exercise-working-with-external-libraries.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"learntools_metadata":{"lesson_index":6,"type":"exercise"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**This notebook is an exercise in the [Python](https://www.kaggle.com/learn/python) course.  You can reference the tutorial at [this link](https://www.kaggle.com/colinmorris/working-with-external-libraries).**\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"# Try It Yourself\n\nThere are only three problems in this last set of exercises, but they're all pretty tricky, so be on guard! \n\nRun the setup code below before working on the questions.","metadata":{}},{"cell_type":"code","source":"from learntools.core import binder; binder.bind(globals())\nfrom learntools.python.ex7 import *\nprint('Setup complete.')","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:10:48.914878Z","iopub.execute_input":"2023-08-19T16:10:48.915256Z","iopub.status.idle":"2023-08-19T16:10:48.995798Z","shell.execute_reply.started":"2023-08-19T16:10:48.915225Z","shell.execute_reply":"2023-08-19T16:10:48.994592Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Setup complete.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 1.\n\nAfter completing the exercises on lists and tuples, Jimmy noticed that, according to his `estimate_average_slot_payout` function, the slot machines at the Learn Python Casino are actually rigged *against* the house, and are profitable to play in the long run.\n\nStarting with $200 in his pocket, Jimmy has played the slots 500 times, recording his new balance in a list after each spin. He used Python's `matplotlib` library to make a graph of his balance over time:","metadata":{}},{"cell_type":"markdown","source":"As you can see, he's hit a bit of bad luck recently. He wants to tweet this along with some choice emojis, but, as it looks right now, his followers will probably find it confusing. He's asked if you can help him make the following changes:\n\n1. Add the title \"Results of 500 slot machine pulls\"\n2. Make the y-axis start at 0. \n3. Add the label \"Balance\" to the y-axis\n\nAfter calling `type(graph)` you see that Jimmy's graph is of type `matplotlib.axes._subplots.AxesSubplot`. Hm, that's a new one. By calling `dir(graph)`, you find three methods that seem like they'll be useful: `.set_title()`, `.set_ylim()`, and `.set_ylabel()`. \n\nUse these methods to complete the function `prettify_graph` according to Jimmy's requests. We've already checked off the first request for you (setting a title).\n\n(Remember: if you don't know what these methods do, use the `help()` function!)","metadata":{}},{"cell_type":"code","source":"# Import the jimmy_slots submodule\nfrom learntools.python import jimmy_slots\n# Call the get_graph() function to get Jimmy's graph\ndef prettify_graph(graph):\n    # Set the title\n    graph.set_title(\"Results of 500 slot machine pulls\")\n    \n    # Set the y-axis limits\n    graph.set_ylim(0)\n    \n    # Set the y-axis label\n    graph.set_ylabel(\"Balance\")\nhelp(graph.set_title)\nhelp(graph.set_ylim)\nhelp(graph.set_ylabel)\n\ngraph = jimmy_slots.get_graph()\ngraph","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:16:31.012551Z","iopub.execute_input":"2023-08-19T16:16:31.013354Z","iopub.status.idle":"2023-08-19T16:16:31.451143Z","shell.execute_reply.started":"2023-08-19T16:16:31.013313Z","shell.execute_reply":"2023-08-19T16:16:31.449755Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Help on method set_title in module matplotlib.axes._axes:\n\nset_title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs) method of matplotlib.axes._axes.Axes instance\n    Set a title for the Axes.\n    \n    Set one of the three available Axes titles. The available titles\n    are positioned above the Axes in the center, flush with the left\n    edge, and flush with the right edge.\n    \n    Parameters\n    ----------\n    label : str\n        Text to use for the title\n    \n    fontdict : dict\n        A dictionary controlling the appearance of the title text,\n        the default *fontdict* is::\n    \n           {'fontsize': rcParams['axes.titlesize'],\n            'fontweight': rcParams['axes.titleweight'],\n            'color': rcParams['axes.titlecolor'],\n            'verticalalignment': 'baseline',\n            'horizontalalignment': loc}\n    \n    loc : {'center', 'left', 'right'}, default: :rc:`axes.titlelocation`\n        Which title to set.\n    \n    y : float, default: :rc:`axes.titley`\n        Vertical Axes location for the title (1.0 is the top).  If\n        None (the default) and :rc:`axes.titley` is also None, y is\n        determined automatically to avoid decorators on the Axes.\n    \n    pad : float, default: :rc:`axes.titlepad`\n        The offset of the title from the top of the Axes, in points.\n    \n    Returns\n    -------\n    `.Text`\n        The matplotlib text instance representing the title\n    \n    Other Parameters\n    ----------------\n    **kwargs : `~matplotlib.text.Text` properties\n        Other keyword arguments are text properties, see `.Text` for a list\n        of valid text properties.\n\nHelp on method set_ylim in module matplotlib.axes._base:\n\nset_ylim(bottom=None, top=None, *, emit=True, auto=False, ymin=None, ymax=None) method of matplotlib.axes._axes.Axes instance\n    Set the y-axis view limits.\n    \n    Parameters\n    ----------\n    bottom : float, optional\n        The bottom ylim in data coordinates. Passing *None* leaves the\n        limit unchanged.\n    \n        The bottom and top ylims may also be passed as the tuple\n        (*bottom*, *top*) as the first positional argument (or as\n        the *bottom* keyword argument).\n    \n        .. ACCEPTS: (bottom: float, top: float)\n    \n    top : float, optional\n        The top ylim in data coordinates. Passing *None* leaves the\n        limit unchanged.\n    \n    emit : bool, default: True\n        Whether to notify observers of limit change.\n    \n    auto : bool or None, default: False\n        Whether to turn on autoscaling of the y-axis. *True* turns on,\n        *False* turns off, *None* leaves unchanged.\n    \n    ymin, ymax : float, optional\n        They are equivalent to bottom and top respectively, and it is an\n        error to pass both *ymin* and *bottom* or *ymax* and *top*.\n    \n    Returns\n    -------\n    bottom, top : (float, float)\n        The new y-axis limits in data coordinates.\n    \n    See Also\n    --------\n    get_ylim\n    set_ybound, get_ybound\n    invert_yaxis, yaxis_inverted\n    \n    Notes\n    -----\n    The *bottom* value may be greater than the *top* value, in which\n    case the y-axis values will decrease from *bottom* to *top*.\n    \n    Examples\n    --------\n    >>> set_ylim(bottom, top)\n    >>> set_ylim((bottom, top))\n    >>> bottom, top = set_ylim(bottom, top)\n    \n    One limit may be left unchanged.\n    \n    >>> set_ylim(top=top_lim)\n    \n    Limits may be passed in reverse order to flip the direction of\n    the y-axis. For example, suppose ``y`` represents depth of the\n    ocean in m. The y-axis limits might be set like the following\n    so 5000 m depth is at the bottom of the plot and the surface,\n    0 m, is at the top.\n    \n    >>> set_ylim(5000, 0)\n\nHelp on method set_ylabel in module matplotlib.axes._base:\n\nset_ylabel(ylabel, fontdict=None, labelpad=None, *, loc=None, **kwargs) method of matplotlib.axes._axes.Axes instance\n    Set the label for the y-axis.\n    \n    Parameters\n    ----------\n    ylabel : str\n        The label text.\n    \n    labelpad : float, default: :rc:`axes.labelpad`\n        Spacing in points from the Axes bounding box including ticks\n        and tick labels.  If None, the previous value is left as is.\n    \n    loc : {'bottom', 'center', 'top'}, default: :rc:`yaxis.labellocation`\n        The label position. This is a high-level alternative for passing\n        parameters *y* and *horizontalalignment*.\n    \n    Other Parameters\n    ----------------\n    **kwargs : `~matplotlib.text.Text` properties\n        `.Text` properties control the appearance of the label.\n    \n    See Also\n    --------\n    text : Documents the properties supported by `.Text`.\n\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x700 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA4wAAAJGCAYAAAAQzgvEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACN5ElEQVR4nOzdeXjU5dn28e9s2TdCyEYCCWRBCSKLyr5DsO772rpSFxARgWpb+/Rp30of3K1W627rWq22WpB9F1H2TUnCHpawk5CQfeb9YzKTBAJkkkl+Seb8HEeO18z8MnPN86Zhzrmv+7pNDofDgYiIiIiIiMhpzEYXICIiIiIiIi2TAqOIiIiIiIjUSYFRRERERERE6qTAKCIiIiIiInVSYBQREREREZE6KTCKiIiIiIhInRQYRUREREREpE5WowtoCLvdzv79+wkNDcVkMhldjoiIiIiISKvicDg4efIk8fHxmM1nX0dslYFx//79JCYmGl2GiIiIiIhIq5abm0tCQsJZ72+VgTE0NBRwvriwsDCDqxEREREREWldCgoKSExMdGers2mVgdHVhhoWFqbAKCIiIiIi0kDn2+KnoTciIiIiIiJSJwVGERERERERqZMCo4iIiIiIiNRJgVFERERERETqpMAoIiIiIiIidVJgFBERERERkTopMIqIiIiIiEidFBhFRERERESkTgqMIiIiIiIiUicFRhEREREREamTAqOIiIiIiIjUSYFRRERERERE6qTAKCIiIiIiInVSYBQREREREZE6KTCKiIiIiIhInRQYRUREREREpE4KjCIiIiIiIlInBUYRERERERGpkwKjiIiIiIiI1EmBUUREREREROqkwCgiItIADoeDHYcLsdsdRpciIiLSZKxGFyAiItIaLco6xL3vrQZg15+vMLgaERGRpqEVRhERkQbYcbgIAJMJtuYVGFyNiIhI01BgFBERaQSHA56dk2V0GSIiIk1CgVFERKQB7I7qvYvzfzrEmt3HDKxGRESkaSgwioiINIDjtFk3M2Zn8cbS7SQ9MZNDJ0uMKUpERMTLFBhFREQawJUXB3Rtj5/VzPc7j/H0rK0APD8327jCREREvEiBUUREpAFcLanxEYH8vF/nWvd9tmYvOw4XGlGWiIiIVykwioiINICrJdVsgkmjUrlvUDJv/qIvoy6IptLuYMRzS4wtUERExAt0DqOIiEgjmDARGmDjqSsvBCAxMpAFWw/hcMDmfflkdAw3uEIREZGG0wqjiIhIA9jtziVGk6n27d1iw7imZzwAM3TchoiItHIKjCIiIg3gGnpjOj0xAo+NTsNqNrE0+zArdxxt3sJERES8SIFRRESkAVx7GOvIi3RuH8ytlyYCMGP2Vhynn8EhIiLSSigwioiINIBrSmodeRGAiSNSCbCZWbvnBAt+OtR8hYmIiHiRAqOIiEgDuNYMzXUtMQLRYQHcPSAZgPv/vtq951FERKQ1UWAUERFpAFeb6VnyIgAPDe1KaIBzIPlXG/Y3R1kiIiJepcAoIiLSANXnMJ49MYYH2XhwaFcAnp+XTVmFvTlKExER8RoFRhERkQZwUL8W03sGJhEV4s+eY6f4dHVuE1clIiLiXQqMIiIiDWA/x5TUmoL8rEwcmQLAywtyOFVW0cSViYiIeI8Co4iISAPUpyXV5dZLOpEYGcjhk6W8t2JX0xYmIiLiRQqMIiIiDeBqST1/XAQ/q5nHRqUBMGN2FvmnypuwMhEREe9RYBQREWkARz1bUl2uubgjaTEhAPxt6fYmqkpERMS7FBhFREQawHWsRn1aUgEsZhNTxqQD8O63uzh0sqTJahMREfEWBUYREZEGcK0w1qsntcroC2Po1SmC4vJKLv3TgiapS0RExJsUGEVERBrA7sHQGxeTycTUTOcqo81iIvfYqaYoTURExGsUGEVERBrAk6E3NQ3oGsXg1CjKKx28MC/b+4WJiIh4kUeB8bXXXuOiiy4iLCyMsLAw+vfvzzfffOO+3+Fw8Pvf/574+HgCAwMZNmwYW7ZsqfUYpaWlPPLII0RFRREcHMzVV1/N3r17vfNqREREmomnQ29qcq0yfrFuH1vzCrxYlYiIiHd5FBgTEhL485//zOrVq1m9ejUjRozgmmuucYfCGTNm8Pzzz/PKK6+watUqYmNjGT16NCdPnnQ/xqRJk/jyyy/55JNPWL58OYWFhVx55ZVUVlZ695WJiIg0IU+H3tR0UUIEP+sRC8Czc7TKKCIiLZdHgfGqq67iZz/7GWlpaaSlpfGnP/2JkJAQVq5cicPh4MUXX+Q3v/kN119/PRkZGbz//vucOnWKjz76CID8/HzefvttnnvuOUaNGkWvXr344IMP2LRpE/Pnz2+SFygiItIUGjDzppbJo9Mxm2D+TwdZs/u4t8oSERHxqgbvYaysrOSTTz6hqKiI/v37s3PnTvLy8hgzZoz7Gn9/f4YOHcqKFSsAWLNmDeXl5bWuiY+PJyMjw31NXUpLSykoKKj1JSIiYiR7Y3pSgZToEG7skwDADa+tcK9YioiItCQeB8ZNmzYREhKCv78/Dz74IF9++SUXXngheXl5AMTExNS6PiYmxn1fXl4efn5+tGvX7qzX1GX69OmEh4e7vxITEz0tW0RExKsc7impDX+MR0el4Wdx/lO8LOeIF6oSERHxLo8DY3p6OuvXr2flypU89NBD3HXXXfz444/u+02nfdLqcDjOuO1057vmySefJD8/3/2Vm5vradkiIiJeVd2S2vDE2DEikDv7dQbgmTlZWmUUEZEWx+PA6OfnR0pKCn379mX69On07NmTl156idhY5+b901cKDx065F51jI2NpaysjOPHj5/1mrr4+/u7J7O6vkRERIzkCncN7Eh1Gz+8K8F+Fjbty+ebzWfvthERETFCo89hdDgclJaWkpycTGxsLPPmzXPfV1ZWxpIlSxgwYAAAffr0wWaz1brmwIEDbN682X2NiIhIa+CNllSA9iH+3De4CwDPzs2iotLeyMpERES8x+rJxb/+9a+5/PLLSUxM5OTJk3zyyScsXryY2bNnYzKZmDRpEk8//TSpqamkpqby9NNPExQUxO233w5AeHg49913H48//jjt27cnMjKSKVOm0KNHD0aNGtUkL1BERKQpVM+8aWRiBMYNTuYf3+1ix+Ei/rV2L7dc0qnRjykiIuINHgXGgwcP8vOf/5wDBw4QHh7ORRddxOzZsxk9ejQA06ZNo7i4mIcffpjjx49z2WWXMXfuXEJDQ92P8cILL2C1Wrn55pspLi5m5MiRvPfee1gsFu++MhERkSZk91JLKkBogI2Hh6Xwp1k/8at/beKaizsSYNO/iyIiYjyToxXusC8oKCA8PJz8/HztZxQREUNM+WwDn6/Zy6/GduOhYV0b/Xgl5ZUMe2YxeQUl3D8omU378vl+5zF2Tv+ZV1YxRUREaqpvpmr0HkYRERFf5M0VRoAAm4VJo1IBeGv5Tr7feQyAhVsPeecJREREGkCBUUREpCG8NPSmphv7JNAlKhgAP6vzn+hn5mRht7e6ZiAREWkjFBhFREQawBvnMJ7OajHz1l19mTImjTmThhAaYGVr3km+3rjfa88hIiLiCQVGERGRBvB2S6pLlw4hTBiRSnJUMA8Ode6NfH5eNuU6bkNERAygwCgiItIA3jxW42zuGZhEVIg/u4+e4tNVuU32PCIiImejwCgiItIA1S2pTSfIz8ojI1IA+O2/N1NcVtmEzyYiInImBUYREZEGaKqW1NPddmknEtoFAvD+d7ua9slEREROo8AoIiLSEO4pqU2bGP2sZh4blQbAa4u3k19c3qTPJyIiUpMCo4iISAM01wojwLW9OpIWE0J+cTk9/3du0z+hiIhIFQVGERGRBmiOoTcuFrOJx8ekAxBos3DoZEmTP6eIiAgoMIqIiDSIo6ontRkWGAEYc2EMFydGUFxeyasLtzXTs4qIiK9TYBQREWkAu3uFsXmez2QyMS3Tucr4/ne7yT12qnmeWEREfJoCo4iISAM4mmnoTU0DUqIYlBIFwAvzs5vteUVExHcpMIqIiDRI87akukytWmX8ct0+sg+ebOZnFxERX6PAKCIi0gDN3ZLq0jMxgsszYnE4YMwLS5v3yUVExOcoMIqIiDSAw32sRnOvMcLjY9IwVz3t2j3Hm/35RUTEdygwioiINEDVAmOzt6QCpESHckPvBACemZ3lDq8iIiLepsAoIiLSAPZmPIexLpNGp+FnMfPdjqMs33bEkBpERKTtU2AUERFpANeqntmYvEjHiEDu6NcJgGfmaJVRRESahgKjiIhIAzgMGnpT0/jhKQT5Wdi4N5/Zm/OMK0RERNosBUYREZEGcOBaYTQuMUaF+HP/oGQAHvpwLRWVdsNqERGRtkmBUUREpAFaSgfo/UO6EBFkA+CLdfsMrkZERNoaBUYREZEGsBt4rEZNYQE2Hh7WFYCX5udQWlFpaD0iItK2KDCKiIg0gGuF0aihNzX9on8SsWEB7DtRzIcr9xhdjoiItCEKjCIiIg1QfQ6j8YkxwGbh0VGpALyyaBuFpRUGVyQiIm2FAqOIiEgDONwtqQYXUuWmPgkkRwVzrKiMt5ftNLocERFpIxQYRUREGqAltaQCWC1mJo9OA+CF+dkcKyozuCIREWkLFBhFREQaoHpIagtJjMAVPeK4MC4MgNcWbzO4GhERaQsUGEVERBrA3sJaUgHMZhNTx6YD8P53uzmQX2xwRSIi0topMIqIiDRAdUtqC0qMwLC0DlyaFElZhZ2XF+QYXY6IiLRyCowiIiIN4B56Y3AdpzOZTEyrWmX85+q97DhcaHBFIiLSmikwioiINIBrD6O5Bf5L2jcpkhHdoqm0Oxjx3BKjyxERkVasBf4zJyIi0vK5WlJbwjmMdZkyJt3935v35RtYiYiItGYKjCIiIg1gr06MLdKF8WFc3TMegGfmZBlcjYiItFYKjCIiIg3QUofe1DR5dBpWs4kl2YdJemKm0eWIiEgrpMAoIiLSAK49jC03LkJSVDC3XJIIQJ/O7dyDekREROpLgVFERKQBHC3wHMa6TByZSoDNzJrdx1m49ZDR5YiISCujwCgiItIAraElFSAmLIC7BiQBcN/7q7HbtcooIiL1p8AoIiLSAA5a5jmMdXloaFdCA6wAfL1xPxWVdo4UlhpclYiItAYKjCIiIg1gbw2bGKtEBPnxwJAuADw7N4vRLyyl7/+bz8a9J4wtTEREWjwFRhERkQZw7WFs6S2pLvcMTCYqxI/cY8XsPFIEwP/N3mpwVSIi0tIpMIqIiDRACz+G8QzB/lYeG50GQJeoYPwsZr7ddpTlOUcMrkxERFoyBUYREZEGcHWkms2tJTLCHZd15usJg/jvxEHcflknAO58+3sdtyEiImelwCgiItIA7mM1DK7DUz0SwgnyszJhRApBfhYA5mzJM7gqERFpqRQYRUREGsA19KaVbGE8Q1SIP/cNSgbg2bnZVOq4DRERqYMCo4iISAO4j9VorYkRGDekCxFBNrYdKuSLtXuNLkdERFogBUYREZEGaG1Db+oSFmDjoaFdAXhxfg6lFZUGVyQiIi2NAqOIiEgDuANjK15hBLhrQBIxYf7sO1HMR9/vMbocERFpYRQYRUREGqD6HEaDC2mkAJuFiSNTAfjfr3+ksLTC4IpERKQlUWAUERFpANeIGFOrbkp1urlvIkntgwB4bm4W97+/mqQnZmLXIBwREZ+nwCgiItIAdtexGq0/L2KzmHlsdBoA7367i/k/HQTgv5sOGFmWiIi0AAqMIiIiDeBo5cdqnO6qi+K5IC4MAJvF+aKen5tFeaXdyLJERMRgCowiIiIN4D6HsQ20pAKYzSZev7M3E4an8M2jg2kf7Meuo6dI/c03RpcmIiIGUmAUERFpkKqhN23oX9LO7YOZkplOSnQo44enABAT5k9JuY7bEBHxVW3onzkREZHm42hjK4ynu6NfJzpGBHKwoJS/f7fL6HJERMQgCowiIiIN0JaG3tTF32ph0ijncRtPz9pKQUm5wRWJiIgRFBhFREQawHXgRGs/h/Fcru+dQEp0CABvLd1hcDUiImIEBUYREZEGcLiPKGy7idFiNjFljPO4jbeW7+RIYanBFYmISHNTYBQREWmAtt6S6pLZPZaeCeGcKquk7/+bb3Q5IiLSzBQYRUREGqJqhdHcxhOjyWRiamY3APwsZvYeP2VwRSIi0pwUGEVERBrA1ZHatuOi06DUKAZ0bU9ZpZ0X5+cYXY6IiDQjBUYREZEGcLWktvUVRpepmekAfL5mLzkHTxpcjYiINBcFRhERkQZwn8PoG3mRXp3aMebCGACem5vNkuzDJD0xk6LSCoMrExGRpuRRYJw+fTqXXHIJoaGhREdHc+2115KVlVXrGpPJVOfXM888475m2LBhZ9x/6623eucViYiINAN79ZhUnzElMx2zCWZvyePud38A4OWFalEVEWnLPAqMS5YsYfz48axcuZJ58+ZRUVHBmDFjKCoqcl9z4MCBWl/vvPMOJpOJG264odZjjRs3rtZ1f/vb37zzikRERJqB+xzGtnwQ42nSYkK5rlcCUL3C+t63uzhYUGJgVSIi0pSsnlw8e/bsWt+/++67REdHs2bNGoYMGQJAbGxsrWv+85//MHz4cLp06VLr9qCgoDOuPZvS0lJKS6vPfiooKPCkbBEREe9ztaQaW0WzmzY2nUMnS7gsOZJFWYdZs/s4lz29gF1/vsLo0kREpAk0ag9jfn4+AJGRkXXef/DgQWbOnMl99913xn0ffvghUVFRdO/enSlTpnDy5Nk30E+fPp3w8HD3V2JiYmPKFhERaTRfOYfxdDFhAfzjvsuYMCKVaVWDcKxmE7uPFnGsqIwTp8oMrlBERLzJoxXGmhwOB5MnT2bQoEFkZGTUec37779PaGgo119/fa3b77jjDpKTk4mNjWXz5s08+eSTbNiwgXnz5tX5OE8++SSTJ092f19QUKDQKCIihnK3pPpaYqzhsi7tGZrWgSXZh3nk43VsPXCSsko7m34/htAAm9HliYiIFzQ4ME6YMIGNGzeyfPnys17zzjvvcMcddxAQEFDr9nHjxrn/OyMjg9TUVPr27cvatWvp3bv3GY/j7++Pv79/Q0sVERHxOodrhdHgOow2NTOdJdmH2bg3333b60u243DAXxdvZ/vTP8PiQ/s8RUTamga1pD7yyCN89dVXLFq0iISEhDqvWbZsGVlZWdx///3nfbzevXtjs9nIydGkNRERaR3sriVGH89CGR3Dub53RwB6JoQD8Oqi7fx18XYAvtqwz7DaRESk8TwKjA6HgwkTJvDFF1+wcOFCkpOTz3rt22+/TZ8+fejZs+d5H3fLli2Ul5cTFxfnSTkiIiKG8+WWVJdnbuzJnElD+PLhgfToGF7rvufnZVNWYTeoMhERaSyPAuP48eP54IMP+OijjwgNDSUvL4+8vDyKi4trXVdQUMBnn31W5+ri9u3b+cMf/sDq1avZtWsXs2bN4qabbqJXr14MHDiwca9GRESkGThqnMGouAgWs4n02FDMZhPP3dyTay6O5+/3XkpUiD+5x4pJ++03gPP/bn9bsp2kJ2Zy+GTpeR5VRERaAo8C42uvvUZ+fj7Dhg0jLi7O/fXpp5/Wuu6TTz7B4XBw2223nfEYfn5+LFiwgMzMTNLT05k4cSJjxoxh/vz5WCyWxr0aERGRZmCvzotaYTxNWkwoL93aiyFpHZg4MgWADqH+FJVW8L9f/8j0b7YC8Py8LCPLFBGRejI5an5M2koUFBQQHh5Ofn4+YWFhRpcjIiI+pqLSTspvnKtm6383moggP4MrapnKKuyMfH4xuceKSY4KZueRIvd9FrOJeY8NoUuHEAMrFBHxXfXNVI06h1FERMQX1VxhNKkp9az8rGYeG5UGwM4jRVjMJl64pScju0VTaXcw4rklBlcoIiLno8AoIiLiIQc19jDqX9JzuubijvTt3I4Am5lXb+/Ndb0SmJKZjquTd/O+/HM/gIiIGEr/zImIiHjIUWuFUc7FYjbx0bh+rH1qNGMzYgG4IC6Mq3vGA/DsXO1lFBFpyRQYRUREPFQrMGrozXn5Wc0E+Vlr3TZ5dBpWs4nFWYf5fsdRgyoTEZHzUWAUERHxUM2WVLPyYoN0bh/MLZckAjBjThatcAafiIhPUGAUERHxkENDb7xi4shUAmxm1uw+zsKth4wuR0RE6qDAKCIi4iF7jcSojtSGiwkL4K4BSQDc9/5q7HatMoqItDQKjCIiIh6qGWsUGBvnoaFdCQ1w7m/8euN+Fm09RNITMw2uSkREXBQYRUREPOSwV/+3WlIbJyLIjweGdAHg119s4p73VgGwbs9xACo9XHXUKqWIiHcpMIqIiHhIQ2+8656ByUSF+FFUVum+7U8zf+Km11fQ9dezOFhQUq/HWZ5zhG6/m60VShERL1JgFBER8ZCO1fCuYH8rf7gmg9iwAB4ZkYKfxczq3cdZtcu5yvjSghy+33GUpCdmUl5pP+vjLNh6kLIKOyH+Vo4VlTVX+SIibZoCo4iIiIdqDb0xsI625Gc94lj565E8PiadO/p1AqB9sB8An67K5fa3vgfg3W93nvUxco+dAqCwtILXFm9r4opFRHyDAqOIiIiHNPSmaT15+QW8dOvFfDNpMMPSO1Bpd7j3Mv518XYKSsrr/Lk9VYER4M1lOzmQX9ws9YqItGUKjCIiIh5SS2rT8rOauebijkSHBvDbKy6ke3wYDw3rStcOwZw4Vc5Fv597xs84HA5yjzkDYuf2QQC8vCCnWesWEWmLFBhFREQ85KhKjMqKTS8lOoSZEwfzq7HdmDImHYAgPwtHCkspLqukpNw5KOdIYRnF5ZWYTDD9+h4A/HP1XnYcLjSsdhGRtkCBUURExEOuBUazEmOzGpsRy0UJ4Zwqq2TSJ+vp8//m0e2p2ZRV2N3tqPHhgQzoGsWIbtFU2h2MeG6JwVWLiLRuCowiIiIecrWkKi42L5PJxNRM5yrj8m1HOFV1DMcnq/a4B94kRgYCuFcjATbvy2/mSkVE2g4FRhEREQ/Z1ZJqmEEpUQxOjQKgS4dgAF5esI2teScB6BTp3L94YXwY11wcD8Azc7IMqFREpG1QYBQREfGQqyVVA2+an8lk4s1f9OWfD/Rn1sTBdIoM4khhKe+tcB63kdguyH3t5NFpWM0mlmQf5vsdR40qWUSkVVNgFBER8ZC96ogHxUVjBNgsXJocSYDNwuTRaQCUlNsB6NS+OjB2bh/MLZckAjBjThZfbdhP0hMzOXyytPmLFhFppRQYRUREGkhDb4x3dc94usWGur9PjAyqdf/EkakE2Mys2X2ciR+vA+DP32xt1hpFRFozBUYREREPuYfeKC8azmw21Rpw0+m0wBgTFsBdA5Jq3fblur3kHDzZHOWJiLR6VqMLEBERaW3cQ28MrkOcRl4QzfjhXbGazUSF+J9x//jhKRwvKqNHQgTf5hxh9pY8Rr+wlF1/vsKAakVEWhcFRhEREQ/pHMaWxXncRrez3h8WYGPGjT0B6N8lkrk/5mF3wPrcE1ycGNFMVYqItE5qSRUREfGQQwcxtlop0aFc3zsBgBmztZdRROR8FBhFREQ8ZFdebNUmjUrFz2JmxfajLM85YnQ5IiItmgKjiIiIx5yJ0WxWZGyNEtoFcftlnQB4Zs7W6hVjERE5gwKjiIiIh9SR2vpNGJFCkJ+FDXvzmbMlz+hyRERaLAVGERERD7lbUjX0ptWKCvHnvkHJADz4wVoq7VplFBGpiwKjiIiIhxyullTlxVZt3JAuRATZAPhi7V6DqxERaZkUGEVERDxkt7v+S4mxNQsLsPHwsK4AvDg/h9KKSoMrEhFpeRQYRUREPKQVxrbjF/2TiA0LYN+JYtJ/O9vockREWhwFRhEREQ+5h94oMLZ6ATYLE0emAtA+2I/C0gqDKxIRaVkUGEVERDxUPSVVibEtuKlvAkntgzhaVMY7y3caXY6ISIuiwCgiIuIhtaS2LTaLmclj0gF4fl42x4vKDK5IRKTlUGAUERHxkEPHarQ5V/aI48K4MABeW7Ld4GpERFoOBUYREREP2R06s6+tMZtNTM10rjK+v2IXefklBlckItIyKDCKiIh4yBUXzfpXtE0Zlt6BS5LaUVphp9/0BUaXIyLSIuifOhEREQ9p6E3bZDKZmDa2GwAWs4mdR4oMrkhExHgKjCIiIh5yVCVGbWFsey5JimR4egcq7Q6en5dtdDkiIoZTYBQREfGQuyVVibFNmprpXGX8esN+tuzPN7gaERFjKTCKiIh4yG6vWmE0uA5pGhfGh3F1z3gAnp2TZXA1IiLGUmAUERHxkGuFUQuMbdfk0WlYzSYWZR3mh53HjC5HRMQwCowiIiIe0jmMbV9SVDA3X5IIwM1/+869b1VExNcoMIqIiHjIPfTG4DqkaU0ckYq/1flWaVHWIYOrERExhgKjiIiIhzT0xjfEhgdw94AkAJ6Zk+3euyoi4ksUGEVERDxU3ZJqbB3S9B4c2pVQfys/HSjg6437jS5HRKTZKTCKiIh4yK79bD6jXbAfvxzSBYDn52VTXmk3uCIRkealwCgiIuIhtaT6lnsHJRMV4sfuo6f45+pco8sREWlWCowiIiIecg+9UV70CcH+VsYPTwHgN19upqS80uCKRESajwKjiIiIh1wdqVph9B23X9aJjhGBALy/YpexxYiINCMFRhEREQ850Aqjr/G3WnhsdBoAf128nfzicoMrEhFpHgqMIiIiHrJXzT1RXvQt1/XqSGp0CPnF5by5dIfR5YiINAsFRhEREQ+5ht6YtMToUyxmE4+PSQfgnW93cvhkqcEViYg0PQVGERERD2noje/K7B5Dz8QITpVV8uqibUaXIyLS5BQYRUREPGSvWmJUXvQ9JpOJaZnOVcb3Vuwi99gpgysSEWlaCowiIiIecyZGTUn1TQNTohiY0h6AF+fnGFyNiEjTUmAUERHxkOtYDeVF3zU1sxsAX67bS87BkwZXIyLSdBQYRUREPFTdkqrE6KsuTowgs3sMdgeMfmGp0eWIiDQZBUYREREP6RxGAZgyJh1z1e/A+twThtYiItJUFBhFREQ8pJZUAUiNCeX63gkAXPvqtwZXIyLSNBQYRUREPGR3aOiNOE0alYqfxfl2annOEYOrERHxPo8C4/Tp07nkkksIDQ0lOjqaa6+9lqysrFrX3H333ZhMplpf/fr1q3VNaWkpjzzyCFFRUQQHB3P11Vezd+/exr8aERGRZqS8KAntgrj9sk4APDNnq/uMThGRtsKjwLhkyRLGjx/PypUrmTdvHhUVFYwZM4aioqJa140dO5YDBw64v2bNmlXr/kmTJvHll1/yySefsHz5cgoLC7nyyiuprKxs/CsSERFpYq4VRg29EYAJI1II8rOwYW8+yU/OOv8PiIi0IlZPLp49e3at7999912io6NZs2YNQ4YMcd/u7+9PbGxsnY+Rn5/P22+/zT/+8Q9GjRoFwAcffEBiYiLz588nMzPT09cgIiLSrLSHUWqKCvHnvkHJ/GXhNlKiQ6i0O7CY9cshIm1Do/Yw5ufnAxAZGVnr9sWLFxMdHU1aWhrjxo3j0KFD7vvWrFlDeXk5Y8aMcd8WHx9PRkYGK1asqPN5SktLKSgoqPUlIiJilOrAqFAgTuOGdCEiyMa2Q4V8sVbbbESk7WhwYHQ4HEyePJlBgwaRkZHhvv3yyy/nww8/ZOHChTz33HOsWrWKESNGUFpaCkBeXh5+fn60a9eu1uPFxMSQl5dX53NNnz6d8PBw91diYmJDyxYREWm06pZUEaewABsPDe0KwNTPN1JaoW02ItI2NDgwTpgwgY0bN/Lxxx/Xuv2WW27hiiuuICMjg6uuuopvvvmG7OxsZs6cec7HczgcZ/2k9sknnyQ/P9/9lZub29CyRUREGs011kRdh1LTXQOSiAnzB+Cj7/cYXI2IiHc0KDA+8sgjfPXVVyxatIiEhIRzXhsXF0fnzp3JyckBIDY2lrKyMo4fP17rukOHDhETE1PnY/j7+xMWFlbrS0RExDBqSZU6BNgsTByZCsArC7dRWFphcEUiIo3nUWB0OBxMmDCBL774goULF5KcnHzenzl69Ci5ubnExcUB0KdPH2w2G/PmzXNfc+DAATZv3syAAQM8LF9ERKT5qSVVzubmvokktQ/iaFEZGf8zx+hyREQazaPAOH78eD744AM++ugjQkNDycvLIy8vj+LiYgAKCwuZMmUK3333Hbt27WLx4sVcddVVREVFcd111wEQHh7Offfdx+OPP86CBQtYt24dd955Jz169HBPTRURaUoOh4Nth05Sadd5adIwrt8crTDK6WwWM4+NTgMg1N/K8aIygysSEWkcjwLja6+9Rn5+PsOGDSMuLs799emnnwJgsVjYtGkT11xzDWlpadx1112kpaXx3XffERoa6n6cF154gWuvvZabb76ZgQMHEhQUxNdff43FYvHuqxMRqcPirMOMen4pXX+t89KkYXSshpzLVRfFc0FcGCdLK3htyXajyxERaRSPzmF0OM79aXxgYCBz5py//SIgIIC//OUv/OUvf/Hk6UVEvGLnkSL3f2/el09Gx3ADq5HWyNWSqqE3Uhez2cS0zHTueW8Vbyzdwb0Dk4kNDzC6LBGRBmnUOYwiIq1RzY++np2bZVgd0nq5W1K1i1HOYlh6By5Jch4h9tKCHIOrERFpOAVGEfE5NbslFmcd5oedxwysRloj1++QWlLlbEwmE9PGdgPgn6tza3U2iIi0JgqMIuJzTu+unzF763lb7kVqcv26mJUY5RwuSYpkeHoHKu0Ohj+72OhyREQaRIFRRHyOo6qhcEDX9vhbzazefZxFWYcMrkpaE/cHDMqLch5TMtPd/71lf76BlYiINIwCo4j4HNdpGrHhAdw9IAmAe99bjV3HbEg92ZUXpZ66x4dzVc94AJ6Zoz3TItL6KDCKiM+p2U744NCuhPo7B0Z/vXG/gVVJa+L6aEEtqVIfk0enYTGbtGdaRFolBUYR8TmullQT0C7Yj18O6QLA8/OyKa+0G1iZtBYaeiOeSI4K5ua+iYBzz3RBSTnfbDpAaUWlwZWJiJyfAqOI+JzTD12/d1AyUSF+7D56in+uzjWuMGk1tIVRPPXoyFT3nunRzy/hoQ/X8tS/NxtdlojIeSkwiojPcbgPXXe+3Q/2tzJ+eAoALy/IoaRcn/rLublWqdWSKvVVc8/0wYJSAD5fs5dth04aWJWIyPkpMIqIzzl9hRHg9ss60TEikIMFpby/YpchdUnr4Z6PpLwoHnhwaFeS2geREh1C/y7tsTvg2TnZRpclInJOCowi4nPcEy5rJEZ/q4VJo1IBmP6Nc4+RyNnoHEZpiHbBfsx9bCjzHhvC/17THbMJZm/JY0PuCaNLExE5KwVGEfE5NYfe1HR97wRSokMAeHPpjmauSlqTs/0OiZyPn9WMyWQiLSaU63olADpuQ0RaNgVGEfE59jpaUgEsZhNTxjgP2f7Lwm0cPlnazJVJa1FXW7OIpyaNSsVmMbF82xGSnphpdDkiInVSYBQR3+M4+8CSzO4x9EyMAODVRduasyppRU4fnCTSEImRQdxxWWcAeiZGuH+vRERaEgVGEfE555pXYjKZmJbpXGX88Pvd5B471Wx1SeuhFUbxlvHDUwjys7Ah9wRzthw0uhwRkTMoMIqIz7G7D12v+93+wJQoBqa0p7zSweAZi5qzNGkl3FNStYtRGqlDqD/3DkwG4MEP1lBZ9ctVUl7JG0u3q1VVRAynwCgiPqc+q0NTM7sBYDZBzkGdkya1VZ/DaHAh0iaMG9KF8EAbAF+u20dRaQX3vreKp2dtJdjPwtFC7acWEeMoMIqIz6luST37u/2LEyPI7B7jPCdtriYYSm1qSRVvCg+08dCwrgC8MC+bn7/9PSu2HwWgqKySVxdtN7I8EfFxCowi4nOqW1LPfd2UMemYTTBny0HW65w0qcE1nORcHzqIeOKu/klEh/qz70Qxa/ecIDzQxtSq/dTvfLuTfSeKDa5QRHyVAqOI+B73oevnviy11jlpW5u4qLMrr7STf6rcsOeXM7lWqdWSKt4S6GfhsdFpAESF+PPpA/14eFhXLkuOBOCl+dlGliciPkyBUUR8jrsltR79hK5z0r7ddpRvtx1p2sLqkHPwJENmLKLnH+ZqYmsLcr7BSSINcesliXx4/2V88+hgusWGOac2j3Xup/58zV62HSo0uEIR8UUKjCLic+z2+rWkQu1z0u546/tmPSdt8758bnljJQfySwB4QSsMLYb2MEpTMJlMDEyJokOov/u2Pp3bMeoC537qUc8vMbA6EfFVCowi4nPqM/SmpgkjnOekAc12TtrqXce47Y2VHCsqo2uHYMA5PTFbE1tbBE9/h0QaY2pmuvvDiU17840tRkR8jgKjiPic+g69cYkK8ee+Qc5z0p6bm+U+J62pLM85ws/f/oGTpRVcmhTJv8cPZGz3WBwOGPPC0iZ9bqkfT3+HRBojPTaUay/uCMAMA/dTi4hvUmAUEZ/jqOfQm5rGDelCRJCNnEOFdP31rKYpDPhh5zHufPt7issrGZLWgffvvZTQABtTMtPc9a7bc7zJnl/qqQG/QyKN8dioNKxmE8tyjrBie/PvpxYR36XAKCI+y5N2wrAAGw8NdZ6T1jEikNKKSn7cX0BJeaVXa/ro+90AjOgWzZu/6ENgVStsSnQoN/R2TmydMTurWfdSypk8GZwk4g2d2gdx26WdAP0NEJHmpcAoIj6noe2Edw1IIibMeU7a9X9dwc9eXka3p2Z79Y3bnqpJqDf0TsDfaql136TRafhZzHy34yjLDZjYKtXcg5MMrkN8yyMjUgiwmVmfe4J5PzbPfmoREQVGEfE51RMuPXu7H2CzMHFkKgBb9he4b1+cfdhrte055jycu1Nk0Bn3dYwI5I5+zhWGZ+ZohcFIWmEUI0SHBXDPQOd+6l/+Y02T76cWEQEFRhHxQQ4avjp0c99ELogLw2I2cWmS80DtZ2ZnuVecALbmFbD3uOdnJp4qq+BIYSlQd2AEGD/cObF14958Zm/Oa8ArEG/QsRpilAeHdCUswArAf9bvM7gaEfEFCowi4nPsjXizb7OY+ddD/Vn1m1H87ed9CPW38uOBAmZuOgDAO8t3MvbFZQz6v0WcOFXm0WPnVq0uhgVYCQ+y1XlNVIg/91dNbH3ow7VUVNo9fxHSaO62ZoPrEN8THmTjwWHO/dQvzM+mrEJ/A0SkaSkwiojPqZ6S2rC3+0F+ViKD/WgX7Me4IV0AeH5eNi/Oz+YP//3Rfd3rS3Z49Li5VfsXO7Wve3XR5f6qia0Ary7azl3v/EDSEzM9ei7xjob+Dok0xj0DkukQ6k/usWLSfvuN0eWISBunwCgiPsfhxdWhewcl0z7Yj51Hinhxfg4Aoy+MAeC9FTs5VFBS78dyDbxJbHfuwBgWYOPhGisMS6r2UK7adczj+qVhdA6jGCnQz8LEESmAs+vgVFmFwRWJSFumwCgiPse9wuiFQ/RC/K2MH57i/v63V1zAGz/vQ+9OEZSU23l5YU69H8sVGM+2f7GmX/RPIjYsAABr1euYMXurBuE0k4YOThLxllsu6URiZCBHCkt599tdRpcjIm2YAqOI+BwH3g1VP+/fmamZ6bzx8z7cP7gLJpOJaWO7AfDJD7nsPlpUr8dxtaQm1iMwBtgsvHVXX8YP78p/JgzE32pm1a7jLM7y3sRWObvGDE4S8QY/q5nJo9MA59Tk/FPlBlckIm2VAqOI+JzGDL2pi81iZvzwFMZ0j3Xf1q9Le4akdaDC7uCFedn1ehxPVhgBMjqGMzWzG93jw7l7QBIAM+ZkceJUGf9as5ei0rrb1HKPneI/6/fVmuwqnvH275BIQ1zdsyPpMaEAvLZku8HViEhbpcAoIj6nsUNv6mtaZjoA/16/n615Bee81uFwkHvcs8BY04NDuxLqb+WnAwWMeG4Jj3+2ge7/M6fOa5/6z2Ye/WQ973y70+PnEafm+h0SOReL2cSUqr8znu6ZFhGpLwVGEfE5zdVOmNExnCt6xAHw7Jysc157uLCUknLnePz4iECPn6tdsB+/rJrYeqyo+jiPDbknzrg2O+8kAH9dvJ3Cs6xCyvmoJVVahlEXRLv3TF/69AIAKu0Ofv/VFpKemFnn3wAREU8oMIqIz2nOQ9cnj0nDYjYx/6dD5zz6wrV/sWNEIH7Whv1pvndQMhfGhXFBXBgjukUDcM2r39a6prSikgNVqxDHisrIOMsqpJybveroOy0witFq7pm2mk1sO1TIo5+s470VuwD436+3aBiWiDSKAqOI+BzXm6fmaCfs2iGEG3snAHBpcuRZ37i5j9SI9Hx10SXY38p/HxnEN48O5n+v7o7N4nx932474r5m3/FiapYQ4m/laGFpg5/TV7lXqZUYpQWouWf62le/5b8bD2CzmPCzmlm75wQLfjpkdIki0oopMIqIz2nuz9ofHZWKn9XMDzuPuc9MPF3usWKgYfsXa3IdFZIYGcTtl3YCnINwXEE197jzedJiQsjoGEZhaQV/XaxhGZ5qzlVqkfpw7ZkuLK3A32rmjV/05Z6BSQDc//fVGnIlIg2mwCgiPsfezGfoxUcE8vN+nQG4+91Vdb5xc68wtmtcYKxpwohUAm0WNuSeIPnJWbWep1NkMFMznW1sby/fyf4TxV57Xl9g19AbaWEyOobzwNAudIoM4v17L2V4ejQPDe1KaIAVgK827OfLdXvP2RovIlIXBUYR8TnVLanN95wPD+tKsJ8FgFmbD5xxvzvItfdeYOwQ6s99g5IBSI0OodLucO+V7BQZxJDUKC5LjgTgpfk5AJSUV3rt+dsyncMoLdGTl1/A0mnD6delPQARQX48OLQrANP+tZHHPt0AwLo9xw2rUURaHwVGEfE57nbCZnzO9iH+3D/YOcX0+bnZVFTaa92f697D6L3ACDBuSBfCA23kHCrky3X72HPUFRgDaw3L+GxNLpM/XU/G/8zRCkQ9qCVVWot7BiYRFeJPWUX135wZs7M0CEdE6k2BUUR8jmt1yNycS4zA/YOTiQz2Y8eRIlJ+84379tKKSvKqJpc2dg/j6cIDbTw0zLnCMOWzDWw/XOh8nqqVzD6d2zHqgmjsDvhi3T4q7A7CA21szStg+jc/KTyeRXMOThJpjCA/K3+6LoOU6BB+/bNu+FnMfLfjqLtNXUTkfBQYRcTnGLHCCBAaYOPhqvAWFx7gbv90TS4N8rPQPtjP6897V/8kokP9Acg55AyMNfdKTslMx2I2YTGbiA71J7+4nCtfXs7fluyga4fgM1ZDpfkHJ4k0Rmb3WOZPHsovh3Tljn7OYVgXJYTjcDg4capMq40ick4KjCLic+wG9hPe2a8z8eEBHMgv4YOVu4HaA2+aYhBPoJ+FiSNTa92WUCMwdosN48uHBzBz4iD+37UZAFRUTXXZfriIL9bt83pNrV1zD04S8Zbxw1MI8rOwcW8+4/6+hl5/nKfVRhE5JwVGEfE5DveEy+Z/7gCbhUdHOcPb/5v5EydLypts/2JNt1ySSOcaA3UCqwbwuFyUEEG32DBGXxjDHZd1YsyFMdXDMj7fSGmFhuHUZMTgJBFviArx5/6qYVjzfzro/nu4ZX++gVWJSEumwCgiPsfVfGUyaMblDb0T6NIhGIC3lu2scdRF0wVGm8XM1Kpz2kZ2iz7rdSaTiT9d14M3ftGXSaNSiQ0LAODDlXuarLbWqPp3SKT1uX9IF6JCnO3vXaKcf4uenZNlZEki0oIpMIqIz3GtDhnVTWi1mHl8tDO8vbVsBwu3HgIgMTKwSZ/3yovi+ddD/Zlx40X1uj7AVt3K+uqibRSWVjRlea1K9e+QIqO0PmEBNr5+ZBCzJw3mnbsvwWo2sSjrMKt2HTO6NBFpgRQYRcTnGNmS6nJ5RiwZHcMoKqtk++EiIoP9yOwe2+TP26dzJO1D/Ot9/U19E0hqH8TRojIy/mdOE1bWurSE3yGRxogLD6RbbBhJUcHcfEkiADNmb9UAHBE5gwKjiPgco1tSwXmkxxNjL8Bsgpgwfz79ZT/iI5p2hbEhbBYzk8c4V0ND/a0cLyozuKKWwf2eWiuM0gZMHJGKv9XMql3HWZx12OhyRKSFUWAUEZ9jN+pcjdMMSo1i7mNDmT95KKkxocYWcw5X9ojjwrgwTpZW8NqS7UaX0yLYNfRG2pDY8ADuHpAEwD3vrcJu1yqjiFRTYBQRn1PdTmj8u/2U6BBCA2xGl3FOZrOJqWOdq4xvLN3BgfxigysyXktYpRbxpgeHdiXU3wrAfzcdMLgaEWlJFBhFxOe4Vof0Vr/+hqV14NKkSABeXpBjcDXGM3pwkoi3tQv245dDugDw/NwsyivtBlckIi2FAqOI+Cyz/gLWm8lkYlrVKuM/V+9lx+FCgysylobeSFt076BkokL82HX0FJ+t3mt0OSLSQujtkoj4nOotjHq374m+SZGM6BZNpd3BiOeWGF2OodSSKm1RsL+V8cNTAHhpQTYl5ZUGVyQiLYECo4j4HLvaCRtsStXEVIDN+/LZkHuC7T642thSBieJeNvtl3WiY0QgBwtKeX/FLqPLEZEWQIFRRHyO+72+EqPHLowP4+qe8QA88I81XPPqt4x8bgmHT5YaXFnzakmDk0S8yd9q4bHRaQBM/2YrBSXlBlckIkZTYBQRn+NAQ28aY/LoNKxmE/tOVE9LfXXRNgMran7VLakibc91vTqSGh0CwJtLdxhcjYgYTYFRRHyO3b3CaGwdrVVSVDAPDO2C1Wzimoudq40ffr+b3GOnDK6s+WhKqrRlFrOJx6vaz99evtPnOghEpDYFRhHxPWonbLSpmd3Y9PtMXrq1FwNT2lNe6eDF+b5z3IZaUqWty+weQ8/ECE6VVfpcB4GI1KbAKCI+Ry2p3hHoZwGc4RHgy3V7yTl40siSmo37d0i/RNJGmUwmpmU6Vxk/+n4Pe4/7TgeBiNSmwCgiPkctqd51cWIEY7vHYnfAs3OzjC6nWdirzjTX4CRpywamRDEwpT1llXaf6iAQkdo8CozTp0/nkksuITQ0lOjoaK699lqysqrfHJSXl/OrX/2KHj16EBwcTHx8PL/4xS/Yv39/rccZNmwYJpOp1tett97qnVckInIe1fvP9GbfW6ZkpmE2wZwtB1mfe8LocpqcVqnFV7g6CD5f4zsdBCJSm0eBccmSJYwfP56VK1cyb948KioqGDNmDEVFRQCcOnWKtWvX8tRTT7F27Vq++OILsrOzufrqq894rHHjxnHgwAH319/+9jfvvCIRkfOw6wg9r0uJDuX63gkAPDNnq8HVND2tUouvuDgxgszuMQA8Nzfb4GpExAhWTy6ePXt2re/fffddoqOjWbNmDUOGDCE8PJx58+bVuuYvf/kLl156KXv27KFTp07u24OCgoiNja3X85aWllJaWj2hq6CgwJOyRURqcR2JoIEl3jVpVCpfrd/Pt9uOkvTETHb9+QqjS2o6GnojPmTKmHTm/XiQ2Vvy2v7/tkXkDI3aw5ifnw9AZGTkOa8xmUxERETUuv3DDz8kKiqK7t27M2XKFE6ePHubw/Tp0wkPD3d/JSYmNqZsEfF1OhKhSSS0C+L2y5wfDPZMCHe3/rZFakkVX5IaU91BMDClvcHViEhza3BgdDgcTJ48mUGDBpGRkVHnNSUlJTzxxBPcfvvthIWFuW+/4447+Pjjj1m8eDFPPfUU//rXv7j++uvP+lxPPvkk+fn57q/c3NyGli0ionbCJjRhRApBfhY27M1nzpY8o8tpMvodEl8zaVQqfhYz3247yrfbjhhdjog0owYHxgkTJrBx40Y+/vjjOu8vLy/n1ltvxW6389e//rXWfePGjWPUqFFkZGRw66238vnnnzN//nzWrl1b52P5+/sTFhZW60tEpKGqj0TQu31viwrx575ByQA8+MFaKu1tc5VRg5PE19TsILjjre/bdAeBiNTWoMD4yCOP8NVXX7Fo0SISEhLOuL+8vJybb76ZnTt3Mm/evPMGvN69e2Oz2cjJ0chmEWl6Dg29aVLjhnQhIsgGwBdr9xpcTdNwvVXW75D4ElcHATgnIouIb/AoMDocDiZMmMAXX3zBwoULSU5OPuMaV1jMyclh/vz5tG9//l73LVu2UF5eTlxcnCfliIg0SHU7od7uN4WwABsPDe0KwIvzcyitqDS4Iu/T75D4opodBM/NzWqzHQQiUptHgXH8+PF88MEHfPTRR4SGhpKXl0deXh7FxcUAVFRUcOONN7J69Wo+/PBDKisr3deUlZUBsH37dv7whz+wevVqdu3axaxZs7jpppvo1asXAwcO9P4rFBE5jauVyqz3+k3mrgFJxIT5s+9EMem/nX3+H2ht9DskPsrVQZBzqJCuv55ldDki0gw8CoyvvfYa+fn5DBs2jLi4OPfXp59+CsDevXv56quv2Lt3LxdffHGta1asWAGAn58fCxYsIDMzk/T0dCZOnMiYMWOYP38+FovF+69QROQsTGoobDIBNguPjkwDoH2wH0WlFQZX5F0aeiO+qmYHQceIwDbZQSAitXl0DuP5NjgnJSWd95rExESWLFniydOKiHiVXatDzeKmvgm8sXQ7u46e4p3lO3lkZKrRJXmNBieJL7trQBLvfLuTfSeK+fj7Pdw98MwtSiLSdjTqHEYRkdbIoYklzcJmMTN5TDoAz83L5nhRmcEVeY8GJ4kvC7BZmFj1AdDvv/6xzXUQiEhtCowi4nNcK4xqSW16V/aI48I456Ts15dsN7ga79HQG/F1N/dNJKl9EADvLN9pcDUi0pQUGEXE57gWGNWS2vTMZhNTM52rjO+t2EVefonBFXmHBieJr6vZQfDG0h1tqoNARGpTYBQR36PVoWY1LL0DlyS1o7TCTr/pC4wux6u0Si2+zNVBcLK0gl5/nGd0OSLSRBQYRcTnuFtS9V6/WZhMJqaN7QaAxWxi55Gic15vtztYn3uCkvKWO31Rv0MitTsI/K3mNtNBICK1KTCKiM9RS2rzuyQpkuHpHai0O3h+XvZZr6uotDPtXxu59tVv6fbU7PNO3jaKQ8dqiAC1OwheXphjdDki0gQUGEXE51RnEL3bb05TqlYivt6wny378+u85ndfbeHzNXvd3y/LOdIstXmqetCufofEt9XsIPjo+z3sOk8HgYi0PgqMIuJz1E5ojO7x4VzVMx6AZ+dknXG/3e5wh8W+ndsB8MycrBa5yqjfIZFqrg4C4JwdBCLSOikwiojPceUPs97tN7vJo9OwmE0syjrMql3Hat136GQpZRV2LGYTf72zN8F+Fjbty+ebzXkGVXsO+h0SqcXVQfDVhv38uL/A4GpExJsUGEXEZ+mtfvNLjgrmlksSAbjp9e9qrR7uOXYKgI4RgUSHBnD/4C4APDs3i4pKe/MXew5aYRSprWYHwc9eXmZwNSLiTQqMIuJz7O4z9PRu3wgTR6Tib3X+87M467D7dldg7BTpPAz8/sHJtAuyseNwEV+s3df8hZ6DBieJnMnVQQCc0UEgIq2XAqOI+BxNuDRWbHgAdw9IAmDGnCzsduf/h+RWBcbEqsAYGmBj/PAUAKb9a2OLOmZDg5NEzpQcFczNfZ0dBDNmb22R+49FxHMKjCLic+x6E2O4B4d2JdTfyk8HCvh6436gZmAMdF93Z7/OxIUHAPDh93uav9CzUEuqSN0eHensIFi163itDgIRab0UGEXE51S3E+rdvlHaBfvxyyHOPYrPz8umvNJ+RksqQIDNwqMjUwF4ddE2Cksrmr/YOmhwkkjdztZBICKtlwKjiPgctaS2DPcOSiYqxI/dR0/xz9W5dQZGgBv7JNAlKphjRWW8tWyHEaWelX6FRM5Us4Pgv5sOGF2OiDSSAqOI+ByH2glbhGB/q3uP4m++3Myhk6XAmYHRajEzeUwaAG8t28mxorLmLbQOakkVObuaHQQTP15HeQubciwinlFgFBGfo5bUluP2yzrRMaJ6z2Kov5XwQNsZ1/0sI47u8WEUllbQ+4/zmrPEOqklVeTcXB0EAJ+t3mtwNSLSGAqMIuJz3CuMBtch4G+1MGlUqvv7xMggTHWEMLPZxNSqg8H9rGb2nyhuthrr4kD7skTOpWYHwUsLslvUlGMR8YwCo4j4HLv2MLYo1/dOICU6BIAfDxSc9bqhaR24NDmSsgo7Ly/Iaa7y6qTfIZHzc3UQHCwopdtTs40uR0QaSIFRRHxO9R5GvdtvCSxmE3+8JoOOEYH89Y7eZ73OZDLxq7HOVcZPVuWy/XCh+76ZGw+Q9MTMZjv3TS2pIudXs4MgIshGQUm5wRWJSEMoMIqIz3FFCr3Vbzn6d23Pt0+M4Gc94s55XZ/OkYzsFg3A83OzcTgcPD83i/EfrQVgzpa8Jq8VNDhJpL5cHQQnTpXz5tKWNeVYROpHgVFEfI5Wh1q3KZnpmEwwc9MBxn+0lpcXbnPf9+zcbCqb4dw3DU4SqR+L2cSUqinHf1m4jcNV05BFpPVQYBQRn6PVodbtgrgwrukZD8CsTc4VxV+N7UZEkI1thwrp+utZTV6DBieJ1F9m91h6JoQD8Oqibee5WkRaGgVGEfE57oElervfak0enY6f1YzZBM/e1JOHhnXl4WFdAegYEUhpRdNOZNTQG5H6M5lMTBvbDYCPvt/D3uOnDK5IRDyhwCgiPsd1JILe7LdendoH8d9HBjF70hBu7JMAwC/6JxEbFsC+E8V8uHJPkz6/BieJeGZgShQDU9pTVmln0P8tMrocEfGAAqOI+ByHVofahLSYUNJiQt3fB9gsPFo1kfEP//2RwtKKJntuDU4S8dzUTOcqo9kEOQdPGlyNiNSXAqOI+JzqwKi3+23NTX0SSI4KBuCd5TvPe/3JknIOFpR4/Dz6HRLx3MWJEWR2j8HugOfmZhtdjojUkwKjiPgcV0uqWe/12xyrxczk0c6JjG8u3cHxorKzXrtm93EG/nkhlz29gN1Hizx6HldLqn6HRDwzZUw6ZhPM3pLHhtwTRpcjIvWgwCgiPsehoTdt2hU94rgwLoyTpRX0+uO8Oq/5cX8BP3/7ewpKnG2rL8w792pHUWkFv/vPZpKemAnUbEnV75CIJ1JjQrmul3Pf8TNzsgyuRkTqQ4FRRHyOXcdqtGlms4mpY9MB8Leaycs/s+X0szW5nCqrpFuscw/kfzbsZ2teQZ2Pl3+qnDvf/p6/f7cbgG+3HdHvkEgjTBqVis1iYvm2I3y77YjR5YjIeSgwiojPca8O6c1+mzUsrQOXJkVSWmHnpQU5Z9yfe8w51v/Ofp25okccDgeMfXFZnY/1wvxs1u054f5+xuytOlZDpBESI4O447LOANzx1vfuFm8RaZkUGEXE56glte1znvvmXGX8+Ic97DhcWOv+3GPFAHSKDGLymDQsVZsR1+w+dsZjbdmfD8Cvf9aNID8LG/bmU1Zhdz+PiHhu/PAUgvwsAMzZctDgakTkXBQYRcSn1PwkWwNL2ra+SZGM6BYNwPM19ig6HA72VK0wJkYG0bVDCDf2du6pmjE764zVDle4vCy5PfcOTK51n36HRBqmQ6g/9w1y/u/publZVNq1yijSUikwiohPqZkFtDrU9k0Z41xl/O/GA2ze51wpPFJYRnF5JSYTdIwIBODRUan4Wc18v/MYS3Oq91SVlFeSV3XsRmJkEOOGdCE80Oa+X6vUIg03bkgXIoJs5Bwq5Mt1+4wuR0TOQoFRRHyKvUZi1Fv9tu/C+DCu7hkPwJV/WQ7gXl2MDw/Ez+r8ZzA+IpCf93PuqXpmzlbsVasde487VxdD/K20C7IRHmjjoWFd3Y+vzxxEGi4swMZDQ53/e3phXjalFZUGVyQidVFgFBGfUrPpyax3+z5h8ug0rFW9oz/sPMbe46521MBa1z08rCvBfhY27yvgm815QPVwnMTIIPeK9F39k0iJDiE2LIB2QX7N9TJE2qS7BiQRE+bPvhPFfPz9HqPLEZE6KDCKiE+ptT1NedEnJEUFc8sliYBzwunuo1UhsF1Qrevah/gzbkgXAMZ/tJaKSrt7NbJTjXAZ6Gfhv48MYsm0Ye4VShFpmACbhYkjUwH4/dc/UlRaYXBFInI6/UsnIj6lVkuqAqPPmDgylQCbmdW7j/PZmlzAOSH1dPcP7kJksHPV8PM1e6tXGE8LlwE2C/5WSxNXLeIbbu6bSFJ75//G3v12p8HViMjpFBhFxGepJdV3xIQFcNeAJKDGkRrtzwyMIf5WHq7ao/jSghxyDhWe9VoR8Q6bxcxjo9MA+NuSHRwvKjO4IhGpSYFRRHxKrSmpxpUhBnhoaFdCA6zu7xPrWGEEuLNfZ+LDAziQX8KS7MPnvFZEvOOqi+K5IC6Mk6UVvL5ku9HliEgNCowi4lPUkuq7IoL8eKBqjyKc2WbqEmCz8Oio1Fq31dW+KiLeYzabmJrpXGV8b8Uu8vJLDK5IRFwUGEXEp2hKqm+7Z2AyF8SF0a9LJFEhZ59wekPvBLp0CHZ/7zqvUUSazvD0aPp2bkdphZ1+0xcYXY6IVFFgFBGfYq81JlV8TbC/lVkTB/HJL/u7j8moi9Vi5vHR6QAktQ8iwKYBNyJNzWQy8avLuwFgNZvYdaTI4IpEBBQYRcTH1MyLWmH0TecKijX9rEcsL916MS/f1quJKxIRl0uSIhme3oEKu4Pn52UbXY6IoMAoIr6m5tAb5UU5B5PJxDUXd+SihAijSxHxKVMynav7X23Yz4/7CwyuRkQUGEXEp9QaemNgHSIiUrfu8eFc1TMegGfnZhlcjYgoMIqIT9HQGxGRlm/y6DQsZhMLtx5i9a5jRpcj4tMUGEXEpzh0rIaISIuXHBXMzX0TAbjx9e9q/e0WkealwCgiPsVeaw+jEqOISEv16MhU/K3Ot6qLsw5TUWnnxKkyg6sS8T0KjCLiUxxVTanKiiIiLVtseAB3DUgC4OlZPzHiuSVc/Id5bDtUaGxhIj5GgVFEfEvVCqPyoohIy/fQ0K6E+lvJOVTInmOnAHhubhZfb9hP0hMzKSytMLhCkbZPgVFEfIqrJVXtqCIiLV+7YD8eGt4VgC5RwZhM8M3mPB75eB0AryzcZmR5Ij5BgVFEfIqrJdWsvCgi0io8NLQr/3qoP/+dOIjrenWsdd97K3ZyqKDEoMpEfIMCo4j4FPcKo5pSRURaBZPJRJ/OkQT5WXlibDcyu8fwx2sz6NO5HSXldi59eoHRJYq0aVajCxARaU6u0ezqSBURaX2iwwL428/7ApAaHcKtb6zEajax+2gRndsHG1ydSNukFUYR8SkO9x5GY+sQEZHG6delPUPSOlBhd/DCvGyjyxFpsxQYRcSnONSSKiLSZkzLTAfg3+v3szWvwOBqRNomBUYR8SkaeiMi0nZkdAznioviAHh2ThYl5ZWs3HEUu2vDuog0mgKjiPgUh47VEBFpUx4fnYbFbGL+T4f42UvLuPWNlfzf7K1GlyXSZigwiohPsbuG3hhch4iIeEeXDiHc1CcBgB1HigB4d8Uu9p8oNrIskTZDgVFEfIqrSUkLjCIibcejo1KJCvEjPjyAHh3DKauw89L8HKPLEmkTFBhFxKeoJVVEpO2JCw9k0ZRhLJ02nN9f3R2AT1fnsu1QocGVibR+HgXG6dOnc8kllxAaGkp0dDTXXnstWVlZta5xOBz8/ve/Jz4+nsDAQIYNG8aWLVtqXVNaWsojjzxCVFQUwcHBXH311ezdu7fxr0ZE5Dx0DqOISNsUGmDDajHTp3M7Rl0QDcDz87LO81Micj4eBcYlS5Ywfvx4Vq5cybx586ioqGDMmDEUFRW5r5kxYwbPP/88r7zyCqtWrSI2NpbRo0dz8uRJ9zWTJk3iyy+/5JNPPmH58uUUFhZy5ZVXUllZ6b1XJiJSB1dLqlmJUUSkzZqSmY7JBLM25bFpb369f+6Hncf4YeexJqxMpPUxOVwftzfA4cOHiY6OZsmSJQwZMgSHw0F8fDyTJk3iV7/6FeBcTYyJieH//u//eOCBB8jPz6dDhw784x//4JZbbgFg//79JCYmMmvWLDIzM8/7vAUFBYSHh5Ofn09YWFhDyxcRH7Q1r4CxLy6jfbAfa54abXQ5IiLSRCZ9so5/r98PwK4/X3He64tKK+jz/+ZRUm5n3mNDSI0JbeoSRQxV30zVqD2M+fnOT2wiIyMB2LlzJ3l5eYwZM8Z9jb+/P0OHDmXFihUArFmzhvLy8lrXxMfHk5GR4b7mdKWlpRQUFNT6EhFpCO1hFBHxDZNHp2OtOnT3u+1Hz3v9nmOnKCm3A/Dc3OwmrU2kNWlwYHQ4HEyePJlBgwaRkZEBQF5eHgAxMTG1ro2JiXHfl5eXh5+fH+3atTvrNaebPn064eHh7q/ExMSGli0iPq46MBpbh4iINK1O7YO47dJOAMyYs5XzNdXtOXbK/d+zt+SxIfdEU5Yn0mo0ODBOmDCBjRs38vHHH59x3+mf3DscjvN+mn+ua5588kny8/PdX7m5uQ0tu8mVVdj1B0akBdM5jCIivuORESkE2Mys23OC+T8dOue1uTUCI8A1r37blKWJtBoNCoyPPPIIX331FYsWLSIhIcF9e2xsLMAZK4WHDh1yrzrGxsZSVlbG8ePHz3rN6fz9/QkLC6v11RLtP1HMyOcXc82r33KsqMzockTkHDT0RkSk7YsOC+CegckAPDsni0r72VcZXYHxyovisFmc/0as2Hak6YsUaeE8CowOh4MJEybwxRdfsHDhQpKTk2vdn5ycTGxsLPPmzXPfVlZWxpIlSxgwYAAAffr0wWaz1brmwIEDbN682X1NaxUbFkB4oA2Avy7aZnA1IlIXtaSKiPiWB4d0JSzAStbBk3T99ayzXudqSR3QNYrbq1pZb3/r+/O2soq0dR4FxvHjx/PBBx/w0UcfERoaSl5eHnl5eRQXFwPOVtRJkybx9NNP8+WXX7J582buvvtugoKCuP322wEIDw/nvvvu4/HHH2fBggWsW7eOO++8kx49ejBq1Cjvv8JmZDabmJrZDYC/r9zN/hPFBlckIqdTS6qIiG8JD7LxwNCuACRGBlJWYae0ohL7aauNrsDYKTKICSNSCbRZAJj748HmLVikhfEoML722mvk5+czbNgw4uLi3F+ffvqp+5pp06YxadIkHn74Yfr27cu+ffuYO3cuoaHVo4lfeOEFrr32Wm6++WYGDhxIUFAQX3/9NRaLxXuvzCBDUqO4LDmSsgo7A/680OhyROQ0rrcHmpIqIuI77hmYRFSIP7nHipn8z/X0+eN8uvx6lnv10G53sPe484P+TpFBdAj1595BScD5W1lF2rpGncNolJZ+DuOa3ce54bUVmE3wxcMDWfjTQV5euK1eZwCJSNNat+c41/11BQntAln+qxFGlyMiIs3k79/t4nf/2VLrtnfu7kv2wUL+/M1WAMwmyPp/l2OzmMkvLmfIjEXkF5cD9TvLUaQ1aZZzGKVufTq3Y9QF0dgdcONrK3h54TbCAqzknyo3ujQRn+f6kFhDb0REfMutl3Sic/sgAOLCAwD45d/XuMMiQHxEIDaL8+1xeKCNB6taWRPaBVJaUUlBSblWG8XnKDA2kSmZ6ZhMUFH1R6WgpIK/Ld1ucFUi4mpKVV4UEfEtflYznz3Qn/fuuYSvJgwi2M/ifp/m0ikyqNb3dw9IIjrUn73Hi3n4g7X0/sM8utZoZRXxBQqMTaRbbBjTMrvRv0t7nrzcOQjnr4u3c+hkicGVifg213sD5UUREd8THRbAsPRoOoT686fretCnczv+eG2G+377aUEw0M/CIyNTAViw9ZA7YC7JPtx8RYsYTIGxCT00rCsf/7IfvxzShV6dIgB4ZeE2HA4H+zRBVcQQDrWkiogIcG2vjvzroQH8vF9nhqZ1AODKi+LPuO7WSxLpEhUM4P5/Z8zOOmPKqkhbZTW6AF9gMpmYmpnO7W9+z8c/7OGnAwWs2nWc6df34Laqc35EpHm424iUF0VEpMqbv+jL9zuP0q9L+zPus1nMfP7QAHYdLSKpfTBDZizixwMFzNx0gKt6nhkwRdoarTA2kwFdoxicGkV5pYNVu44DzjHNhaUVBlcm4lvUkioiIqfzs5oZnNrBPfDmdJHBfvTu1I7IYD/uH5wMwPPzsimvtDdnmSKGUGBsRtMyu+FnMRMeaKNjRCBHi8p4Z/lO9/0Oh4OsvJNU6I+PSJNxVA29UUuqiIg0xP2DuxAZ7MfOI0V8vmav0eWINDkFxmbUIyGcuY8NYcnUYTxRNQjn+XnZHC8qo6LSztTPN5L54lJSfvONwZWKtGGuFUblRRERaYAQfyvjh6cA8OQXmygprzS4IpGmpcDYzJKigokI8uOKHnFcGOc8IPOlBTlM/GRdrU+p1ueeMKhCkbatuiVViVFERBrmjss6EV91luM/vtttcDUiTUuB0SBms4mpY9MBeG/FLmZtysNmMdEzMQKAZ+ZsPcdPi0hDOXQOo4iINFKAzcKkUWkA/HXxNk6WlBtckUjTUWA00LC0DlyaFOn+/q27LuGV23phs5j4dttRvt12xMDqRNom95BUJUYREWmE63t3pGuHYI6fKufNZTvP/wMirZQCo4FMJhPP3HQRt/RN5F8PDWBoWgcSI4O447LOAMyYk1V9BICIeIXrUGaz8qKIiDSC1WLm8THObrG3l+3gaGGpwRWJNA0FRoN1bh/M/914EX06t3PfNn54CkF+FjbknmDOloMGVifS9rg+gtECo4iINNblGbH06BhOUVklry7abnQ5Ik1CgbEF6hDqz70DnWf8PPjBGirtWmUU8RbXqr2G3oiISGOZTCamZjpXGd/5dif7ThQbXJGI9ykwtlDjhnQhPNAGwIvzs7nxtRUkPTHT4KpEWj9Xl7daUkVExBsGp0bRr4tzJsVL87MNrkbE+xQYW6jwQBsPDesKwF8WbmP17uMArN51zMiyRFo9h3pSRUTEi0wmE9PGOs/X/nzNXrYdKjS4IhHvUmBswe7qn0RMmD8Afhbn/1dpEI5I49jdLakiIiLe0btTO0ZfGIPdAaOeX2J0OSJepcDYggX6WXj37kuZmpnOfycOws9q5oedx1iSfdjo0kRaLdfHLWpJFRERb5oyJt3dvLJx7wlDaxHxJgXGFu7C+DDGD08hLSaUu/pXHbcxOwu7BuGINIjOYRQRkaaQHhvKdRd3BOCZOVkGVyPiPQqMrchDw1II8bfy44ECZm46YHQ5Iq2SQy2pIiLSRB4bnYbNYmJZzhFWbD9idDkiXqHA2IpEBvsxbnAXAB75eB3llXaDKxJpfapbUhUZRUTEuxIjg7jt0k6AsyNMcyekLVBgbGXuG5xM+2A/wDmJS0Q8Uz0l1dAyRESkjZowIoVAm4X1uSdIfnKW0eWINJoCYysT4m/l4eEpALw0P4eS8kqDKxJpXVxTUjX0RkREmkJ0aAD3DEwCIC0mhMp6zp1wOByUVah7TFoeBcZW6I7LOhEfHkBeQQndnpptdDkirUr1AqMSo4iINI0HhnQlLMBK9sFC/rN+33mv33P0FKNfWErab7/heFFZM1QoUn8KjK1QgM3CpFFpALQLsnGypNzgikRaD/fQG+VFERFpIuFBNh4c1hWAyf/ccM6Vw5yDJ7nx9RVsO1QIwOtLtjNnSx5JT8ykQvMqpAVQYGylru/dka4dgjl+qpw3l+00uhyRVsO1h1FDb0REpCndMyCZDqH+AHyyak+d12zel8/Nf/uOQydLiQlzXvv28p088I81APxzteZViPEUGFspq8XMlDHpALy8IIejhaUGVyTSOjjQCqOIiDS9QD8LE0emAvDygm2cKquodf/qXce47Y2VHD9VzkUJ4cx+dAh9O7ejosaex5cWZGtehRhOgbEVG5sRy0UJ4QC8umi7wdWItA52dfeIiEgzuaVvIp0igzhSWMqFv5vjvn1ZzmFufP07TpZWcGlSJB/efxntgv3447UZ9EyM4Nc/60bHiEAOFpRqXoUYToGxFTOZTEzNdK4yfrByN/tOFBtckUjLp3MYRUSkufhZzUwe7Zw7ERZgJf9UOXO25HHfe6sBGJrWgffvvZTQABsAF8SF8Z/xA/nlkK48Osq5OhkRZKOgpJzjRWVabRRDKDC2coNSoujfpT1llXYG/nmh0eWItHgaeiMiIs3p6p7xdIsNpaCkggc+WM3DH66lrNLO5RmxvPGLPgT6Wer8uet7OedVnDhVzgN/X0PfP82n21Oz631Mh4i3KDC2ciaTialjnauMZhNsO3TS4IpEWjbX0BvlRRERaQ5ms8k9d2LljmNU2h3c0DuBv9zWC39r3WERas+r+G7HUXdQrM8xHSLepMDYBvTu1I7RF8Zgd8Bzc7ONLkekRXMNvVFLqoiINJeRF0RzWXIkAL/o35lnbrwIq+X8b8PHZsTSp3M7ALrFhgLwwvzscx7TIeJtVqMLEO+YMiad+T8d5JvNeWzce4KLEiKMLkmkRXJ18igviohIczGZTLx/76VsO1RI9/gwTPX8R8hkMvGP+y5lx+EiunYIYegzi8g9Vswnq/bwi/5JTVu0SBWtMLYR6bGhXNerIwDPzMkyuBqRlsvdkqrEKCIizSjAZiGjY7jH//4E+VnJ6BhOoJ+FR85xTIdIU1FgbEMeG5WGzWJiWc4RVmw/YnQ5Ii2S+xxGg+sQERHxVM1jOt79dpfR5YiPUGBsQxIjg7j90k4A3P7m9+5pkCJSTS2pIiLSWtU8puOZOVnknyo3uCLxBQqMbcyEEakE2pwTt+b9eNDgakRaIIeG3oiISOvlOqYD4LUl2w2uRnyBAmMb0yHUn3sHJQHw7NwsndUjchrX/yKUF0VEpDWqeUzHeyt2cqigxOCKpK1TYGyDfjmkK+GBNrIPFuqsHpHT2O2uPYxKjCIi0jqNvCCaPp3bUVJu5+WFOUaXI22cAmMbFB5o48GhXQF4fp7O6hGpSSuMIiLS2plMJqZlOlcZP/khl91HiwyuSNoyBcY26u4BSUSH+rP3eDEf/7DH6HJEWgwdqyEiIm3BZV3aMzStAxV2By/Myza6HGnDFBjbqEA/CxOrzur5n6+26KwekSp2h47VEBGRtmFq1Srjv9fv56cDBQZXI22VAmMbdssliXRuHwSgs3pETmNWYhQRkVYuo2M4V1wUB8Czc7IMrkbaKgXGNsxmqT6r5/Ul2zlxqszgikSM515hVEuqiIi0AY+PTsNiNrFg6yFW7zpmdDnSBikwtnFXXeQ8q+dkSQWvL9lhdDkihqvew2hsHSIiIt7QpUMIN/VJAODG17/D4dCRauJdCoxtnNlscve366wekRpTUrWLUURE2ohHR6XiZ3W+rV+SfdjgaqStUWD0ASO6VZ/Vc+nTC4wuR8RQ1S2pBhciIiLiJXHhgfyiX2cA7n53lfvM4bIKOx+s3E3SEzONLE9aOQVGH1DzrB6r2cTuo0U4HA4qKnU+o/geV6eOht6IiEhb8vDwFEL8rQDM2nyAkvJKHvxgDb/992bCAqzknyo3uEJprRQYfUTNs3qe+s8WRr+wlJTffMPJEv3xEN+kllQREWlLIoP9GDe4CwDPzc3mnndXsXDrIQAKSir429LtRpYnrZgCow9x7WVcmn2YbYcKAXhr2U4jSxJpdq42HbWkiohIW3Pf4GTaB/ux80gR3+04Soi/lfHDuwLw18XbOXRSsyzEcwqMPiSjYzhX9YwHoEOoPwBvLdvB0cJSI8sSaVbuoTdKjCIi0sY4A2IKAO2CbHw07jKmjEmnV6cIAF5ZuM3A6qS1UmD0Mc/ceBF/ua0X8ycPJaNjGEVllfT5f/ONLkuk2ehYDRERacvuGZjE337eh5kTB3NRQgQmU/XE/I9/2MOeo6cMrlBaGwVGHxNgs3BVz3jCA21MzewGgJ/FzL4TxQZXJtI83FNSDa5DRESkKZhMJjK7xxIfEei+bUDXKAanRlFe6WDIM4sMrE5aIwVGHzYkNYrLkiMpq7Tz0vxso8sRaRaullSzlhhFRMSHuFYZTSbYmldgcDXSmigw+jCTycS0sc5Vxn+u3usehCPSljl0DqOIiPigixIiuDwjFocDnp2jhQKpPwVGH9encztGXRADwPPzsvhy3V6SnphJYWmFwZWJNI3qcxiVGEVExLc8PiYNswnm/3SQNbuPG12OtBIKjMLUzHRMJpi1KY/HPt0AwF8XaYqWtE0Od1OqiIiIb0mJDuXGPgkAPDNnq7vrRuRcFBiF9NhQrr24Y63b3v12F4cKdFaPtD12TUkVEREf9uioNPwsZlbuOMaynCNGlyOtgAKjAPDk5d34WY9Ypl/fg96dIigur+TSpxcYXZaI16klVUREfFnHiEDu7NcZgF+884NWGeW8FBgFgOiwAP56Rx9uu7ST+7gNq9mks3qkzXG1pCouioiIrxo/vCvBfhYAvtmcZ3A10tIpMMoZ+ndtz+DUKCrsDl7QcRvSxjjUkioiIj6ufYg/9w3uAsCzc7OoqLQbXJG0ZAqMUqdpVauMX67bp7N6pE1xtd6oJVVERHzZuMHJtAuyseNwESm/+cbocqQFU2CUOvVICOdnPWIBndUjbYt7q4byooiI+LDQABsPD0sBID48gJLySoMrkpbK48C4dOlSrrrqKuLj4zGZTPz73/+udb/JZKrz65lnnnFfM2zYsDPuv/XWWxv9YsS7Jo9O11k90ua4p6QqMYqIiI/7ef/OxIYFsD+/hA9W7ja6HGmhPA6MRUVF9OzZk1deeaXO+w8cOFDr65133sFkMnHDDTfUum7cuHG1rvvb3/7WsFcgTSYlOsR9Vs8Nr63QFC1pE1xDb8zKiyIi4uMCbBYeHZUKwP+b+ROFpRUGVyQtkdXTH7j88su5/PLLz3p/bGxsre//85//MHz4cLp06VLr9qCgoDOulZbn0VFp/Hvdfsoq7SzLOcKQtA5GlyTSKBp6IyIiUu2mPgm8sXQHO48U8dayHUwalWZ0SdLCNOkexoMHDzJz5kzuu+++M+778MMPiYqKonv37kyZMoWTJ0+e9XFKS0spKCio9SXNo+ZZPc/MycJu1yqjtG4aeiMiIlLNajHz+BhnSHxr2U6OFZUZXJG0NE0aGN9//31CQ0O5/vrra91+xx138PHHH7N48WKeeuop/vWvf51xTU3Tp08nPDzc/ZWYmNiUZctpXGf1bNqXr7N6pNXTzBsREZHafpYRR/f4MApLK+j9x3lGlyMtTJMGxnfeeYc77riDgICAWrePGzeOUaNGkZGRwa233srnn3/O/PnzWbt2bZ2P8+STT5Kfn+/+ys3Nbcqy5TQ1z+p5bl4Wx4vK+Gx1LgUl5QZXJuI5u3pSRUREajGbTUzNTAfAz2pm/4ligyuSlqTJAuOyZcvIysri/vvvP++1vXv3xmazkZOTU+f9/v7+hIWF1fqS5lXzrJ4Rzy1m6ucbuej3c40uS8RjrryooTciIiLVhqZ14NLkSMoq7Ly8oO735OKbmiwwvv322/Tp04eePXue99otW7ZQXl5OXFxcU5UjjVTzrJ7jp6pXFlfuOGpUSSINUt2SqsQoIiLiYjKZ+NVY5yrjJ6ty2XG40OCKpKXwODAWFhayfv161q9fD8DOnTtZv349e/bscV9TUFDAZ599Vufq4vbt2/nDH/7A6tWr2bVrF7NmzeKmm26iV69eDBw4sOGvRJrcz/t35uLECLrFhpLZPQaAW99YqeM2pFVx/b6qI1VERKS2Pp0jGdktGoDn5mUbXI20FB4HxtWrV9OrVy969eoFwOTJk+nVqxe/+93v3Nd88sknOBwObrvttjN+3s/PjwULFpCZmUl6ejoTJ05kzJgxzJ8/H4vF0oiXIk0twGbhi4cGMHvSEP5wTQYBNuevz4KfDhlcmUj9qSVVRETk7KZkpmMywcyNB9i8L9/ocqQFMDla4fJQQUEB4eHh5Ofnaz+jgf78zVZeX7KdbrGhzJo4GLPegUsr8KvPN/Lp6lymZqYzfniK0eWIiIi0OI9+so7/rN8PwK4/X2FwNdJU6pupmnRKqrRtDw3tSmiAla15J+ny61lGlyNSL3a1pIqIiJzT5NFpWKsWAjSvQhQYpcHCg2w8OLQrAJ0igyirsBtckcj5aeiNiIjIuXVuH8ytlzrPPZ8xe6vmVfg4BUZplHsGJhEV4s+eY6f4dLXOx5SWTyuMIiIi5zdxRCoBNjNr95zQvAofp8AojRLkZ+WREc59YE/9ezPFZZUGVyRyHhp6IyIicl7RYQHcPSAZgGfnZmG3a5XRVykwSqPddmknEtoFAvDeil3GFiNyHmpJFRERqZ+a8yq+2rDf6HLEIAqM0mh+VjOPjUoD4LXF28g/VW5wRSJnp5ZUERGR+qk5r2LSp+s1r8JHKTCKV1zbqyNpMSEUlFTQ8w9zjS5H5Kxc+/ZNSowiIiLn5ZpXAWhehY9SYBSvsJhNPD4mHYBAm4VDJ0sMrkikbtUtqSIiInI+NedV/GVBjuZV+CAFRvGaMRfGcHFiBMXllbyycJvR5YjUSS2pIiIinnHNqzh0slTzKnyQAqN4jclkYtpY5yrj37/bTe6xUwZXJFIH95RUJUYREZH6qDmv4vUl28kv1rwKX6LAKF41oGsUg1OjAHhhXrbB1YicyYFWGEVERDzlmleRX1zOG0u3G12ONCMFRvG6qZnOVcYv1+8jK++kwdWI1GavGvCmoTciIiL1ZzGbmFI1r+LVRds1r8KHKDCK112UEMHlGbE4HJD54lKjyxGpxb3CaHAdIiIirc3oC2Po1SkCgBfm5fD0rJ9IemImpRUahNOWKTBKk3h8TBrmqnfka/ccN7YYkRrs7mM1jK1DRESktTGZTO5Oso9/2MMbS3cA8OHKPUaWJU1MgVGaREp0KDf0TgBgxuytOFyH34kYzKGhNyIiIg1Wc16FyyuLtlFYWmFQRdLUFBilyUwanYafxczKHcdYlnPEK4+55+gprnllOUlPzPTK44kvUkuqiIhIY0y/vge3X9aJT3/Zj6T2QRwrKiPjf+YYXZY0EQVGaTIdIwK5o18nAJ6Zk+WVVcavN+5nw958wgNtGuksDaKWVBERkcZJaBfE09f14LIu7ZlcNQgnxN/KsaIygyuTpqDAKE1q/PAUgvwsbNqXzzeb8xr9eHuOOs92zC8u582qvnkRT7g+uNCUVBERkca7skccF8aFUVhawWuLtxldjjQBBUZpUlEh/tw/KBmAhz9cS0WlvVGPt+fYKfd/v7Jom0Y6i8dc69yKiyIiIo1nNpuYOta5yvjmsp0cyC82uCLxNgVGaXL3D+lCRJANgC/W7mvUY+UedwZG1+O9ulCfZIlnqltSFRlFRES8YVhaBy5NigTg5QU5Xn/8E6fK+HLdXorLdHyHERQYpcmFBdgYPywFgBfnZ1NS3rD/sZdX2tl/wvmp1e+v6g7ARz/sIbfGqqPI+bhaUs3KiyIiIl5hMpmYVrXK+M/Ve9lxuNBrj30gv5jrX1vBY59u4ILfzfba40r9KTBKs/h5/87EhgWwP7+Ebk817H/s+08UY3eAv9XM1T3jGZQSRXmlg8EzFjW4rvzickY+t1hTV32IQ0NvREREvK5vUiQjukVTaXcw4rklXnnM3UeLuOn179hxuMh925b9+V55bKk/BUZpFgE2C4+OSgUgMtivQWf1uPYvJkYGOfvlqw6ONZkg++DJBtW1aucxtlf9Efph57EGPYa0Lg5cK4xKjCIiIt40pWpiKsCmvfn8e90+1ueeaNBj7ThcyE2vf8fe48UktQ9iSFoHAJ6dk+WNUsUDCozSbG7qk0ByVDDHisp4e9lOj38+95izHbVTZBAAPRMjGNs9Foej4X88ag7RmTF76xlHf9jtjT8KRFoWL5zuIiIiInW4MD6Mq3vGA3D7myuZ9Ol6rn31Ww6fLPX4sV5ZuI1DJ0tJjwnlnw/253+v7o7FbGJR1mFW7dKH/M1JgVGajdViZvLoNABemJ/t8Vk9rnDnCowAUzLTMJtg7o8HWbfnuMc1uYboAKzefZxFWYectx87xc9eWkaXX8+iqAGrodJy2XWshoiISJOZPDoNq9nEyRrvn15d5PmQwu1V+yAfG51KdGgAyVHB3Nw3Eaj7Q35pOgqM0qyuqDqrB/D4rB7XcJuEdoHu21KiQ7mhdwIAzzRgldH1mHHhAQDMmJ3FtkMnGTxjET8eKADgneWer4ZKy+X690VDb0RERLwvKSqYx0anER8ewINDuwLw3opdHg8prF4oCHbf9ujIVPytZlbtOk7yk7O8V7SckwKjNKuaZ/W8/91u99TT+qhrhRFg0ug0/CxmVmw/yvKcIx7V43rMJy7vRqi/la15J7ni5eWAc68lwBtLd3Dcw9VQabmqz2FUYhQREWkK44en8O0TI3ji8m4MTGkPwIvz63/cxsmSco6fKgcgMbJ6oSA2PIC7BiQBcEFcGHa7g9KKSq02NjEFRml2rrN6yirsDPjzwnr/nKt9tFP72oGxY0Qgd/TrBMCdb39f7z8aDofDHRh7JkTwwNAuAJRW2MnoGMbcx4bQLTaUk6UV9PrjvHrXKS2bw92SanAhIiIibZhr68fUzG4AfLluLzn1HFLomlvRLshGaICt1n0PDe1KqL+Vnw4U8PhnG7j4f+dptbGJKTBKs6t5Vo/FbGJD7gmen5t1zqMt8ovLOeH6pKld0Bn3jx+eQpCfBYA5W/LqVcfhwlJKyu2YTRAfEcg9A5MZmNKesd1j+WhcP6JC/N2TWP2tZvLySzx6ndIyqSVVRESk+VycGEFm9xjsDhj9wtJ6/czZusoA2gX7MW6I80P+L9fto7jqfO+Ne094p2A5gwKjGKLmWT03vLaClxduIzzQRn5xeZ3Xu/re2wf7EexvPeP+qBB/7h+UDMCzc7OprMd0U9enV3HhgfhZzQT7W/nw/n68/vM+hFV9mjWiWzR9O7ejtMLOywvr30ohLVf1b4YSo4iISHOYMibd/UFtfYYU5tY4Sq0u9w1KJirEH6ieQ9GQWRZSPwqMYpipmemYTFBRFe7yi8t5c+mOOq893x8OgPuHdCEiyMa2Q4V8sXbveZ+/+jEDz3qNczXU2Urx0fd72HWk6KzXSutgV0uqiIhIs0qNCeW6XvUfUujehnSW933B/la+fHgAH95/Gf98oD82i4llOUdYsd2zWRZSPwqMYpgL4sKYMiadfl0ieeJyZyh7ZdG2Os/qOd8fDoCwABsPD3NO43pxfg5rdh8n6YmZ7D7qDHkOh4N/rsol6YmZlFXYz9nuUNOlyZEMS3ceFvv8vGwPX6W0NNUtqUqMIiIizWXSqFRsFhMrth895zYkOHdLqktiZBADU6JIjAzi9kudsyxmzM7SAJwmoMAohho/PIVPftmfB4Z0oWdiBACvLMzB4XDUmqBa33D3i/5JxIYFsO9EMTe+vgKAKZ9twOFw8OdvtjLtXxsB+PiHPfV+THC2UgB8tWE/P+4v8OxFVikpr9Q+yBbAPfTG4DpERER8SWJkEHdc1hmAngnh5wx2e+rRWVbThBGpBNosrM89wbwfDza+WKlFgVFaBJPJxK+qBsx89MMebnr9Owb8eSGfrtoDwJ6q/YbnC3cBNgsTR6YC1StJq3Yd5/Y3v+dvNdpd/7JwG99tPwrU749RRsdwrrwoDoCfvbzMg1dW7ZGP19Fv+gLW7D7WoJ8X73D982TWXz8REZFm5RpSuGFv/lmHFNrtDvbW832fS4dQf+4dlATAL/+xpl6zLKT+9JZJWowBKVEMSomivNLB6t3ODdHPzMmiqLSCvVWfNCWcY7+hy019E7ilbyJ39e/M3VVn9Xy34ygmE/zxmu50igziSGEp+04UExsWwNC0DvWq7/Ex6Viqdmyv3uV56Pthp/Nn/vzNVrVLGMj1f3qdwygiItK8OoT6c1/VkMIHP1hbZ7A7dLKUsko7UD3Qpj5+OaQr4YHOoYX/XrfPC9WKiwKjtCjTxqZjs5gID7TRMSKQI4VlvLVsJ3uP1/+TJpvFzP/deBH/e00Gj45MJTLYD6vZxEu39uLn/ZP4VdUQm06RQXz2YH8igvzqVVtyVDA393Vu2Pa0Rz6/uNw9AXbVruMszjpc758V77JXJ0YRERFpZuOqhhQCdQ4prLllyGqpf1QJD7Tx4FDnLIsX5mdTVmH3QrUCCozSwlyUEMHcx4ayZOowflU1COeF+dmUVdqxmk3EhZ9/hbGmdsF+fPPoYJZMG87VPeMBuOKiOOZMGsLsSYPr3RvvMnFkKn5WMz/sOsbi7PqHPtdEVpd73luFXe0ShtDQGxEREeOEBdh4aGj1kMLSispa9++pxxT7s7l7QBLRof7sPV7Mxz/saXyxAigwSguUHBVMRJAfV/aI48K4MPftCe0C3S2hnogJC6BjRO0/OumxoQT5nXme4/nEhQdyV3/nhu1nZmfVO/S5AmOXDsGEVp0j+fXG/R4/vzSe6//HFBdFRESMcdeAJGLC/Nl3opj0386udZ8nQwlPF+hXPcviLwu3caqsovHFigKjtFxms4mpVYNwoP6TspraQ8NSCPG38uOBAmZuOlDnNRWVdr7bfpTyqh581x+/jPhwfjmkC+A8osN1vzQfh85hFBERMVTNIYXtg/0oLK0Odns9nJB6ulsuSaRze+e8ine/3dXoWkWBUVq4YekduCSpHQDLclrGYayRwX6MG+wMfY98vK7O0PfZmr3c9uZKUn/zDVD7HMl7ByUTFeLH7qOn+Ofq3OYrXAC1pIqIiLQEN/dNJKl9EEeLynhn+U737Y1ZYQTnLIvJo9MA5/DEE6fKGl+sj1NglBbNZDIx48ae3NA7gQWPDzW6HLf7BifTPtg5LOfzNWdu2N649wTgXMXamldQ61iQYH8r44enAPDyghxKyivP+HmjHS0s5ebXvzvrwbqrdh0j6YmZrNtzvJkrazwHOodRRETEaDaLmceqgt3z87I5XuQMdo0NjABXXRRPt9hQAF6Yl82UzzaQ9MRMKtTZ1SAKjNLiJUcF89zNPenaIcToUtxCaoS+F+dnnxH6cqsCosMBz87Jdu9hdLVX3H5ZJzpGBHKwoJT3V+xqvsLrad6PB/lh1zECbGYOFZSccf8nPzhXRp/8YlOrG95j1yZGERGRFuGqi+K5oGpexWtLtlNSXsmhk6UAJLZreGCsua3p/e92uz/cr+tDfjk/BUaRBrqjX3Xo+/t3u2rdt6fGVNT5Px1k19EiADq1d/7x87damDTK2bs//ZutFJSUN0/R9eSqv6TczssLc8643xWAt+adbHXDe1x7GNWSKiIiYiyz2cQ0V7BbsYvVu5ydS6H+VvfRGw01ols0fTo7tzW5Zia+1EI7u1o6BUaRBvK3Wni0KvT9dfF2d+irqLSz74RzhXFIWgfAudJos5iIDas+gPb63gmkRDtXTd9cuqM5Sz+vmoH3g5V72F0VeOu6/9FP1reqs450DKOIiEjL4ZpXUVph5863vwecHVmmRn6wazKZePGWi7lvUDJfTRhEfHgAB/JL6PbU7PP/sNSiwCjSCNf36kjXDsGcOFXOW1Wh70B+CZV2B35WM09fl4Ff1aGzHSNqHwtiMZuYMsbZu//28p0crmrBaC7fbT/q3mt5OtcKYliA8wiQ5+dlu+8rKa8kr6pN1XX/p61oeI+rI9XcgCNaRERExLtMJhPTxnardVtj9i/WlBgZxFNXXkhGx3D3h/ztgmycbGGdXS2dAqNII1gtZqaMcbZSvLxwG0cKS92rbwntAkloF8Sd/ZznNu46euqMn8/sHkvPhHBOlVXy6qJtzVb30cJSfvHO91z9yrfsr1oNrcn1Gn53VXcAvtqwn58OFACw97jz+hB/K49Xvfa/LMihuKx1tHi4j9UwuA4RERFxuiQpkuHpHdzfu7bweNMNvRPo0iGY46fKeXPZzvP/gLgpMIo00tiMWC5KCAfg1UXbzpjuNSUzjcmj05g9afAZP1vzU7WPvt/D3uNnhkpP/XSggP7TF5x1winAjiNFlFc6g9PLC2rvUTxZUs7xU85P3sZmxHLFRXE4HHD5S8uA6iNCEiODuO3STiS0C+TQyVIu+J2zxaO0opKpVdPIco81/vV4m2vojbYwioiItBxTap693S7Q649f60P+BTkcLWzezq7WTIFRpJFMpupJXB+u3MN3248C1YExyM/KxJGpdIsNq/PnB6ZEMTClPWWVdl6cf+aAGU99+P1uDuQ7W0b3nyjmn6ty2XPa6mbN7z9bs5cdhwvd37smvEYG+zlXEUenuVtpV+86Vj3xtV0gflYzj41yttWGB9o4WFDC/e+v5rOqKWQz5mSdt941u48ze/OBhr5cj7mP1VBiFBERaTG6x4dz94AkAm0WBqZENclzXJ4RS4+Org/5tzfJc7RFCowiXjAoJYr+XZyh76sNzqmhnvTfT810rjJ+vmYvOQdP1vvnXO2VNZ2q0Ro69sWlTPvXRoY8s6jWYJqaQ2sq7Q6eq7FHcc9pR4B06RDCTX0SAJgxO4vdR2uvoF7bqyNpMSHkF5cz+vklLMs54n6srzfsZ8v+/HPW/8A/1vDgB2tZmn243q+7MTT0RkREpGX6n6suZPP/ZtKliY5Sq/kh/wcrd7uHFMq5KTCKeIHJZGLq2PRatyV6EBgvTowgs3sMAM/NzT7P1WC3O/jD1z+S/OQsluXUDlp7j1X/8SsoqXD/98c/7HH/t2uV8Oqe8ZhMMHPjATbvy691X83A++ioVPysZn7Ydaw6EFftL7CYTe69jAUlFYQGWPnXQ/25qmc8AFe8vPysr+PEqXKOVLWEPDMnq84A7G3uwKgVRhERkRbFZDLVGhDYFAanRtGvSyRllXYG/nlhkz5XW6HAKOIlvTu1Y/SFMe7vPT1wdsqYdMwmmL0l75z7Dysq7Uz5bAPvfOvcsP0/X22hovLM1cOU6BA6RQZxZ79O7utOlTkDpGsf4pjuMVxdFeyu/MvyWvd1iqzePxAXHsgvqob3uKa51gzEYy6M4YoecXSJCubjcf3o0zmSyTVaWVftOlbna6m50rlpXz7JT846x/+FvKP6HMYmfyoRERFpYWrOjzCbYNuhwvP8hCgwinjR1Exn6PO3mj2e8JUaE8p1vZytnwNT2td5TWlFJeM/WssX6/ZhMZsI8bey43ARX6zdB9Q+8uKTX/ZjydRh/O7K7u7Vwne/3QXUaDttF8Tk0WlYq9LT9zuO1rqvpoeHpxDib3V/X/N+k8nEK7f3YsHjQ8mo2huQHBXMLZckAjBj9tY6Vw/3nDYUp0uH4Frhtym4qjCpKVVERMQn9e7UjlEXxGB3wPPzzj9vwdcpMIp4UVpMKB/cdxnv3nNJrXBVX5NGpWKzmPh221G+3Xak1n3FZZXc//5q5mw5iJ/FzOt39uHRkc4zhab9ayMl5ZXuXvxgPwvtg/0wmUz4Wc1MHu0cTPP6ku3k5ZdwsMC5StgpMojO7auD3Z9m/cSW/QXu+2qKDPZj3OAu7u8TTptgZjKZzmjznDgiFX+rmVW7jrM468w9iq7AOObCGNoF2WqF36Zidx2robwoIiLis6ZmpmMywaxNeWzae/Z5C6LAKOJ1A1KiGNC1YdO9EiODuOMyZ+vnHW99716VKygp5xfvfM+ynCME2iy8c/cljL4whp/370xsWAAAH36/p9bAmprh7eqe8XSLDeVkSQX9pi8AINTfSkSQDYCJI1MJsJnZuDefwydLiQnzp2dixBn13Tc4mUuS2nFTnwQCbJbzvp7Y8ADuHpAEOCem2u21Vxld+yUviAtj/PAUoDr8NhWHjtUQERHxeemxoVx3cUcAZszZanA1LZsCo0gLM354CkF+zjA2Z8tBjheVcceb37Nq13FCA6x8cP+lDEp1BtIAm4VHRzlXGV9dtI2fDjhXB08fuGM2m9xnD7nUDJUxYQHcMzAZgI4RgXz6y/4E17FCGuJv5bMHB/DMTT3r/XoeHNqVUH8rPx0o4L+bah+fUb1fMog7+3UmLrw6/DYV9zmMakkVERHxaY+NTsNmMbEs54j7WDQ5kwKjSAvTIdSfe6vC24w5W7nlje/YtC+fyGA/90CZmm7qk0ByVDDHisqYMdvZh1/XkR4jL4imd6cI9/eJkbVbSqeMSef1O/vw1YSBJEUFe+31tAv245dDnK2sEz9eR3kdA3o6tQ9yht+R1eG3sLTizAfziqqhN/rrJyIi4tMSI4O47VLncMDb3lzZLNPaWyO9ZRJpgX45tAsRVXv6sg8WEhPmzz8f6OceKFOT1VK9R9GlrsBoMpn4VdVUsLqusZhNjM2IpX2Iv5deRbV7ByUTFeIHwCercnlz6Q6SnpjJ/hMltWq5sU8CXarCb8b/zPF6HVDzHEatMIqIiPi6CSNSCKzaZjNrUx4vzc8557R6X6TAKNIChQXYmFC1py8xMpDPHhhASnToWa+/okccF8aFub8/ffXQ5bIu7RnRLRqgzvDZVIL9re49ir/7z2b+NOsnACrtDvytZjpUhVSrxczkMc7wG+Jv5VhRmddr0dAbERERcYkODeCegUkATPh4LS/Mz8Zkgq15BRSXVZJ/qtzYAlsABUaRFuq+Qcl8NO4y/jth8HmP6DCbTUwdW71H8VxnQL5yey/evfsSrrwo3mu11sftl3WiY0Qgp3d7dGwXiLnGoYg/y4gjo2MYhaUV/HXRNq/X4Xp6ncMoIiIiAA8M6UpYgNX9HsXhgF99vpHLnp5Pzz/MbZIPsFsTBUaRFspkMjGgaxThVZNMz2dYWgfuHZjMjX0S6Noh5KzXBflZGd4tGkszJyZ/q4Vnb+rJgK7tef7m6qE5Ow4X1brObDYxNdPZOvvW8p3srzoqxFuqA6sSo4iIiEB4kI0ZN/ZkYEp7nrupJ2YTbNibT0GJc55CU3yA3ZooMIq0ESaTid9ddSHP3tSz1opdS9K/a3s+GteP63sncP8g52CfSVVTXmsakhrFZcnO4T4vL8ih0u5g26FCr9SgllQRERE53diMWD68vx839ElwD8LpFuvcDvT3lbu9/gF2a2JytMJxQAUFBYSHh5Ofn09YWNj5f0BEWhyHw8HaPSfoHh9W55mOa3Yf54bXVmAxm8iID2PD3nx+d+WF3FsVNBuqx//M4WRpBYumDCPZi9NgRUREpG2otDvYtC+fjPgwbn/re37YeQyAXX++wuDKvKu+mcrjFcalS5dy1VVXER8fj8lk4t///net+++++25MJlOtr379+tW6prS0lEceeYSoqCiCg4O5+uqr2bt3r6eliEgrZjKZ6NO5XZ1hEaBP53aMuiCaSruDDXvzAXhpQQ75xefefD5nSx5JT8zk0EnnBNai0gp+++9NJD0xE4fD4d7DqAVGERERqYvFbOLixAisFjO/qpoRYTGb2HHYO91OrY3HgbGoqIiePXvyyiuvnPWasWPHcuDAAffXrFmzat0/adIkvvzySz755BOWL19OYWEhV155JZWVlZ6/AhFps6ZkphNgMxMZ7Efn9kHkF5fzxtLt5/yZCR+tBWDc+6vJP1XOnW9/zwcr9wCwLOeIuyXVrJ5UEREROY8+nSPdH2A/Ny/b6HIMYfX0By6//HIuv/zyc17j7+9PbGxsnffl5+fz9ttv849//INRo0YB8MEHH5CYmMj8+fPJzMz0tCQRaaO6xYax8PFhhAXa+HbbER74xxpeXbSduwYkER0aUOfPlFc6A+GGvfnc8PqKWnsfn5mTpT2MIiIi4pEpmeks2HqImRsP8NDQ/GY9mqwlaJKhN4sXLyY6Opq0tDTGjRvHoUOH3PetWbOG8vJyxowZ474tPj6ejIwMVqxYUefjlZaWUlBQUOtLRHxDfEQgIf5WxlwYw8WJEQC8urDuaWUFJbXbVbcdKiQqxJ+Pxl1GsJ+FTfvyKSm3N3XJIiIi0oZ0iw3jmp7O48iemZNlcDXNz+uB8fLLL+fDDz9k4cKFPPfcc6xatYoRI0ZQWloKQF5eHn5+frRr167Wz8XExJCXl1fnY06fPp3w8HD3V2JiorfLFpEWzmQyMS3TuY/gox/2kHvs1BnX1LwtxN9Kx4hAPnuwPwO6RnH/4C61rm2pk2RFRESk5XlsdBpWs4kl2YdZueOo0eU0K68HxltuuYUrrriCjIwMrrrqKr755huys7OZOXPmOX/O4XBgOkuP2JNPPkl+fr77Kzc319tli0grMCAlisGpUZRXOhg8Y9EZ9+cec4687pkYwZKpw1g8tXoS6v2Dk2lX40xLxUURERGpr87tg7n1Uuei1a1vrKQVHjTRYE1+DmNcXBydO3cmJycHgNjYWMrKyjh+/Hit6w4dOkRMTEydj+Hv709YWFitLxHxTVOrVhlNJsjKO1nrPtcKY6fIINqH+GOzVP+JCw2wMX54ivt77WEUERERT0wckUqAzfneYsFPh85zddvR5IHx6NGj5ObmEhcXB0CfPn2w2WzMmzfPfc2BAwfYvHkzAwYMaOpyRKSVuyghgsszYnE44Nm5tfcR7HEHxsA6f/bOfp1JjwklNiyAyGC/Jq9VRERE2o7osADuHuA8D/rZuVnY7b6xyuhxYCwsLGT9+vWsX78egJ07d7J+/Xr27NlDYWEhU6ZM4bvvvmPXrl0sXryYq666iqioKK677joAwsPDue+++3j88cdZsGAB69at484776RHjx7uqakiIufy+Jg0zCaY9+NB1u6p7lZwBcbEdkF1/lyAzcJ/Jgxk6bTh+FvrPv9RRERE5GweGtqV0AArW/NO8tWG/UaX0yw8PlZj9erVDB8+3P395MmTAbjrrrt47bXX2LRpE3//+985ceIEcXFxDB8+nE8//ZTQ0FD3z7zwwgtYrVZuvvlmiouLGTlyJO+99x4Wi97Aicj5pUSHckPvBD5bs5c/f7OVhHaBfLF2HyH+zj9pnSLrDozgDI0iIiIiDREeZOPBoV15Zk4Wz8/L5mc94vCzNnnTpqFMjla4Y7OgoIDw8HDy8/O1n1HER+07UczwZxZTVnnmMRnLpg0n8RyhUURERKShTpVVMGTGYo4UlvLHazP4eb/ORpfUIPXNVG07DotIm9UxIpA7z/IHOi48oJmrEREREV8R5GflkRHOQXpP/XszxWWVBlfUtBQYRaTVmjY2nd9ecQFzHxtCdKi/+3arRX/aREREpOncdmknEto5h+y9t2KXscU0Mb2rEpFWK8Bm4f7BXUiLCeXD+y8jOtSfh4d1NbosERERaeP8rGYmj04D4P9mbyW/uNzgipqOAqOItAmpMaF8/+uRTBvbzehSRERExAdcc3FH0mJCAHhj6XaDq2k6Cowi0maYTCajSxAREREfYTGbmDImHYB3lu/i0MkSgytqGgqMIiIiIiIiDTD6whh6dYqguLySS/+0wOhymoQCo4iIiIiISAOYTCamZjpXGW0WE7nHTlFRaaekvO1MTrUaXYCIiIiIiEhrNaBrFINTo1iWc4Rf/Wsj2w4VcuhkKZv/N5MQ/9Yft7TCKCIiIiIi0giuVcYV249y6GQpAG8v22lkSV6jwCgiIiIiItIIFyVEcMVFcfz/9u4tNso6D+P4M2UOdGunoeEwnZRoPQBBYLK0oCVqWarNEushXqy6XpD1CkMJXc1uPFyUZE3aeNFEFg9xNURvdryQGm80jCsMEEO2lDaM1ZgmRaha7JoVOhRobfvbC5c3O9u37oa+nbcz+X6SSej//0/zm+TJME/fzltJzt9n/MuxQf1zbMLPsTxBYQQAAACAOer8TUIHfrdJqd83aG1VVJfGJ7XxTym/x5ozCiMAAAAAzFEkuEi/Wr1cpeFF+sOvf/oV1XCwRMMXr/g82dxQGAEAAADAQ1tXLdPmmyo1MTmtfX8b8HucOaEwAgAAAICHAoGA/vjvq4x//fuQBv9xyeeJrl/h3+cVAAAAABaYupsq1bhmuS5PTGlq2vwe57pRGAEAAABgHvz5t79UaWiRAoGA36NcNwojAAAAAMyDX4QLv27xGUYAAAAAgCsKIwAAAADAFYURAAAAAOCKwggAAAAAcEVhBAAAAAC4ojACAAAAAFxRGAEAAAAAriiMAAAAAABXFEYAAAAAgCsKIwAAAADAFYURAAAAAOCKwggAAAAAcEVhBAAAAAC4ojACAAAAAFxRGAEAAAAAriiMAAAAAABXFEYAAAAAgCsKIwAAAADAFYURAAAAAOCKwggAAAAAcBX0e4DrYWaSpNHRUZ8nAQAAAIDCc61LXetWsynIwpjNZiVJK1eu9HkSAAAAAChc2WxWFRUVs+4H7H9VygVoenpa3377rcrLyxUIBPweJ8fo6KhWrlypoaEhRaNRv8dBESNryCfyhnwha8gXsoZ8Woh5MzNls1nF43GVlMz+ScWCvMJYUlKi6upqv8f4WdFodMGEAcWNrCGfyBvyhawhX8ga8mmh5e3nrixew01vAAAAAACuKIwAAAAAAFcURo9FIhG1tbUpEon4PQqKHFlDPpE35AtZQ76QNeRTIeetIG96AwAAAACYf1xhBAAAAAC4ojACAAAAAFxRGAEAAAAAriiMAAAAAABXFEYAAAAAgCsKo8deffVV1dTUaPHixaqtrdWxY8f8HgkF5ujRo3rggQcUj8cVCAT0/vvv5+ybmfbu3at4PK7S0lJt3bpV/f39OWfGx8e1e/duLV26VGVlZXrwwQf19ddf5/FZYKFrb2/Xpk2bVF5eruXLl+vhhx/Wl19+mXOGrMErr732mjZs2KBoNKpoNKr6+np9+OGHzj5Zw3xpb29XIBBQa2urs0be4IW9e/cqEAjkPGKxmLNfTDmjMHro3XffVWtrq1544QX19vbq7rvv1vbt23Xu3Dm/R0MBGRsbUyKR0P79+133X3rpJXV2dmr//v3q7u5WLBbTfffdp2w265xpbW1VV1eXksmkjh8/rkuXLqm5uVlTU1P5ehpY4NLptHbt2qUTJ04olUppcnJSTU1NGhsbc86QNXilurpaHR0dOnnypE6ePKlt27bpoYcect48kTXMh+7ubr3xxhvasGFDzjp5g1duv/12DQ8PO49MJuPsFVXODJ7ZvHmz7dy5M2dtzZo19uyzz/o0EQqdJOvq6nK+np6etlgsZh0dHc7a1atXraKiwl5//XUzM7tw4YKFQiFLJpPOmW+++cZKSkrso48+ytvsKCwjIyMmydLptJmRNcy/JUuW2JtvvknWMC+y2azddtttlkqlrKGhwfbs2WNmvLbBO21tbZZIJFz3ii1nXGH0yMTEhHp6etTU1JSz3tTUpE8//dSnqVBszpw5o/Pnz+fkLBKJqKGhwclZT0+Pfvzxx5wz8Xhc69atI4uY1cWLFyVJlZWVksga5s/U1JSSyaTGxsZUX19P1jAvdu3apfvvv1/33ntvzjp5g5cGBgYUj8dVU1Ojxx57TIODg5KKL2dBvwcoFt9//72mpqa0YsWKnPUVK1bo/PnzPk2FYnMtS245O3v2rHMmHA5ryZIlM86QRbgxMz399NO66667tG7dOklkDd7LZDKqr6/X1atXdcMNN6irq0tr16513hiRNXglmUzq1KlT6u7unrHHaxu8cscdd+idd97RqlWr9N133+nFF1/Uli1b1N/fX3Q5ozB6LBAI5HxtZjPWgLm6npyRRcympaVFp0+f1vHjx2fskTV4ZfXq1err69OFCxf03nvvaceOHUqn084+WYMXhoaGtGfPHh06dEiLFy+e9Rx5w1xt377d+ff69etVX1+vW265RW+//bbuvPNOScWTM34l1SNLly7VokWLZvxEYGRkZMZPF4Drde3uWz+Xs1gspomJCf3www+zngGu2b17tz744AMdPnxY1dXVzjpZg9fC4bBuvfVW1dXVqb29XYlEQi+//DJZg6d6eno0MjKi2tpaBYNBBYNBpdNp7du3T8Fg0MkLeYPXysrKtH79eg0MDBTd6xqF0SPhcFi1tbVKpVI566lUSlu2bPFpKhSbmpoaxWKxnJxNTEwonU47OautrVUoFMo5Mzw8rM8++4wswmFmamlp0cGDB/XJJ5+opqYmZ5+sYb6ZmcbHx8kaPNXY2KhMJqO+vj7nUVdXpyeeeEJ9fX26+eabyRvmxfj4uL744gtVVVUV3+uaH3faKVbJZNJCoZC99dZb9vnnn1tra6uVlZXZV1995fdoKCDZbNZ6e3utt7fXJFlnZ6f19vba2bNnzcyso6PDKioq7ODBg5bJZOzxxx+3qqoqGx0ddb7Hzp07rbq62j7++GM7deqUbdu2zRKJhE1OTvr1tLDAPPXUU1ZRUWFHjhyx4eFh53H58mXnDFmDV5577jk7evSonTlzxk6fPm3PP/+8lZSU2KFDh8yMrGF+/eddUs3IG7zxzDPP2JEjR2xwcNBOnDhhzc3NVl5e7rzvL6acURg99sorr9iNN95o4XDYNm7c6NyiHvh/HT582CTNeOzYscPMfrpVc1tbm8ViMYtEInbPPfdYJpPJ+R5XrlyxlpYWq6ystNLSUmtubrZz58758GywULllTJIdOHDAOUPW4JUnn3zS+b9x2bJl1tjY6JRFM7KG+fXfhZG8wQuPPvqoVVVVWSgUsng8bo888oj19/c7+8WUs4CZmT/XNgEAAAAACxmfYQQAAAAAuKIwAgAAAABcURgBAAAAAK4ojAAAAAAAVxRGAAAAAIArCiMAAAAAwBWFEQAAAADgisIIAAAAAHBFYQQAAAAAuKIwAgAAAABcURgBAAAAAK7+BdyYDy85UXkFAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"def prettify_graph(graph):\n    \"\"\"Modify the given graph according to Jimmy's requests: add a title, make the y-axis\n    start at 0, label the y-axis. (And, if you're feeling ambitious, format the tick marks\n    as dollar amounts using the \"$\" symbol.)\n    \"\"\"\n    graph.set_title(\"Results of 500 slot machine pulls\")\n    # Complete steps 2 and 3 here\n    graph.set_title(\"Results of 500 slot machine pulls\")\n    # Make the y-axis begin at 0\n    graph.set_ylim(bottom=0)\n    # Label the y-axis\n    graph.set_ylabel(\"Balance\")\n    # Bonus: format the numbers on the y-axis as dollar amounts\n    # An array of the values displayed on the y-axis (150, 175, 200, etc.)\n    ticks = graph.get_yticks()\n    # Format those values into strings beginning with dollar sign\n    new_labels = ['${}'.format(int(amt)) for amt in ticks]\n\ngraph = jimmy_slots.get_graph()\nprettify_graph(graph)\ngraph","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:21:06.993055Z","iopub.execute_input":"2023-08-19T16:21:06.993462Z","iopub.status.idle":"2023-08-19T16:21:07.344813Z","shell.execute_reply.started":"2023-08-19T16:21:06.993432Z","shell.execute_reply":"2023-08-19T16:21:07.343655Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<Axes: title={'center': 'Results of 500 slot machine pulls'}, ylabel='Balance'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x700 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Bonus:** Can you format the numbers on the y-axis so they look like dollar amounts? e.g. $200 instead of just 200.\n\n(We're not going to tell you what method(s) to use here. You'll need to go digging yourself with `dir(graph)` and/or `help(graph)`.)","metadata":{}},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nq1.solution()","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:19:36.335589Z","iopub.execute_input":"2023-08-19T16:19:36.336219Z","iopub.status.idle":"2023-08-19T16:19:36.343396Z","shell.execute_reply.started":"2023-08-19T16:19:36.336186Z","shell.execute_reply":"2023-08-19T16:19:36.342226Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"1_JimmySlots\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\ndef prettify_graph(graph):\n    graph.set_title(\"Results of 500 slot machine pulls\")\n    # Make the y-axis begin at 0\n    graph.set_ylim(bottom=0)\n    # Label the y-axis\n    graph.set_ylabel(\"Balance\")\n    # Bonus: format the numbers on the y-axis as dollar amounts\n    # An array of the values displayed on the y-axis (150, 175, 200, etc.)\n    ticks = graph.get_yticks()\n    # Format those values into strings beginning with dollar sign\n    new_labels = ['${}'.format(int(amt)) for amt in ticks]\n    # Set the new labels\n    graph.set_yticklabels(new_labels)\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\ndef prettify_graph(graph):\n    graph.set_title(\"Results of 500 slot machine pulls\")\n    # Make the y-axis begin at 0\n    graph.set_ylim(bottom=0)\n    # Label the y-axis\n    graph.set_ylabel(\"Balance\")\n    # Bonus: format the numbers on the y-axis as dollar amounts\n    # An array of the values displayed on the y-axis (150, 175, 200, etc.)\n    ticks = graph.get_yticks()\n    # Format those values into strings beginning with dollar sign\n    new_labels = ['${}'.format(int(amt)) for amt in ticks]\n    # Set the new labels\n    graph.set_yticklabels(new_labels)\n```"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2. <span title=\"Spicy\" style=\"color: coral\">🌶️🌶️</span>\n\nThis is a very challenging problem.  Don't forget that you can receive a hint!\n\nLuigi is trying to perform an analysis to determine the best items for winning races on the Mario Kart circuit. He has some data in the form of lists of dictionaries that look like...\n\n    [\n        {'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 3},\n        {'name': 'Bowser', 'items': ['green shell',], 'finish': 1},\n        # Sometimes the racer's name wasn't recorded\n        {'name': None, 'items': ['mushroom',], 'finish': 2},\n        {'name': 'Toad', 'items': ['green shell', 'mushroom'], 'finish': 1},\n    ]\n\n`'items'` is a list of all the power-up items the racer picked up in that race, and `'finish'` was their placement in the race (1 for first place, 3 for third, etc.).\n\nHe wrote the function below to take a list like this and return a dictionary mapping each item to how many times it was picked up by first-place finishers.","metadata":{}},{"cell_type":"code","source":"def best_items(racers):\n    \"\"\"Given a list of racer dictionaries, return a dictionary mapping items to the number\n    of times those items were picked up by racers who finished in first place.\n    \"\"\"\n    winner_item_counts = {}\n    for i in range(len(racers)):\n        # The i'th racer dictionary\n        racer = racers[i]\n        # We're only interested in racers who finished in first\n        if racer['finish'] == 1:\n            for i in racer['items']:\n                # Add one to the count for this item (adding it to the dict if necessary)\n                if i not in winner_item_counts:\n                    winner_item_counts[i] = 0\n                winner_item_counts[i] += 1\n\n        # Data quality issues :/ Print a warning about racers with no name set. We'll take care of it later.\n        if racer['name'] is None:\n            print(\"WARNING: Encountered racer with unknown name on iteration {}/{} (racer = {})\".format(\n                i+1, len(racers), racer['name'])\n                 )\n    return winner_item_counts","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"He tried it on a small example list above and it seemed to work correctly:","metadata":{}},{"cell_type":"markdown","source":"However, when he tried running it on his full dataset, the program crashed with a `TypeError`.\n\nCan you guess why? Try running the code cell below to see the error message Luigi is getting. Once you've identified the bug, fix it in the cell below (so that it runs without any errors).\n\nHint: Luigi's bug is similar to one we encountered in the [tutorial](https://www.kaggle.com/colinmorris/working-with-external-libraries) when we talked about star imports.","metadata":{}},{"cell_type":"code","source":"sample = [\n    {'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 3},\n    {'name': 'Bowser', 'items': ['green shell',], 'finish': 1},\n    {'name': None, 'items': ['mushroom',], 'finish': 2},\n    {'name': 'Toad', 'items': ['green shell', 'mushroom'], 'finish': 1},\n]\nbest_items(sample)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Import luigi's full dataset of race data\nfrom learntools.python.luigi_analysis import full_dataset\n\n# Fix me!\ndef best_items(racers):\n    winner_item_counts = {}\n    for racer in racers:  # Use a different variable name here\n        # We're only interested in racers who finished in first\n        if racer['finish'] == 1:\n            for item in racer['items']:  # Use a different variable name here\n                # Add one to the count for this item (adding it to the dict if necessary)\n                if item not in winner_item_counts:\n                    winner_item_counts[item] = 0\n                winner_item_counts[item] += 1\n\n        # Data quality issues :/ Print a warning about racers with no name set. We'll take care of it later.\n        if racer['name'] is None:\n            print(\"WARNING: Encountered racer with unknown name on iteration {}/{} (racer = {})\".format(\n                racers.index(racer) + 1, len(racers), racer['name'])\n            )\n    return winner_item_counts\n\n# Try analyzing the imported full dataset\nbest_items(full_dataset)\n","metadata":{"tags":["raises-exception"],"execution":{"iopub.status.busy":"2023-08-19T16:29:17.839741Z","iopub.execute_input":"2023-08-19T16:29:17.840167Z","iopub.status.idle":"2023-08-19T16:29:17.854099Z","shell.execute_reply.started":"2023-08-19T16:29:17.840131Z","shell.execute_reply":"2023-08-19T16:29:17.853162Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"WARNING: Encountered racer with unknown name on iteration 4/8 (racer = None)\nWARNING: Encountered racer with unknown name on iteration 6/8 (racer = None)\n","output_type":"stream"},{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"{'green shell': 4, 'banana': 1, 'red shell': 1, 'blue shell': 1, 'star': 1}"},"metadata":{}}]},{"cell_type":"code","source":"#q2.hint()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nq2.solution()","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:23:43.274654Z","iopub.execute_input":"2023-08-19T16:23:43.277169Z","iopub.status.idle":"2023-08-19T16:23:43.285519Z","shell.execute_reply.started":"2023-08-19T16:23:43.277126Z","shell.execute_reply":"2023-08-19T16:23:43.284687Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"2_LuigiAnalysis\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: Luigi used the variable name `i` to represent each item in racer['items'].\nHowever, he also used `i` as the loop variable for the outer loop (`for i in range(len(racers))`).\nThese i's are clobbering each other. This becomes a problem only if we encounter a racer\nwith a finish of 1 and a name of `None`. If that happens, when we try to print the \"WARNING\" message,\n`i` refers to a string like \"green shell\", which python can't add to an integer, hence a `TypeError`.\n\nThis is similar to the issue we saw when we imported * from `math` and `numpy`. They both contained variables called `log`, and the one we got when we tried to call it was the wrong one.\n\nWe can fix this by using different loop variables for the inner and outer loops. `i` wasn't a very\ngood variable name for the inner loop anyways. `for item in racer['items']` fixes the bug and is \neasier to read.\n\nVariable shadowing bugs like this don't come up super often, but when they do they can take an infuriating amount of time to diagnose!","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> Luigi used the variable name `i` to represent each item in racer['items'].\nHowever, he also used `i` as the loop variable for the outer loop (`for i in range(len(racers))`).\nThese i's are clobbering each other. This becomes a problem only if we encounter a racer\nwith a finish of 1 and a name of `None`. If that happens, when we try to print the \"WARNING\" message,\n`i` refers to a string like \"green shell\", which python can't add to an integer, hence a `TypeError`.\n\nThis is similar to the issue we saw when we imported * from `math` and `numpy`. They both contained variables called `log`, and the one we got when we tried to call it was the wrong one.\n\nWe can fix this by using different loop variables for the inner and outer loops. `i` wasn't a very\ngood variable name for the inner loop anyways. `for item in racer['items']` fixes the bug and is \neasier to read.\n\nVariable shadowing bugs like this don't come up super often, but when they do they can take an infuriating amount of time to diagnose!\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. <span title=\"A bit spicy\" style=\"color: darkgreen \">🌶️</span>\n\nSuppose we wanted to create a new type to represent hands in blackjack. One thing we might want to do with this type is overload the comparison operators like `>` and `<=` so that we could use them to check whether one hand beats another. e.g. it'd be cool if we could do this:\n\n```python\n>>> hand1 = BlackjackHand(['K', 'A'])\n>>> hand2 = BlackjackHand(['7', '10', 'A'])\n>>> hand1 > hand2\nTrue\n```\n\nWell, we're not going to do all that in this question (defining custom classes is a bit beyond the scope of these lessons), but the code we're asking you to write in the function below is very similar to what we'd have to write if we were defining our own `BlackjackHand` class. (We'd put it in the `__gt__` magic method to define our custom behaviour for `>`.)\n\nFill in the body of the `blackjack_hand_greater_than` function according to the docstring.","metadata":{}},{"cell_type":"code","source":"def blackjack_hand_greater_than(hand_1, hand_2):\n    \"\"\"\n    Return True if hand_1 beats hand_2, and False otherwise.\n    \n    In order for hand_1 to beat hand_2 the following must be true:\n    - The total of hand_1 must not exceed 21\n    - The total of hand_1 must exceed the total of hand_2 OR hand_2's total must exceed 21\n    \n    Hands are represented as a list of cards. Each card is represented by a string.\n    \n    When adding up a hand's total, cards with numbers count for that many points. Face\n    cards ('J', 'Q', and 'K') are worth 10 points. 'A' can count for 1 or 11.\n    \n    When determining a hand's total, you should try to count aces in the way that \n    maximizes the hand's total without going over 21. e.g. the total of ['A', 'A', '9'] is 21,\n    the total of ['A', 'A', '9', '3'] is 14.\n    \n    Examples:\n    >>> blackjack_hand_greater_than(['K'], ['3', '4'])\n    True\n    >>> blackjack_hand_greater_than(['K'], ['10'])\n    False\n    >>> blackjack_hand_greater_than(['K', 'K', '2'], ['3'])\n    False\n    \"\"\"\n    pass\n    total_1 = calculate_total(hand_1)\n    total_2 = calculate_total(hand_2)\n    return total_1 <= 21 and (total_1 > total_2 or total_2 > 21)\n\ndef calculate_total(hand):\n    total = 0\n    # Count the number of aces and deal with how to apply them at the end.\n    aces = 0\n    for card in hand:\n        if card in ['J', 'Q', 'K']:\n            total += 10\n        elif card == 'A':\n            aces += 1\n        else:\n            # Convert number cards (e.g. '7') to ints\n            total += int(card)\n    # At this point, total is the sum of this hand's cards *not counting aces*.\n    \n    # Add aces, counting them as 1 for now. This is the smallest total we can make from this hand.\n    total += aces\n    # \"Upgrade\" aces from 1 to 11 as long as it helps us get closer to 21 without busting.\n    while total + 10 <= 21 and aces > 0:\n        # Upgrade an ace from 1 to 11\n        total += 10\n        aces -= 1\n    return total\n\n# Check your answer\nq3.check()\n","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:31:46.779532Z","iopub.execute_input":"2023-08-19T16:31:46.780014Z","iopub.status.idle":"2023-08-19T16:31:46.794460Z","shell.execute_reply.started":"2023-08-19T16:31:46.779978Z","shell.execute_reply":"2023-08-19T16:31:46.793662Z"},"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_BlackjackCmp\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"#q3.hint()\nq3.solution()","metadata":{"execution":{"iopub.status.busy":"2023-08-19T16:29:26.631494Z","iopub.execute_input":"2023-08-19T16:29:26.632269Z","iopub.status.idle":"2023-08-19T16:29:26.642722Z","shell.execute_reply.started":"2023-08-19T16:29:26.632217Z","shell.execute_reply":"2023-08-19T16:29:26.641491Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 2, \"questionId\": \"3_BlackjackCmp\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\ndef hand_total(hand):\n    \"\"\"Helper function to calculate the total points of a blackjack hand.\n    \"\"\"\n    total = 0\n    # Count the number of aces and deal with how to apply them at the end.\n    aces = 0\n    for card in hand:\n        if card in ['J', 'Q', 'K']:\n            total += 10\n        elif card == 'A':\n            aces += 1\n        else:\n            # Convert number cards (e.g. '7') to ints\n            total += int(card)\n    # At this point, total is the sum of this hand's cards *not counting aces*.\n\n    # Add aces, counting them as 1 for now. This is the smallest total we can make from this hand\n    total += aces\n    # \"Upgrade\" aces from 1 to 11 as long as it helps us get closer to 21\n    # without busting\n    while total + 10 <= 21 and aces > 0:\n        # Upgrade an ace from 1 to 11\n        total += 10\n        aces -= 1\n    return total\n\ndef blackjack_hand_greater_than(hand_1, hand_2):\n    total_1 = hand_total(hand_1)\n    total_2 = hand_total(hand_2)\n    return total_1 <= 21 and (total_1 > total_2 or total_2 > 21)\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\ndef hand_total(hand):\n    \"\"\"Helper function to calculate the total points of a blackjack hand.\n    \"\"\"\n    total = 0\n    # Count the number of aces and deal with how to apply them at the end.\n    aces = 0\n    for card in hand:\n        if card in ['J', 'Q', 'K']:\n            total += 10\n        elif card == 'A':\n            aces += 1\n        else:\n            # Convert number cards (e.g. '7') to ints\n            total += int(card)\n    # At this point, total is the sum of this hand's cards *not counting aces*.\n\n    # Add aces, counting them as 1 for now. This is the smallest total we can make from this hand\n    total += aces\n    # \"Upgrade\" aces from 1 to 11 as long as it helps us get closer to 21\n    # without busting\n    while total + 10 <= 21 and aces > 0:\n        # Upgrade an ace from 1 to 11\n        total += 10\n        aces -= 1\n    return total\n\ndef blackjack_hand_greater_than(hand_1, hand_2):\n    total_1 = hand_total(hand_1)\n    total_2 = hand_total(hand_2)\n    return total_1 <= 21 and (total_1 > total_2 or total_2 > 21)\n```"},"metadata":{}}]},{"cell_type":"markdown","source":"# The End\n\nYou've finished the Python course. Congrats!\n\nYou probably didn't put in all these hours of learning Python just to play silly games of chance, right? If you're interested in applying your newfound Python skills to some data science tasks, we strongly recommend **[this tutorial](https://www.kaggle.com/alexisbcook/titanic-tutorial)**, which will teach you how to make your very first submission to a Kaggle competition.\n\nYou can also check out some of our other **[Kaggle Courses](https://www.kaggle.com/learn/overview)**. Some good next steps are:\n\n1. [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n2. [Pandas for data manipulation](https://www.kaggle.com/learn/pandas)\n3. [Data Visualization](https://www.kaggle.com/learn/data-visualization)\n\nHappy Pythoning!","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/python/discussion) to chat with other learners.*","metadata":{}}]}