc\nRNHcH6OFWhLJJ007yGAtl2gcGOi4VBT5jEJJQQ9p587/hk6qvzA0SDvS5KKlhsEwLQcLtThIpurM\nfx4nVjxofaJ4d4WfMCob9IPq+DA18wHQHB0fQznISH33mYvETXlUOE4tLvB7bShMczFWfc54/RNA\nUaGSKCEN/5k/6QqVRCEjqCa+X3yL0PETQwlDtEOka1sw48VBMrXUPQvBhAn/WTPN6HgZzleM1rc3\ne41FQ+EmghI1DOeZaylWakjRjhNJBQZm3CocJ/GUMwDa0W8uQoMU9gbCcDFW28pYbRv6mf8kZjwo\nNLzKir+ZK+5O4nCdGa5Vz6SZoZ0Frj8xMHeo5ezWolvfoJUi4iWcGi3uzGYG4ZqToRw8s1YRhgez\n5sXAFHhoghcrhWoEJ9VYytUoPH3ghpVIypmk/Zvh2leAwqeslDMAN42fSmY0+60qHLfW2v0hCjNu\n4rQiBpBXdxx0nMRiJwXOgcZJf3VOFvb61k8qhxmg5aOh0FCkarn4lBk7KYG71BR6u67wi96j0Kkl\nhlpivll45v2q+4Cua62a8TBQO8p55BBm+KnVIyhSwzip0ikm7cz+vfNGm/GQQAFDtEPEcBoACy4G\nasdxqSgKGd4NFyg1fIRTwbk95rs/OicKuwkvcRQHbpiI00rQz1zArNW+GdlRRBp+rf/O+HYuaviI\nMw/muptbtLquC51qErVTDOEQJs2LhwhKVFqLwz8rSaSSJBSmQOPAhr3V81cTH7gGUDe0tZQYSgHQ\nMUjS8gJDWxU6HmXFrdWdW6FzSvqzRSf0m8KuYRCFg8EcYah2kHCqATBrbmJwoCAwEqUvXcAU3UTT\n8BJOOSmUa1DXLeElRjtNBseb6ZNWmDQ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       "text": [
        "<matplotlib.figure.Figure at 0xf158cf8>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}