Mariam Omolola Olasunkanmi-Ojo

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Untitled2.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"16rVXrgD0qju2xGbcsZL2HRaU6F4lLHuC","authorship_tag":"ABX9TyNLqugejJO139XhD0Wxq0hp"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RPsgdnyK78fW","executionInfo":{"status":"ok","timestamp":1606270710512,"user_tz":-60,"elapsed":4832,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"76e415e7-a3d1-4493-cf31-b9c5d9c95266"},"source":["from google.colab import drive\n","drive.mount(\"/content/drive\")"],"execution_count":50,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8qIA-pyeBcL1","executionInfo":{"status":"ok","timestamp":1606270727844,"user_tz":-60,"elapsed":4520,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns"],"execution_count":51,"outputs":[]},{"cell_type":"code","metadata":{"id":"Vjc0tpc3FRde","executionInfo":{"status":"ok","timestamp":1606270730412,"user_tz":-60,"elapsed":5893,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df = pd.read_csv('/content/drive/MyDrive/Global ai/churn.csv')"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195},"id":"I-bnth8jGAHX","executionInfo":{"status":"ok","timestamp":1606270740244,"user_tz":-60,"elapsed":7826,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"b8a4796e-7660-4c4b-ffaf-efb56c80db4e"},"source":["df.head(4)"],"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Churn</th>\n","      <th>AccountWeeks</th>\n","      <th>ContractRenewal</th>\n","      <th>DataPlan</th>\n","      <th>DataUsage</th>\n","      <th>CustServCalls</th>\n","      <th>DayMins</th>\n","      <th>DayCalls</th>\n","      <th>MonthlyCharge</th>\n","      <th>OverageFee</th>\n","      <th>RoamMins</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>128</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2.7</td>\n","      <td>1</td>\n","      <td>265.1</td>\n","      <td>110</td>\n","      <td>89.0</td>\n","      <td>9.87</td>\n","      <td>10.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>107</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3.7</td>\n","      <td>1</td>\n","      <td>161.6</td>\n","      <td>123</td>\n","      <td>82.0</td>\n","      <td>9.78</td>\n","      <td>13.7</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>243.4</td>\n","      <td>114</td>\n","      <td>52.0</td>\n","      <td>6.06</td>\n","      <td>12.2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>84</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>299.4</td>\n","      <td>71</td>\n","      <td>57.0</td>\n","      <td>3.10</td>\n","      <td>6.6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Churn  AccountWeeks  ContractRenewal  ...  MonthlyCharge  OverageFee  RoamMins\n","0      0           128                1  ...           89.0        9.87      10.0\n","1      0           107                1  ...           82.0        9.78      13.7\n","2      0           137                1  ...           52.0        6.06      12.2\n","3      0            84                0  ...           57.0        3.10       6.6\n","\n","[4 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QWyP6yyhGGvL","executionInfo":{"status":"ok","timestamp":1606270740252,"user_tz":-60,"elapsed":6567,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"610b4b78-e15c-4e52-970a-fe7c0996afe6"},"source":["df.info()"],"execution_count":54,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 3333 entries, 0 to 3332\n","Data columns (total 11 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   Churn            3333 non-null   int64  \n"," 1   AccountWeeks     3333 non-null   int64  \n"," 2   ContractRenewal  3333 non-null   int64  \n"," 3   DataPlan         3333 non-null   int64  \n"," 4   DataUsage        3333 non-null   float64\n"," 5   CustServCalls    3333 non-null   int64  \n"," 6   DayMins          3333 non-null   float64\n"," 7   DayCalls         3333 non-null   int64  \n"," 8   MonthlyCharge    3333 non-null   float64\n"," 9   OverageFee       3333 non-null   float64\n"," 10  RoamMins         3333 non-null   float64\n","dtypes: float64(5), int64(6)\n","memory usage: 286.6 KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5wYJERVUGlXp","executionInfo":{"status":"ok","timestamp":1606270740259,"user_tz":-60,"elapsed":5229,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"cf1d3b65-39b6-4271-fb64-4847f2758286"},"source":["df.isnull().sum()"],"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Churn              0\n","AccountWeeks       0\n","ContractRenewal    0\n","DataPlan           0\n","DataUsage          0\n","CustServCalls      0\n","DayMins            0\n","DayCalls           0\n","MonthlyCharge      0\n","OverageFee         0\n","RoamMins           0\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":320},"id":"4rB-i3TIG1y1","executionInfo":{"status":"ok","timestamp":1606270740267,"user_tz":-60,"elapsed":4307,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"d5938d9b-1c9c-4031-9065-cdf5dcc0e63b"},"source":["df.describe()"],"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Churn</th>\n","      <th>AccountWeeks</th>\n","      <th>ContractRenewal</th>\n","      <th>DataPlan</th>\n","      <th>DataUsage</th>\n","      <th>CustServCalls</th>\n","      <th>DayMins</th>\n","      <th>DayCalls</th>\n","      <th>MonthlyCharge</th>\n","      <th>OverageFee</th>\n","      <th>RoamMins</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","      <td>3333.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.144914</td>\n","      <td>101.064806</td>\n","      <td>0.903090</td>\n","      <td>0.276628</td>\n","      <td>0.816475</td>\n","      <td>1.562856</td>\n","      <td>179.775098</td>\n","      <td>100.435644</td>\n","      <td>56.305161</td>\n","      <td>10.051488</td>\n","      <td>10.237294</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.352067</td>\n","      <td>39.822106</td>\n","      <td>0.295879</td>\n","      <td>0.447398</td>\n","      <td>1.272668</td>\n","      <td>1.315491</td>\n","      <td>54.467389</td>\n","      <td>20.069084</td>\n","      <td>16.426032</td>\n","      <td>2.535712</td>\n","      <td>2.791840</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","      <td>74.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>143.700000</td>\n","      <td>87.000000</td>\n","      <td>45.000000</td>\n","      <td>8.330000</td>\n","      <td>8.500000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.000000</td>\n","      <td>101.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>179.400000</td>\n","      <td>101.000000</td>\n","      <td>53.500000</td>\n","      <td>10.070000</td>\n","      <td>10.300000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.000000</td>\n","      <td>127.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.780000</td>\n","      <td>2.000000</td>\n","      <td>216.400000</td>\n","      <td>114.000000</td>\n","      <td>66.200000</td>\n","      <td>11.770000</td>\n","      <td>12.100000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>243.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>5.400000</td>\n","      <td>9.000000</td>\n","      <td>350.800000</td>\n","      <td>165.000000</td>\n","      <td>111.300000</td>\n","      <td>18.190000</td>\n","      <td>20.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             Churn  AccountWeeks  ...   OverageFee     RoamMins\n","count  3333.000000   3333.000000  ...  3333.000000  3333.000000\n","mean      0.144914    101.064806  ...    10.051488    10.237294\n","std       0.352067     39.822106  ...     2.535712     2.791840\n","min       0.000000      1.000000  ...     0.000000     0.000000\n","25%       0.000000     74.000000  ...     8.330000     8.500000\n","50%       0.000000    101.000000  ...    10.070000    10.300000\n","75%       0.000000    127.000000  ...    11.770000    12.100000\n","max       1.000000    243.000000  ...    18.190000    20.000000\n","\n","[8 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":56}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":163},"id":"MCgn7QHFG989","executionInfo":{"status":"ok","timestamp":1606270740274,"user_tz":-60,"elapsed":3344,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"32a1442c-802d-4226-e949-17a33909bb3a"},"source":["                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      df.groupby(by='Churn').count()                                                                                                "],"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>AccountWeeks</th>\n","      <th>ContractRenewal</th>\n","      <th>DataPlan</th>\n","      <th>DataUsage</th>\n","      <th>CustServCalls</th>\n","      <th>DayMins</th>\n","      <th>DayCalls</th>\n","      <th>MonthlyCharge</th>\n","      <th>OverageFee</th>\n","      <th>RoamMins</th>\n","    </tr>\n","    <tr>\n","      <th>Churn</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","      <td>2850</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","      <td>483</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       AccountWeeks  ContractRenewal  ...  OverageFee  RoamMins\n","Churn                                 ...                      \n","0              2850             2850  ...        2850      2850\n","1               483              483  ...         483       483\n","\n","[2 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":481},"id":"_Pzn3jO1sMO9","executionInfo":{"status":"ok","timestamp":1606270741251,"user_tz":-60,"elapsed":3294,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"d367a699-6dbd-42f8-cba6-b0faf872ef8c"},"source":["#@title Default title text\n","plt.figure(figsize=(16,10))\n","sns.distplot(df['OverageFee'])"],"execution_count":58,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f08038c12e8>"]},"metadata":{"tags":[]},"execution_count":58},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA7YAAAJNCAYAAAAF2On2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3Sc12Hn/d+dgt4b0Qn2DnaqF1uxLNmSaMtSIslN2SSKT6Ikx968b5xkXyfrZNfxxptN4jiJbMdNDi3JslVNW11UJUVS7A0EG3rvAFFm5r5/ENJSMEiCJB7cKd/POTiceZ5nBj/ZOpr58d7nXmOtFQAAAAAAscrnOgAAAAAAAJeDYgsAAAAAiGkUWwAAAABATKPYAgAAAABiGsUWAAAAABDTKLYAAAAAgJgWcB1guhQUFNiqqirXMQAAAAAAHti5c2eHtbZwsnNxU2yrqqq0Y8cO1zEAAAAAAB4wxpw61zmmIgMAAAAAYhrFFgAAAAAQ0yi2AAAAAICYRrEFAAAAAMQ0ii0AAAAAIKZRbAEAAAAAMY1iCwAAAACIaRRbAAAAAEBMo9gCAAAAAGIaxRYAAAAAENMotgAAAACAmEaxBQAAAADENIotAAAAACCmUWwBAAAAADGNYgsAAAAAiGkUWwAAAABATKPYAgAAAABiGsUWAAAAABDTKLYAAAAAgJhGsQUAAAAAxDSKLQAAAAAgplFsAQAAAAAxjWILAAAAAIhpFFsAAAAAQEwLuA4AAAAgSZu21U3L+9x3ReW0vA8AIHYwYgsAAAAAiGkUWwAAAABATKPYAgAAAABiGsUWAAAAABDTKLYAAAAAgJhGsQUAAAAAxDSKLQAAAAAgplFsAQAAAAAxjWILAAAAAIhpFFsAAAAAQEyj2AIAAAAAYhrFFgAAAAAQ0yi2AAAAAICYRrEFAAAAAMQ0ii0AAAAAIKZRbAEAAAAAMY1iCwAAAACIaRRbAAAAAEBMo9gCAAAAAGIaxRYAAAAAENMotgAAAACAmEaxBQAAAADENIotAAAAACCmUWwBAAAAADGNYgsAAAAAiGkUWwAAAABATKPYAgAAAABiGsUWAAAAABDTPC22xphbjDFHjDG1xpgvT3L+emPMu8aYkDHmrgnnKo0xzxtjDhljDhpjqrzMCgAAAACITZ4VW2OMX9K3JN0qaamke40xSydcVifpfkmbJnmLH0n6e2vtEkkbJLV5lRUAAAAAELsCHr73Bkm11trjkmSMeUTSRkkH37vAWnty/Fzk7BeOF+CAtfaF8esGPMwJAAAAAIhhXk5FLpNUf9bzhvFjU7FQUo8x5ufGmF3GmL8fHwEGAAAAAOADonXxqICk6yT9qaT1kubqzJTlDzDGPGCM2WGM2dHe3j6zCQEAAAAAUcHLYtsoqeKs5+Xjx6aiQdJua+1xa21I0pOS1ky8yFr7bWvtOmvtusLCwssODAAAAACIPV4W2+2SFhhj5hhjkiTdI+npi3htjjHmvbb6YZ11by4AAAAAAO/xrNiOj7Q+KOk5SYckPWatPWCM+aox5g5JMsasN8Y0SLpb0kPGmAPjrw3rzDTkl4wx+yQZSd/xKisAAAAAIHZ5uSqyrLWbJW2ecOwrZz3erjNTlCd77QuSqr3MBwAAAACIfdG6eBQAAAAAAFNCsQUAAAAAxDSKLQAAAAAgpnl6jy0AAMB7hkZDenJXkx7dXqfOwVFZ+3/Pra/K1ez8dM3KSnEXEAAQsyi2AADAUw3dQ3r47VN6ZHu9ek+PaUlJljbMyZMkGRmNhSN6/mCrhkbDWlqSpQ8tKlJZbqrj1ACAWEKxBQAAnrDWatM7dfrvTx9U2FrdsqxY919TpXWzc2WM+cC13YOj+q8/3aO3jnXoYHOf1s3O1SdWl8k34ToAACZDsQUAANNueCys//bkfj2+s0E3LCzU1+5codKcc4/C5qYn6TeWzNK18wv0ypE2vX60Q6GI1V1ryym3AIALotgCAIBpVdc5pC/8eKcONvfpT25aoD+5aYF8vqmV05SgX7cuL1FK0K8XDrbKWqu71lbIP8XXAwASE8UWAABMm/2Nvfr0d7fJWqvv3b9OH14865Le50OLimQkPX+wVVbS3ZRbAMB5UGwBAMC0qGnt12f/Y5sykgPa9HtXaHZ++mW9343j5fa5g61K8vt055ry6QkKAIg7FFsAAHDZTnYM6jPf3aag3zctpfY9Nywq0nAooi017VpSkqUlJVnT8r4AgPjicx0AAADEtqae0/r0d7dpLBzRf/7u9JXa99y0pEjFWSl6clejhkZD0/reAID4wIgtAAC4ZJ0DI/rMd7epY2BEv3vdXG0/2a3tJ7un9XcEfD7dtbZc//pqrX6xt1l3r6uY1vcHAMQ+RmwBAMAlGQtH9OCmXWrsOa37r65S2Xm287lcpTmpumFhkXbV9+hQc59nvwcAEJsotgAA4JL8z82H9PbxTn3tzhXTPv14Mh9aXMiUZADApCi2AADgov1sZ4O+/+ZJ/Zdr5szYasXvTUkeHA1p877mGfmdAIDYQLEFAAAXZW9Dj/78iX26am6+/uJji2f0d5fmpOra+QXaVdejlr7hGf3dAIDoRbEFAABT1jkwot9/eKcKM5L1L/etVsA/818lrl9QqKSATy8dap3x3w0AiE4UWwAAMCXWWv3Zz/aqc3BUD312rfIzkp3kSEsO6Op5BTrQ1KemntNOMgAAogvFFgAATMkj2+v14qE2/dkti7W8LNtplmvnFygl6NNLh9uc5gAARAeKLQAAuKATHYP66jMHde38Av321VWu4yg1ya9r5hfoUHOfGrsZtQWAREexBQAA5xUKR/TFR3crKeDTN+5eKZ/PuI4kSbpmXoFSg369yL22AJDwKLYAAOC8/uWVWu2u79H/+ORyFWenuI7zvpSgX9ctKNCR1n7Vdw25jgMAcIhiCwAAzmlXXbe++XKt7lxdptuqS13H+TVXzc1XWhKjtgCQ6Ci2AABgUoMjIX3x0d0qzkrRX29c5jrOpJKDfl07v0BH2wbUyr62AJCwKLYAAGBSf/uLQzrVNaR/+M2VykoJuo5zTuuq8hTwGW093uk6CgDAEYotAAD4NS8ebNVP3qnTA9fP1RVz813HOa+M5ICqy7O1q65Hw2Nh13EAAA5QbAEAwAe094/oz362V0tLsvSljyx0HWdKrppboNFwRDtPdbuOAgBwIOA6AAAAiB7WWn35Z3vVPxLST+5ZpeSA33WkKSnLTVVFbqq2Hu9UJGKjZksiAMDMYMQWAAC87yfv1Oulw2368i2LtXBWpus4F+WqeQXqHBzVa0fbXUcBAMwwRmwBAEhgm7bVvf+4o39E33zlqOYXZSgp4PvAuViwvCxLm/cF9KO3T+nGRUWu4wAAZhAjtgAAQOGI1WM76xXw+fSpNeXymdibyhvw+bS+Kk+vHGnTqc5B13EAADOIYgsAAPTKkTY1dJ/WJ1aXKTs1erf2uZAr5uTJb4wefvuU6ygAgBlEsQUAIMHVdQ3p1SNtWl2RoxVl2a7jXJas1KA+urxYj+2oZ+sfAEggFFsAABLYSCisx3bUKys1qNtXlrqOMy3u21CpvuGQnj/Y6joKAGCGUGwBAEhgm/c1q3twVHevrVBKMDa29rmQq+bmqzQ7RY/vbHAdBQAwQyi2AAAkqBcOtmr7yW5dv7BQcwrSXceZNj6f0afWluuNo+1q6R12HQcAMAMotgAAJKD2/hF9+Wd7VZqdopuWxN/WOJ9aU66IlX6+i1FbAEgEFFsAABKMtVZ/9rO9GhgJ6e51FQr44u/rQFVBujZU5enxnQ2y1rqOAwDwWPx9kgEAgPPa9E6dXj7cpj+/dbFmZaW4juOZu9aW63j7oHbV97iOAgDwWMB1AAAAYsmmbXWX/R73XVE5DUkuzfH2Af3ts4d03YICfe6qKj2yvd5ZFq99rLpEf/X0AT2+s0FrKnNdxwEAeIgRWwAAEsRoKKI/eWS3koM+fePulfL5jOtInspIDujW5cV6Zk8Te9oCQJyj2AIAkCD+/rnD2tfYq69/qjqupyCf7a615epnT1sAiHsUWwAAEsArR9r0nddP6LNXztZHlxW7jjNjrpybr7KcVPa0BYA4xz22AADEubb+Yf3pY3u0uDhTf/nxJa7jeG7ifdALZ2Xq1SNt+vdXjykrNTil93B5HzQA4OIxYgsAQByLRKy+9OgeDY6G9M17Vysl6HcdacatqsiRlbS3sdd1FACARyi2AADEsYdeO643ajv0V7cv04JZma7jOFGYmazSnBTtbWDbHwCIVxRbAADi1Fu1Hfr75w7r49Ulumd9hes4Tq0sz1FD92l1Doy4jgIA8ADFFgCAONTYc1oP/mSX5hVm6OufqpYx8b21z4VUl+fISNrDqC0AxCWKLQAAcWZ4LKwvPLxTY6GI/v2za5WRzFqR2alBzc5P1576XllrXccBAEwzii0AAHHEWqv/78n92tfYq3/4rVWaV5jhOlLUWFmRrfaBETX3DruOAgCYZhRbAADiyH9uq9NPdzbojz88Xx9ZOst1nKiyvDRbPiMWkQKAOOTp3CRjzC2S/kmSX9J3rbV/N+H89ZL+UVK1pHustY9POJ8l6aCkJ621D3qZFQCAWDNxv9aa1n796O2TWjgrQ0VZKb92PtGlJwe0oChTexp6dfOyYvkS/L5jAIgnno3YGmP8kr4l6VZJSyXda4xZOuGyOkn3S9p0jrf5G0mveZURAIB40dhzWpu21WlWVoruWV9JaTuHlRXZ6j09prrOIddRAADTyMupyBsk1Vprj1trRyU9Imnj2RdYa09aa/dKikx8sTFmraRZkp73MCMAADGve3BUP3zrpNKS/Pr8VVVKCfpdR4paS0qyFPQbVkcGgDjjZbEtk1R/1vOG8WMXZIzxSfrfkv7Ug1wAAMSNoZGQvv/WSYUiEd1/dZWyUoOuI0W15IBfi4uztK+xV+EIqyMDQLyI1sWj/kDSZmttw/kuMsY8YIzZYYzZ0d7ePkPRAACIDsNjYf1o6yl1D43qs1dWqSgrxXWkmLCyPFtDo2Edax9wHQUAME28LLaNkirOel4+fmwqrpL0oDHmpKRvSPqcMebvJl5krf22tXadtXZdYWHh5eYFACBm9A2P6ftvnlBD95B+a12F5hSku44UMxbMylRywKd9Db2uowAApomXxXa7pAXGmDnGmCRJ90h6eiovtNZ+2lpbaa2t0pnpyD+y1n7Zu6gAAMSO3qExfea729TUM6z7NlRqeVm260gxJej3aWlJlg409yoU+bVlPgAAMcizYmutDUl6UNJzkg5Jesxae8AY81VjzB2SZIxZb4xpkHS3pIeMMQe8ygMAQDzoHhzVfd/dqsPN/fr0FZVaWkqpvRQryrM1PBZRbSvTkQEgHni6j621drOkzROOfeWsx9t1Zory+d7jB5J+4EE8AABiSmPPaf3OD7breMegHvrcWjX3DLuOFLPmF2UoNejX3sZeLS7Jch0HAHCZonXxKAAAcJZddd3a+C9vqrH7tL73+fX60KIi15FiWsDn09LSLB1q7tNYmOnIABDrKLYAAES5Z/Y06Z5vb1Vqkk8//4Orde2CAteR4kJ1WbZGQhHVtPa7jgIAuEwUWwAAolQkYvWPL9boj36yS9Xl2XryD67RglmZrmPFjbmFGUpL8mtfI6sjA0Cs8/QeWwAAcGna+0f0pcd26/WjHbpzTZm+ducKJQf8rmPFFb/PaHlptnbX92g0FFFSgL/vB4BYxX/BAQCIMq8fbdet//S63jnRpa/duUL/++6VlFqPrCjP1mg4oiNMRwaAmMaILQAAUWIsHNE/vFCjf3v1mBbOytB//u4VWlTM1GMvzSlIV2ZyQHsberSC/YABIGZRbAEAiAL1XUP640d2aVddj+7dUKmv3LZUqUmM0nrNZ4yWlWVrx8kujYyFlRzkf3MAiEVMRQYAwLFf7mvWx/75ddW2Duib967W1+5cQamdQSvKshWKWB1qYToyAMQqRmwBAHBkeCysv3n2oP5zW51WVuTom/esVmV+mutYCWd2fpqyUgLa19CjVRU5ruMAAC4BxRYAAAdaeof1+z/eqT31PXrg+rn605sXsSqvIz5jtKIsW1tPdOn0aJjRcgCIQXyCAgAww3ac7NJt33xDta39+vfPrNVffGwJpdaxFeU5CkesDjX3uY4CALgEfIoCADCD3jnRpXu/s1UZyX498YfX6Jblxa4jQVJFbqpy0oLa19jrOgoA4BIwFRkAgBlgrdVLh9v08uE23bCwUP98z2plpwVdx8I4Mz4d+c3aDg2NhlzHAQBcJEZsAQDwmLVWLx46U2rXVubqe/evp9RGoeqyHEWsdLCJ6cgAEGsYsQUAwENnSm2rXjnSrnWzc/WJ1WXy+4zrWJhEaU6K8tKTtJfpyAAQcxixBQDAI9ZavTCh1PoMpTZaGWNUXZat4+0D6hwYcR0HAHARKLYAAHjkrWOdevVIu9ZXUWpjxYrybEWs9Mv9La6jAAAuAsUWAAAPnOoc1C/3N2tJSZY2rqLUxorirBQVZiTr2b1NrqMAAC4CxRYAgGk2MBLST96pU05aku5aU06pjSHGGK0oz9a2E11q6xt2HQcAMEUUWwAAplE4YvXIO3UaGg3r01dUKjXJ7zoSLtKKsmxZK23e1+w6CgBgiii2AABMoxcPtep4x6A2ripTSXaq6zi4BLOyUrRoVqae3UuxBYBYQbEFAGCa1LT2a0vNmcWi1s7OdR0Hl+G26hLtONWt5t7TrqMAAKaAYgsAwDQYC0f09J4mFWYk67bqUtdxcJluW3nm/8NfMGoLADGBYgsAwDR4/WiHugZHdfvKUgX9fLzGujkF6VpWmsV0ZACIEXzyAgBwmbqHRrWlpk3LS7M0vyjDdRxMk9uqS7W7vkf1XUOuowAALiDgOgAAALHuvdVzP7aiZErXb9pW52UcTJPbqkv09V8d1i/2NesLN8xzHQcAcB6M2AIAcBmOtvbrQFOfPrSoSDlpSa7jYBpV5KVpZUWOnt3b5DoKAOACKLYAAFyiUCSiZ/Y2Kz89SdfOL3AdBx64vbpE+xv7dLJj0HUUAMB5UGwBALhEW493qWNgRLdVlyrAglFx6b3p5YzaAkB041MYAIBLMBaO6PWj7ZpbmK5FxZmu48AjpTmpWjc7l9WRASDKUWwBALgE79Z1q384pBsXFrmOAo99vLpEh1v6VdvW7zoKAOAcKLYAAFykcMTq9aMdKs9N1bzCdNdx4LGPryiRz0hP72Y6MgBEK4otAAAXaX9jr7oGR3XjwkIZY1zHgceKslJ0zfwCPbm7SdZa13EAAJOg2AIAcBGstdpS066izGQtLslyHQcz5BOrylTXNaR363pcRwEATIJiCwDARTjS0q+WvmFdv7BQPkZrE8ZHlxcrJejTk7saXUcBAEyCYgsAwBRZa/VqTbty0oJaWZ7jOg5mUEZyQB9ZWqxn9zZpNBRxHQcAMAHFFgCAKdp2okt1XUO6bkGh/D5GaxPNJ1eXqntoTK/VtLuOAgCYgGILAMAUfe+NE0pL8mvd7FzXUeDAdQsKlZeepCd2Mx0ZAKINxRYAgClo6xvWS4fbtHZ2roJ+Pj4TUdDv0+3VJXrxYKv6h8dcxwEAnIVPZgAApuDxdxsUjlitn53nOgoc+sTqMo2EIvrV/hbXUQAAZ6HYAgBwAZGI1aPb67VhTp4KMpNdx4FDqypyNDs/TU8yHRkAogrFFgCAC9h6vFOnOod074YK11HgmDFGn1hVpreOdaqld9h1HADAOIotAAAX8Mj2emWlBHTr8hLXURAFPrG6TNaKUVsAiCIUWwAAzqN7cFS/2t+iT64uU0rQ7zoOosCcgnStnZ2rx3c2yFrrOg4AQBRbAADO6+e7GjUajuieDZWuoyCK3L22XLVtA9pd3+M6CgBAFFsAAM7JWqtH3qnTyoocLSnJch0HUeTj1SVKCfr0050NrqMAAESxBQDgnN6t69HRtgHds55Fo/BBmSlBfWx5iZ7Z06ThsbDrOACQ8Ci2AACcw2Pb65WW5NftK0tdR0EUumtdufqHQ3ruAHvaAoBrFFsAACYxGorol/ubdcuyYmUkB1zHQRS6ck6+ynNT9dMdTEcGANcotgAATOL1o+3qGw7ptpVs8YPJ+XxGd60t15vHOtTQPeQ6DgAkNIotAACTeHZvs7JTg7p2fqHrKIhin1pTLmuln+1kT1sAcMnTuVXGmFsk/ZMkv6TvWmv/bsL56yX9o6RqSfdYax8fP75K0r9JypIUlvQ/rLWPepkVABD/Nm2rm9J1Y+GINu9r1oqybD3OqrcJaar/rkjSvMJ0/eCtE8rPSJLPmPeP33cFW0QBwEzxbMTWGOOX9C1Jt0paKuleY8zSCZfVSbpf0qYJx4ckfc5au0zSLZL+0RiT41VWAADOVtPar5FQRCvKs11HQQxYOztX3UNjOtkx6DoKACQsL6cib5BUa609bq0dlfSIpI1nX2CtPWmt3SspMuF4jbX26PjjJkltkpgLBgCYEXsbepWe5NfcggzXURADlpZkKyXo045T3a6jAEDC8rLYlkmqP+t5w/ixi2KM2SApSdKxacoFAMA5jYYiOtzSp2Vl2fL7zIVfgISXFPBpVUWO9jf2amgk5DoOACSkqF48yhhTIulhSb9trY1Mcv4BY8wOY8yO9vb2mQ8IAIg7h1v6NBa2qmYaMi7Chqp8hSJW79YxagsALnhZbBslVZz1vHz82JQYY7Ik/ULSX1prt052jbX229baddbadYWFzFQGAFy+vQ29ykwJqCo/3XUUxJDi7BRV5qXpnZNdsta6jgMACcfLYrtd0gJjzBxjTJKkeyQ9PZUXjl//hKQfvbdSMgAAXhseC6umtV/Ly7I/sLotMBUb5uSpY2BUx1lECgBmnGfF1lobkvSgpOckHZL0mLX2gDHmq8aYOyTJGLPeGNMg6W5JDxljDoy//DclXS/pfmPM7vGfVV5lBQBAkg419ykUsaouYxoyLt6KsmylBv1650SX6ygAkHA83cfWWrtZ0uYJx75y1uPtOjNFeeLrfizpx15mAwBgon2NvcpODaoiL811FMSgoN+nNZU52nq8SwMsIgUAMyqqF48CAGCmjIYiqm0b0LLSLKYh45Ktr8pT2FrtZOsfAJhRFFsAACQdbetXKGK1pCTLdRTEsKKsFFXlp2v7yS5FIiwiBQAzhWILAIDO3F+bGvSzGjIu24Y5eeoaHNUbtR2uowBAwqDYAgASXsRaHW7p16LiTPl9TEPG5VlemqW0JL8e3nrKdRQASBgUWwBAwjvVOaSh0TDTkDEtAn6f1lfl6aVDrarvGnIdBwASAsUWAJDwDjX3ye8zWliU4ToK4sQVc/JkjGHUFgBmCMUWAJDQrLU61NyneYXpSg76XcdBnMhJS9Ity4v1yDt1Ghpl6x8A8BrFFgCQ0Nr7R9Q5OKrFxUxDxvT67aur1Dcc0hO7Gl1HAYC4R7EFACS0Q819ksT9tZh2a2fnanlZln7w5klZy9Y/AOAlii0AIKEdbO5TWU6qslODrqMgzhhjdP/Vc3S0bUBv1na6jgMAcY1iCwBIWP3DY2roPs1oLTxz+8oSFWQk6QdvnXAdBQDiGsUWAJCwDrf0y0paUpLpOgriVHLAr/s2VOqlw2061TnoOg4AxC2KLQAgYR1q7lNuWlDFWSmuoyCOffrK2fIbox++xdY/AOAVii0AICGNhSM61j6gRcVZMsa4joM4NisrRbdVl+jR7XXqPT3mOg4AxCWKLQAgIZ3qHNJY2GrRrAzXUZAAfu/6uRocDWvTtjrXUQAgLlFsAQAJqaa1XwGf0ZwCii28t6w0W9fOL9D33zyhkVDYdRwAiDsUWwBAQqpp7VdVQbqSAnwUYmY8cP1ctfWP6KndTa6jAEDc4dMcAJBweoZG1dY/ooVFjNZi5ly3oEBLSrL0ndeOKxKxruMAQFyh2AIAEs7RtgFJ0oJZbPODmWOM0QPXz9HRtgG9WtPmOg4AxBWKLQAg4dS09is7NaiizGTXUZBgbqsuVUl2ir792nHXUQAgrlBsAQAJJRyxqm0b0IKiDLb5wYwL+n36L9fM0dbjXdrb0OM6DgDEDYotACCh1HcNaSQU0UKmIcORezZUKDM5oIe2MGoLANOFYgsASCg1bf3yGWleIQtHwY3MlKA+c9Vsbd7frGPtA67jAEBcoNgCABLK0dYBVeSlKTXJ7zoKEtjvXDtHyQGf/vWVY66jAEBcoNgCABLGwEhIjT2nmYYM5woyknXfhtl6cnej6ruGXMcBgJhHsQUAJIzatn5J0sIiii3ce+D6ufIbo3/bwqgtAFwuii0AIGHUtA4oPcmvkpwU11EAFWen6O515Xp8R4Oae0+7jgMAMY1iCwBICJGI1dHWfi2YlSkf2/wgSnzhhnkKW8u+tgBwmSi2AICEcKCpT4OjYS2cxWrIiB4VeWn65Ooy/eSdOrX3j7iOAwAxi2ILAEgIW2raJEnzub8WUeYPbpynkVBE//HGCddRACBmUWwBAAlhS027ynJSlZEccB0F+IC5hRm6rbpUD799Uj1Do67jAEBMotgCAOJe7+kxvVvXowVMQ0aU+sMPzdPgaFjff/Ok6ygAEJMotgCAuPdWbYfCEcs2P4hai4uzdPPSWfr+myfUPzzmOg4AxByKLQAg7m2paVdmSkAVeWmuowDn9OCH56tvOKSHt55yHQUAYg7FFgAQ16y1eq2mXdfOL5DfxzY/iF7V5Tm6YWGhvvv6CQ2NhlzHAYCYQrEFAMS12rYBNfUO64aFha6jABf0Rx+er67BUW3aVuc6CgDEFIotACCubalplyRdT7FFDFhXlacr5+bp268d1/BY2HUcAIgZFFsAQFzbUtOuhbMyVJqT6joKMCV/9OEFausf0U93NriOAgAxg2ILAIhbQ6MhbTvexTRkxJSr5+VrdWWO/v3VYxoNRVzHAYCYQLEFAMStbce7NBqO6IaFRa6jAFNmjNEff3iBGntO64ldjNoCwFRQbAEAcWtLTbtSgj6tq8p1HQW4KDcuKtSKsmz9yyu1GgszagsAF0KxBQDErS017bpqbr5Sgn7XUYCLYozRH9+0QPVdp/XU7ibXcQAg6lFsAQBx6VTnoAmk9zwAACAASURBVE50DHJ/LWLWbywp0pKSLH3rlVqFGLUFgPOi2AIA4tJr49v83LCI+2sRm4wx+pOb5utEx6Ce3dvsOg4ARDWKLQAgLm2paVdlXpqq8tNcRwEu2c1Li7VoVqa++fJRhSPWdRwAiFoB1wEAAJhuo6GI3jrWqU+tKZcxxnUcJKhN2+qm5X1WV+boke31+ssn9qm6POeS3uO+KyqnJQsARCtGbAEAcWfHqS4NjYa5vxZxYXlZtgozk/Xy4TZFLKO2ADAZii0AIO5sqWlX0G901bx811GAy+YzRh9aVKS2/hEdbOpzHQcAohLFFgAQd7Ycadf6qjylJ3PHDeJDdXm2CjKSGLUFgHOg2AIA4kpr37AOt/QzDRlxxWeMblxUpJa+YR1u7ncdBwCiDsUWABBXtoxv83M9xRZxZmV5jvLSk/TykVZZRm0B4AMotgCAuLKlpl1FmclaXJzpOgowrfw+oxsXFqqpZ1hHWhm1BYCzeVpsjTG3GGOOGGNqjTFfnuT89caYd40xIWPMXRPOfd4Yc3T85/Ne5gQAxIdQOKI3jnbohoWFbPODuLS6Mlc5aUG9fLiNUVsAOItnxdYY45f0LUm3Sloq6V5jzNIJl9VJul/SpgmvzZP0V5KukLRB0l8ZY3K9ygoAiA97GnrVe3pMNyxiGjLi05lR2yI1dJ9WbduA6zgAEDW8HLHdIKnWWnvcWjsq6RFJG8++wFp70lq7V1Jkwms/KukFa22XtbZb0guSbvEwKwAgDmypaZfPSNfOL3AdBfDMmsocZacG9RKjtgDwPi+LbZmk+rOeN4wf8/q1AIAEtaWmXasqcpSTluQ6CuCZgN+nGxYWqq5rSCc6Bl3HAYCoENOLRxljHjDG7DDG7Ghvb3cdBwDgUPfgqPY29OiGhUWuowCeWzs7VxnJAb1aw/cfAJC8LbaNkirOel4+fmzaXmut/ba1dp21dl1hIfdTAUAie722Q9aK+2uREIJ+n66dX6DatgE1dA+5jgMAznlZbLdLWmCMmWOMSZJ0j6Snp/ja5yTdbIzJHV806ubxYwAATGrLkXblpgW1oizbdRRgRmyYk6eUoO/9vZsBIJF5VmyttSFJD+pMIT0k6TFr7QFjzFeNMXdIkjFmvTGmQdLdkh4yxhwYf22XpL/RmXK8XdJXx48BAPBrIhGrLTXtum5Bofw+tvlBYkgJ+nXV3HwdaOpTW9+w6zgA4FTAyze31m6WtHnCsa+c9Xi7zkwznuy135P0PS/zAQDiw6GWPnUMjOj6hUxDRmK5el6B3qjt0GtH23XX2ooLvwAA4lRMLx4FAICk96diXr+AbX6QWNKTA1pflafd9T3qHhx1HQcAnKHYAgBi3pYj7VpakqWirBTXUYAZd+38AhkZvV7LvbYAEhfFFgAQ0/qHx7TzVDerISNh5aQlaXVljnac7Fb/8JjrOADgBMUWABDT3jrWqVDE6gbur0UCu35BocIRq63HO11HAQAnKLYAgJi2paZdGckBranMdR0FcKYgM1lLSrK09XiXRkMR13EAYMZRbAEAMctaq9dq2nX1vHwlBfhIQ2K7bkGBTo+FtfMUOyQCSDx8CwAAxKzjHYNq6D7N/bWApNn56arMS9MbtR0KR6zrOAAwoyi2AICYteXIe9v8UGwB6cyobffQmA409bqOAgAzimILAIhZW2raNa8wXRV5aa6jAFFhSUmW8tOT9PrRDlnLqC2AxDGlYmuM+bkx5uPGGIowACAqDI+FtfV4p25YWOQ6ChA1fMbo2gUFauw5rRMdg67jAMCMmWpR/VdJ90k6aoz5O2PMIg8zAQBwQVuPd2okFNH1CwtcRwGiyprKXKUn+fX60Q7XUQBgxkyp2FprX7TWflrSGkknJb1ojHnLGPPbxpiglwEBAJjMq0falRL06cq5+a6jAFEl6Pfpynn5OtLar9a+YddxAGBGTHlqsTEmX9L9kn5X0i5J/6QzRfcFT5IBAHAO1lq9fLhN18wrUErQ7zoOEHWunJOvgM/orWOdrqMAwIyY6j22T0h6XVKapNuttXdYax+11v6RpAwvAwIAMNGJjkHVdQ3pxsXcXwtMJj05oFUVOdpd362hkZDrOADguamO2H7HWrvUWvs1a22zJBljkiXJWrvOs3QAAEzilfFtfm5cyDY/wLlcPa9AY2Gr7Se7XEcBAM9Ntdj+7STH3p7OIAAATNWrR9q0oCiDbX6A8yjOTtG8wnRtPdGlsXDEdRwA8FTgfCeNMcWSyiSlGmNWSzLjp7J0ZloyAACe27St7v3HI6Gw3jrWqavn5n/gOIBfd/W8Aj289ZSeO9Ci26pLXccBAM+ct9hK+qjOLBhVLukfzjreL+kvPMoEAMA5HW8fVDhitbA403UUIOotKs5UXnqSvvfGCYotgLh23mJrrf2hpB8aYz5lrf3ZDGUCAOCcjrT0Kzng0+x8Jg4BF+IzRlfPy9eze5u1p75HKytyXEcCAE+c9x5bY8xnxh9WGWO+NPFnBvIBAPA+a62OtPZrflGGAr4p71gHJLS1lbnKTA7o+2+ecB0FADxzoW8F6eN/ZkjKnOQHAIAZ09o/ot7TY1o4i48gYKqSg37dva5Cz+5tVmvfsOs4AOCJC01Ffmj8z/8+M3EAADi3mpZ+SaLYAhfp/qur9P23TmjTtjp98SMLXccBgGk3pXlcxpj/ZYzJMsYEjTEvGWPaz5qmDADAjDjS2q+S7BRlpwZdRwFiSmV+mm5cWKifvFPH1j8A4tJUb1C62VrbJ+k2SSclzZf0/3gVCgCAiYbHwjrVOchoLXCJPnvVbLX1j+iFg62uowDAtJtqsX1vyvLHJf3UWtvrUR4AACZV09qviJUWUWyBS3LDwiKV56bqR2+fdB0FAKbdVIvts8aYw5LWSnrJGFMoidUHAAAz5nBLv9KS/Kpkmx/gkvh9Rp++Yra2Hu/S0dZ+13EAYFpNqdhaa78s6WpJ66y1Y5IGJW30MhgAAO8JR6yOtPRr0axM+YxxHQeIWb+5rlxJfp9+vPWU6ygAMK0uZhPAxZJ+yxjzOUl3SbrZm0gAAHxQXdeQTo+Ftbgky3UUIKblZyTr49Ul+tm7jRocCbmOAwDTZqqrIj8s6RuSrpW0fvxnnYe5AAB43+GWPvmN0YKiDNdRgJj32atma2AkpCd3N7qOAgDT5rz72J5lnaSl1lrrZRgAACZzuLlfcwrSlRL0u44CxLzVFTlaVpqlh98+pfs2VMowvR9AHJjqVOT9koq9DAIAwGROdgyqfWBEi0tYDRmYDsYYffbK2Trc0q/tJ7tdxwGAaTHVYlsg6aAx5jljzNPv/XgZDAAASXrx0Jk9NxcXc38tMF3uWFWqzOSAfvJOnesoADAtpjoV+a+9DAEAwLm8dKhNRZnJyktPch0FiFmbtv16gV1amqVn9jRpeWm2UpOmNs3/visqpzsaAEyLqW73s0XSSUnB8cfbJb3rYS4AANR7ekzbT3ZpCashA9NufVWeQhGrXfVMRwYQ+6a6KvLvSXpc0kPjh8okPelVKAAAJGlLTbtCEavFxdxfC0y30pxUleWkavvJLrE+KIBYN9V7bP9Q0jWS+iTJWntUUpFXoQAAkKSXDrUqLz1JFXlprqMAcWl9VZ5a+0ZU333adRQAuCxTLbYj1trR954YYwKS+Ks9AIBnQuGIXj3Srg8tKpKP7UgAT6wsz1aS36ftJ7tcRwGAyzLVYrvFGPMXklKNMR+R9FNJz3gXCwCQ6Hac6lbv6TH9xhImCAFeSQ76VV2erb0NPRoeC7uOAwCXbKrF9suS2iXtk/T7kjZL+m9ehQIA4PkDrUoK+HTdwkLXUYC4tr4qT2Nhqz0NPa6jAMAlm9J2P9baiDHmSUlPWmvbPc4EAEhw1lo9f7BF184vUEbyVHemA3ApynNTVZyVou0nu3TFnHzXcQDgkpx3xNac8dfGmA5JRyQdMca0G2O+MjPxAACJ6FBzvxq6T+ujy2a5jgLEPWOM1s/JU1PPsBpZRApAjLrQVOQv6sxqyOuttXnW2jxJV0i6xhjzRc/TAQAS0nMHWmSMdNMSii0wE1aV5yjgM9pxikWkAMSmCxXbz0q611p74r0D1trjkj4j6XNeBgMAJK7nD7Zq3excFWQku44CJITUJL+WlWZpT0OPxsIR13EA4KJdqNgGrbUdEw+O32cb9CYSACCR1XcN6VBznz66rNh1FCChrJ2dp+GxiA4297mOAgAX7ULFdvQSzwEAcEmeO9AiSbp5KcUWmElzC9OVkxbUu6e6XUcBgIt2oaUmVxpjJvtrOyMpxYM8AIAE9/zBVi0uzlRlfprrKEBC8RmjNZW5euVwm3qGRpWTluQ6EgBM2XlHbK21fmtt1iQ/mdZapiIDAKZV58CIdpzs0s1MQwacWFOZKyvp3Tr2tAUQWy40FRkAgBnz0qE2Rax081JWQwZcyEtP0pyCdL1b1y1rres4ADBlFFsAQNR4/mCLynJStaw0y3UUIGGtnZ2rrsFRnewcch0FAKaMYgsAiAqDIyG9drRDNy+bJWOM6zhAwlpemq3kgE87WUQKQAyh2AIAosJrNe0aDUVYDRlwLCng04qybO1v7NVIKOw6DgBMiafF1hhzizHmiDGm1hjz5UnOJxtjHh0/v80YUzV+PGiM+aExZp8x5pAx5s+9zAkAcO+X+1uUn56k9VW5rqMACW/t7FyNhiPa39jrOgoATIlnxdYY45f0LUm3Sloq6V5jzNIJl/2OpG5r7XxJ/0fS18eP3y0p2Vq7QtJaSb//XukFAMSf4bGwXjrUqpuXFSvgZzIR4FplXpoKMpJYHRlAzPDy28MGSbXW2uPW2lFJj0jaOOGajZJ+OP74cUk3mTM3VllJ6caYgKRUSaOSJttPFwAQB14/2qHB0bA+toJpyEA0MMZoVUWOTnQMqmdo1HUcALggL4ttmaT6s543jB+b9BprbUhSr6R8nSm5g5KaJdVJ+oa1tsvDrAAAh365r1k5aUFdOTffdRQA41ZVnLktYE89o7YAol+0zvfaICksqVTSHEn/1Rgzd+JFxpgHjDE7jDE72tvbZzojAGAajITCeuFQq25eOktBpiEDUSMvPUmz89K0q76HPW0BRD0vv0E0Sqo463n5+LFJrxmfdpwtqVPSfZJ+Za0ds9a2SXpT0rqJv8Ba+21r7Tpr7brCwkIP/hEAAF57q7ZT/cMh3bq8xHUUABOsqsxRW/+ImnqHXUcBgPPysthul7TAGDPHGJMk6R5JT0+45mlJnx9/fJekl+2ZvxKsk/RhSTLGpEu6UtJhD7MCABzZvK9ZmSkBXT2fachAtFlRli2/z2h3HXvaAohunhXb8XtmH5T0nKRDkh6z1h4wxnzVGHPH+GX/ISnfGFMr6UuS3tsS6FuSMowxB3SmIH/fWrvXq6wAADfGwhE9f7BVH1kyS8kBv+s4ACZISwpo0axM7WnoVTjCdGQA0Svg5ZtbazdL2jzh2FfOejysM1v7THzdwGTHAQDx5e1jneo9PaZbVzANGYhWqypydLC5T8faB1xHAYBzYpUOAIAzv9zfrPQkv65bUOA6CoBzWFycqdSgX7tZHRlAFKPYAgCcCIUjeu5Aq25aMkspQaYhA9Eq4PdpRVm2DjT1anAk5DoOAEzK06nIAABs2lY36fFj7QPqGhxVRnLgnNcAiA6rKnL0zskuPXegRXeuKXcdBwB+DSO2AAAn9jX0Kug3Wjgr03UUABcwOz9NuWlBPbFr4s6NABAdKLYAgBkXjljtb+rVkpIsJQX4KAKinTFGqypy9GZth1r72NMWQPTh2wQAYMYdax/Q0GhY1WXZrqMAmKLVFbmKWOnp3U2uowDAr6HYAgBm3L6GXiUHfFrANGQgZhRkJmtlRY5+znRkAFGIYgsAmFGhcEQHmnu1tCRLQT8fQ0As+eSqUh1q7tPhlj7XUQDgA/hGAQCYUUfbBjQ8FlF1OdOQgVhz+8pS+X2GRaQARB2KLQBgRu1r7FVq0K95RRmuowC4SPkZybphYaGe2tWkSMS6jgMA76PYAgBmzFg4ooPNfVpWmqWAj48gIBZ9cnWZWvqGtfV4p+soAPA+vlUAAGbMkZZ+jYYiqi7PcR0FwCX6yNJZykgOsIgUgKhCsQUAzJi9jb1KT/JrTkG66ygALlFK0K9blxfrV/tbdHo07DoOAEii2AIAZshIKKwjLX1aXpYtv8+4jgPgMnxyTZkGRkJ64VCr6ygAIIliCwCYIYdb+jUWtkxDBuLAlXPyVZKdoieZjgwgSlBsAQAzYk99j7JSApqdn+Y6CoDL5PMZ3bGyVK/VtKtrcNR1HACg2AIAvDc0ElJNa7+qy3PkM0xDBuLBHatKFYpYbd7X7DoKAFBsAQDe29fUq4iVVlUwDRmIF0tLsrSgKENP725yHQUAKLYAAO/tqe9RYUaySrJTXEcBME2MMdq4qlTvnOxSY89p13EAJDiKLQDAUz1DozrZOaSVFTkyTEMG4sodK8skSc/sYdQWgFsUWwCAp/Y09EpiGjIQjyrz07S6MkdPMR0ZgGMUWwCAp/bU96giN1V56UmuowDwwMaVpTrU3Kea1n7XUQAkMIotAMAzh1v61NI3zGgtEMc+Xl0qnxGLSAFwimILAPDMU7ub5DPSinKKLRCvCjOTdc38Aj21p1HWWtdxACQoii0AwBORiNXTu5s0vyhDGckB13EAeGjjqjLVd53Wrvoe11EAJCiKLQDAEzvrutXYc1orGa0F4t5Hl81SUsDHdGQAzlBsAQCeeGJXo1KCPi0tyXIdBYDHMlOC+o0lRXp2b5NC4YjrOAASEMUWADDtRkJhPbunSR9dVqzkoN91HAAz4I6VZeoYGNVbxzpdRwGQgCi2AIBp9/KhNvUNh3TnmnLXUQDMkBsXFSozJaAndze6jgIgAVFsAQDT7ue7GlWUmaxr5uW7jgJghqQE/bp1ebGe29+i4bGw6zgAEgzFFgAwrboGR/XK4TZtXFWqgJ+PGSCRbFxVpsHRsF461OY6CoAEwzcOAMC0emZPk0IRyzRkIAFdOTdfRZnJeorpyABmGMUWADCtfr6rUUtKsrSE1ZCBhOP3Gd2+slSvHmlX79CY6zgAEgjFFgAwbWrbBrSnvkefWlPmOgoARzauKtVoOKJfHWh2HQVAAqHYAgCmzRO7GuQz0h0rS11HAeDIirJszSlI11O7m1xHAZBAKLYAgGkRiVg9uatJ1y0oVFFWius4ABwxxuiOlaV6+3inWvuGXccBkCAotgCAabHtRJcae07rTqYhAwnvjlWlsvbMYnIAMBMotgCAafHzdxuUkRzQzUuLXUcB4Ni8wgytKMvW0xRbADMk4DoAACA6bdpWN+VrR0JhPbW7SSvKs/XELrb5AOLVxfx3oSI3VZv3t+ifXzyqgszk94/fd0WlF9EAJDhGbAEAl+1AY59GwxGtrcx1HQVAlFhRniMjaU9Dj+soABIAxRYAcNl21nUrPz1Js/PTXEcBECWyU4OaU5CuPQ09sta6jgMgzlFsAQCXpWtwVCc6BrVmdq6MMa7jAIgiKyty1DEwqqYeVkcG4C2KLQDgsrxb1y0jaXVFjusoAKLM8tJs+Y1hOjIAz1FsAQCXLGKt3q3r1vyiDOWkJbmOAyDKpCb5tbA4U3sbehRhOjIAD1FsAQCX7Hj7oHqGxrRmNotGAZjcyvJs9Q2HdKJj0HUUAHGMYgsAuGTv1nUrJejT0pIs11EARKnFxVlKCvi0p57pyAC8Q7EFAFyS4bGwDjT1qro8R0E/HycAJpcU8GlZSZb2N/UqFI64jgMgTvFNBABwSfY19GosbNm7FsAFrazI0fBYRDWt/a6jAIhTFFsAwCXZWdetosxkleemuo4CIMrNK8xQepJfuxt6XUcBEKcotgCAi9baN6y6riGtZe9aAFPg9xmtKM/R4eY+9Q+PuY4DIA5RbAEAF23nqW75jdFqpiEDmKJV5dkKRayeP9DqOgqAOESxBQBclFA4onfrurWkJFMZyQHXcQDEiIq8NOWmBfXUnibXUQDEIU+LrTHmFmPMEWNMrTHmy5OcTzbGPDp+fpsxpuqsc9XGmLeNMQeMMfuMMSleZgUATM2hln4NjYa1rirPdRQAMcQYo5XlOXqztkPt/SOu4wCIM54VW2OMX9K3JN0qaamke40xSydc9juSuq218yX9H0lfH39tQNKPJX3BWrtM0o2SuCEDAKLAjpNdyk4Nan5RhusoAGLMyoochSNWm/c1u44CIM54OWK7QVKttfa4tXZU0iOSNk64ZqOkH44/flzSTebMKiQ3S9prrd0jSdbaTmtt2MOsAIAp6B4aVW3bgNbOzpWPRaMAXKRZWSlaXJypp3Y3uo4CIM54WWzLJNWf9bxh/Nik11hrQ5J6JeVLWijJGmOeM8a8a4z5fz3MCQCYop2nuiVJa2ezaBSAS7NxVZneretRXeeQ6ygA4ki0Lh4VkHStpE+P//lJY8xNEy8yxjxgjNlhjNnR3t4+0xkBIKFErNXOU92aX5Sh3LQk13EAxKjbV5ZIkp7ZyyJSAKbP/9/enYfJVdZ5//98q3rf13Sn9yydhCwkIQlhCwgKCMqiArIoMOqDOuD6qMPjPC6jvxkdR0VnXB5RUUARERSigGxhz74vZE866SS9pfd9qbp/f1QRmpiEQLr6VHW9X9fVV586dbr607kq1fXp+5z7jmSxPSipfNjtsvC+Yx4Tvq42W1KzQqO7LznnDjvneiQ9IemMo7+Bc+5u59x859z8wsLCCPwIAIDX7WrsUnvvIJNGATglZblpWlCVq0fXHZRzzus4AMaISBbbVZKqzWyCmSVJul7S4qOOWSzplvD2NZKWuNAr3FOSZplZWrjwXiDptQhmBQC8hdU1LUpL8uu04kyvowCIcVfOKdXOxi5tq+/0OgqAMSJixTZ8zewdCpXUrZIecs5tMbNvmdmV4cN+LSnfzHZJ+qKkO8Nf2yrphwqV4/WS1jrnHo9UVgDAiXX2Deq1ug7NLc9Rgj9ar2IBECveN2u8Enymx9ZzOjKAkZEQyQd3zj2h0GnEw/d9fdh2n6Rrj/O1v1NoyR8AgMfW7GtV0ElnTsj3OgqAMSAvPUmLqgv01w2H9JVLp8rnY5Z1AKeGP7sDAE4o6JxW1bRoYmG6CjOTvY4DYIy4ak6pDrb1as3+Vq+jABgDKLYAgBPa2dCl1p5BncmkUQBG0MXTi5Sa6GdNWwAjgmILADihlXublZ6coOklWV5HATCGpCcn6OLpRXp8Y50GA0Gv4wCIcRRbAMBxtfUMaFt9p+ZX5irBx68MACPrqjklau0Z1Ms7m7yOAiDG8S4FAHBcq/eFrn1bwGnIACJgUXWhctISmR0ZwCmj2AIAjikQdFpd06LqogzlpSd5HQfAGJSU4NPls8br6S0N6hkY8joOgBhGsQUAHNP2+g519A1pIUv8AIigq2aXqHcwoGdea/A6CoAYRrEFABzTir0tykpJ0JSiTK+jABjDFlTlaXx2ihZzOjKAU0CxBQD8g/3NPdrZ2KUFVXny+8zrOADGMJ/PdOXsEr24o0mt3QNexwEQoyi2AIB/8MDK/fKZNJ9JowCMgivnlGgo6PTE5jqvowCIURRbAMCb9A8F9KfVtZpWnKXs1ESv4wCIA9PHZ2nyuAxmRwbwjiV4HQAAEF2e2tKg5u4BXTG7xOsoAMagB1bsP+b+qvx0Pbu1QT97fpdy0k48E/uNCysiEQ1ADGPEFgDwJg+s2KfyvFRNHpfhdRQAcWRueY4kaV1tm8dJAMQiii0A4IhdjV1avqdFN5xZIZ8xaRSA0ZObnqQJBelau69Vzjmv4wCIMRRbAMARD6zYr0S/6dp55V5HARCH5pbnqLl7QLUtPV5HARBjKLYAAElS32BAD6+p1aUzilWYmex1HABxaGZpthL9prX7OR0ZwNtDsQUASJL+trFOHX1DumlhpddRAMSplES/ZpRka+PBNg0Ggl7HARBDKLYAAEnS71fs08TCdJ01kbVrAXhnbkWO+gaD2lbf6XUUADGEYgsA0OaD7Vq3v003LayUMWkUAA9NKsxQVkqC1u5r9ToKgBhCsQUA6N6lNUpN9OuaeWVeRwEQ53xmmlOeq52NnersG/Q6DoAYQbEFgDjX2j2gxzYc0gfPKFV2aqLXcQBAZ1TkKOikDaxpC+AkUWwBIM49uKpWA0NB3Xx2lddRAECSNC4rRWW5qcyODOCkUWwBII4Fgk6/W75PZ0/M19TiTK/jAMARcytyVd/Rp0NtvV5HARADKLYAEMee3dqgg229uuUclvgBEF1ml2XL7zOtYRIpACeBYgsAcey+ZTUqyU7Re04r8joKALxJWlKCpo/P0vpa1rQF8NYotgAQp3Y2dOrVXc266axKJfj5dQAg+syvzFXvYEBb6zq8jgIgyvFOBgDi1H3L9ikpwafrF5R7HQUAjmnSuAxlpyZyOjKAt0SxBYA41NE3qEfWHtAVp5coPyPZ6zgAcEw+M82rzNWuxi619Qx4HQdAFKPYAkAcemTNAfUMBJg0CkDUO6MiV07S2v2M2gI4PootAMSZYNDp/mX7NLciR6eX5XgdBwBOKC89SRML07VmX6uCznkdB0CUotgCQJx5eddh7TncrVvOrvI6CgCclPmVuWrtGdTew91eRwEQpSi2ABBn7ltao4KMJF02q9jrKABwUmaUZCsl0cckUgCOi2ILAHFkf3OPlmxv1I1nVig5we91HAA4KYl+n2aX5WjzwXb1DgS8jgMgClFsASCO3L+8Rn4z3biQSaMAxJZ5lbkaCjptONDmdRQAUYhiCwBxoncgoD+uqtWlM4tVnJ3idRwAeFtKc1JVkp2ilXtb5JhECsBRKLYAECceXX9QHX1DTBoFICaZmRZMyFN9R582HGj3Og6AKEOxBYA44JzTvUtrdNr4LC2oyvU6DgC8I7PLcpTk9+mBFfu8jgIgyiR4HQAAMPIeWLH/8ySGkQAAIABJREFUTbf3HO7StvpOfWBuqf6wstajVABwalIS/Tq9LFt/3VCn//v+6cpKSfQ6EoAowYgtAMSBpbualZbk15zyHK+jAMApOXNCnnoHA3ps/SGvowCIIhRbABjjWroHtLWuQwuq8pTo52UfQGwrzUnV9PFZemDFfiaRAnAE73AAYIxbvqdZZtJZE/O9jgIAp8zMdMPCCm2t69BGJpECEEaxBYAxrH8woFU1LZpZmq3sVK5FAzA2XD2nRKmJ/n+YTwBA/KLYAsAYtnZ/q/qHgjpnUoHXUQBgxGSmJOrK2SVavOGQOvsGvY4DIApQbAFgjAo6p6W7m1Wem6qKvDSv4wDAiLphYYV6BwN6dN1Br6MAiAIUWwAYo3Y0dKq5e4DRWgBj0uyybM0szdJ9y/YxiRQAii0AjFVLdzcrKyVBM0uzvY4CACPOzHTzWVXa2dil5XtavI4DwGMUWwAYgxo6+rSrsUtnTcyX32dexwGAiLhidomyUxN1//Iar6MA8BjFFgDGoKW7m5XgMy2oyvM6CgBETGqSX9fNL9NTWxrU0NHndRwAHqLYAsAY09o9oPW1rZpTnqP05ASv4wBARH3krEoFnWPpHyDOUWwBYIz5w6r9Ggw4Jo0CEBcq89N1wZRC/WHlfg0Ggl7HAeARii0AjCGDgaDuX7ZPEwvTVZyd4nUcABgVN59dqcbOfj21pd7rKAA8QrEFgDHk75vrVdfep3MZrQUQRy6YMk7leam6b9k+r6MA8EhEi62ZvdfMtpvZLjO78xj3J5vZH8P3rzCzqqPurzCzLjP7UiRzAsBY8ZtX96oyP01TizO9jgIAo8bvM31kYaVW7m3RtvoOr+MA8EDEiq2Z+SX9VNJlkqZLusHMph912McltTrnJku6S9J/HnX/DyU9GamMADCWrK9t09r9bbr1nCr5jCV+AMSX6+aXKznBp3uXMmoLxKNIjtieKWmXc26Pc25A0oOSrjrqmKsk3RvefljSu81C78bM7GpJeyVtiWBGABgzfvPqXmUkJ+iaeWVeRwGAUZebnqQPnlGqP689oNbuAa/jABhlkSy2pZJqh90+EN53zGOcc0OS2iXlm1mGpH+R9G8RzAcAY0ZDR58e31ina+eXKTMl0es4AOCJfzp3gvqHgnpgJUv/APEmWieP+qaku5xzXSc6yMxuM7PVZra6qalpdJIBQBS6d2mNAs7p1nOqvI4CAJ6ZUpSpRdUFum9ZDUv/AHEmksX2oKTyYbfLwvuOeYyZJUjKltQsaaGk75lZjaTPS/qqmd1x9Ddwzt3tnJvvnJtfWFg48j8BAMSAnoEh/X7Ffl06vViV+elexwEAT33s3Alq6OjXE5vqvI4CYBRFstiuklRtZhPMLEnS9ZIWH3XMYkm3hLevkbTEhSxyzlU556ok/UjSfzjnfhLBrAAQs/60+oDaewf1iUUTvI4CAJ67YEqhJhak655X9so553UcAKMkIVIP7JwbCo+yPiXJL+ke59wWM/uWpNXOucWSfi3pfjPbJalFofILADhJgaDTr1/ZqznlOZpXmet1HAAYFQ+sOPE1tDNKs/XXDYf0n09uU8UJzmS5cWHFSEcD4JGIFVtJcs49IemJo/Z9fdh2n6Rr3+IxvhmRcAAwBjzzWr32t/ToX947TcYSPwAgSTqjIkfPvFavV3c3n7DYAhg7onXyKADASfjly3tVlpuqS2cUeR0FAKJGcoJfCyrztOVQu9p6WPoHiAcUWwCIUWv3t2rNvlZ97NwJSvDzcg4Aw501KV/OSct2N3sdBcAo4J0QAMSoX7+8V5kpCbpuQflbHwwAcSY3LUmzyrK1sqZFfYMBr+MAiDCKLQDEoNqWHj25uU43LqxQRnJEp0sAgJi1qLpQ/UNBrdzb4nUUABFGsQWAGHTPq3vlM9Ot51R5HQUAolZpTqomF2bo1d2HNRQIeh0HQARRbAEgxrT3DuqhVbW6YnaJxmeneh0HAKLaoikF6uwb0oYDbV5HARBBFFsAiDF/WLlf3QMBfWLRBK+jAEDUm1yYofHZKXpp52EFnfM6DoAIodgCQAwZGArqt6/W6JxJ+ZpRku11HACIemam86sL1dTZr+31nV7HARAhFFsAiCGPbzqk+o4+/a9FE72OAgAxY2ZptnLSEvXSziavowCIEIotAMQI55x++dJeTR6XoQumFHodBwBiht9nOm9ygfY192hfc7fXcQBEAMUWAGLEst3Neq2uQ584b4J8PvM6DgDElPmVeUpN9OvFHYzaAmMRxRYAYsSvXtmrgowkXT231OsoABBzkhJ8OndyvrbVd+pQW6/XcQCMMIotAMSAXY2dWrKtUR89q0opiX6v4wBATDp7YoGSE3x6YXuj11EAjDCKLQDEgF++tFfJCT595KwKr6MAQMxKTfLr7En52nKoQw0dfV7HATCCErwOAAB4swdW7H/T7fbeQT285oAWTMjVU1saPEoFAGPDuZMKtHRXs17Y3qgvXDzF6zgARggjtgAQ5V7ddVhOTosmMxMyAJyq9OQELZyYp40H2rX3MDMkA2MFxRYAoljPwJBW7m3R6WU5yk1P8joOAIwJ500ukN9n+tnzu7yOAmCEUGwBIIot39OsgUBQ51czWgsAIyUzJVELJuTpL+sOqralx+s4AEYAxRYAotTAUFBLdzdrWnGmirNTvI4DAGPK+dWF8pnpZy/s9joKgBFAsQWAKLV6X4t6BgK6YAqjtQAw0rJTE3X9meX60+pa7W9m1BaIdRRbAIhCgaDTyzsPqyo/TZX56V7HAYAx6Y4LJyvBb/rRszu8jgLgFFFsASAKbahtU3vvIKO1ABBB47JSdMvZVfrL+oPa2dDpdRwAp4BiCwBRJuicXtjRqPHZKZpSlOl1HAAY0z51wSSlJyXoh88wagvEMootAESZjQfadbhrQBdOHScz8zoOAIxpuelJ+vh5E/Tk5nptOtDudRwA7xDFFgCiSCDo9Py2RhVlJWt6SZbXcQAgLnxi0QTlpCXq+09v9zoKgHeIYgsAUeSJTXVq6urXRdOK5GO0FgBGRWZKoj51wSS9uKNJq2pavI4D4B2g2AJAlAgGnf5nyU6Ny0zWDEZrAWBU3XJ2lQozk/W9v2+Tc87rOADeJootAESJJzfXa0dDly6cNo7RWgAYZalJfn3hPVO0qqZVT22p9zoOgLeJYgsAUeD10dpJhemaVZrtdRwAiEvXzS/TlKIMfefJbRoYCnodB8DbQLEFgCjw9Gv12lbfqc9cVM1oLQB4JMHv01cvP037mnt037Iar+MAeBsotgDgsUDQ6YfP7NDEgnS9//TxXscBgLj2rqnjdP6UQv3Pkl1q6xnwOg6Ak0SxBQCPPbb+oHY0dOmLl0xRgp+XZQDw2r9efpo6+wb14+d2eh0FwEniHRQAeKh/KKAfPrNDM0uzdPlMRmsBIBpMLc7UhxeU6/5l+7SnqcvrOABOAsUWADz0hxX7daC1V1+5dJp8Pq6tBYBo8YWLpyg5waf/eGKb11EAnASKLQB4pLt/SP+zZJfOnpivRdUFXscBAAwzLjNFt180Wc9ubdCSbQ1exwHwFii2AOCRe17Zq+buAX3lvVNlzIQMAFHnE+dN1KTCdH1j8Rb1DQa8jgPgBCi2AOCBlu4B3f3SHl06o0hzK3K9jgMAOIakBJ++ffVM1bb06qfP7/I6DoAToNgCgAd+9vwudQ8M6UuXTPU6CgDgBM6ZVKCr55ToFy/u0W4mkgKiFsUWAEbZnqYu3busRtfMK1N1UabXcQAAb+Gr7ztNyYk+feOxLXLOeR0HwDFQbAFglP1/j29VSoJfX750mtdRAAAnYVxmir586VS9suuw/raxzus4AI6BYgsAo+j57Y1asq1Rn313tQozk72OAwA4STctrNSs0mz9219fU2v3gNdxAByFYgsAo2RgKKhv//U1TSxI1y3nVHkdBwDwNvh9pu9+aJbaegb0zb9u8ToOgKNQbAFglNy3rEZ7Dnfra++frqQEXn4BINbMKMnWZy6q1mPrD+nvmzklGYgmvLMCgFHQ1NmvHz+7U++aWqgLp43zOg4A4B365wsnaWZplv71L5vV3NXvdRwAYRRbABgF//XUNvUOBvS190/3OgoA4BQk+n36wbVz1Nk3pK89tplZkoEoQbEFgAhbuuuwHlp9QB9fNEGTCjO8jgMAOEVTizP1+Yur9cSmemZJBqIExRYAIqh3IKA7/7xJVflp+vy7p3gdBwAwQm5bNFFzynP0tcc261Bbr9dxgLhHsQWACPrB09u1v6VH3/3Q6UpN8nsdBwAwQhL8Pt314TkaCjh95g/rNBgIeh0JiGsUWwCIkHX7W3XPq3t108IKnTUx3+s4AIARNqEgXd/54Cyt2deq7z+13es4QFyj2AJABAwMBfUvj2xUUVaK7rxsmtdxAAARcsXsEn3krAr94qU9em5rg9dxgLhlY2Umt/nz57vVq1d7HQNAHHtgxf4j289ubdCSbY26+exKTSvO8jAVACCSblxYob7BgD7086U62Narxz+7SKU5qV7HAsYkM1vjnJt/rPsiOmJrZu81s+1mtsvM7jzG/clm9sfw/SvMrCq8/2IzW2Nmm8KfL4pkTgAYSfuau/XC9kbNKc+h1AJAHEhJ9OunN56hoYDTHQ+sVf9QwOtIQNyJWLE1M7+kn0q6TNJ0STeY2dELOH5cUqtzbrKkuyT9Z3j/YUlXOOdmSbpF0v2RygkAI6lnYEgPrqpVTlqSrpxd4nUcAMAoqSpI1/euOV3r9rfpq39mfVtgtEVyxPZMSbucc3uccwOSHpR01VHHXCXp3vD2w5LebWbmnFvnnDsU3r9FUqqZJUcwKwCcMuec/rz2oLr6hnT9gnKlJDILMgDEk8tnjdcX3jNFj6w9oJ+/uNvrOEBciWSxLZVUO+z2gfC+Yx7jnBuS1C7p6KlDPyRprXOuP0I5AWBELNvTrNfqOnTpzGKV5aZ5HQcA4IHPvnuyrpxdou/9fbv+vrnO6zhA3IjqWZHNbIZCpyd/8jj332Zmq81sdVNT0+iGA4BhNh9s15Ob6zWtOFPnTmJpHwCIV2am711zuuZW5Ojzf1yvTQfavY4ExIWECD72QUnlw26Xhfcd65gDZpYgKVtSsySZWZmkv0i62Tl3zHM5nHN3S7pbCs2KPKLpAeAktXYP6PYH1iojOUHXnFEmM/M6EgBglAyfEX+4984o1t6mbt30q+X65AWTlJuWdMLHuXFhRSTiAXEjkiO2qyRVm9kEM0uSdL2kxUcds1ihyaEk6RpJS5xzzsxyJD0u6U7n3KsRzAgAp6R/KKBP3r9Gde19umFBudKSI/n3QgBArMhMSdTNZ1dpIBDUr1/Zq46+Qa8jAWNaxIpt+JrZOyQ9JWmrpIecc1vM7FtmdmX4sF9LyjezXZK+KOn1JYHukDRZ0tfNbH34Y1yksgLAO+Gc052PbNLKmhb94NrZqshP9zoSACCKFGen6Nazq9TVN6TfvLpXPf1DXkcCxiwbK1ORz58/361evdrrGADiyI+f3am7nt2hL10yRXdcVH3c09EAAPFtV2OX7ltWo+LsFH3s3AnHnDWfU5GBt2Zma5xz8491X1RPHgUA0erRdQd117M79KEzynT7hZO9jgMAiGKTx2XohjMrdKitV/ct26eBoaDXkYAxh2ILAG/Tc1sb9OWHN+isiXn6zgdnMVkUAOAtnTY+S9fOK9e+5m795tW96hsMeB0JGFMotgDwNjy3tUGf+t0anTY+S7/46HwlJfAyCgA4ObPLc3T9mRWqbe3Rr17Zo26uuQVGDO/IAOAkDS+19398obJTE72OBACIMbNKs/XRsyrV2NGvX768Rx29zJYMjASKLQCcBEotAGCkTC3O0q3nVKmtd1B3v7xHzV39XkcCYh7FFgDewp/XHnij1H6MUgsAOHUTCzP08XMnqG8woJ+/uFura1q8jgTENIotAByHc04/fGaHvvjQBs2vzAuN1KZRagEAI6M8L02fumCSUhP9uvFXK7R4wyGvIwExi2ILAMfQPxTQF/64Xv/93E5dO69M937sTEZqAQAjriAjWZ++YJLmlOXos39Yp58s2SnnnNexgJhDsQWAozR29ukjv1qhR9cf0pcvnarvXXM6sx8DACImLTlB93/iTF09p0Tff3qHPvfgevUOsBwQ8HYkeB0AAKLJKzsP6/N/XKeu/iH99w1zdeXsEq8jAQDiQHKCX3d9eI6qizL1/ae3a0dDp+7+6HxV5Kd5HQ2ICQxBAICkoUBQ339quz56zwrlpiVp8R3nUWoBAKPKzHT7hZP1m1sXqK69T1f85BW9sL3R61hATKDYAoh7tS09uvGXK/ST53fpmjPK9Ngd52pKUabXsQAAcepdU8fpr3ecp5KcVP3Tb1fpR8/uUCDIdbfAiXAqMoC4FQw6/W7FPn33yW0yST+8brY+eEaZ17EAAFBFfpr+/Olz9K+PbtKPnt2pFXta9OPr52hcVorX0YCoxIgtgLhUc7hbN/xyub7+2BbNq8zVU184n1ILAIgqqUl+/fC6Ofqva07X+to2Xfbjl/XSjiavYwFRiRFbADHtgRX739bxQ8GgXt15WEu2N8rvM31wbqnmVeaqLJfJOQAA0ena+eWaU56jOx5Yp5vvWalPnj9RX7xkipIT/F5HA6IGxRZA3NjV2KXFGw7pcFe/po/P0hWzS1ibFgAQE6qLMvXo7efq24+/pl+8tEcv7mjSj66fo2nFWV5HA6ICxRbAmNfWM6AnN9dr08F25aUn6ZazKzWVNwIAgChysmcgzSzJ1s1nVeqRdQf1vv9+RZdOL9I5kwvkM9ONCysinBKIXhRbAGNW32BAL+5o0qu7DkuS3n3aOJ1fXahEP9MLAABi17TxWfpcXpr+su6gnthcr231nbpmHvNEIL5RbAGMOYGg06qaFj23tUHdAwHNKc/RxdOLlJuW5HU0AABGREZygj6ysEJr9rXqb5vq9N9LdqokJ1VXzSmRmXkdDxh1FFsAY4ZzTtvrO/Xk5no1dfWrKj9dt8wqZmIoAMCYZGaaX5WniYUZemh1rT7/x/V6ZmuD/v3qmcrhj7mIMxRbAGPCobZePbG5TnuaulWQkaSPLKzUaeMz+as1AGDMy0tP0m3nT1R776DuemaHVu5t0b9fPVOXzCj2Ohowaii2AGJaS/eAntvaoPW1bUpN8uuK08frzAn58vsotACA+OEz0+0XTtYFUwr15Yc36rb71+iK2SX6tytnKC+d0VuMfRRbADGpqbNfP1myU79bvl9m0qLqAl0wZZxSk97Zmn5vdz1cAACi0czSbD12+7n6+Qu79ZPnd2rprsP69tUzdfms8V5HAyKKYgsgpnT0DeruF/fonlf3qn8oqHmVubpw2jjWowUAICwpwafPvadal84s0pf/tFH//Pu1unxWsb511UwVZCR7HQ+ICIotAM+8nVHSwUBQy3Y368UdTeodDOj0smy957QifkEDAHAc04qz9Jd/Pke/eGmPfvzsTi3b/aK+eeUMXTmbmZMx9lBsAUS1oWBQa/a16vltjeroG9KUogxdMr1YJTmpXkcDACDqJfh9uv3CybpkepG+/PBGfe7B9frrhjp966oZ/C7FmEKxBRCVAkGndftbtWR7o9p6BlWRl6brFpRrYkGG19EAAIhKb3Um1DXzyjQ+O0XPbm3Qu77fpItPK9LZk/LlGzZ6e+PCikjHBCKCYgsgqgSCTutr2/T89ka1dA+oLDdVV88pVfW4DE6bAgDgFPjMtKi6UDNLsvXYhoN6fFOd1te26eo5pSrNZfQWsY1iCyAqBJ3Thto2LdnWqObuAZXkpOjmsys1tYi1aAEAGEm56Um65ewqbT7Uob9tOKSfvbBL50zK13umF3kdDXjHKLYAPBUIOm062KYl25p0uKtf47NT9NGzKjWtmEILAECkmJlmlWZrcmGGnn6tXkt3N2vzoQ6V5abpYgouYhDFFoAn+gYDWr6nWS/vbFJrz6CKs1J008IKnTY+603X+gAAgMhJTfLrqjmlmlueo0fXH9L/um+1LplepP/7vumqyE/zOh5w0sw553WGETF//ny3evVqr2MAeAsdfYO6f9k+/ebVvTrcNaDy3FS9a+o4TS3OpNACAOChQNCpq39I/7Nkp4YCTp9YNEH/fOFkZSQzFoboYGZrnHPzj3Ufz1IAo6Kxs0/3vFKj3y/fp87+IZ0/pVBTijI0IT+dU44BAIgCfp/p0++apA/MLdX3/r5NP3tht/605oC+culUfeiMMvl8/L5G9GLEFkBE7Wzo1G+W1ujhNQc0GAjq8lnj9ekLJmlmafZbLksAAABG1/Dlftbtb9W3/vaa1u1v0+ll2fr6+6drflWeh+kQ7xixBTCqAkGnJdsade/SGr2y67CSEnz64NxSffKCSZpQkO51PAAAcBLmVuTqkU+do8UbDum7T27TNf9vma6YXaI7L5um0hyWB0J0odgCGDHtPYN6aHWt7lteo9qWXo3PTtGXL52q6xeUKz8j2et4AADgbfL5TFfPLdUlM4r0/17YrV+8tEdPb6nXredU6VMXTFJuepLXEQFJnIoMYARsr+/Ub5fW6NF1B9U7GNCZVXm69dwqXTK9SAl+33G/jlORAQCILa09A3r2tQatr21TUoJP500u0LmTC5SS6H/TccNPaQZGCqciAxhxHX2D+tuGOv1pTa3W7W9TcoJPV88p1S3nVGl6SZbX8QAAQATkpiXp2vnlOn9KoZ7d2qDntjVq6e5mnTs5X2dPLFBqkv+tHwSIAIotgJMWDDot39us7/19u7YcatdgwGlcZrIum1mseRW5SktO0PraNq2vbfM6KgAAiKCirBTdtLBSB1t79dy2Bj27tVEv7zyshRPydO7kAq/jIQ5RbAG8pQOtPXpkzUE9vLZWtS29Sk7waW5FruZV5KosN5XlegAAiFOluam6+ewq1bX36sUdTXp552Et3d2s3U1duuWcKs0oyfY6IuIE19gCOKbGjj49ublej2+s06p9LXJOOndyvq6bX662nkElnuDaWQAAEJ8Od/XrlZ2Htelgu3oHA1pQlatbz5mgS2YU8d4Bp+xE19hSbAEc0djZp7+Hy+zKmlCZnVqUqctnjdcHzyhVeV6aJCZ9AgAAJ/a+WeP1pzW1undZaKWE/PQkXTWnVNfMK2MuDrxjTB4F4Ljq2/v0zNYGPb7xkFbsDZXZyeMy9Ll3V+t9s8aruijT64gAACDGZKcl6hOLJuqfzp2gF3c06k+rD+j+5TW659W9mlGSpStnl+iymeNVkZ/mdVSMERRbIM4MBYJau79NL2xv1PPbm7S1rkOSNKkwXZ+5qFrvP328plBmAQDACPD7TBdNK9JF04rU2j2gxRsO6ZG1B/SdJ7fpO09u08zSLF02c7wunl6k6nEZzNuBd4xTkYE40NjZpxe3N+mFHU16bmuD+gaD8plUmZ+uKUWZmlqcqaLMZH6ZAACAEfFW69jWtvToyc11enJzvdbtD62mMD47RRdMKdQFUwp19qR8PbGpflSyIHZwKjIQZ5o6+7WqpkUr97Zo+Z5mbavvlCQVZiZrxvhsTSnO1OTCDNaaAwAAnijPS9Nt50/SbedPUn17n17c0agXdzTp8U11enBVrcykoswUVRWka0JBuiry0pSdmuh1bEQxii0wBhxq69XKvS1asbdZK/a2aE9TtyQpNdGveZW5+tIl4/WuqeM0fXyWHlxV63FaAACANxRnp+jDCyr04QUVGgoEtb62Tcv3NOvR9Ye0dl+rlu9pliRlpiSoNCdVpbmpKstJU2luqjKSqTMI4ZkAxJiOvkFtOtCuDQfatLG2XRsPtOlQe5+k0Av+gqo8XTe/XGdOyNOs0mym1gcAADEjwe/T/Ko8za/KU156sgJBp0Ntvapt7dHB1l4daO3V9vpOvX4xZU5qokpzU1Wak6rirBQVZacoJzWRy6viEMUWiGKdfYPaVt+pTQdCBXbjgXbtOdx95P7K/DTNq8rTJ8pztHBinqYVZ8nv44UcAACMDX6fqTwv7ciSg5LUNxjQofbeI0X3YFuvthzqOHJ/coLvSMktzkpRdVGGphZnKiuFU5nHMootEAUCQaf9LT3aWtehbXUd2lrfqa11HTrQ2nvkmOKsFM0qy9YHzyjV6WU5Or0sWzlpSR6mBgAAGH0piX5NLMjQxIKMI/v6BgNq6OhTfUef6ttDnzceaNPKwaAWbzgkSSrNSdXU4kxNKw5NnFk9LlMTC9OVksicI2MBsyIDo+CBFfslhQpsa8+Amjr71djZr6bOPjV29quho0+DgdD/RZNUkJms4qwUjQ//pbEkJ1VZTJgAAABw0pxzau8d1LTxmdpW36nt9Z3aVtep3U1dGgqG3nf5TKrIS9PkcZmqLspQ9bgMVY/L1KRx6UpLYgww2jArMjCKnHNq6uzX/pYe7Wvu0b7mbi3Z1qjGzn41dw8oEHzjj0mZKQkqzEzWgqq8cJFN1bisZK6LBQAAOEVmppy0pCPr6L5uYCioPYe7tKuxSzsbwp8bO/XijsYjAw2SVJabGiq6RaHVJCYUpqsyP02FGSyRGI0iOmJrZu+V9GNJfkm/cs5996j7kyXdJ2mepGZJH3bO1YTv+z+SPi4pIOmzzrmnTvS9GLHFaAkGnQ5396uhvV/1HX2qa+8NF9ge1bb0aH9Lj3oHA0eO95mUm5akwsxkjctMVmFmsgozU1SYkcxyOwAAAFEiEHRq7u5XY0fozLrGzj41dvTrcFf/kRFeSUpP8qsyP11VBWmqzE/XhPxQ4S3NTVVRVgoDFBHkyYitmfkl/VTSxZIOSFplZoudc68NO+zjklqdc5PN7HpJ/ynpw2Y2XdL1kmZIKpH0rJlNcc4FBERA32BArT0Dau0eVFvPgFp7BtXaM6C2ngEd7ho4cs1GQ3vo1OHhL25SaFmdirw0VeSn6bzqAlXmhyY5qMwLvcg9suYd5SM0AAAKGUlEQVSgRz8ZAAAATobfZxqXmaJxmSlv2h8IOi2qLlBNc7dqDnerJnxG3ra6Tj29peFN7wvNpMKMZI3PDp2JV5ydopKcFBVnp6owI1l56UnKTU9UbloSBXiERfJU5DMl7XLO7ZEkM3tQ0lWShhfbqyR9M7z9sKSfWGhc/ypJDzrn+iXtNbNd4cdbFsG8nnPOyTkp6JycdGRbw7Zd+Ligk+SkwWBQgaDTYOD1z+5Nt4eCwX/Y9/rtN+4LfR4KBDUUdKGPQHhfMKihwLB9wWHHBdyRfJKOnJKxr/mNWXvtqPte3+czk1lov8/0xnb42BmlWfKZyW8mny90zBvbJr8v9Bg+MwVdKF8gnCkQDB65PRhw6hsKqHcgoJ6BIfUMvL79xu3OvqE3jbAeLTnBp6yURGWlJqgoK0XVRZnKSk1UdnhfdmqiMpIT/uGUlLq2PtW19Z3CMwIAAABe8/tMVQXpqipIl6a++b6hQFCH2vpU09ytuvZeHWoLTV51qL1Xu5u69Mquw+rqHzrm42YmJyg3PUm56UnKS0tUVmqi0pMTlJ7kV1pSgtKT3/w5NdGvBL8p0e9Tgi/0OdHvC+3z+Y7cl+g3JYSPOfKeW29+/z0WRbLYlkqqHXb7gKSFxzvGOTdkZu2S8sP7lx/1taWRixp5n7h3tV7Z1XSkkDqFyqkbVmKjkd9nR/7j+H0W+o8S/o+T4AsVTUl6fTExJ6mjd/BNjzH8Rxv+875e0I981hvFflVNi4Lh/YHg2//HeT2n32dKSfQpNcmvtMQEpSX7lZbkV05aktKSQtuZKQnKSUtSblqSctMSQ9vhv6Rlpybqz2sZbQUAAMA/SvD7VJEfOmvveDr7BlXX3qfDXf1q7R5US8+AWrsHwmcLDqilZ1BNXf3ac7hb3f1vDL5E0rHKrkn62vun6yNnVUb0e0dKTE8eZWa3SbotfLPLzLZ7mQdHFEg67HUIwAM89xHPeP4jXvHcjwM3eR1glHz036WPnvzhXjz3j9u6I1lsD0oqH3a7LLzvWMccMLMESdkKTSJ1Ml8r59zdku4ewcwYAWa2+ngXdQNjGc99xDOe/4hXPPcRr6LtuR/JK5ZXSao2swlmlqTQZFCLjzpmsaRbwtvXSFriQtM0L5Z0vZklm9kESdWSVkYwKwAAAAAgRkVsxDZ8zewdkp5SaLmfe5xzW8zsW5JWO+cWS/q1pPvDk0O1KFR+FT7uIYUmmhqSdDszIgMAAAAAjiWi69giPpnZbeHTxIG4wnMf8YznP+IVz33Eq2h77lNsAQAAAAAxjVWBAQAAAAAxjWKLEWNm7zWz7Wa2y8zu9DoPMJrMrMbMNpnZejNb7XUeIFLM7B4zazSzzcP25ZnZM2a2M/w518uMQKQc5/n/TTM7GH79X29ml3uZEYgEMys3s+fN7DUz22Jmnwvvj5rXf4otRoSZ+SX9VNJlkqZLusHMpnubChh1Fzrn5kTT1PdABPxW0nuP2nenpOecc9WSngvfBsai3+ofn/+SdFf49X+Oc+6JUc4EjIYhSf/bOTdd0lmSbg+/14+a13+KLUbKmZJ2Oef2OOcGJD0o6SqPMwEARphz7iWFVjIY7ipJ94a375V09aiGAkbJcZ7/wJjnnKtzzq0Nb3dK2iqpVFH0+k+xxUgplVQ77PaB8D4gXjhJT5vZGjO7zeswwCgrcs7VhbfrJRV5GQbwwB1mtjF8qjKn4mNMM7MqSXMlrVAUvf5TbAFgZJznnDtDodPxbzez870OBHjBhZZbYMkFxJOfS5okaY6kOkk/8DYOEDlmliHpEUmfd851DL/P69d/ii1GykFJ5cNul4X3AXHBOXcw/LlR0l8UOj0fiBcNZjZeksKfGz3OA4wa51yDcy7gnAtK+qV4/ccYZWaJCpXa3zvn/hzeHTWv/xRbjJRVkqrNbIKZJUm6XtJijzMBo8LM0s0s8/VtSZdI2nzirwLGlMWSbglv3yLpMQ+zAKPq9Tf1YR8Qr/8Yg8zMJP1a0lbn3A+H3RU1r/8WGjEGTl14evsfSfJLusc59+8eRwJGhZlNVGiUVpISJD3A8x9jlZn9QdK7JBVIapD0DUmPSnpIUoWkfZKuc84xwQ7GnOM8/9+l0GnITlKNpE8Ou+YQGBPM7DxJL0vaJCkY3v1Vha6zjYrXf4otAAAAACCmcSoyAAAAACCmUWwBAAAAADGNYgsAAAAAiGkUWwAAAABATKPYAgAAAABiGsUWAIARYGZlZvaYme00s91m9uPwut5Rw8y+aWYHzWx9+OO7XmcCAGAkUGwBADhF4YXr/yzpUedctaQpkjIkndJ6xmaWMALxjnaXc25O+OPOCDw+AACjjmILAMCpu0hSn3PuN5LknAtI+oKkj5nZSjOb8fqBZvaCmc03s3Qzuyd8/zozuyp8/61mttjMlkh6zswyzOw5M1trZptePy587NfMbLuZvWJmfzCzL4X3TzKzv5vZGjN72cymHS+4mfnN7L/MbJWZbTSzTw6778vD9v/bSP+jAQAwUiLxl2AAAOLNDElrhu9wznWY2X5Jj0u6TtI3zGy8pPHOudVm9h+SljjnPmZmOZJWmtmz4S8/Q9LpzrmW8KjtB8KPVyBpuZktljRf0ockzZaUKGntsAx3S/qUc26nmS2U9DOFyrckfcHMPhLe/hdJlZLanXMLzCxZ0qtm9rSk6vDHmZJM0mIzO98599KI/asBADBCKLYAAETWCwoVy28oVHAfDu+/RNKVr4+ySkqRVBHefsY51xLeNkn/YWbnSwpKKpVUJOlcSY855/ok9ZnZXyXJzDIknSPpT6EzpCVJycPy3OWc+/7rN8zsYUmnm9k14V3ZChXaS8If68L7M8L7KbYAgKhDsQUA4NS9Juma4TvMLEuhorpKUrOZnS7pw5I+9fohkj7knNt+1NctlNQ9bNdNkgolzXPODZpZjUIl+Hh8ktqcc3NOMrtJ+oxz7qmjclwq6TvOuV+c5OMAAOAZrrEFAODUPScpzcxulkLXrUr6gaTfOud6JP1R0lckZTvnNoa/5ilJnwlPPCUzm3ucx86W1BgutRcqdOqwJL0q6QozSwmP0r5fCp0CLWmvmV0bflwzs9knyP6UpE+bWWL4+Clmlh7e/7HwY8vMSs1s3Nv8dwEAYFRQbAEAOEXOOSfpA5KuNbOdknZI6pP01fAhD0u6XtJDw77s2wpdG7vRzLaEbx/L7yXNN7NNkm6WtC38PVdJWixpo6QnJW2S1B7+mpskfdzMNkjaIumqox90mF8pNOK81sw2S/qFpATn3NOSHpC0LPy9H5aU+db/GgAAjD4L/S4GAACxxswynHNdZpam0LWvtznn1nqdCwCA0cY1tgAAxK67zWy6Qtfc3kupBQDEK0ZsAQAAAAAxjWtsAQAAAAAxjWILAAAAAIhpFFsAAAAAQEyj2AIAAAAAYhrFFgAAAAAQ0yi2AAAAAICY9v8DiFzH+pg8VKEAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1152x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"nAsG78uWt6jY","executionInfo":{"status":"ok","timestamp":1606270741257,"user_tz":-60,"elapsed":2360,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['Roam_bin']=np.array(np.floor(np.array(df['RoamMins'])/10.))"],"execution_count":59,"outputs":[]},{"cell_type":"code","metadata":{"id":"-XEeWmHfNZId","executionInfo":{"status":"ok","timestamp":1606270742594,"user_tz":-60,"elapsed":1978,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["\n","df['Overage_bin']=np.array(np.floor(np.array(df['OverageFee'])/10.))"],"execution_count":60,"outputs":[]},{"cell_type":"code","metadata":{"id":"30kzvvIkOp61","executionInfo":{"status":"ok","timestamp":1606270742604,"user_tz":-60,"elapsed":1139,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['MonthlyCharge_bin']=np.array(np.floor(np.array(df['MonthlyCharge'])/10.))"],"execution_count":61,"outputs":[]},{"cell_type":"code","metadata":{"id":"y6vmRG_YC8hR","executionInfo":{"status":"ok","timestamp":1606270743254,"user_tz":-60,"elapsed":1067,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['DayMins_bin']=np.array(np.floor(np.array(df['DayMins'])/10.))"],"execution_count":62,"outputs":[]},{"cell_type":"code","metadata":{"id":"iry-T9bWDPtt","executionInfo":{"status":"ok","timestamp":1606270744030,"user_tz":-60,"elapsed":1097,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['AccountWeeks_bin']=np.array(np.floor(np.array(df['AccountWeeks'])/10.))"],"execution_count":63,"outputs":[]},{"cell_type":"code","metadata":{"id":"3M9Qf7y1DYaq","executionInfo":{"status":"ok","timestamp":1606270744625,"user_tz":-60,"elapsed":924,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['DayCalls_bin']=np.array(np.floor(np.array(df['DayCalls'])/10.))"],"execution_count":64,"outputs":[]},{"cell_type":"code","metadata":{"id":"BZYgXfC3EyaH","executionInfo":{"status":"ok","timestamp":1606270745415,"user_tz":-60,"elapsed":898,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df['DataUsage_bin']=np.array(np.floor(np.array(df['DataUsage'])/10.))"],"execution_count":65,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195},"id":"j2FbPD8eE_tm","executionInfo":{"status":"ok","timestamp":1606270748520,"user_tz":-60,"elapsed":2368,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"18f70dee-7b3a-44b9-e9f4-1be2928f8efc"},"source":["\n","df.head(4)"],"execution_count":66,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Churn</th>\n","      <th>AccountWeeks</th>\n","      <th>ContractRenewal</th>\n","      <th>DataPlan</th>\n","      <th>DataUsage</th>\n","      <th>CustServCalls</th>\n","      <th>DayMins</th>\n","      <th>DayCalls</th>\n","      <th>MonthlyCharge</th>\n","      <th>OverageFee</th>\n","      <th>RoamMins</th>\n","      <th>Roam_bin</th>\n","      <th>Overage_bin</th>\n","      <th>MonthlyCharge_bin</th>\n","      <th>DayMins_bin</th>\n","      <th>AccountWeeks_bin</th>\n","      <th>DayCalls_bin</th>\n","      <th>DataUsage_bin</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>128</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2.7</td>\n","      <td>1</td>\n","      <td>265.1</td>\n","      <td>110</td>\n","      <td>89.0</td>\n","      <td>9.87</td>\n","      <td>10.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>8.0</td>\n","      <td>26.0</td>\n","      <td>12.0</td>\n","      <td>11.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>107</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3.7</td>\n","      <td>1</td>\n","      <td>161.6</td>\n","      <td>123</td>\n","      <td>82.0</td>\n","      <td>9.78</td>\n","      <td>13.7</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>8.0</td>\n","      <td>16.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>243.4</td>\n","      <td>114</td>\n","      <td>52.0</td>\n","      <td>6.06</td>\n","      <td>12.2</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>24.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>84</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>299.4</td>\n","      <td>71</td>\n","      <td>57.0</td>\n","      <td>3.10</td>\n","      <td>6.6</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>29.0</td>\n","      <td>8.0</td>\n","      <td>7.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Churn  AccountWeeks  ...  DayCalls_bin  DataUsage_bin\n","0      0           128  ...          11.0            0.0\n","1      0           107  ...          12.0            0.0\n","2      0           137  ...          11.0            0.0\n","3      0            84  ...           7.0            0.0\n","\n","[4 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":66}]},{"cell_type":"code","metadata":{"id":"OoiLeRfxFHL_","executionInfo":{"status":"ok","timestamp":1606270749035,"user_tz":-60,"elapsed":2094,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["df=df.drop(['AccountWeeks','DataUsage','DayMins','DayCalls','MonthlyCharge','OverageFee','RoamMins'],axis=1)"],"execution_count":67,"outputs":[]},{"cell_type":"code","metadata":{"id":"buTDUurFGGCZ","executionInfo":{"status":"ok","timestamp":1606270752548,"user_tz":-60,"elapsed":1490,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["x = df.drop(['Churn'], axis = 1)\n","y = df[['Churn']]"],"execution_count":68,"outputs":[]},{"cell_type":"code","metadata":{"id":"-tbwa9wsGLfx","executionInfo":{"status":"ok","timestamp":1606270753217,"user_tz":-60,"elapsed":908,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["from sklearn import preprocessing\n","from sklearn.model_selection import train_test_split"],"execution_count":69,"outputs":[]},{"cell_type":"code","metadata":{"id":"gn-ECA6AII60","executionInfo":{"status":"ok","timestamp":1606270755100,"user_tz":-60,"elapsed":1497,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"],"execution_count":70,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6pdsapFgIWRu","executionInfo":{"status":"ok","timestamp":1606270762590,"user_tz":-60,"elapsed":7800,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"5ed6c6c0-0fb6-430e-9670-591bf9f88cdb"},"source":["x_train.shape"],"execution_count":71,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2333, 10)"]},"metadata":{"tags":[]},"execution_count":71}]},{"cell_type":"code","metadata":{"id":"aZYi62YQIpM6","executionInfo":{"status":"ok","timestamp":1606270766975,"user_tz":-60,"elapsed":6340,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import r2_score"],"execution_count":72,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g2T_v1jwI7ec","executionInfo":{"status":"ok","timestamp":1606270775616,"user_tz":-60,"elapsed":7290,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"72d1148d-a6dc-4865-c5e6-c49ff745852d"},"source":["from sklearn.model_selection import cross_validate\n","clf=LogisticRegression(random_state=42,n_jobs=-1)\n","clf.fit(x_train,y_train)\n","print('Accuracy of train:',clf.score(x_train,y_train))\n","print('Accuracy of test:',clf.score(x_test,y_test))"],"execution_count":73,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n","  y = column_or_1d(y, warn=True)\n"],"name":"stderr"},{"output_type":"stream","text":["Accuracy of train: 0.8598371195885126\n","Accuracy of test: 0.86\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Q0R2J2WnJkd9","executionInfo":{"status":"ok","timestamp":1606274683886,"user_tz":-60,"elapsed":2830,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["#Bias for training = 1-0.859=0.141\n","#Bias for test = 1- 0.86 = 0.14\n","\n","#Variance=0.001"],"execution_count":74,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DWwaIpabboPZ","executionInfo":{"status":"ok","timestamp":1606275411218,"user_tz":-60,"elapsed":3247,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"0806ba44-fe89-45b7-d549-0c403cc77ae5"},"source":["from sklearn.ensemble import RandomForestClassifier\n","classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n","classifier.fit(x_train, y_train)\n","accuracy=classifier.score(x_test, y_test)"],"execution_count":75,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n","  This is separate from the ipykernel package so we can avoid doing imports until\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TMdFqpe4eZ30","executionInfo":{"status":"ok","timestamp":1606275481532,"user_tz":-60,"elapsed":2037,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"427c34ff-8d61-4db3-9e23-7ce89c945de0"},"source":["print(accuracy)"],"execution_count":76,"outputs":[{"output_type":"stream","text":["0.889\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BxBUnIeverXm","executionInfo":{"status":"ok","timestamp":1606275823069,"user_tz":-60,"elapsed":3054,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"ed089fa9-194b-4375-a41a-b2b52fa7e811"},"source":["from sklearn.metrics import precision_score, recall_score, accuracy_score, classification_report, f1_score\n","prediction=classifier.predict(x_test)\n","print(classification_report(y_test, prediction))"],"execution_count":77,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.91      0.96      0.94       857\n","           1       0.67      0.44      0.53       143\n","\n","    accuracy                           0.89      1000\n","   macro avg       0.79      0.70      0.73      1000\n","weighted avg       0.88      0.89      0.88      1000\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ubZU84Q2f-h1","executionInfo":{"status":"ok","timestamp":1606275999873,"user_tz":-60,"elapsed":1552,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"13cf3547-498b-4cbd-b2cd-dabb2d8c9a2e"},"source":["print(\"Precision = {}\".format(precision_score(y_test, prediction, average='macro')))\n","print(\"Recall = {}\".format(recall_score(y_test, prediction, average='macro')))\n","print(\"Accuracy = {}\".format(accuracy_score(y_test, prediction)))\n","print(\"F1 Score = {}\".format(f1_score(y_test, prediction,average='macro')))"],"execution_count":78,"outputs":[{"output_type":"stream","text":["Precision = 0.7909562726034475\n","Recall = 0.7021933725550995\n","Accuracy = 0.889\n","F1 Score = 0.7343423537267459\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"x7e1MNcZgqD-","executionInfo":{"status":"ok","timestamp":1606276541588,"user_tz":-60,"elapsed":1391,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["from sklearn.metrics import confusion_matrix\n","\n","def conftable(test,pred, imagename):\n","    confmatrix= metrics.confusion_matrix(y_test, prediction)\n","    plt.matshow(confmatrix)\n","    plt.title('Confusion matrix')\n","    plt.colorbar()\n","    plt.ylabel('True Label')\n","    plt.xlabel('Predicted Label')\n","    plt.savefig(imagename)\n","    \n","    plt.show()\n","    print(confmatrix)"],"execution_count":79,"outputs":[]},{"cell_type":"code","metadata":{"id":"80y5xnWziuYI","executionInfo":{"status":"ok","timestamp":1606276685226,"user_tz":-60,"elapsed":1204,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}}},"source":["from sklearn import metrics"],"execution_count":82,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":294},"id":"SUWYzkori4Wj","executionInfo":{"status":"ok","timestamp":1606276697741,"user_tz":-60,"elapsed":4133,"user":{"displayName":"mariam olasunkanmi-ojo","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgBFz_-tte9lJ9nuW-p8uc5F0GFBzUkXgK2wuCp=s64","userId":"15947454563810535958"}},"outputId":"48534d31-87f3-470b-86ad-4654776fe362"},"source":["conftable(y_test,prediction,\"conf\")"],"execution_count":83,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQwAAADwCAYAAAD1q1pFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZBUlEQVR4nO3de7hcdX3v8fcnFyAIREICxkAajkQspQUxJQEUkYsNaAunB7mIevChT2y91Gp9Cno4Ba310B57EIvaRhDjpRHkco4CElKEIpZLAqQUAki4aSAQLuEWBJK9v+eP32+SyXbvPb/ZmdlrZvbn9Tzr2bMus9ZvdjLf/but9VVEYGZWYlzVBTCz7uGAYWbFHDDMrJgDhpkVc8Aws2IOGGZWzAGjYpImSfqxpOcl/XArznOKpGtbWbaqSHqHpPurLof9JnkeRhlJ7wc+DbwFeBFYAfxtRNy0lef9IPAJ4OCI2LjVBe1wkgKYHRGrqi6LNc81jAKSPg18BfgSsBswE/g6cGwLTv9bwC/GQrAoIWlC1WWwYUSEl2EWYDLwEvC+YY7ZlhRQHs/LV4Bt877DgNXAXwJrgTXAh/O+zwOvARvyNU4Dzga+V3fuWUAAE/L6qcBDpFrOw8ApddtvqnvfwcAy4Pn88+C6fTcAfwP8PJ/nWmDqEJ+tVv6/qiv/ccAxwC+AZ4HP1R1/IHAz8Fw+9nxgm7zvxvxZ1ufPe2Ld+U8HngC+W9uW3/OmfI0D8vobgaeAw6r+v9GK5d2HbR9v+71tixbgmqrL62je2EHAdsAVwxzzP4B5wP6kL8T/A84E/mfe/wZS4JkBHAVcKun/RsRZuYq+V0R8AEDS2UNdRNLrgK8Cvx8R90uaDkwZ5LgpwFXAnwOLgfcBV0naKyKeyYe9Hzga+BXwE+AzwBlDXPoN+XcwgxSYvgksBd5Gqm0tl7Q4Ih4G+oBPAcuB3fO5Pwp8JSIOzZ93v8hNEkmH5fNPIdW2xgFzaxeOiAclnQ58T9Ic4CJgUUTcMNTvqZs8/Wwfty7ZvejYidMfnNrm4jTkJkljuwBPx/BNhlOAL0TE2oh4ilRz+GDd/g15/4aIuJr013XvEZanH9hX0qSIWBMR9wxyzHuAByLiuxGxMSIWA/cBf1h3zEUR8YuI+DVwCSnYDWUDqb9mA/ADYCpwXkS8mK+/EtgPICJuj4hb8nUfAf4ZeGfBZzorIl7N5dlCRHwTWAXcCkwnBegeEfRFf9HSCRwwGnsGmNqgbf1G4NG69Ufztk3nGBBwXgZ2aLYgEbGeVI3/U2CNpKskvaWgPLUyzahbf6KJ8jwTEX35de0L/WTd/l/X3i/pzZKulPSEpBdI/T6N/jI+FRGvNDjmm8C+wD9GxKsNju0aAfQTRUsJSZ+SdI+kuyUtlrSdpD0l3SpplaSLJW2Tj902r6/K+2c1Or8DRmM3A6+S2u1DeZxUna6ZmbeNxHpg+7r1N9TvjIglEXEU6S/tfaQvUqPy1Mr02AjL1IxvkMo1OyJ2Aj4HqMF7hv02SNqB1C90IXB2bnL1hCDYEH1FSyOSZpCaoXMiYl9gPHAS8HfAuRGxF7CO1FdG/rkubz83HzcsB4wGIuJ54K+Br0k6TtL2kiZKOlPSs5JWkf56nylpmqSp+fjvjfCSK4BDJc2UNBn4bG2HpN0kHZv7Ml4lNW0Gq6teDbxZ0vslTZB0IrAPcOUIy9SMHYEXgJdy7efPBux/EvgvBefZWdJaSXcD5wHLI+JPSH0z/9TKAletlTUMYAIwKdeItyd1PB8OXJr3L2LzH79j8zp5/xGShg3uDhgFIuIfSHMwziT10P+K9EVeQPoiTiWNXNwF/CdwB/DFEV5rKXBxPtftbPklH5fL8Thp5OCd/OYXktyx+V7SyMwzpBGO90bE0yMpU5M+Q+pQfZFU+7l4wP6zgUWSnpN0wjDnWQ/MJwWg+Wz+nJ8GDpB0SisLXZUA+oiipeG5Ih4Dvgz8khQonif9H3qurkm8ms1N0xmk/8vk/c+T+uyG5IlbIyDpIODsiPiDvP5ZgIj4X5UWrMfkNvWVuXrdk/bfb5tY+pNpRcfuOuPxR4H6oL8wIhbWViTtDFxG6ud6DvghqeZwdm52IGkP4CcRsW+uvc2PiNV534PA3OH+sHhYdWQ2ReZsNXVDgWalAugr/6P9dETMGWb/kcDDeaQOSZcDhwCvlzQh1yJ2Z3Nf1mPAHsDq3ISZTKqRDslNErOK9RcuBX4JzMv9bAKOIA15Xw8cn4/576R5QgA/yuvk/T+NBk0O1zBGphaZa+qjtlmxKOyfKDpXxK2SLiX1oW0E7gQWkjqKfyDpi3nbhfktFwLfzR33z5JGVIblgDEyy4DZkvYkBYqTSB19Zk2JgA0t7EaMiLOAswZsfog0ZX/gsa+QZgEXc5NkBHJb8OPAEuBe4JIhZlzaCElaTJoDs7ek1ZJOa/Se7iT6CpdO4BrGCOUp3ldXXY5eFREnV12G0RBAfxcNVDpgmFWsU2oPJRwwzCqUJm45YJhZof5wwDCzAq5hmFmxQGyI8VUXo5iHVbeCpAVVl6HX9frvuFbD6JZhVQeMrdPT/5k7RI//jkVfjCtaOoGbJGYVSk/c6oxgUKKjAsbUKeNj1h4Tqy5GsZkzJjBnv+26aNoN/OKu7Rsf1EG2Y3t20pSu+h2/wnpei1eL2xCd0two0VEBY9YeE7ltyR6ND7QR+4M3DvesX2uFW+O64mMj1DHNjRIdFTDMxqJ+1zDMrEQgXovu+Rp2T0nNepA7Pc2sKX2eGm5mJQLR5xqGmZXq9yiJmZVIU8O7J2B0T0nNelDt5rOSpRFJe0taUbe8IOkvJE2RtFTSA/nnzvl4Sfpqzq16l6QDGl3DAcOsQhG07F6SiLg/IvaPiP2Bt5GSbF8BnAFcFxGzgevyOsDRwOy8LCDlxR2WA4ZZpUR/4dKkI4AHI+JRtsyhOjC36nciuYWU8Gj6cCd1H4ZZhVLms7b83T4JWJxf7xYRa/LrJ4Dd8uvBMvjNIOVlHZQDhlnFmuj0nCpped36FrlVayRtA/wRKWH4FiIiJI34Zj4HDLMKBWrmmZ6NcqvWHA3cERFP5vUnJU2PiDW5ybE2b286g5/7MMwq1se4oqUJJ7O5OQJb5lAdmFv1Q3m0ZB7wfF3TZVCuYZhVqNXP9JT0OuAo4CN1m88BLsnZ4x4FTsjbrwaOAVaRRlQ+3Oj8DhhmFUqZz1pX0Y+I9cAuA7Y9Qxo1GXhsAB9r5vwOGGYV8xO3zKxIhHwviZmV8yP6zKxIeoCOmyRmVsQPATazQgFdlSrRAcOsQk3O9KycA4ZZxfwQYDMrkp6H4RqGmRVyk8TMiqQ+DDdJzKyQp4abWZFAbOz3sKqZFfJMTzMr4lESM2uKOz3NrIhneppZU9yHYWZF0iP6uidgdE/jyawXRRpWLVlKSHq9pEsl3SfpXkkHObeqWY+oPUCnhakSzwOuiYi3APsB9+Lcqma9oz9UtDQiaTJwKHAhQES8FhHP0cLcqg4YZhWq9WG0ImAAewJPARdJulPSBTlPSbO5VYfU1oAhab6k+3Mb6YzG7zAbe5oIGFMlLa9bFgw41QTgAOAbEfFWYD2bmx/AplwknZdbVdJ44GukLEyrgWWSfhQRK9t1TbNu0+LcqquB1RFxa16/lBQwuiK36oHAqoh4KCJeA35AajOZWU3AxhhXtDQ8VcQTwK8k7Z03HQGspEtyqw7WPprbxuuZdZ02zMP4BPB9SdsAD5HypY6jV3Kr5nbYAoCZMyovjtmoa2XAiIgVwGDNlpbkVm1nk6SofRQRCyNiTkTMmbZL9zwXwKwVan0YLRolabt2BoxlwGxJe+bq0UmkNpOZ1YlQ0dIJ2tYGiIiNkj4OLAHGA9+KiHvadT2zbuWbz7KIuJrUsWJmg4jorpvP3MtoVinR1989E64dMMwq1in9EyUcMMwq1G3Pw3DAMKtSpH6MbuGAYVYxj5KYWZHAfRhmVqxzZnGWcMAwq1h/vwOGmRWIcJPEzJrgJomZFfOwqpkVc5PEzIoEnXPregkHDLOKdVGLxAHDrFIB0QvDqo3yLEbEHa0vjtnY08omiaRHgBeBPmBjRMyRNAW4GJgFPAKcEBHrJImUWvEY0kOAT230vR6uhvEPw+wL4PDCz2Bmw2jDKMm7IuLpuvVabtVzckKxM4DT2TK36lxSbtVhn+w/ZMCIiHdtbanNbHijdC/JscBh+fUi4AZSwNiUWxW4JWd+nz5cbpKGj/qRtL2kMyUtzOuzJb13Kz+AmUGOGCpbys94raTb61Iptiy3akmn50XA7cDBef0x4IfAlQXvNbMGmmiSTJW0vG59YUQsHHDM2yPiMUm7Aksl3bfltSIktTW36psi4kRJJ+cLvpw7S8ysFcq/vo1yqxIRj+WfayVdQUpZOqq5VV+TNIn8sSS9CXi14H1m1pCI/rKl4Zmk10nasfYaeDdwN6OcW/Us4BpgD0nfBw4BTi14n5k10tq7VXcDrsgNgAnAv0TENZKWMVq5VSNiqaQ7gHmAgE8OGLIxs63RomHViHgI2G+Q7c/QotyqpTM93wm8nfTRJgJXNHMRMxtO93QJNgwYkr4O7AUszps+IunIiGgqMpnZELroZpKSGsbhwG/n6guSFgHOkWrWKj0WMFYBM0mdJZCGYVa1rURmY0kP3Xz2Y1Ls2xG4V9JteX0ucNvoFM9sDOiRGsaXR60UZmNZLzxAJyL+bTQLYjZWjXyi9ugruflsnqRlkl6S9JqkPkkvjEbhzHpeNLF0gJJOz/OBk0g3nM0BPgS8uZ2FMhs7mroTtXIl95IQEauA8RHRFxEXAfPbWyyzMaTHahgvS9oGWCHp74E1FAYaMyvQX3UBypV88T+Yj/s4sJ40D+OP21koszGj9Q/QaauSm89qE7ZeAT4PIOli4MQ2lstszOimUZKRphk4qKWlMBvLxkDAaIsHVu7EMfsdVXUxetr4nfzso3bTS73bxTeSvCQi3eJuZi3QK02S4fKS3DfMPjNrRod0aJZwXhKzKgVdNazaUX0YZmNRNzVJerd3xqxbtHimp6Txku6UdGVe31PSrZJWSbo4T8RE0rZ5fVXeP6vRuR0wzKrW+qnhnwTurVv/O+DciNgLWAeclrefBqzL28/Nxw2r5G5VSfqApL/O6zMlHdhU8c1sUIrypeh80u7Ae4AL8rpIj9m8NB+yCDguvz42r5P3H9EoSVlJDePrpIlaJ+f1F4GvlRXfzBpq7dTwrwB/xeau1F2A5yJiY16vz5+6Kbdq3v98Pn5IJQFjbn5C+Cv5xOuAbUpLb2YNlDdJpkpaXrcsqD9NTpK+NiJub1dRS0ZJNkgaz+ZUidPoqoEgs86m8m9To9yqhwB/JOkYYDtgJ+A84PWSJuRaRH3+1Fpu1dWSJgCTgWeGK0BJDeOrpMRFu0r6W+Am4EsF7zOzRlrYhxERn42I3SNiFumhVz+NiFOA64Hj82EDc6vWcq4en48f9kold6t+X9LtpFRrAo6LiHsbvM3MSrV/HsbpwA8kfRG4E7gwb78Q+K6kVcCzpCAzrJLMZzNJiVp/XL8tIn45goKb2UBtCBgRcQNwQ379EPAbI5sR8QrwvmbOW9KHcRXpI4nULtoTuB/4nWYuZGaD66aZniVNkt+tX893sX60bSUys47V9L0kEXGHpLntKIzZmNRLNQxJn65bHQccADzethKZjSXR1LBq5UpqGDvWvd5I6tO4rD3FMRuDeqWGkSds7RgRnxml8piNKaJHOj1rM8MkHTKaBTIbc3ohYAC3kforVkj6ESlV4vrazoi4vM1lM+t9TdyJ2glK+jC2I80vP5zN8zECcMAwa4UeCRi75hGSu9kcKGq66COadbZeGSUZD+zAloGixgHDrFW66Ns0XMBYExFfGLWSmI1FHZSZvcRwAaN7kiWYdbFe6fQ8YtRKYTaW9ULAiIhnR7MgZmNVr9QwzGw0OGCYWYlmUgh0AgcMs6o5YJhZqW6qYThVolnVWpQqUdJ2km6T9B+S7pH0+bzduVXNekbrcqu+ChweEfsB+wPzJc1jNHOrmlkbtTYvSUTES3l1Yl6CUc6tambt1MLs7ZLGS1oBrAWWAg/Swtyq7vQ0q1gTd6tOlbS8bn1hRCysPyAi+oD9Jb2elLHwLS0pZOaAYVaxJkZJGuVW3SQinpN0PXAQo5xb1czapbQ5UjZKMi3XLJA0CTgKuJfRzK1qZm3WunkY04FF+eHd44BLIuJKSSsZrdyqZtY+rXxqeETcBbx1kO0ty63atiaJpG9JWivp7nZdw6wntHCUpN3a2YfxbWB+G89v1hMUUbR0grY1SSLixpKppmZjWg+mSjSzduqMykORygOGpAXAAoDtxu1QcWnMRp/vVm1CRCyMiDkRMWebcZOqLo7Z6OuiTs/KaxhmY1qXPXGrncOqi4Gbgb0lrZZ0WqP3mI1JrmFARJzcrnOb9YpWTtwaDW6SmFVM/d0TMRwwzKrUQc2NEg4YZhXzxC0zK+cahpmVcqenmZUJoENuLCvhgGFWMfdhmFkRz8Mws3IRbpKYWTnXMMysXBcFjMpvbzcb61qVKlHSHpKul7QyJ2P+ZN4+RdJSSQ/knzvn7ZL01ZyM+S5JBzS6hgOGWZUC6I+ypbGNwF9GxD7APOBjkvYBzgCui4jZwHV5HeBoYHZeFgDfaHQBBwyziqm/bGkkItZExB359YukJEYz2DLp8sBkzN/JSZxvIWVImz7cNdyHYVa18lGShrlVa/IDuN8K3ArsFhFr8q4ngN3y603JmLNaouY1DMEBw6xirc6tKmkH4DLgLyLiBUmb9kVESCMfl3GTxKxKLcytCiBpIilYfD8iLs+bn6w1NfLPtXl7LRlzTX2i5kE5YJhVKM30bE0iI6WqxIXAvRHxf+p21SddHpiM+UN5tGQe8Hxd02VQbpKYVa1195IcAnwQ+E9JK/K2zwHnAJfk5+o+CpyQ910NHAOsAl4GPtzoAg4YZhVrVRrEiLiJVGkZzBGDHB/Ax5q5hgOGWZWieI5FR3DAMKuY7yUxs3K+W9XMijh7u5k1xTUMMyvWPfHCAcOsaq0aVh0NDhhmVQqgzwHDzAqIsmnfncIBw6xqDhhmVswBw8yKBK28+aztHDDMKuY+DDMr54BhZkUioL972iQOGGZV65544YBhVjX3YZhZOQcMMytSy3zWJToqYLyw8amnl6z9xqNVl6MJU4Gnqy5Ej+vG3/FvlR8aLa1hSPoW8F5gbUTsm7dNAS4GZgGPACdExLr8lPHzSA8Cfhk4tZY5bSgdFTAiYlrVZWiGpOUliWVs5MbE77i1TZJvA+cD36nbVsuteo6kM/L66WyZW3UuKbfq3OFO7rwkZlUKoK+/bCk5XcSNwLMDNju3qllvCIjicdXi3KoDOLdqhyj5x7Kt0/u/4/ImSVFu1eEv5dyqlRkuukvqk7RC0t2Sfihp+5FeR9K3JR2fX18gaZ9hjj1M0sEjuMYjkqaWbh/iHKdKOr8V160p/AvavWqjJCXLyDm3ahf4dUTsn3uqXwP+tH6npBHV7iLiTyJi5TCHHAY0HTCsQhFly8i1LLeqA8bo+BmwV/7r/zNJPwJWShov6X9LWibpLkkfgZRUV9L5ku6X9K/ArrUTSbpB0pz8er6kOyT9h6TrJM0iBaZP5drNOyRNk3RZvsYySYfk9+4i6VpJ90i6gKFT7P0GSQdKulnSnZL+XdLedbv3yGV8QNJZde/5gKTbcrn+WdL4Ef82e00LA4akxcDNwN6SVud8qucAR0l6ADgyr0PKrfoQKbfqN4GPNjq/+zDaLNckjgauyZsOAPaNiIclLSBF9d+XtC3wc0nXAm8F9gb2IXVQrQS+NeC800j/yIfmc02JiGcl/RPwUkR8OR/3L8C5EXGTpJnAEuC3gbOAmyLiC5LeA5zWxMe6D3hHRGyUdCTwJeC/5X0HAvuSxvWXSboKWA+cCBwSERskfR04hS2H/samCOjra+Hp4uQhdjm3aoebVJdB+2fAhaSmwm0R8XDe/m7g92r9E8Bk0pj4ocDiiOgDHpf000HOPw+4sXauiBg4lFZzJLBPmqMDwE6SdsjX+OP83qskrWvis00GFkmaTWqFT6zbtzQingGQdDnwdmAj8DZSAAGYxOZ2tHlquJH7MOo35C/L+vpNwCciYsmA445pYTnGAfMi4pVByjJSfwNcHxH/NTeDbqjbN/B/f5A+56KI+OzWXLRndVHAcB9GtZYAfyZpIoCkN0t6HXAjcGLu45gOvGuQ994CHCppz/zeKXn7i8COdcddC3yitiKpFsRuBN6ftx0N7NxEuSezuTf91AH7jpI0RdIk0gShnwPXAcdL2rVWVklNTJ/uZYUjJB1yv4lrGNW6gDS//448r/8p0pfsCuBwUt/FL0mdWFuIiKdyH8jlksaRqvhHAT8GLpV0LClQ/DnwNUl3kf69byR1jH4eWCzpHuDf83WGcpe0KQPoJcDfk5okZwJXDTj2NuAy0hDd9yJiOUA+9tpc1g2ktnM33TfUHgFRPnGrcoouqg6Z9ZrJE6bFQTsd1/hAYMm6C26v+r4a1zDMqtZFf7QdMMyq1OJh1XZzwDCrWPghwGZWprUP0Gk3BwyzKvkRfWbWlC4aVnXAMKtQAOEahpkViaaeuFU5BwyzikUXDat6pqdZhSRdQ0qlUOLpiJjfzvI04oBhZsV8t6qZFXPAMLNiDhhmVswBw8yKOWCYWbH/D8/j3uvJAlpFAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 288x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["[[826  31]\n"," [ 80  63]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yCfh1xJjjTvN"},"source":[""],"execution_count":null,"outputs":[]}]}