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{"cells":[{"cell_type":"markdown","metadata":{"id":"NnMXsHDsBh4V"},"source":["# Statistics about EWS in Mimic-iv-ED"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7aPKx7T-Bh4a"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n"]},{"cell_type":"markdown","metadata":{"id":"NxmoUGL7Bh4c"},"source":["# reading vital sign ED csv"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rN2QUHmbBh4d","outputId":"d7abe0af-73df-4a75-8493-e5688b2e40fe"},"outputs":[{"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>subject_id</th>\n"," <th>stay_id</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>dbp</th>\n"," <th>rhythm</th>\n"," <th>pain</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," <td>51.0</td>\n"," <td>NaN</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," <td>39.0</td>\n"," <td>NaN</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>NaN</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.0</td>\n"," <td>75.0</td>\n"," <td>39.0</td>\n"," <td>NaN</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>NaN</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.0</td>\n"," <td>86.0</td>\n"," <td>51.0</td>\n"," <td>NaN</td>\n"," <td>NaN</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.4</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.0</td>\n"," <td>65.0</td>\n"," <td>37.0</td>\n"," <td>NaN</td>\n"," <td>NaN</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>1646971</th>\n"," <td>19999828</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 17:10:00</td>\n"," <td>98.1</td>\n"," <td>109.0</td>\n"," <td>15.0</td>\n"," <td>96.0</td>\n"," <td>111.0</td>\n"," <td>78.0</td>\n"," <td>NaN</td>\n"," <td>NaN</td>\n"," </tr>\n"," <tr>\n"," <th>1646972</th>\n"," <td>19999914</td>\n"," <td>32002659</td>\n"," <td>2158-12-24 11:43:00</td>\n"," <td>99.5</td>\n"," <td>81.0</td>\n"," <td>10.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," <td>55.0</td>\n"," <td>NaN</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>1646973</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:40:00</td>\n"," <td>NaN</td>\n"," <td>112.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>118.0</td>\n"," <td>83.0</td>\n"," <td>NaN</td>\n"," <td>NaN</td>\n"," </tr>\n"," <tr>\n"," <th>1646974</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 20:11:00</td>\n"," <td>NaN</td>\n"," <td>111.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>123.0</td>\n"," <td>82.0</td>\n"," <td>NaN</td>\n"," <td>unable</td>\n"," </tr>\n"," <tr>\n"," <th>1646975</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 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24.0 97.0 90.0 51.0 NaN 0 \n","1 22.0 98.0 76.0 39.0 NaN 0 \n","2 22.0 97.0 75.0 39.0 NaN 0 \n","3 20.0 99.0 86.0 51.0 NaN NaN \n","4 20.0 98.0 65.0 37.0 NaN NaN \n","... ... ... ... ... ... ... \n","1646971 15.0 96.0 111.0 78.0 NaN NaN \n","1646972 10.0 100.0 93.0 55.0 NaN 0 \n","1646973 18.0 NaN 118.0 83.0 NaN NaN \n","1646974 18.0 NaN 123.0 82.0 NaN unable \n","1646975 20.0 NaN 113.0 79.0 Sinus Tachycardia unable \n","\n","[1646976 rows x 11 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('//Users/shayan/Desktop/HiWis/mimiciv-ed-2.0/vitalsign.csv.gz')\n","df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gQ5Y4wSgBh4e","outputId":"7716f26c-4824-4a3c-abc9-85b55b4fbffe"},"outputs":[{"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>subject_id</th>\n"," <th>stay_id</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>NaN</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.0</td>\n"," <td>75.0</td>\n"," 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<td>10.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646973</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:40:00</td>\n"," <td>NaN</td>\n"," <td>112.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>118.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646974</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 20:11:00</td>\n"," <td>NaN</td>\n"," <td>111.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646975</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1646976 rows × 8 columns</p>\n","</div>"],"text/plain":[" subject_id stay_id charttime temperature heartrate \\\n","0 10000032 32952584 2180-07-22 16:36:00 NaN 83.0 \n","1 10000032 32952584 2180-07-22 16:43:00 NaN 85.0 \n","2 10000032 32952584 2180-07-22 16:45:00 NaN 84.0 \n","3 10000032 32952584 2180-07-22 17:56:00 NaN 84.0 \n","4 10000032 32952584 2180-07-22 18:37:00 98.4 86.0 \n","... ... ... ... ... ... \n","1646971 19999828 32917002 2149-01-08 17:10:00 98.1 109.0 \n","1646972 19999914 32002659 2158-12-24 11:43:00 99.5 81.0 \n","1646973 19999987 34731548 2145-11-02 19:40:00 NaN 112.0 \n","1646974 19999987 34731548 2145-11-02 20:11:00 NaN 111.0 \n","1646975 19999987 34731548 2145-11-02 21:51:00 99.3 103.0 \n","\n"," resprate o2sat sbp \n","0 24.0 97.0 90.0 \n","1 22.0 98.0 76.0 \n","2 22.0 97.0 75.0 \n","3 20.0 99.0 86.0 \n","4 20.0 98.0 65.0 \n","... ... ... ... \n","1646971 15.0 96.0 111.0 \n","1646972 10.0 100.0 93.0 \n","1646973 18.0 NaN 118.0 \n","1646974 18.0 NaN 123.0 \n","1646975 20.0 NaN 113.0 \n","\n","[1646976 rows x 8 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# we dont need [dbp,rhythm and pain]\n","# so droping these columns\n","df =df.drop(['dbp','rhythm','pain'],axis=1)\n","df"]},{"cell_type":"markdown","metadata":{"id":"iVm5np8BBh4f"},"source":["### set the data from ascending according to charttime"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wO56chLCBh4g","outputId":"4e6842d5-69ef-4461-b354-b81c023899bd"},"outputs":[{"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>subject_id</th>\n"," <th>stay_id</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>7</th>\n"," <td>10000032</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.7</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>10</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.9</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.0</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>11</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.9</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.0</td>\n"," <td>96.0</td>\n"," </tr>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," 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<td>NaN</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646975</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1646976 rows × 8 columns</p>\n","</div>"],"text/plain":[" subject_id stay_id charttime temperature heartrate \\\n","7 10000032 33258284 2180-05-06 23:04:00 97.7 79.0 \n","10 10000032 38112554 2180-06-26 18:42:00 97.9 76.0 \n","11 10000032 38112554 2180-06-26 20:54:00 97.9 86.0 \n","0 10000032 32952584 2180-07-22 16:36:00 NaN 83.0 \n","1 10000032 32952584 2180-07-22 16:43:00 NaN 85.0 \n","... ... ... ... ... ... \n","1646971 19999828 32917002 2149-01-08 17:10:00 98.1 109.0 \n","1646972 19999914 32002659 2158-12-24 11:43:00 99.5 81.0 \n","1646973 19999987 34731548 2145-11-02 19:40:00 NaN 112.0 \n","1646974 19999987 34731548 2145-11-02 20:11:00 NaN 111.0 \n","1646975 19999987 34731548 2145-11-02 21:51:00 99.3 103.0 \n","\n"," resprate o2sat sbp \n","7 16.0 98.0 107.0 \n","10 18.0 95.0 95.0 \n","11 17.0 93.0 96.0 \n","0 24.0 97.0 90.0 \n","1 22.0 98.0 76.0 \n","... ... ... ... \n","1646971 15.0 96.0 111.0 \n","1646972 10.0 100.0 93.0 \n","1646973 18.0 NaN 118.0 \n","1646974 18.0 NaN 123.0 \n","1646975 20.0 NaN 113.0 \n","\n","[1646976 rows x 8 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.sort_values(by=['subject_id','charttime'],ascending= True,inplace= True)\n","df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d0LgdoW8Bh4h","outputId":"1ee02dbe-38f2-49ad-e057-8816365f1360"},"outputs":[{"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>subject_id</th>\n"," <th>stay_id</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.7</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.9</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.0</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.9</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.0</td>\n"," <td>96.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>1646971</th>\n"," <td>19999828</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 17:10:00</td>\n"," <td>98.1</td>\n"," <td>109.0</td>\n"," <td>15.0</td>\n"," <td>96.0</td>\n"," <td>111.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646972</th>\n"," <td>19999914</td>\n"," <td>32002659</td>\n"," <td>2158-12-24 11:43:00</td>\n"," <td>99.5</td>\n"," <td>81.0</td>\n"," <td>10.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646973</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:40:00</td>\n"," <td>NaN</td>\n"," <td>112.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>118.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646974</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 20:11:00</td>\n"," <td>NaN</td>\n"," <td>111.0</td>\n"," <td>18.0</td>\n"," <td>NaN</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646975</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1646976 rows × 8 columns</p>\n","</div>"],"text/plain":[" subject_id stay_id charttime temperature heartrate \\\n","0 10000032 33258284 2180-05-06 23:04:00 97.7 79.0 \n","1 10000032 38112554 2180-06-26 18:42:00 97.9 76.0 \n","2 10000032 38112554 2180-06-26 20:54:00 97.9 86.0 \n","3 10000032 32952584 2180-07-22 16:36:00 NaN 83.0 \n","4 10000032 32952584 2180-07-22 16:43:00 NaN 85.0 \n","... ... ... ... ... ... \n","1646971 19999828 32917002 2149-01-08 17:10:00 98.1 109.0 \n","1646972 19999914 32002659 2158-12-24 11:43:00 99.5 81.0 \n","1646973 19999987 34731548 2145-11-02 19:40:00 NaN 112.0 \n","1646974 19999987 34731548 2145-11-02 20:11:00 NaN 111.0 \n","1646975 19999987 34731548 2145-11-02 21:51:00 99.3 103.0 \n","\n"," resprate o2sat sbp \n","0 16.0 98.0 107.0 \n","1 18.0 95.0 95.0 \n","2 17.0 93.0 96.0 \n","3 24.0 97.0 90.0 \n","4 22.0 98.0 76.0 \n","... ... ... ... \n","1646971 15.0 96.0 111.0 \n","1646972 10.0 100.0 93.0 \n","1646973 18.0 NaN 118.0 \n","1646974 18.0 NaN 123.0 \n","1646975 20.0 NaN 113.0 \n","\n","[1646976 rows x 8 columns]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.reset_index(inplace= True,drop = True)\n","df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G6QSbpCTBh4h"},"outputs":[],"source":["df['charttime'] = pd.to_datetime(df['charttime'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pXIbB-cFBh4i"},"outputs":[],"source":["# Keep only the rows with at least 7 non-NA values\n","\n","def null_dropping(x):\n","\n"," x= x.dropna(thresh=7)\n"," return x\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aMExNq-0Bh4j","outputId":"64fa56e5-9eaa-4b91-f164-d594afdf0f39"},"outputs":[{"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>subject_id</th>\n"," <th>stay_id</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.7</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.9</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.0</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.9</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.0</td>\n"," <td>96.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>1646969</th>\n"," <td>19999828</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 14:53:00</td>\n"," <td>97.9</td>\n"," <td>104.0</td>\n"," <td>16.0</td>\n"," <td>96.0</td>\n"," <td>116.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646970</th>\n"," <td>19999828</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 16:00:00</td>\n"," <td>98.1</td>\n"," <td>102.0</td>\n"," <td>17.0</td>\n"," <td>96.0</td>\n"," <td>121.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646971</th>\n"," <td>19999828</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 17:10:00</td>\n"," <td>98.1</td>\n"," <td>109.0</td>\n"," <td>15.0</td>\n"," <td>96.0</td>\n"," <td>111.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646972</th>\n"," <td>19999914</td>\n"," <td>32002659</td>\n"," <td>2158-12-24 11:43:00</td>\n"," <td>99.5</td>\n"," <td>81.0</td>\n"," <td>10.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1646975</th>\n"," <td>19999987</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1512715 rows × 8 columns</p>\n","</div>"],"text/plain":[" subject_id stay_id charttime temperature heartrate \\\n","0 10000032 33258284 2180-05-06 23:04:00 97.7 79.0 \n","1 10000032 38112554 2180-06-26 18:42:00 97.9 76.0 \n","2 10000032 38112554 2180-06-26 20:54:00 97.9 86.0 \n","3 10000032 32952584 2180-07-22 16:36:00 NaN 83.0 \n","4 10000032 32952584 2180-07-22 16:43:00 NaN 85.0 \n","... ... ... ... ... ... \n","1646969 19999828 32917002 2149-01-08 14:53:00 97.9 104.0 \n","1646970 19999828 32917002 2149-01-08 16:00:00 98.1 102.0 \n","1646971 19999828 32917002 2149-01-08 17:10:00 98.1 109.0 \n","1646972 19999914 32002659 2158-12-24 11:43:00 99.5 81.0 \n","1646975 19999987 34731548 2145-11-02 21:51:00 99.3 103.0 \n","\n"," resprate o2sat sbp \n","0 16.0 98.0 107.0 \n","1 18.0 95.0 95.0 \n","2 17.0 93.0 96.0 \n","3 24.0 97.0 90.0 \n","4 22.0 98.0 76.0 \n","... ... ... ... \n","1646969 16.0 96.0 116.0 \n","1646970 17.0 96.0 121.0 \n","1646971 15.0 96.0 111.0 \n","1646972 10.0 100.0 93.0 \n","1646975 20.0 NaN 113.0 \n","\n","[1512715 rows x 8 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df = null_dropping(df)\n","df"]},{"cell_type":"markdown","metadata":{"id":"qMIDp02vBh4j"},"source":["# reading edstays csv"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"S1p0jC17Bh4k","outputId":"7affe397-7cb5-48ce-f908-27d385ed7a7d"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>gender</th>\n"," <th>race</th>\n"," <th>arrival_transport</th>\n"," <th>disposition</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>22595853.0</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 19:17:00</td>\n"," <td>2180-05-06 23:30:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>HOME</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>39399961</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>2180-07-23 14:00:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>447707</th>\n"," <td>19999784</td>\n"," <td>26194817.0</td>\n"," <td>35692999</td>\n"," <td>2119-06-18 14:21:00</td>\n"," <td>2119-06-18 21:09:29</td>\n"," <td>M</td>\n"," <td>BLACK/AFRICAN AMERICAN</td>\n"," <td>WALK IN</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>447708</th>\n"," <td>19999828</td>\n"," <td>25744818.0</td>\n"," <td>32917002</td>\n"," <td>2149-01-08 09:11:00</td>\n"," <td>2149-01-08 18:12:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>447709</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>F</td>\n"," <td>WHITE</td>\n"," <td>WALK IN</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," <tr>\n"," <th>447710</th>\n"," <td>19999914</td>\n"," <td>NaN</td>\n"," <td>32002659</td>\n"," <td>2158-12-24 11:41:00</td>\n"," <td>2158-12-24 11:56:00</td>\n"," <td>F</td>\n"," <td>UNKNOWN</td>\n"," <td>UNKNOWN</td>\n"," <td>ELOPED</td>\n"," </tr>\n"," <tr>\n"," <th>447711</th>\n"," <td>19999987</td>\n"," <td>23865745.0</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:28:00</td>\n"," <td>2145-11-02 22:59:00</td>\n"," <td>F</td>\n"," <td>UNKNOWN</td>\n"," <td>AMBULANCE</td>\n"," <td>ADMITTED</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>447712 rows × 9 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 22595853.0 33258284 2180-05-06 19:17:00 \n","1 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","2 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 39399961 2180-07-23 05:54:00 \n","... ... ... ... ... \n","447707 19999784 26194817.0 35692999 2119-06-18 14:21:00 \n","447708 19999828 25744818.0 32917002 2149-01-08 09:11:00 \n","447709 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","447710 19999914 NaN 32002659 2158-12-24 11:41:00 \n","447711 19999987 23865745.0 34731548 2145-11-02 19:28:00 \n","\n"," outtime gender race arrival_transport \\\n","0 2180-05-06 23:30:00 F WHITE AMBULANCE \n","1 2180-06-26 21:31:00 F WHITE AMBULANCE \n","2 2180-08-06 01:44:00 F WHITE AMBULANCE \n","3 2180-07-23 05:54:00 F WHITE AMBULANCE \n","4 2180-07-23 14:00:00 F WHITE AMBULANCE \n","... ... ... ... ... \n","447707 2119-06-18 21:09:29 M BLACK/AFRICAN AMERICAN WALK IN \n","447708 2149-01-08 18:12:00 F WHITE AMBULANCE \n","447709 2147-07-18 17:34:00 F WHITE WALK IN \n","447710 2158-12-24 11:56:00 F UNKNOWN UNKNOWN \n","447711 2145-11-02 22:59:00 F UNKNOWN AMBULANCE \n","\n"," disposition \n","0 ADMITTED \n","1 ADMITTED \n","2 ADMITTED \n","3 HOME \n","4 ADMITTED \n","... ... \n","447707 ADMITTED \n","447708 ADMITTED \n","447709 ADMITTED \n","447710 ELOPED \n","447711 ADMITTED \n","\n","[447712 rows x 9 columns]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_eds = pd.read_csv('/Users/shayan/Desktop/HiWis/mimiciv-ed-2.0/edstays.csv')\n","df_eds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6Ezg525DBh4k","outputId":"845035cf-0d72-4816-be48-ea49b512d481"},"outputs":[{"data":{"text/plain":["HOME 254545\n","ADMITTED 166303\n","TRANSFER 7436\n","LEFT WITHOUT BEING SEEN 6516\n","ELOPED 6011\n","OTHER 4520\n","LEFT AGAINST MEDICAL ADVICE 1986\n","EXPIRED 395\n","Name: disposition, dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_eds['disposition'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"3bDMlWgyBh4l"},"source":["### we need information about HOME, ADMITTED and EXPIRED"]},{"cell_type":"markdown","metadata":{"id":"tfqCawR3Bh4l"},"source":["# dividing dataframe into three dataframes (df_home,df_adm,df_exp)"]},{"cell_type":"markdown","metadata":{"id":"_8H8vywaBh4l"},"source":["### HOME"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1FupsA6bBh4m","outputId":"249bb625-5560-467b-dd50-fbd644c402f5"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>NaN</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>NaN</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>NaN</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.0</td>\n"," <td>75.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>NaN</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.0</td>\n"," <td>86.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.4</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.0</td>\n"," <td>65.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>703465</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-08 23:38:00</td>\n"," <td>NaN</td>\n"," <td>50.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>102.0</td>\n"," </tr>\n"," <tr>\n"," <th>703466</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-09 02:51:00</td>\n"," <td>98.1</td>\n"," <td>54.0</td>\n"," <td>16.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>703467</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>97.7</td>\n"," <td>89.0</td>\n"," <td>22.0</td>\n"," <td>100.0</td>\n"," <td>176.0</td>\n"," </tr>\n"," <tr>\n"," <th>703468</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 16:19:00</td>\n"," <td>98.6</td>\n"," <td>82.0</td>\n"," <td>18.0</td>\n"," <td>97.0</td>\n"," <td>148.0</td>\n"," </tr>\n"," <tr>\n"," <th>703469</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 18:37:00</td>\n"," <td>97.0</td>\n"," <td>80.0</td>\n"," <td>18.0</td>\n"," <td>100.0</td>\n"," <td>156.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>703470 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","... ... ... ... ... \n","703465 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703466 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703467 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703468 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703469 19999750 NaN 38224473 2144-03-22 14:27:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 NaN \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 NaN \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 NaN \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 NaN \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.4 \n","... ... ... ... ... \n","703465 2152-07-09 03:45:00 HOME 2152-07-08 23:38:00 NaN \n","703466 2152-07-09 03:45:00 HOME 2152-07-09 02:51:00 98.1 \n","703467 2144-03-22 18:47:00 HOME 2144-03-22 14:27:00 97.7 \n","703468 2144-03-22 18:47:00 HOME 2144-03-22 16:19:00 98.6 \n","703469 2144-03-22 18:47:00 HOME 2144-03-22 18:37:00 97.0 \n","\n"," heartrate resprate o2sat sbp \n","0 83.0 24.0 97.0 90.0 \n","1 85.0 22.0 98.0 76.0 \n","2 84.0 22.0 97.0 75.0 \n","3 84.0 20.0 99.0 86.0 \n","4 86.0 20.0 98.0 65.0 \n","... ... ... ... ... \n","703465 50.0 16.0 98.0 102.0 \n","703466 54.0 16.0 100.0 93.0 \n","703467 89.0 22.0 100.0 176.0 \n","703468 82.0 18.0 97.0 148.0 \n","703469 80.0 18.0 100.0 156.0 \n","\n","[703470 rows x 12 columns]"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["def home(df_eds,df):\n"," home_eds= df_eds.loc[df_eds['disposition']== 'HOME']\n"," df_home = pd.merge(home_eds,df,on = 'stay_id')\n"," df_home = df_home.drop(['race','arrival_transport','subject_id_y','gender'],axis = 1)\n"," df_home = df_home.rename(columns={'subject_id_x': 'subject_id'})\n"," return df_home\n","df_home= home(df_eds,df)\n","df_home"]},{"cell_type":"markdown","metadata":{"id":"KEftuhJvBh4m"},"source":["### ADMITTED"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wwweDYVrBh4m","outputId":"0f030a84-7b30-49d1-c73d-c1904e836c87"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>22595853.0</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 19:17:00</td>\n"," <td>2180-05-06 23:30:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.7</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.9</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.0</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.9</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.0</td>\n"," <td>96.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-05 23:50:00</td>\n"," <td>98.5</td>\n"," <td>96.0</td>\n"," <td>17.0</td>\n"," <td>100.0</td>\n"," <td>102.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-06 01:07:00</td>\n"," <td>98.1</td>\n"," <td>91.0</td>\n"," <td>18.0</td>\n"," <td>99.0</td>\n"," <td>98.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>742327</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 09:01:00</td>\n"," <td>98.4</td>\n"," <td>67.0</td>\n"," <td>18.0</td>\n"," <td>99.0</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>742328</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:05:00</td>\n"," <td>98.6</td>\n"," <td>72.0</td>\n"," <td>15.0</td>\n"," <td>100.0</td>\n"," <td>87.0</td>\n"," </tr>\n"," <tr>\n"," <th>742329</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:50:00</td>\n"," <td>NaN</td>\n"," <td>72.0</td>\n"," <td>16.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>742330</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 16:35:00</td>\n"," <td>99.6</td>\n"," <td>78.0</td>\n"," <td>17.0</td>\n"," <td>99.0</td>\n"," <td>108.0</td>\n"," </tr>\n"," <tr>\n"," <th>742331</th>\n"," <td>19999987</td>\n"," <td>23865745.0</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:28:00</td>\n"," <td>2145-11-02 22:59:00</td>\n"," <td>ADMITTED</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>742332 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 22595853.0 33258284 2180-05-06 19:17:00 \n","1 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","2 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","3 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","4 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","... ... ... ... ... \n","742327 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742328 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742329 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742330 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742331 19999987 23865745.0 34731548 2145-11-02 19:28:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-05-06 23:30:00 ADMITTED 2180-05-06 23:04:00 97.7 \n","1 2180-06-26 21:31:00 ADMITTED 2180-06-26 18:42:00 97.9 \n","2 2180-06-26 21:31:00 ADMITTED 2180-06-26 20:54:00 97.9 \n","3 2180-08-06 01:44:00 ADMITTED 2180-08-05 23:50:00 98.5 \n","4 2180-08-06 01:44:00 ADMITTED 2180-08-06 01:07:00 98.1 \n","... ... ... ... ... \n","742327 2147-07-18 17:34:00 ADMITTED 2147-07-18 09:01:00 98.4 \n","742328 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:05:00 98.6 \n","742329 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:50:00 NaN \n","742330 2147-07-18 17:34:00 ADMITTED 2147-07-18 16:35:00 99.6 \n","742331 2145-11-02 22:59:00 ADMITTED 2145-11-02 21:51:00 99.3 \n","\n"," heartrate resprate o2sat sbp \n","0 79.0 16.0 98.0 107.0 \n","1 76.0 18.0 95.0 95.0 \n","2 86.0 17.0 93.0 96.0 \n","3 96.0 17.0 100.0 102.0 \n","4 91.0 18.0 99.0 98.0 \n","... ... ... ... ... \n","742327 67.0 18.0 99.0 95.0 \n","742328 72.0 15.0 100.0 87.0 \n","742329 72.0 16.0 100.0 93.0 \n","742330 78.0 17.0 99.0 108.0 \n","742331 103.0 20.0 NaN 113.0 \n","\n","[742332 rows x 12 columns]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["def admission(df_eds,df):\n"," adm_eds= df_eds.loc[df_eds['disposition']== 'ADMITTED']\n"," df_adm = pd.merge(adm_eds,df,on = 'stay_id')\n"," df_adm = df_adm.drop(['race','arrival_transport','subject_id_y','gender'],axis = 1)\n"," df_adm = df_adm.rename(columns={'subject_id_x': 'subject_id'})\n"," return df_adm\n","df_adm = admission(df_eds,df)\n","df_adm"]},{"cell_type":"markdown","metadata":{"id":"DEZOm0B5Bh4n"},"source":["### EXPIRED"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oeYoFvHzBh4n","outputId":"f4fe5f91-94f8-42fd-ffab-f1ba792bbec4"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 19:51:00</td>\n"," <td>NaN</td>\n"," <td>113.0</td>\n"," <td>18.0</td>\n"," <td>43.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 20:51:00</td>\n"," <td>NaN</td>\n"," <td>105.0</td>\n"," <td>18.0</td>\n"," <td>60.0</td>\n"," <td>128.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-05 23:58:00</td>\n"," <td>NaN</td>\n"," <td>59.0</td>\n"," <td>15.0</td>\n"," <td>100.0</td>\n"," <td>121.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:09:00</td>\n"," <td>NaN</td>\n"," <td>51.0</td>\n"," <td>14.0</td>\n"," <td>100.0</td>\n"," <td>115.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:45:00</td>\n"," <td>NaN</td>\n"," <td>58.0</td>\n"," <td>14.0</td>\n"," <td>100.0</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>NaN</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.0</td>\n"," <td>110.0</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>NaN</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.0</td>\n"," <td>124.0</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>NaN</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.0</td>\n"," <td>99.0</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>NaN</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>132.0</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.6</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>NaN</td>\n"," <td>117.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>507 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime outtime \\\n","0 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","1 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","2 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","3 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","4 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n",".. ... ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","\n"," disposition charttime temperature heartrate resprate o2sat \\\n","0 EXPIRED 2142-06-24 19:51:00 NaN 113.0 18.0 43.0 \n","1 EXPIRED 2142-06-24 20:51:00 NaN 105.0 18.0 60.0 \n","2 EXPIRED 2127-02-05 23:58:00 NaN 59.0 15.0 100.0 \n","3 EXPIRED 2127-02-06 00:09:00 NaN 51.0 14.0 100.0 \n","4 EXPIRED 2127-02-06 00:45:00 NaN 58.0 14.0 100.0 \n",".. ... ... ... ... ... ... \n","502 EXPIRED 2120-01-06 21:51:00 NaN 142.0 24.0 85.0 \n","503 EXPIRED 2120-01-06 21:53:00 NaN 131.0 18.0 89.0 \n","504 EXPIRED 2120-01-06 21:57:00 NaN 126.0 24.0 88.0 \n","505 EXPIRED 2148-12-08 17:26:00 NaN 95.0 22.0 98.0 \n","506 EXPIRED 2148-12-08 18:11:00 97.6 93.0 17.0 NaN \n","\n"," sbp \n","0 93.0 \n","1 128.0 \n","2 121.0 \n","3 115.0 \n","4 123.0 \n",".. ... \n","502 110.0 \n","503 124.0 \n","504 99.0 \n","505 132.0 \n","506 117.0 \n","\n","[507 rows x 12 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["def expired(df_eds,df):\n"," exp_eds= df_eds.loc[df_eds['disposition']== 'EXPIRED']\n"," df_exp = pd.merge(exp_eds,df,on = 'stay_id')\n"," df_exp= df_exp.drop(['race','arrival_transport','subject_id_y','gender'],axis = 1)\n"," df_exp = df_exp.rename(columns={'subject_id_x': 'subject_id'})\n"," return df_exp\n","df_exp = expired(df_eds,df)\n","df_exp"]},{"cell_type":"markdown","metadata":{"id":"CqmgvmcoBh4o"},"source":["### correlation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E3STkQasBh4o"},"outputs":[],"source":["df2 = df.drop(['subject_id','hadm_id','stay_id','intime','outtime','charttime'],axis =1)\n","df2\n","pearsoncorr=df2.corr(method = 'pearson')\n","pearsoncorr\n","import seaborn as sns\n","sns.heatmap(pearsoncorr, \n"," xticklabels=pearsoncorr.columns,\n"," yticklabels=pearsoncorr.columns,\n"," cmap='RdBu_r',\n"," annot=True,\n"," linewidth=0.5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Em7zijY9Bh4p"},"outputs":[],"source":["import seaborn as sns"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bwowbR_ABh4p"},"outputs":[],"source":["def pearson(x):\n"," df_e = x.drop(['subject_id','hadm_id','stay_id','intime','outtime','disposition','charttime'],axis =1)\n"," pearsoncorr=df_e.corr(method = 'pearson')\n"," \n"," \n"," plot = sns.heatmap(pearsoncorr, \n"," xticklabels=pearsoncorr.columns,\n"," yticklabels=pearsoncorr.columns,\n"," cmap='RdBu_r',\n"," annot=True,\n"," linewidth=0.5)\n"," return plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TESbiMFyBh4q","outputId":"dc2aa358-f638-410c-de19-f9832375be79"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":27,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_home)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DgDR1hbMBh4q","outputId":"06c8c29f-845f-4751-c713-9fda8ecf7d3e"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":28,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_exp)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1tp0XjSFBh4r","outputId":"192fab6e-e575-4653-d439-c5ab404de9c1"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":29,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_adm)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0D-Kmg76Bh4r"},"outputs":[],"source":["df_e = df.drop(['subject_id','hadm_id','stay_id','intime','outtime','disposition','charttime'],axis =1)\n","df_e"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YJBfyestBh4s"},"outputs":[],"source":["pearsoncorr=df2.corr(method = 'pearson')\n","pearsoncorr"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5q0-gJ5LBh4s"},"outputs":[],"source":["import seaborn as sns\n","sns.heatmap(pearsoncorr, \n"," xticklabels=pearsoncorr.columns,\n"," yticklabels=pearsoncorr.columns,\n"," cmap='RdBu_r',\n"," annot=True,\n"," linewidth=0.5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l89PIu05Bh4s"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZM6HKls2Bh4s"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"yGmvGYBKBh4s"},"source":["# filling null values of df_adm, df_exp, df_home with their corresponding mean values"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"drEzqTz0Bh4t"},"outputs":[],"source":["# defining a function for filling these null values\n","# these ranges of temperature are desired\n","def filling(x):\n"," \n","\n","\n"," x[['temperature','heartrate','resprate','o2sat','sbp']] = x[['temperature','heartrate','resprate','o2sat','sbp']].fillna(x.mean())\n"," \n"," return x\n"," \n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T8r2Wyn2Bh4t","outputId":"78e144d5-db0d-4acf-f109-1d18147f2455"},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_3672/1494638634.py:7: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n"," x[['temperature','heartrate','resprate','o2sat','sbp']] = x[['temperature','heartrate','resprate','o2sat','sbp']].fillna(x.mean())\n"]}],"source":["df_adm = filling(df_adm)\n","df_home = filling(df_home)\n","df_exp = filling(df_exp)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q9mMinQRBh4t"},"outputs":[],"source":["df_adm .to_csv('admissionED.csv',index=False)\n","df_home .to_csv('homeED.csv',index=False)\n","df_exp.to_csv('expiredED.csv',index=False)\n"]},{"cell_type":"markdown","metadata":{"id":"7ozd70a2Bh4t"},"source":["## df_home"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i35SmDxGBh4t","outputId":"63a505a7-0c20-432a-8fe6-748e3d0514f6"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>98.119213</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>98.119213</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>98.119213</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.0</td>\n"," <td>75.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>98.119213</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.0</td>\n"," <td>86.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.400000</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.0</td>\n"," <td>65.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>703465</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-08 23:38:00</td>\n"," <td>98.119213</td>\n"," <td>50.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>102.0</td>\n"," </tr>\n"," <tr>\n"," <th>703466</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-09 02:51:00</td>\n"," <td>98.100000</td>\n"," <td>54.0</td>\n"," <td>16.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>703467</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>97.700000</td>\n"," <td>89.0</td>\n"," <td>22.0</td>\n"," <td>100.0</td>\n"," <td>176.0</td>\n"," </tr>\n"," <tr>\n"," <th>703468</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 16:19:00</td>\n"," <td>98.600000</td>\n"," <td>82.0</td>\n"," <td>18.0</td>\n"," <td>97.0</td>\n"," <td>148.0</td>\n"," </tr>\n"," <tr>\n"," <th>703469</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 18:37:00</td>\n"," <td>97.000000</td>\n"," <td>80.0</td>\n"," <td>18.0</td>\n"," <td>100.0</td>\n"," <td>156.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>703470 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","... ... ... ... ... \n","703465 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703466 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703467 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703468 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703469 19999750 NaN 38224473 2144-03-22 14:27:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 98.119213 \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 98.119213 \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 98.119213 \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 98.119213 \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.400000 \n","... ... ... ... ... \n","703465 2152-07-09 03:45:00 HOME 2152-07-08 23:38:00 98.119213 \n","703466 2152-07-09 03:45:00 HOME 2152-07-09 02:51:00 98.100000 \n","703467 2144-03-22 18:47:00 HOME 2144-03-22 14:27:00 97.700000 \n","703468 2144-03-22 18:47:00 HOME 2144-03-22 16:19:00 98.600000 \n","703469 2144-03-22 18:47:00 HOME 2144-03-22 18:37:00 97.000000 \n","\n"," heartrate resprate o2sat sbp \n","0 83.0 24.0 97.0 90.0 \n","1 85.0 22.0 98.0 76.0 \n","2 84.0 22.0 97.0 75.0 \n","3 84.0 20.0 99.0 86.0 \n","4 86.0 20.0 98.0 65.0 \n","... ... ... ... ... \n","703465 50.0 16.0 98.0 102.0 \n","703466 54.0 16.0 100.0 93.0 \n","703467 89.0 22.0 100.0 176.0 \n","703468 82.0 18.0 97.0 148.0 \n","703469 80.0 18.0 100.0 156.0 \n","\n","[703470 rows x 12 columns]"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["df_home = pd.read_csv('/Users/shayan/Desktop/HiWis/PLRI-HIWI/files_shayan/homeED.csv')\n","df_home"]},{"cell_type":"markdown","metadata":{"id":"V8gvk9vgBh4u"},"source":["## df_adm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y7FxrTBABh4u","outputId":"205985f4-405a-4d72-eb01-6c9f9954fa14"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>22595853.0</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 19:17:00</td>\n"," <td>2180-05-06 23:30:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.70000</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.000000</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.90000</td>\n"," 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<td>98.10000</td>\n"," <td>91.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>98.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>742327</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 09:01:00</td>\n"," <td>98.40000</td>\n"," <td>67.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>742328</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:05:00</td>\n"," <td>98.60000</td>\n"," <td>72.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>87.0</td>\n"," </tr>\n"," <tr>\n"," <th>742329</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:50:00</td>\n"," <td>97.82373</td>\n"," <td>72.0</td>\n"," <td>16.0</td>\n"," <td>100.000000</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>742330</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 16:35:00</td>\n"," <td>99.60000</td>\n"," <td>78.0</td>\n"," <td>17.0</td>\n"," <td>99.000000</td>\n"," <td>108.0</td>\n"," </tr>\n"," <tr>\n"," <th>742331</th>\n"," <td>19999987</td>\n"," <td>23865745.0</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:28:00</td>\n"," <td>2145-11-02 22:59:00</td>\n"," <td>ADMITTED</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.30000</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>97.571799</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>742332 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 22595853.0 33258284 2180-05-06 19:17:00 \n","1 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","2 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","3 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","4 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","... ... ... ... ... \n","742327 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742328 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742329 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742330 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742331 19999987 23865745.0 34731548 2145-11-02 19:28:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-05-06 23:30:00 ADMITTED 2180-05-06 23:04:00 97.70000 \n","1 2180-06-26 21:31:00 ADMITTED 2180-06-26 18:42:00 97.90000 \n","2 2180-06-26 21:31:00 ADMITTED 2180-06-26 20:54:00 97.90000 \n","3 2180-08-06 01:44:00 ADMITTED 2180-08-05 23:50:00 98.50000 \n","4 2180-08-06 01:44:00 ADMITTED 2180-08-06 01:07:00 98.10000 \n","... ... ... ... ... \n","742327 2147-07-18 17:34:00 ADMITTED 2147-07-18 09:01:00 98.40000 \n","742328 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:05:00 98.60000 \n","742329 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:50:00 97.82373 \n","742330 2147-07-18 17:34:00 ADMITTED 2147-07-18 16:35:00 99.60000 \n","742331 2145-11-02 22:59:00 ADMITTED 2145-11-02 21:51:00 99.30000 \n","\n"," heartrate resprate o2sat sbp \n","0 79.0 16.0 98.000000 107.0 \n","1 76.0 18.0 95.000000 95.0 \n","2 86.0 17.0 93.000000 96.0 \n","3 96.0 17.0 100.000000 102.0 \n","4 91.0 18.0 99.000000 98.0 \n","... ... ... ... ... \n","742327 67.0 18.0 99.000000 95.0 \n","742328 72.0 15.0 100.000000 87.0 \n","742329 72.0 16.0 100.000000 93.0 \n","742330 78.0 17.0 99.000000 108.0 \n","742331 103.0 20.0 97.571799 113.0 \n","\n","[742332 rows x 12 columns]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["df_adm = pd.read_csv('/Users/shayan/Desktop/HiWis/PLRI-HIWI/files_shayan/admissionED.csv')\n","df_adm"]},{"cell_type":"markdown","metadata":{"id":"z8OZyedLBh4u"},"source":["## df_exp"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r2XeKcDEBh4u","outputId":"90eecb4f-8802-4bbb-8e4d-02de778063fa"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 19:51:00</td>\n"," <td>90.027513</td>\n"," <td>113.0</td>\n"," <td>18.0</td>\n"," <td>43.000000</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 20:51:00</td>\n"," <td>90.027513</td>\n"," <td>105.0</td>\n"," <td>18.0</td>\n"," <td>60.000000</td>\n"," <td>128.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-05 23:58:00</td>\n"," <td>90.027513</td>\n"," <td>59.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>121.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:09:00</td>\n"," <td>90.027513</td>\n"," <td>51.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>115.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:45:00</td>\n"," <td>90.027513</td>\n"," <td>58.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.027513</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.027513</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.027513</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.027513</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.600000</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>507 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime outtime \\\n","0 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","1 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","2 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","3 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","4 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n",".. ... ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","\n"," disposition charttime temperature heartrate resprate \\\n","0 EXPIRED 2142-06-24 19:51:00 90.027513 113.0 18.0 \n","1 EXPIRED 2142-06-24 20:51:00 90.027513 105.0 18.0 \n","2 EXPIRED 2127-02-05 23:58:00 90.027513 59.0 15.0 \n","3 EXPIRED 2127-02-06 00:09:00 90.027513 51.0 14.0 \n","4 EXPIRED 2127-02-06 00:45:00 90.027513 58.0 14.0 \n",".. ... ... ... ... ... \n","502 EXPIRED 2120-01-06 21:51:00 90.027513 142.0 24.0 \n","503 EXPIRED 2120-01-06 21:53:00 90.027513 131.0 18.0 \n","504 EXPIRED 2120-01-06 21:57:00 90.027513 126.0 24.0 \n","505 EXPIRED 2148-12-08 17:26:00 90.027513 95.0 22.0 \n","506 EXPIRED 2148-12-08 18:11:00 97.600000 93.0 17.0 \n","\n"," o2sat sbp \n","0 43.000000 93.0 \n","1 60.000000 128.0 \n","2 100.000000 121.0 \n","3 100.000000 115.0 \n","4 100.000000 123.0 \n",".. ... ... \n","502 85.000000 110.0 \n","503 89.000000 124.0 \n","504 88.000000 99.0 \n","505 98.000000 132.0 \n","506 96.334677 117.0 \n","\n","[507 rows x 12 columns]"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["df_exp = pd.read_csv('/Users/shayan/Desktop/HiWis/PLRI-HIWI/files_shayan/expiredED.csv')\n","df_exp"]},{"cell_type":"markdown","metadata":{"id":"Xn7vb9AIBh4v"},"source":["## keeping desired range of vital signs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ISqsn7SwBh4v"},"outputs":[],"source":["def desired(x):\n","\n"," x = x.loc[(x['temperature']>=86) & (x['temperature']<=113) & (x['sbp']>=30) & (x['sbp']<=300) &\n"," (x['heartrate']>=10) & (x['heartrate']<=300) & (x['resprate']>=3) & (x['resprate']<=60)& (x['o2sat']>=0) & (x['o2sat']<=100)]\n"," \n"," \n"," return x\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2FvHXN2LBh4v","outputId":"c6063daf-bd52-47ca-d869-4cb219cc170c"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," 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<td>176.0</td>\n"," </tr>\n"," <tr>\n"," <th>703468</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 16:19:00</td>\n"," <td>98.600000</td>\n"," <td>82.0</td>\n"," <td>18.0</td>\n"," <td>97.0</td>\n"," <td>148.0</td>\n"," </tr>\n"," <tr>\n"," <th>703469</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 18:37:00</td>\n"," <td>97.000000</td>\n"," <td>80.0</td>\n"," <td>18.0</td>\n"," <td>100.0</td>\n"," <td>156.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>701405 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","... ... ... ... ... \n","703465 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703466 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703467 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703468 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703469 19999750 NaN 38224473 2144-03-22 14:27:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 98.119213 \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 98.119213 \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 98.119213 \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 98.119213 \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.400000 \n","... ... ... ... ... \n","703465 2152-07-09 03:45:00 HOME 2152-07-08 23:38:00 98.119213 \n","703466 2152-07-09 03:45:00 HOME 2152-07-09 02:51:00 98.100000 \n","703467 2144-03-22 18:47:00 HOME 2144-03-22 14:27:00 97.700000 \n","703468 2144-03-22 18:47:00 HOME 2144-03-22 16:19:00 98.600000 \n","703469 2144-03-22 18:47:00 HOME 2144-03-22 18:37:00 97.000000 \n","\n"," heartrate resprate o2sat sbp \n","0 83.0 24.0 97.0 90.0 \n","1 85.0 22.0 98.0 76.0 \n","2 84.0 22.0 97.0 75.0 \n","3 84.0 20.0 99.0 86.0 \n","4 86.0 20.0 98.0 65.0 \n","... ... ... ... ... \n","703465 50.0 16.0 98.0 102.0 \n","703466 54.0 16.0 100.0 93.0 \n","703467 89.0 22.0 100.0 176.0 \n","703468 82.0 18.0 97.0 148.0 \n","703469 80.0 18.0 100.0 156.0 \n","\n","[701405 rows x 12 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["df_home = desired(df_home)\n","df_home"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XSMfSHkcBh4v","outputId":"e1828c82-1bcf-43c4-8fd2-589512befdbb"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>22595853.0</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 19:17:00</td>\n"," <td>2180-05-06 23:30:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.70000</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.000000</td>\n"," <td>107.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.90000</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.000000</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.90000</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.000000</td>\n"," <td>96.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-05 23:50:00</td>\n"," <td>98.50000</td>\n"," <td>96.0</td>\n"," <td>17.0</td>\n"," <td>100.000000</td>\n"," <td>102.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-06 01:07:00</td>\n"," <td>98.10000</td>\n"," <td>91.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>98.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>742327</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 09:01:00</td>\n"," <td>98.40000</td>\n"," <td>67.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>95.0</td>\n"," </tr>\n"," <tr>\n"," <th>742328</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:05:00</td>\n"," <td>98.60000</td>\n"," <td>72.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>87.0</td>\n"," </tr>\n"," <tr>\n"," <th>742329</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:50:00</td>\n"," <td>97.82373</td>\n"," <td>72.0</td>\n"," <td>16.0</td>\n"," <td>100.000000</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>742330</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 16:35:00</td>\n"," <td>99.60000</td>\n"," <td>78.0</td>\n"," <td>17.0</td>\n"," <td>99.000000</td>\n"," <td>108.0</td>\n"," </tr>\n"," <tr>\n"," <th>742331</th>\n"," <td>19999987</td>\n"," <td>23865745.0</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:28:00</td>\n"," <td>2145-11-02 22:59:00</td>\n"," <td>ADMITTED</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.30000</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>97.571799</td>\n"," <td>113.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>737074 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 22595853.0 33258284 2180-05-06 19:17:00 \n","1 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","2 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","3 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","4 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","... ... ... ... ... \n","742327 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742328 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742329 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742330 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742331 19999987 23865745.0 34731548 2145-11-02 19:28:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-05-06 23:30:00 ADMITTED 2180-05-06 23:04:00 97.70000 \n","1 2180-06-26 21:31:00 ADMITTED 2180-06-26 18:42:00 97.90000 \n","2 2180-06-26 21:31:00 ADMITTED 2180-06-26 20:54:00 97.90000 \n","3 2180-08-06 01:44:00 ADMITTED 2180-08-05 23:50:00 98.50000 \n","4 2180-08-06 01:44:00 ADMITTED 2180-08-06 01:07:00 98.10000 \n","... ... ... ... ... \n","742327 2147-07-18 17:34:00 ADMITTED 2147-07-18 09:01:00 98.40000 \n","742328 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:05:00 98.60000 \n","742329 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:50:00 97.82373 \n","742330 2147-07-18 17:34:00 ADMITTED 2147-07-18 16:35:00 99.60000 \n","742331 2145-11-02 22:59:00 ADMITTED 2145-11-02 21:51:00 99.30000 \n","\n"," heartrate resprate o2sat sbp \n","0 79.0 16.0 98.000000 107.0 \n","1 76.0 18.0 95.000000 95.0 \n","2 86.0 17.0 93.000000 96.0 \n","3 96.0 17.0 100.000000 102.0 \n","4 91.0 18.0 99.000000 98.0 \n","... ... ... ... ... \n","742327 67.0 18.0 99.000000 95.0 \n","742328 72.0 15.0 100.000000 87.0 \n","742329 72.0 16.0 100.000000 93.0 \n","742330 78.0 17.0 99.000000 108.0 \n","742331 103.0 20.0 97.571799 113.0 \n","\n","[737074 rows x 12 columns]"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["df_adm = desired(df_adm)\n","df_adm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZiHsyKgaBh4v","outputId":"9e20f3a8-93d1-4cd9-eb4e-24b50641b420"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 19:51:00</td>\n"," <td>90.027513</td>\n"," <td>113.0</td>\n"," <td>18.0</td>\n"," <td>43.000000</td>\n"," <td>93.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 20:51:00</td>\n"," <td>90.027513</td>\n"," <td>105.0</td>\n"," <td>18.0</td>\n"," <td>60.000000</td>\n"," <td>128.0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-05 23:58:00</td>\n"," <td>90.027513</td>\n"," <td>59.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>121.0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:09:00</td>\n"," <td>90.027513</td>\n"," <td>51.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>115.0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:45:00</td>\n"," <td>90.027513</td>\n"," <td>58.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>123.0</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.027513</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.027513</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.027513</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.027513</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.600000</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>483 rows × 12 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime outtime \\\n","0 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","1 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","2 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","3 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","4 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n",".. ... ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","\n"," disposition charttime temperature heartrate resprate \\\n","0 EXPIRED 2142-06-24 19:51:00 90.027513 113.0 18.0 \n","1 EXPIRED 2142-06-24 20:51:00 90.027513 105.0 18.0 \n","2 EXPIRED 2127-02-05 23:58:00 90.027513 59.0 15.0 \n","3 EXPIRED 2127-02-06 00:09:00 90.027513 51.0 14.0 \n","4 EXPIRED 2127-02-06 00:45:00 90.027513 58.0 14.0 \n",".. ... ... ... ... ... \n","502 EXPIRED 2120-01-06 21:51:00 90.027513 142.0 24.0 \n","503 EXPIRED 2120-01-06 21:53:00 90.027513 131.0 18.0 \n","504 EXPIRED 2120-01-06 21:57:00 90.027513 126.0 24.0 \n","505 EXPIRED 2148-12-08 17:26:00 90.027513 95.0 22.0 \n","506 EXPIRED 2148-12-08 18:11:00 97.600000 93.0 17.0 \n","\n"," o2sat sbp \n","0 43.000000 93.0 \n","1 60.000000 128.0 \n","2 100.000000 121.0 \n","3 100.000000 115.0 \n","4 100.000000 123.0 \n",".. ... ... \n","502 85.000000 110.0 \n","503 89.000000 124.0 \n","504 88.000000 99.0 \n","505 98.000000 132.0 \n","506 96.334677 117.0 \n","\n","[483 rows x 12 columns]"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["df_exp = desired(df_exp)\n","df_exp"]},{"cell_type":"markdown","metadata":{"id":"vG-TxcIMBh4w"},"source":["# computing EWS"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mjL-ijkGBh4w"},"outputs":[],"source":["# round the temperature column to one decimal place\n","\n","def EWS(df):\n"," \n"," df['temperature']= df['temperature'].round(decimals = 1)\n"," #df['resprate']= df['resprate'].round(decimals = 1)\n","\n"," EWS_Scores = []\n"," for row in df.itertuples():\n"," row_ews = []\n","\n","\n"," if row.temperature >= 96.9 and row.temperature <= 100.4:\n"," row_ews.append(0)\n"," elif (row.temperature >= 95.1 and row.temperature <= 96.8) or (row.temperature >= 100.5 and row.temperature <= 102.3):\n"," row_ews.append(1)\n"," elif row.temperature >= 102.4:\n"," row_ews.append(2)\n"," elif row.temperature <= 95:\n"," row_ews.append(3)\n","\n","\n"," if row.heartrate >= 51.0 and row.heartrate <= 90.0:\n"," row_ews.append(0)\n"," elif (row.heartrate >= 41.0 and row.heartrate <= 50.0) or (row.heartrate >= 91.0 and row.heartrate <= 110.0):\n"," row_ews.append(1)\n"," elif row.heartrate >= 111.0 and row.heartrate <= 130.0:\n"," row_ews.append(2)\n"," elif row.heartrate <= 40.0 or row.heartrate >= 131.0:\n"," row_ews.append(3)\n","\n","\n"," \n"," if row.resprate >= 12.0 and row.resprate <= 20.0:\n"," row_ews.append(0)\n"," elif row.resprate >= 9.0 and row.resprate <= 11.0:\n"," row_ews.append(1)\n"," elif row.resprate >= 21.0 and row.resprate <= 24.0:\n"," row_ews.append(2)\n"," elif row.resprate <= 8.0 or row.resprate >= 25.0:\n"," row_ews.append(3)\n","\n"," if row.o2sat >= 96.0:\n"," row_ews.append(0)\n"," elif row.o2sat >= 94.0 and row.o2sat <= 95.0:\n"," row_ews.append(1)\n"," elif row.o2sat >= 92.0 and row.o2sat <= 93.0:\n"," row_ews.append(2)\n"," elif row.o2sat <= 91.0:\n"," row_ews.append(3)\n"," \n"," if row.sbp >= 111.0 and row.sbp <= 219.0:\n"," row_ews.append(0)\n"," elif row.sbp >= 101.0 and row.sbp <= 110.0:\n"," row_ews.append(1)\n"," elif row.sbp >= 91.0 and row.sbp<= 100.0:\n"," row_ews.append(2)\n"," elif row.sbp <= 90.0 or row.sbp >= 220.0:\n"," row_ews.append(3)\n"," \n"," \n"," \n"," \n","\n"," \n"," if len(row_ews) == 5:\n"," EWS_Scores.append(row_ews)\n"," \n"," \n"," else:\n"," print('error:', len(row_ews))\n"," print(row)\n"," print(row_ews)\n"," \n"," #print(EWS_Scores)\n","\n"," ews_list = []\n"," for scores in EWS_Scores:\n"," ews = sum(scores)\n"," ews_list.append(ews)\n","\n"," df['EWS'] = ews_list\n"," df.head(10)\n"," return df\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OADtiY_3Bh4w","outputId":"44f09e6f-09e3-4887-8aa7-d40e6d12da85"},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:5: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['temperature']= df['temperature'].round(decimals = 1)\n","/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:82: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['EWS'] = ews_list\n","/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:5: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['temperature']= df['temperature'].round(decimals = 1)\n","/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:82: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['EWS'] = ews_list\n","/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:5: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['temperature']= df['temperature'].round(decimals = 1)\n","/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/52834638.py:82: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df['EWS'] = ews_list\n"]},{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 19:51:00</td>\n"," <td>90.0</td>\n"," <td>113.0</td>\n"," <td>18.0</td>\n"," <td>43.000000</td>\n"," <td>93.0</td>\n"," <td>10</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 20:51:00</td>\n"," <td>90.0</td>\n"," <td>105.0</td>\n"," <td>18.0</td>\n"," <td>60.000000</td>\n"," <td>128.0</td>\n"," <td>7</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-05 23:58:00</td>\n"," <td>90.0</td>\n"," <td>59.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>121.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:09:00</td>\n"," <td>90.0</td>\n"," <td>51.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>115.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:45:00</td>\n"," <td>90.0</td>\n"," <td>58.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>123.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.0</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.0</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," <td>9</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.0</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.0</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," <td>6</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.6</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>483 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime outtime \\\n","0 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","1 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","2 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","3 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","4 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n",".. ... ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","\n"," disposition charttime temperature heartrate resprate \\\n","0 EXPIRED 2142-06-24 19:51:00 90.0 113.0 18.0 \n","1 EXPIRED 2142-06-24 20:51:00 90.0 105.0 18.0 \n","2 EXPIRED 2127-02-05 23:58:00 90.0 59.0 15.0 \n","3 EXPIRED 2127-02-06 00:09:00 90.0 51.0 14.0 \n","4 EXPIRED 2127-02-06 00:45:00 90.0 58.0 14.0 \n",".. ... ... ... ... ... \n","502 EXPIRED 2120-01-06 21:51:00 90.0 142.0 24.0 \n","503 EXPIRED 2120-01-06 21:53:00 90.0 131.0 18.0 \n","504 EXPIRED 2120-01-06 21:57:00 90.0 126.0 24.0 \n","505 EXPIRED 2148-12-08 17:26:00 90.0 95.0 22.0 \n","506 EXPIRED 2148-12-08 18:11:00 97.6 93.0 17.0 \n","\n"," o2sat sbp EWS \n","0 43.000000 93.0 10 \n","1 60.000000 128.0 7 \n","2 100.000000 121.0 3 \n","3 100.000000 115.0 3 \n","4 100.000000 123.0 3 \n",".. ... ... ... \n","502 85.000000 110.0 12 \n","503 89.000000 124.0 9 \n","504 88.000000 99.0 12 \n","505 98.000000 132.0 6 \n","506 96.334677 117.0 1 \n","\n","[483 rows x 13 columns]"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["EWS(df_home)\n","EWS(df_adm)\n","EWS(df_exp)"]},{"cell_type":"markdown","metadata":{"id":"6aeS2vzmBh4w"},"source":["# combining df_home, df_adm, df_exp"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LxDwlzz9Bh4w","outputId":"a4cb9731-7322-4160-df38-443812b72e0c"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>98.1</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.000000</td>\n"," <td>90.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>98.1</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>76.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.000000</td>\n"," <td>75.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.000000</td>\n"," <td>86.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.4</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.000000</td>\n"," <td>65.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.0</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.0</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," <td>9</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.0</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.0</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," <td>6</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.6</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1438962 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n",".. ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 98.1 \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 98.1 \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 98.1 \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 98.1 \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.4 \n",".. ... ... ... ... \n","502 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:51:00 90.0 \n","503 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:53:00 90.0 \n","504 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:57:00 90.0 \n","505 2148-12-08 22:11:00 EXPIRED 2148-12-08 17:26:00 90.0 \n","506 2148-12-08 22:11:00 EXPIRED 2148-12-08 18:11:00 97.6 \n","\n"," heartrate resprate o2sat sbp EWS \n","0 83.0 24.0 97.000000 90.0 5 \n","1 85.0 22.0 98.000000 76.0 5 \n","2 84.0 22.0 97.000000 75.0 5 \n","3 84.0 20.0 99.000000 86.0 3 \n","4 86.0 20.0 98.000000 65.0 3 \n",".. ... ... ... ... ... \n","502 142.0 24.0 85.000000 110.0 12 \n","503 131.0 18.0 89.000000 124.0 9 \n","504 126.0 24.0 88.000000 99.0 12 \n","505 95.0 22.0 98.000000 132.0 6 \n","506 93.0 17.0 96.334677 117.0 1 \n","\n","[1438962 rows x 13 columns]"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.concat([df_home, df_adm,df_exp])\n","df"]},{"cell_type":"markdown","metadata":{"id":"Te_eCl9YBh4x"},"source":["### saving df as a csv file"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"awbNexaqBh4x"},"outputs":[],"source":["df.to_csv('/Users/shayan/Desktop/HiWis/PLRI-HIWI/files_shayan/vitalsign_edstays.csv',index = False)"]},{"cell_type":"markdown","metadata":{"id":"xWMu4YVoBh4x"},"source":["# some statistics on dataframes for EWS"]},{"cell_type":"markdown","metadata":{"id":"JKU0CKlfBh4x"},"source":["## Correlation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IKmkbwS6Bh4x"},"outputs":[],"source":["def pearson(x):\n"," df_e = x.drop(['subject_id','hadm_id','stay_id','intime','outtime','disposition','charttime'],axis =1)\n"," pearsoncorr=df_e.corr(method = 'pearson')\n"," \n"," \n"," plot = sns.heatmap(pearsoncorr, \n"," xticklabels=pearsoncorr.columns,\n"," yticklabels=pearsoncorr.columns,\n"," cmap='RdBu_r',\n"," annot=True,\n"," linewidth=0.5)\n"," return plot"]},{"cell_type":"markdown","metadata":{"id":"x7MI5DyRBh4x"},"source":["## df_home"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GjcYtSL1Bh4y","outputId":"23f67cd7-799b-4b77-d4e7-2843addcd605"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>98.1</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.0</td>\n"," <td>90.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>98.1</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.0</td>\n"," <td>76.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.0</td>\n"," <td>75.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.0</td>\n"," <td>86.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.4</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.0</td>\n"," <td>65.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>703465</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-08 23:38:00</td>\n"," <td>98.1</td>\n"," <td>50.0</td>\n"," <td>16.0</td>\n"," <td>98.0</td>\n"," <td>102.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>703466</th>\n"," <td>19999733</td>\n"," <td>27674281.0</td>\n"," <td>30940569</td>\n"," <td>2152-07-08 20:15:00</td>\n"," <td>2152-07-09 03:45:00</td>\n"," <td>HOME</td>\n"," <td>2152-07-09 02:51:00</td>\n"," <td>98.1</td>\n"," <td>54.0</td>\n"," <td>16.0</td>\n"," <td>100.0</td>\n"," <td>93.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>703467</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>97.7</td>\n"," <td>89.0</td>\n"," <td>22.0</td>\n"," <td>100.0</td>\n"," <td>176.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>703468</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 16:19:00</td>\n"," <td>98.6</td>\n"," <td>82.0</td>\n"," <td>18.0</td>\n"," <td>97.0</td>\n"," <td>148.0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>703469</th>\n"," <td>19999750</td>\n"," <td>NaN</td>\n"," <td>38224473</td>\n"," <td>2144-03-22 14:27:00</td>\n"," <td>2144-03-22 18:47:00</td>\n"," <td>HOME</td>\n"," <td>2144-03-22 18:37:00</td>\n"," <td>97.0</td>\n"," <td>80.0</td>\n"," <td>18.0</td>\n"," <td>100.0</td>\n"," <td>156.0</td>\n"," <td>0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>701405 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","... ... ... ... ... \n","703465 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703466 19999733 27674281.0 30940569 2152-07-08 20:15:00 \n","703467 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703468 19999750 NaN 38224473 2144-03-22 14:27:00 \n","703469 19999750 NaN 38224473 2144-03-22 14:27:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 98.1 \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 98.1 \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 98.1 \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 98.1 \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.4 \n","... ... ... ... ... \n","703465 2152-07-09 03:45:00 HOME 2152-07-08 23:38:00 98.1 \n","703466 2152-07-09 03:45:00 HOME 2152-07-09 02:51:00 98.1 \n","703467 2144-03-22 18:47:00 HOME 2144-03-22 14:27:00 97.7 \n","703468 2144-03-22 18:47:00 HOME 2144-03-22 16:19:00 98.6 \n","703469 2144-03-22 18:47:00 HOME 2144-03-22 18:37:00 97.0 \n","\n"," heartrate resprate o2sat sbp EWS \n","0 83.0 24.0 97.0 90.0 5 \n","1 85.0 22.0 98.0 76.0 5 \n","2 84.0 22.0 97.0 75.0 5 \n","3 84.0 20.0 99.0 86.0 3 \n","4 86.0 20.0 98.0 65.0 3 \n","... ... ... ... ... ... \n","703465 50.0 16.0 98.0 102.0 2 \n","703466 54.0 16.0 100.0 93.0 2 \n","703467 89.0 22.0 100.0 176.0 2 \n","703468 82.0 18.0 97.0 148.0 0 \n","703469 80.0 18.0 100.0 156.0 0 \n","\n","[701405 rows x 13 columns]"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["df_home"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mRUoFXNaBh4y"},"outputs":[],"source":["# average, median and mode for EWS-Score in df_home\n","def stat(df):\n"," a =print(\"\\n----------- Mean of EWS -----------\\n\")\n"," b =print(df['EWS'].mean())\n","\n"," c = print(\"\\n----------- Median of EWS -----------\\n\")\n"," d = print(df['EWS'].median())\n","\n"," e = print(\"\\n----------- Mode of EWS -----------\\n\")\n"," f = print(df['EWS'].mode())\n","\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8SkFem5uBh4y","outputId":"49d09084-28eb-4ac9-b861-f94de2675409"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","----------- Mean of EWS -----------\n","\n","0.6563169638083561\n","\n","----------- Median of EWS -----------\n","\n","0.0\n","\n","----------- Mode of EWS -----------\n","\n","0 0\n","Name: EWS, dtype: int64\n"]}],"source":["stat(df_home)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bNl-L5bqBh4y","outputId":"624728ae-52fb-48b6-f746-65e703fc01e3"},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'number of records')"]},"execution_count":32,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.hist(df_home['EWS'])\n","plt.xlabel('EWS')\n","plt.title('EWS for cases transfering to Home')\n","plt.ylabel('number of records')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"A7tLw7XSBh4z","outputId":"4a381e7b-0c18-4ee0-b4b4-909998e4d863"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":35,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlgAAAHsCAYAAAAO1dMiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAACtmklEQVR4nOzdd3RUxdvA8e+mbXqvpEMgEEoooYXegmBDVEBfRQRURFBAFBFQiooNBSzYQPAHAkoRFaQJkV4SikBCS0ghpJDeN23fPyIblmQpy4YFfT7n3HPY2ZnZmUt28+wzc28UarVajRBCCCGEMBgTYw9ACCGEEOLfRgIsIYQQQggDkwBLCCGEEMLAJMASQgghhDAwCbCEEEIIIQxMAiwhhBBCCAOTAEsIIYQQwsAkwBJCCCGEMDAJsIQQQgghDEwCLCGEEEIIA5MASwghhBD3lF27dvHggw/SoEEDFAoFv/zyyw3b/PXXX7Rr1w5LS0saNmzIV199Va9jlABLCCGEEPeUoqIiQkND+fzzz2+q/oULFxg4cCDdunXj6NGjvPnmm7z88susXbu23saokD/2LIQQQoh7lUKhYP369QwaNEhnnSlTpvDrr78SGxurKRszZgzHjx9n//799TIuyWAJIYQQwqhUKhX5+flah0qlMlj/+/fvJyIiQqusf//+REVFUV5ebrDXuZpZvfQqhBBCiH+9MYoAg/Tj+fYIZs2apVX29ttvM3PmTIP0n5aWhoeHh1aZh4cHFRUVZGZm4uXlZZDXuZoEWPcYQ/0w38u+Uiew6niKsYdhdMNCvRm16qixh3FXWDysDX0/32PsYRjd9nFd6frBTmMPw+j2TOnF9D9ib1zxX+6dAc2MPYSbNnXqVCZNmqRVplQqDfoaCoVC6/GVHVLXlhuKBFhCCCGE0IupgWITpVJp8IDqap6enqSlpWmVZWRkYGZmhouLS728pgRYQgghhNCLaT1lfwytc+fO/Pbbb1plW7duJSwsDHNz83p5TdnkLoQQQoh7SmFhIceOHePYsWNA9W0Yjh07RlJSElC95Dh8+HBN/TFjxpCYmMikSZOIjY1lyZIlLF68mMmTJ9fbGCWDJYQQQgi9GGqJ8FZFRUXRq1cvzeMr+7eeeeYZli5dSmpqqibYAggMDGTTpk1MnDiRL774ggYNGrBw4UIeffTRehujBFhCCCGE0Iuxlgh79uzJ9W7juXTp0lplPXr04MiRI/U4Km0SYAkhhBBCL8bKYN0LZA+WEEIIIYSBSQZLCCGEEHq5V64iNAYJsIQQQgihF1ki1E2WCIUQQgghDEwyWEIIIYTQiywR6iYBlhBCCCH0Istgusm5EUIIIYQwMMlgCSGEEEIvskSomwRYQgghhNCLXEWomywRCiGEEEIYmGSwhBBCCKEXWSLUTQIsIYQQQuhFlgh1kwBLCCGEEHqRDJZusgdLCCGEEMLAJIMlhBBCCL3IEqFuEmAJIYQQQi+yRKibLBEKIYQQQhjYLWWwevbsSevWrZk/f349DefuMHPmTH755ReOHTtm7KHcFYK6dSDitefxa9cSxwYeLBr0PMc3bDX2sAzq0JYN7P11NYW5Wbj5BDBgxEv4N2tVZ92CnCy2/LCIS/FnyU5LoeOARxgwYpxWnaORm/nlyw9rtZ2+fDPmFhb1Modb1SvIlf5N3XG0Miclr5RVRy9y7nKRzvpN3GwZ2sYbbwdLckvK+SM2nb/isjTPt/Vx4P4QT9xtLTA1UZBeoGLrmQz2J+Ro6jzUwpOHW3hp9ZtXUs6kDScNP8FbNLyDHwObe2CnNON0eiEL/4ojMbv4um26NXJhREd/vBwsSc0rZcmBRPbG15yTlg3sGdLGh8buNrjaKHlrYwz7LmTX6sfPyYrR4QGENnBAoYDE7GLmbD5DRqHK4PO8kZFdAngotAF2lmbEpObzybazXMi8/nno0cSN0d0C8Xa0IiW3hG93xbPrXGaddZ/q5MeYHo34KSqZhX+e15TvmdKrzvpf7DzPykPJ+k9ID+f3bOLMjl8ozc/B3tOX1o+Mwq1Rc531L58/ybFflpCfloyVgzPBvR+hUZf7NM9HfjaNy3GnarXzDGlHt+dn1CqP3baGkxuX07j7A7QePNowk6onskSo239qibCsrAyLO/jLTa1WU1lZiZnZvX2alTbWXDwey77vf2bMuq+NPRyDO7lvJ5uXfsH9o1/BL7gFUdt/Y/l7b/DSp9/j6OpRq35FeTnW9o50H/wU+zeu0dmv0sqG8QuWaZXdLcFVe19HhrXxZnn0Rc5nFtKjkSsTujdixh+xZBeX16rvamPBhB4N2RWXxXcHEghyteWpdj4UqiqIvpgHQFFZJb+fSiOtoJSKKjWhDRx4toM/+aUVnEor0PSVklvCx5E1v1ir1PU/3xsZ2tabR1s34KPt57iYW8L/hfnywcPNeXb5EUrKK+ts08zTjun9m7L0YCJ74rLo2siFGf2DmbDub06nFwJgaWZKfGYhW2LTmTmwWZ39eNlbMv/RVvwRk84PB5MoKqvAz8massqqepuvLv/X0Y+h7X15d1MsydklPBPuz6dDWvPEdwcpKav7PDRvYM+sh0P4bvcFdp3NpHsTV2Y/3JyxK44Sk5qvVbeppx0PhTbgfEZhrX4e+nyv1uNODZ15Y0BT/jpz2XATvAnJR/ZwbP0S2j72Aq6BTYnft4XdX8/hvqmfYe3kVqt+UVY6u7+ZQ8NO/ej41EQyL5zmyJqvUdra4xMaDkD4yDeoqqzQtFEVFbDtown4/vP81bKTzhG/fysODQLqbY6GJAGWbje9RDhixAj++usvFixYgEKhQKFQkJCQQExMDAMHDsTW1hYPDw+efvppMjNrvrn07NmT8ePHM2HCBJycnPDw8OCbb76hqKiIZ599Fjs7Oxo1asQff/yhaRMZGYlCoWDjxo2EhoZiaWlJx44dOXHihNaY9u3bR/fu3bGyssLX15eXX36ZoqKab+ABAQG88847jBgxAgcHB5577jkApkyZQpMmTbC2tqZhw4bMmDGD8vLqXypLly5l1qxZHD9+XDPPpUuXkpCQgEKh0Mpq5ebmolAoiIyM1Br3li1bCAsLQ6lUsnv3btRqNR9++CENGzbEysqK0NBQ1qzR/Yv5bnNqcyS/zpjHsfVbjD2UerHv959p03sA7frcj5uPPwNGjMPe1Z3DW3+ts76TuycDnx1H6x4RWFrb6OxXoQA7R2et424R0dSd3fFZ7I7PIjVfxaqjKWQXl9MzyLXO+j2DXMkqKmfV0RRS81Xsjs9iz4Vs+jetCUDPZBRyNCWP1HwVlwvL2H72MhdzS2jsZqvVV6VaTX5pheYoVFVc+3J33OBQb36MSmZPfBYJ2cV8uP0slmam9G5S+xfqFY+GNiA6OYeV0RdJzi1hZfRFjl7MY3Cot6bO4aQcvj+YxJ6rslrXGtnJn4MJOXy7L4HzmUWk5qs4mJhDbkntQLe+PR7mww/7E9l1NpMLmUW8uzEWpbkJEc1qf9G4YkiYD1EJOSw/kERSdjHLDyQRnZjDkDAfrXpW5qa8/WAIH24+Q0Fp7bllF5VpHV2DXDmSmMulvFKDz/N6zkZuILBjXxp27ledvRo8GmtHV+L2bK6zftzezVg7utF68GjsPX1p2LkfgR37cGbHBk0dCxs7LO2dNEf6mWOYmivxad1Fq68KVQkH//cpYUNfwsJK92eLuDfcdIC1YMECOnfuzHPPPUdqaiqpqamYm5vTo0cPWrduTVRUFJs3byY9PZ0hQ4ZotV22bBmurq4cOnSI8ePH8+KLL/L4448THh7OkSNH6N+/P08//TTFxdpp6Ndee42PP/6Yw4cP4+7uzkMPPaQJhE6cOEH//v0ZPHgwf//9N6tXr2bPnj2MG6e9VPPRRx/RokULoqOjmTGjOhVrZ2fH0qVLiYmJYcGCBXz77bd8+umnAAwdOpRXX32V5s2ba+Y5dOjQWzqpr7/+OnPnziU2NpZWrVoxffp0vv/+exYtWsSpU6eYOHEiTz31FH/99dct9SsMr6KinNT4swSFhmmVN2oVRvKZ2in9W1FWWsInY4cxb8wQVrz/JqkXzt1Wf4ZiaqLA38laK6sEEJOWT5Br3R/qjVxsiEnTzkacTM3H39la5zfYZh62eNorOXtNtsLDTsm8h1vw/gMhvNA5AFcb42b1vOyVuNhYEJ2Uqykrr1Lzd0oezb3sdLYL8bTTagMQlZRDc0/dba6lADoGOHExt4T3H2rOzyM78NljoYQH3vlgvIGDJa62Sg5dtYRZXqnmWHIuLbztdbZr4e2g1Qbg4IVsWng7aJVN6teYfXFZRCXmcCNO1uaEN3Jh49+XbnEWt6eqopyci3F4Nm2tVe7RtDWZCafrbJOVcAaPa+p7Nm1DTvJ5razV1S4c3I5v266YKS21yo+s+QavkHZ4BIfqPYc7zVShMMjxb3TTa1cODg5YWFhgbW2Np6cnAG+99RZt27blvffe09RbsmQJvr6+nD17liZNmgAQGhrK9OnTAZg6dSrvv/8+rq6umozSW2+9xaJFi/j777/p1KmTpq+3336bfv36AdVBmo+PD+vXr2fIkCF89NFHPPnkk0yYMAGAxo0bs3DhQnr06MGiRYuwtKz+we3duzeTJ0/WmsuVsUB1luvVV19l9erVvP7661hZWWFra4uZmZlmnrdq9uzZmnEXFRXxySefsGPHDjp37gxAw4YN2bNnD19//TU9evTQ6zWEYRTn51FVVYWNg5NWua2DE4W5tffK3CzXBn4MGjsFD7+GqEqKOLBpHYtnvMyLH32Li5fPjTuoR3YWppiaKMgv1f7wz1NV0MLSvM429pZm5F2TacovrcDMRIGt0oy8f/qyMjfh44daYGZqglqtZnlUMjHpNYFcfFYxiw8kklagwt7SnAeae/Bm3ybM+COWIh1LUPXNybo6wMu5JmOUU1KOh53yuu3qauN0CwGjo7U51hZmDGvnw9IDiXy7L4H2fk7MHNiMyetP8Pel/Bt3YiDOttXjzi4u0yrPKSrHw8GyribV7WwsyCm6tk0Zzledhz7N3Gniacdzy6JvaiwDWnhRXFbJX2fr3sdVX1RFBairqlDaOWqVW9o5UJpfd2BYWpCLpZ12MKm0c0RdVYmqMB8rB+1gOTvxLPmpSbQfpp0MSDqym5yLcfSd9PHtT+QOkiVC3W5rc1B0dDQ7d+7E1ta21nNxcXGaAKtVq5rNwqampri4uNCyZUtNmYdHdfo5IyNDq48rAQmAs7MzwcHBxMbGal77/PnzrFixQlNHrVZTVVXFhQsXaNaser9DWJh2ZgJgzZo1zJ8/n/Pnz1NYWEhFRQX29rq/od2qq18zJiaG0tJSTcB1RVlZGW3atNHZh0qlQqXS3uCqVOr+sBe3R3HNNyg16lplt8K3SQi+TUJqHge34OspL3Dwj/UMHDle734NS3vzk6JWyXWrc+X0XF1cWl7FrC2nUZqZ0szDjqFtvLlcVMaZf7JYJ6/ak5OSV0pcZhHvPxBCl0Bntt6hvTa9m7gxsWeQ5vG036szlWp1HefjBvvDrm1TXXjzYzH55yTuv5DF2uPV2Zq4zCJCvOx4oIVXvQZY/UI8eK1/E83j19f8swXj2vHfxImo9axCgfqfUnc7Ja/0acyk1cduel/Z/a082RqTbpR9aFA9ZS3q2p8R123xz/mqq82FA9ux9/LD2b/m3BfnXObYuu/o/uJMTM3vjn2aN+vfmn0yhNsKsKqqqnjwwQf54IMPaj3n5VVzpZC5ufa3YoVCoVV25YewqurGb6ar677wwgu8/PLLter4+flp/m1jo73kceDAAYYNG8asWbPo378/Dg4OrFq1innz5l33dU1MqldTr/5AvbJcea2rX/PKnDZu3Ii3t7dWvesFTHPnzmXWrFlaZW+//fZ1xyhunbW9AyYmJrWyVUV5ubWyWrfDxMSEBo2CyUpLMVif+iooq6SySo39Ndkqe6UZ+XXsjYHqbJWDpfbHhZ3SjIoqNUVXZbbUQEZhdTYjObcEL3slA5t5aAKsa5VVVnExrwR32zv35WH/hWxOpx/VPDY3rX5vO1tbaG3wd7QyJ6ekrFb7K3KKy3C21v5l6GRlTk6x7jbXyispp6KyisTsEq3ypOwSWjQw3Je+uuw5n0nMVQGchVn1Z6uzjQVZV2WknKzNyS7SPafsa7JVV9rkFFWfy2BPO5xtLPhuRM0XTzMTE0J9HRnc1pveH/+ldaFDKx8H/F1seHvD7S3R60NpY4fCxITSglyt8tLCvFpZrSss7Rxr1VcV5qEwMcXCRnu5uKJMRdLRPbQY8IRWeU5yHKrCPLbPe1VTpq6q4nJ8DOf3bOLRj39GYWKq97yEcdxSgGVhYUFlZU0av23btqxdu5aAgIB6uVLuwIEDmmApJyeHs2fP0rRpU81rnzp1iqCgoOt1UcvevXvx9/dn2rRpmrLExEStOtfOE8DNrXqza2pqqibzdDO3cQgJCUGpVJKUlHRLy4FTp05l0qRJWmVKpZJXZi296T7EjZmZmePVsAlxf0fTrEM3TXn839EEt699hY++1Go1aYlxePgGGqxPfVVWqUnMKaa5px1HU/I05SHXPL5aXFYRodf8wm/uaUdidjGV10luKBQKzK6zhmBmosDL3vK6t4cwtJLySkrytN/fWUVltPV15HxmkWZcrbwd+HZfgs5+YtIKaOvrqMk8AbTzc6y1t+16KqrUnMkoxMfRSqvcx9GKjIL63dxdUlZJSpl2YJdZqKJ9gDPn/gmIzUwUtPZ15KvIeJ39nEzJo32AMz9FXdSUdQh05uQ/P0tRiTk8vfiQVps3BzYlMauYFQeTal1F+kArL06n5nP+Dv5MXGFiZo6TTyPSzxzDu1XNdpX0M8fwbtGxzjYuAcFcOnVYqyzt9DGcfIMwMdX+vXjx6B6qKsrxC9P+XeDeJJSIKQu0yg7/+Bl2Ht407TP4rg6uZIlQt1uKigICAjh48CAJCQnY2try0ksv8e233/LEE0/w2muv4erqyvnz51m1ahXffvstpqa390Mxe/ZsXFxc8PDwYNq0abi6ujJo0CCg+krATp068dJLL/Hcc89hY2NDbGws27Zt47PPPtPZZ1BQEElJSaxatYr27duzceNG1q9fX2ueFy5c4NixY/j4+GBnZ4eVlRWdOnXi/fffJyAggMzMTK29XLrY2dkxefJkJk6cSFVVFV27diU/P599+/Zha2vLM888U2c7pVJ51ywJKm2scQsK0Dx2DfTFJzSEouxccpLv7CbU+hD+wOOs+2wuDRoG49skhKjtv5OXmU77fg8CsO3HbynIzmTwuKmaNqkJ1bcZKCstoSg/j9SE85iameHuEwDAzp+X4ds4BGcvb1QlxRzctI60hPPcP6p2xtUYtp7OYHQnfxKyi4nLKqJ7I1ecrS3463z1npfBrbxwsrJg8cHqLx+R5zPp3diVoa292RWfSSMXG7o1dOGb/QmaPgc28yAhu5iMQlV1gNLAns4BziyPqrmH0ZDWDTiWkk92cRl2SjMeaO6Jlbkp+y7ovsruTlh3PIUnw3xJySslJbeEJ8N8KK2oZMfZmmXLKX2bkFmkYvH+xH/aXOLTwa0Y2tabffHZhDd0pq2PIxPW/a1pY2lugrdDTfDkZW9JI1cbCkorNPe4+uloCtP7B3PiUh7HUvJo7+dE50BnXl2vfdX0nfBz1EWe7uzHxZxiknNKGN7ZH1V5FVtj0zV1pt/fjMsFKr7eVR10/Rx9kc+fbMP/dfRj97lMujV2JczfibErqrOEJWWVXMjUDpZKyyvJLy2vVW5tYUqvYHc+33keY2nS82EOrpiPk28QLgHBxO/fSnFOJg279AfgxG//oyQviw5PTQCgUZf7OL9nE8fWL6Fh535kJZzhwsHtdBo+qVbfFw5ux7tlR5Q22l9WzC2tcPDy1yozs1CitLarVX63kSVC3W4pwJo8eTLPPPMMISEhlJSUcOHCBfbu3cuUKVPo378/KpUKf39/7rvvPs2S2u14//33eeWVVzh37hyhoaH8+uuvmvtYtWrVir/++otp06bRrVs31Go1jRo1uuEVfw8//DATJ05k3LhxqFQq7r//fmbMmMHMmTM1dR599FHWrVtHr169yM3N5fvvv2fEiBEsWbKEkSNHEhYWRnBwMB9++CERERE3nMecOXNwd3dn7ty5xMfH4+joSNu2bXnzzTdv6/zcKf5hrZgUuUrz+PFPq6/G3L90Dcuenayr2T2jRXgvigvy+WvtDxTkZOPuG8D/TZ2Lo1v1RQ6FOdnkZWrvD/zq9ec1/74Uf5YTe/7E0c2DiV+sBKC0qJBfv5lHYW4OltY2eAYGMXLWfHyC6r4X0p12ODkXW6UZD7bwxMGy+kajC3bFkfXPEpmjlTnONjVLiJlFZcz/K55hbbzp1diV3JJyfjxyUXMPLAClmQlPhfngZGVBeWUVqQWlfLc/gcPJuZo6TlYWvBAegK2FKQWqCuKzinl321nN6xrL6iMpKM1MeblHI+yUZsSmF/DGhlNa98Byt1NSddUWgZi0At7ZcppnO/kzoqM/l/JKeWfLGc09sACC3e2Y90jNftMXuzUEYEtsOh/9WX1V6d74LBZExjGsnQ8vdW9Ick4Js/6I1dqvdqesOJiE0syESRFNqm80eqmAiT8d17oHloe99nk4mZLPzF9jeK5bIKO7BZKSW8Jbv56qdQ+sm9G3mTsKBWyPSb9x5Xri27YrquJ8Yrasrr7RqJcf3V6YgY2zOwAl+dkU59QE3jYuHnR7fgbHfllC3J5NWDo402bwaM09sK4oyEghMz6W7i/OvJPTEUakUNe5S9O4IiMj6dWrFzk5OTg6Ohp7OHeVMYoAYw/B6L5SJ7DquPH3MhnbsFBvRq06euOK/wGLh7Wh7+d7jD0Mo9s+ritdP9hp7GEY3Z4pvZj+R6yxh2F07wyo/y906z113+H+VjySduf33NW3e/sW40IIIYQwGlki1E3+2LMQQgghhIHdlRmsnj171n1/GSGEEELcNUwkg6XTXRlgCSGEEOLup5D7NOgkS4RCCCGEEAYmGSwhhBBC6MVEMlg6SYAlhBBCCL0oTGUhTBcJsIQQQgihF9mDpZuEnkIIIYQQBiYZLCGEEELoRfZg6SYBlhBCCCH0ojDA3x3+t5IzI4QQQghhYJLBEkIIIYReZIlQNwmwhBBCCKEXuYpQN1kiFEIIIYQwMMlgCSGEEEIvcqNR3STAEkIIIYReZA+WbhJ6CiGEEEIYmARYQgghhNCLwkRhkEMfX375JYGBgVhaWtKuXTt279593forVqwgNDQUa2trvLy8ePbZZ8nKytLrtW+GBFhCCCGE0IuJqYlBjlu1evVqJkyYwLRp0zh69CjdunVjwIABJCUl1Vl/z549DB8+nFGjRnHq1Cl+/vlnDh8+zOjRo2/3FOgkAZYQQggh9KIwVRjkuFWffPIJo0aNYvTo0TRr1oz58+fj6+vLokWL6qx/4MABAgICePnllwkMDKRr16688MILREVF3e4p0EkCLCGEEEIYlUqlIj8/X+tQqVR11i0rKyM6OpqIiAit8oiICPbt21dnm/DwcC5evMimTZtQq9Wkp6ezZs0a7r//foPP5QoJsIQQQgihF0NlsObOnYuDg4PWMXfu3DpfMzMzk8rKSjw8PLTKPTw8SEtLq7NNeHg4K1asYOjQoVhYWODp6YmjoyOfffaZwc/JFRJgCSGEEEIvhtqDNXXqVPLy8rSOqVOnXve1FQrtpUW1Wl2r7IqYmBhefvll3nrrLaKjo9m8eTMXLlxgzJgxBjsX15L7YAkhhBDCqJRKJUql8qbqurq6YmpqWitblZGRUSurdcXcuXPp0qULr732GgCtWrXCxsaGbt268c477+Dl5XV7E6iDZLCEEEIIoRdjbHK3sLCgXbt2bNu2Tat827ZthIeH19mmuLgYExPtkMfU1BSoznzVB4W6vnoWQgghxL9a1MA+BuknbNOft1R/9erVPP3003z11Vd07tyZb775hm+//ZZTp07h7+/P1KlTSUlJ4YcffgBg6dKlPPfccyxcuJD+/fuTmprKhAkTMDEx4eDBgwaZw7VkifAes+p4irGHYHTDQr0Zowgw9jCM7it1Ar/G1L2h87/moRBP+iy8/k0G/wv+fLkbr6w/YexhGN2CR1pSliPvDQsnT2MPod4MHTqUrKwsZs+eTWpqKi1atGDTpk34+/sDkJqaqnVPrBEjRlBQUMDnn3/Oq6++iqOjI7179+aDDz6otzFKgCWEEEIIvRjzjz2PHTuWsWPH1vnc0qVLa5WNHz+e8ePH1/OoakiAJYQQQgi9yB971k02uQshhBBCGJhksIQQQgihF33+zM1/hQRYQgghhNCLMfdg3e0kwBJCCCGEXmQPlm4SegohhBBCGJhksIQQQgihF4WJZLB0kQBLCCGEEHoxkT1YOsmZEUIIIYQwMMlgCSGEEEIvcpsG3STAEkIIIYRe5DYNusmZEUIIIYQwMMlgCSGEEEIvChPJ0+giAZYQQggh9CJXEeomZ0YIIYQQwsAkgyWEEEIIvcgmd90kwBJCCCGEXiTA0k0CLCGEEELoRTa56yZnRgghhBDCwCSDJYQQQgi9KExNjT2Eu5YEWEIIIYTQi+zB0u2uODM9e/ZkwoQJxh6GEEIIIYRB/KczWAkJCQQGBnL06FFat2592/1FRkbSq1cvcnJycHR0vO3+7oRDWzaw99fVFOZm4eYTwIARL+HfrFWddQtystjywyIuxZ8lOy2FjgMeYcCIcVp1jkZu5pcvP6zVdvryzZhbWNTLHO6koG4diHjtefzatcSxgQeLBj3P8Q1bjT0sg9n3x3oif1lFQU42Hr4BPDRqHA1DQuusm5+dxW9LvyAl7iyZqRfpcv+jPDxqvFadRdNfIf7UsVptm7brxKjpH9THFPQ2vKMf9zf3xM7SjNi0AhZGxpGYXXzdNt0aufBs5wC8HCxJzStl8b4E9sZnaZ5v2cCeoe18aOxmi6utkrd+j9F6HsDJypznugTSzs8RW6UZf1/K4/PIOFLySutlntfTNdCZ3o3dsLc0Iy1fxboTl4jP0n0OGrnY8EhLLzztleSVVrDj7GX2JmRrnu/g58j/tfOt1e7VDSepqFL/04c1vRu74etohYOVOd8dSOREar7hJ3cbVq1Zz9IVq7iclU2jwACmTBxHu9Z1vy+udvT4CZ4d+wpBDQNZ87/FmvLz8Rf44pslxJw+y6W0NF6fMI6nhz1en1OoNyayyV2n/2yAVVZWdkt1Lf4FwcG1Tu7byealX3D/6FfwC25B1PbfWP7eG7z06fc4unrUql9RXo61vSPdBz/F/o1rdPartLJh/IJlWmX/huAKQGljzcXjsez7/mfGrPva2MMxqGN7dvDrks955PmJBDRtwYGtv7F4zhQmL1yGk1sdPw8VZdjaO9L7safY/dvPdfb5zJQ5VFSUax4XF+Tz6cRRtArvWV/T0Muwdj481sabD7ed5WJOCU918OPDQS0Y8b9oSsor62wT4mnHjAHN+P5AAnvisujayIW3BjTllTV/czq9AAArc1PiLhexOSadWfeH1NnP7AdCqKhS89bvMRSVVfJ4G28+eqQlI5dHU1pRVW9zvlYbbwceaeXFz8cucSG7mPAAZ8aEBzB3+zlySspr1Xe2NueF8AD2J2Tzv+hkAp2tebx1AwrLKjh+qSZAKimv5N1tZ7XaXgmuACzMTEjJK+VgUg6jOvrX3wT1tHnbDj6Y/znTX5tIm1Yt+PmX33hx4hQ2rFyGl2ft98UVBYWFvDn7PTqGtSUrO0frudLSUny8GxDRpycfzv+8vqdQr2SJULe75sxUVVXx+uuv4+zsjKenJzNnztQ8l5eXx/PPP4+7uzv29vb07t2b48ePa56Pi4vj4YcfxsPDA1tbW9q3b8/27du1+g8ICOCdd95hxIgRODg48NxzzxEYGAhAmzZtUCgU9OzZE4ARI0YwaNAg5s6dS4MGDWjSpAkAy5cvJywsDDs7Ozw9PXnyySfJyMgAqrNhvXr1AsDJyQmFQsGIESMAUKvVfPjhhzRs2BArKytCQ0NZs0Z3gHKn7Pv9Z9r0HkC7Pvfj5uPPgBHjsHd15/DWX+us7+TuycBnx9G6RwSW1jY6+1UowM7RWev4tzi1OZJfZ8zj2Potxh6Kwe369Sfa9xlIx34P4OEbwMOjxuPo4sb+zRvqrO/s7sXDo18mrNd9WFrb1lnH2s4eeycXzXHueBTmSiWhd1mANbi1Nz8eTmZPXBYJ2cV8sO0Mluam9Al2u26b6KQcVkZdJDmnhJVRFzlyMZdHWzfQ1DmUmMP3BxLZE5dVZx8+jlaEeNkzf+d5zmQUcjG3hAWR57EyN6X3dV67PvQMcuVAQg4HEnNIL1Cx/kQqOSXldAms+/3bJdCFnJIy1p9IJb1AxYHEHA4m5tCrsfa41WooUFVoHVeLTS9kU2w6f1+6u7JWV/yw8icGPziQRx9+gIaBAUyZOB5PdzdWr6v7fXHF7PfnMTCiL6Etmtd6rkVIM14d/yID+vXBwvzf8eVT1HbXBFjLli3DxsaGgwcP8uGHHzJ79my2bduGWq3m/vvvJy0tjU2bNhEdHU3btm3p06cP2dnVqejCwkIGDhzI9u3bOXr0KP379+fBBx8kKSlJ6zU++ugjWrRoQXR0NDNmzODQoUMAbN++ndTUVNatW6ep++effxIbG8u2bdv4/fffgepM1pw5czh+/Di//PILFy5c0ARRvr6+rF27FoAzZ86QmprKggULAJg+fTrff/89ixYt4tSpU0ycOJGnnnqKv/76q17P6fVUVJSTGn+WoNAwrfJGrcJIPnPqtvouKy3hk7HDmDdmCCvef5PUC+duqz9R/yrKy0mJO0uT1u21ypu0bk/i6ZMGe51D2zfSumtvLCytDNbn7fKyt8TFxoKopJosQ3mlmuMpeTT3stfZLsTLTqsNQFRiznXbXMvcVAFAWWVNpqpKDeVValp4Odx0P7fLVKHA19GKMxmFWuVn0gsJdLGus02AszVn0rXrn04vxM/RChNFTZnSzIS3+wcz676mPN/ZH28HS4OPv76Ul5cTc+Ys4R213xfhHdtz7ITu98X63zeRnJLCi6Oeqe8hGp3C1MQgx7/RXbNE2KpVK95++20AGjduzOeff86ff/6JqakpJ06cICMjA6VSCcDHH3/ML7/8wpo1a3j++ecJDQ0lNLRmPfydd95h/fr1/Prrr4wbV7NHqHfv3kyePFnzOCEhAQAXFxc8PT21xmNjY8N3332ntTQ4cuRIzb8bNmzIwoUL6dChA4WFhdja2uLsXP1Nz93dXbMHq6ioiE8++YQdO3bQuXNnTds9e/bw9ddf06NHj9s9dXopzs+jqqoKGwcnrXJbBycKc7N1tLox1wZ+DBo7BQ+/hqhKijiwaR2LZ7zMix99i4uXz+0OW9STooI8qqoqa2UbbR2dKLiNn4erJZ2NJS3pAo+/NMUg/RmKk7U5ADnF2stgOcVleNjpDgacrS3qaFOOk83NZySSckpIyy9ldHgAn+44T2l5JY+18cbFxgLnW+jndtkoTTE1UZB/TXapQFWBndK8zjb2lmacvqZ+vqoCUxMFthZm5KsqSC9Q8eORi1zKK8XS3IQejVyZ0L0RH+44x+Wim9+mYSw5uXlUVlbi4qz9vnBxdiIrq+73RWLSReZ/8Q3Lvv4MM7O75ldsvZEbjep21/zvt2qlvbHay8uLjIwMoqOjKSwsxMXFRev5kpIS4uLigOogZtasWfz+++9cunSJiooKSkpKamWwwsK0szXX07Jly1r7ro4ePcrMmTM5duwY2dnZVFVVf+tMSkoiJKTu/RUxMTGUlpbSr18/rfKysjLatGmj8/VVKhUqlUqr7EqAaUgKhULrsRp1rbJb4dskBN8mNefCN7gFX095gYN/rGfgyPHXaSnuSmqq13wN4NCfG/H0C8SvSTOD9KevPsFuTOzVWPP4zd+qM7ZqtVqrnoLq98P1XPusQlFH4XVUVqmZuTGWyX0bs+GFzlRWqYlOzuFggmGCWsPQPaFa87+mPDGnhMScEs3zF7KSmNwriG6NXFj3d6pBR1mvrnkLqHW8LyorK5ny9mxeeu5ZAvxqb+4X/y13TYBlbq79LUmhUFBVVUVVVRVeXl5ERkbWanMlS/Taa6+xZcsWPv74Y4KCgrCysuKxxx6rtZHdxkb3vqFrXVu3qKiIiIgIIiIiWL58OW5ubiQlJdG/f//rbpi/EoRt3LgRb29vreeuFzDNnTuXWbNmaZW9/fbbNH3kuZuew/VY2ztgYmJSK1tVlJdbK6t1O0xMTGjQKJistBSD9SkMz8bOARMT01rZqsK8HOwM8PNQpirl+J4dRAwbeePK9WxffDaxaUc0j83/WZ5wtrEg+6qMlKO1BbnFtTd3X5FdXIaztfbnlqOVOTnFt5aZOXe5kBdWHsXGwhQzUxPySsr5fEgoZ69ZrqtPRapKKqvU2Cu1fyXYKc1q7Zm6Ir+0os76lVVqisrqbqMGknJLcLMx/JfF+uDk6ICpqWmtbFV2Tg4uzrXfF0XFxZyKPcPps+d5b171FpGqqirUajWtu/Tm6wUf0zGs7R0Z+53yb13eM4S7JsDSpW3btqSlpWFmZkZAQECddXbv3s2IESN45JFHgOo9WVeW/67nSoaqsrLuq4Sudvr0aTIzM3n//ffx9a3+ZhIVFXXD/kJCQlAqlSQlJd3ScuDUqVOZNGmSVplSqWT96cyb7uN6zMzM8WrYhLi/o2nWoZumPP7vaILbhxvkNaA6K5CWGIeHb6DB+hSGZ2ZujnejJpw7HkXLTt015WePR9G8Q9fb7v/43p1UlJfTtke/G1euZyXllZTkab/ns4rKaOfrxPnLRQCYmSgI9Xbg270XdPYTk1pAOz8n1h67pCkL83PilJ63GCgqqwQq8XawpIm7Hd8fSNSrH31UqtUk55YQ7G7L31eNP9jdVuctExKyi2nhaadVFuxuS1JuCVXXyeL5OFhyKf/O34JCH+bm5oQEN2H/oSj69Kx5X+w/FEWv7rXfF7Y2Nqxb8b1W2eq1v3Aw+iifvDcL7wZe9T7mO00CLN3u+gCrb9++dO7cmUGDBvHBBx8QHBzMpUuX2LRpE4MGDSIsLIygoCDWrVvHgw8+iEKhYMaMGZrM0fW4u7tjZWXF5s2b8fHxwdLSEgeHujeW+vn5YWFhwWeffcaYMWM4efIkc+bM0arj7++PQqHg999/Z+DAgVhZWWFnZ8fkyZOZOHEiVVVVdO3alfz8fPbt24etrS3PPFP3JkilUlkvS4JXC3/gcdZ9NpcGDYPxbRJC1PbfyctMp32/BwHY9uO3FGRnMnjcVE2b1ITzQPVG9qL8PFITzmNqZoa7TwAAO39ehm/jEJy9vFGVFHNw0zrSEs5z/6iX63Uud4rSxhq3oADNY9dAX3xCQyjKziUn+ZLuhveA7g8NYdWCd/FpFIx/cHMObvud3MwMOvd/CIBN//uGvOzLPPHKNE2blH8uYKj+ecgl5cI5zMzM8fAN0Or78PaNNO/YFRv7O7dx+1asO5bCk+19uZhbQkpuCU+296W0vJI/z1zW1JnSrwmZRWUs3pegaTP/sVCGtfNhb3wWXRq60NbXkVfW/K1pY2lugrdDzYZ+T3sljVxtKCitIKOwegtA9yBX8krKyShQEehqzUvdG7E3PovopNw7MvcrIs9n8lSYD0m5JST8c5sGJ2tz9l6ozt48EOKBg5U5K6IvArD3QhbdGrowqKUX+xOyCXC2plOAEz8cTtb0eV9TdxKyi7lcqMLS3JTujVzwdrDi5+M17xULUxPcbGu2Y7hYm+PtYElxWWWdt4e404Y/MYSps96lebNgQls05+cNv5OansGQR6rfF/O//IaMy5d57+1pmJiY0LhRQ632zk5OKC0stMrLy8uJu5BQ/e+KcjIuZ3L67Dmsrazw87239qqaSICl010fYCkUCjZt2sS0adMYOXIkly9fxtPTk+7du+PhUX0Pkk8//ZSRI0cSHh6Oq6srU6ZMIT//xt8izczMWLhwIbNnz+att96iW7dudS5FAri5ubF06VLefPNNFi5cSNu2bfn444956KGHNHW8vb2ZNWsWb7zxBs8++yzDhw9n6dKlzJkzB3d3d+bOnUt8fDyOjo60bduWN9980yDnSF8twntRXJDPX2t/oCAnG3ffAP5v6lwc3ao3/BfmZJOXmaHV5qvXn9f8+1L8WU7s+RNHNw8mfrESgNKiQn79Zh6FuTlYWtvgGRjEyFnz8Qky7r4bQ/EPa8WkyFWax49/OgOA/UvXsOzZybqa3RNad+1NcUEe23/6gfycLDz9Ahk1/QOc3Kt/HvJzssi9rP3zMH/SaM2/L8ad4eiu7Ti5efLmN6s15ZdTkrkQe4Ln3v74zkxED6uiL2JhZsIrvYKwU5oRm17AlF9Oat0Dy91OydXbtGLSCnhn82me7eTPiE7+XMorZc7m05p7YAEEu9vxyaM1+0vHdm8EwJaYdD7cXn1vKBcbC17s1hAna3Oyi8rYejqD5Ye094/eCUdT8rCxMKV/sDsOlmak5qv4el+CJsixtzTHyapmSTS7uJyv9yXwSCsvugU6k1dawbrjqVr3wLIyN2VoG2/slWaUVFRxMbeEhbvjSbpqX5afkxXju9UEH4+0qr7NxcHEHH48crG+p31D9/XrTW5eHl8t/oHLWVkENQzky08+oIFX9fvicmYWqWkZN+hFW8blTB4fXvPeWbpiFUtXrCKsTWu+X7TAoOMXxqNQX7uzU9zVVh2XvUzDQr0Zowgw9jCM7it1Ar/GpBl7GHeFh0I86bNwt7GHYXR/vtyNV9afMPYwjG7BIy0py5H3hoWT540r3aaMj18xSD/uk/99geVdn8ESQgghxN1J9mDpJmdGCCGEEMLAJIMlhBBCCL1IBks3CbCEEEIIoRe5k7tucmaEEEIIIQxMMlhCCCGE0IuJqamxh3DXkgBLCCGEEHqRPVi6yZkRQgghhDAwyWAJIYQQQi+SwdJNAiwhhBBC6EWuItRNAiwhhBBC6EUyWLrJmRFCCCGEMDDJYAkhhBBCL5LB0k0CLCGEEELoRfZg6SZnRgghhBD3nC+//JLAwEAsLS1p164du3fvvm59lUrFtGnT8Pf3R6lU0qhRI5YsWVJv45MMlhBCCCH0ojAxzp3cV69ezYQJE/jyyy/p0qULX3/9NQMGDCAmJgY/P7862wwZMoT09HQWL15MUFAQGRkZVFRU1NsYJcASQgghhH6MFGB98sknjBo1itGjRwMwf/58tmzZwqJFi5g7d26t+ps3b+avv/4iPj4eZ2dnAAICAup1jLJEKIQQQgijUqlU5Ofnax0qlarOumVlZURHRxMREaFVHhERwb59++ps8+uvvxIWFsaHH36It7c3TZo0YfLkyZSUlBh8LldIgCWEEEII/ZiYGOSYO3cuDg4OWkddmSiAzMxMKisr8fDw0Cr38PAgLS2tzjbx8fHs2bOHkydPsn79eubPn8+aNWt46aWXDH5KrpAlQiGEEELoRWFqmCXCqVOnMmnSJK0ypVJ5/ddWKLQeq9XqWmVXVFVVoVAoWLFiBQ4ODkD1MuNjjz3GF198gZWV1W2Mvm4SYAkhhBDCqJRK5Q0DqitcXV0xNTWtla3KyMioldW6wsvLC29vb01wBdCsWTPUajUXL16kcePG+g9eB1kiFEIIIYR+TEwNc9wCCwsL2rVrx7Zt27TKt23bRnh4eJ1tunTpwqVLlygsLNSUnT17FhMTE3x8fG593jdBAiwhhBBC6McIARbApEmT+O6771iyZAmxsbFMnDiRpKQkxowZA1QvOQ4fPlxT/8knn8TFxYVnn32WmJgYdu3axWuvvcbIkSPrZXkQZIlQCCGEEHoy1p3chw4dSlZWFrNnzyY1NZUWLVqwadMm/P39AUhNTSUpKUlT39bWlm3btjF+/HjCwsJwcXFhyJAhvPPOO/U2RgmwhBBCCHHPGTt2LGPHjq3zuaVLl9Yqa9q0aa1lxfqkUKvV6jv2akIIIYT41yjd/I1B+rG873mD9HM3kQzWPWbUqqPGHoLRLR7Whl9j6r7XyX/JQyGejFEEGHsYd4Wv1An0/PQvYw/D6CIn9mD2tjPGHobRvdUvmKTswhtX/Jfzc7at/xcx0p3c7wWyyV0IIYQQwsAkgyWEEEIIvRhrk/u9QAIsIYQQQuhHlgh1ktBTCCGEEMLAJIMlhBBCCP1IBksnCbCEEEIIoRdD/bHnfyNZIhRCCCGEMDDJYAkhhBBCP3IVoU4SYAkhhBBCP7IHSycJsIQQQgihF4UEWDpJbk8IIYQQwsAkgyWEEEII/cgeLJ0kwBJCCCGEXmSJUDcJPYUQQgghDEwyWEIIIYTQj2SwdJIASwghhBD6kT1YOsmZEUIIIYQwMMlgCSGEEEIv8rcIdZMASwghhBD6kT1YOskSoRBCCCGEgUkGSwghhBD6kQyWThJgCSGEEEIvCrmKUCcJsIQQQgihH8lg6SShpwEtXboUR0dHYw9DCCGEEEZ2z2WwysrKsLCw+Ne/Zn3oFeRK/6buOFqZk5JXyqqjFzl3uUhn/SZutgxt4423gyW5JeX8EZvOX3FZmufb+jhwf4gn7rYWmJooSC9QsfVMBvsTcjR1HmrhycMtvLT6zSspZ9KGk4af4G3Y98d6In9ZRUFONh6+ATw0ahwNQ0LrrJufncVvS78gJe4smakX6XL/ozw8arxWnUXTXyH+1LFabZu268So6R/UxxTuqKBuHYh47Xn82rXEsYEHiwY9z/ENW409LIMa0cmfB1p6YWdpRmxqAfN3niMhq/i6bboHuTIyPIAGDlZcyivhu70X2HPVe+ahVl483KoBnvaWACRkFbPsYCKHErLrdS436+yuTcT8uY6SvBwcvfxo9+ho3IOa66yffu4kR9YtJjc1CWsHZ5r1HUyTbgO06pzeuYGzuzdTnHMZpY09fm3Caf3QcEzNa3+mntzyM8d/+x/BPR8k7LHnDD4/ff269id+XvE/srIyCQhsyIsTJtOydZs66+6O3MHv69YQd+4M5WXl+DdsyNOjnqd9p3BNnYqKclYu+55tf/xO5uXL+Pr5M3rsy7TvHF5nn3c1heRpdLnrz0zPnj0ZN24ckyZNwtXVlX79+hETE8PAgQOxtbXFw8ODp59+mszMTE2bNWvW0LJlS6ysrHBxcaFv374UFVUHEiNGjGDQoEHMmjULd3d37O3teeGFFygrK7vuawJ88skntGzZEhsbG3x9fRk7diyFhYUAREZG8uyzz5KXl4dCoUChUDBz5kygOkB7/fXX8fb2xsbGho4dOxIZGXlnTuA/2vs6MqyNNxtj0pm15TTnLhcyoXsjnK3N66zvamPBhB4NOXe5kFlbTrMxJp0n2/rQzsdBU6eorJLfT6Xx3vazvL35NHsvZPNsB3+ae9pp9ZWSW8LEX05ojrc2n67Xud6qY3t28OuSz+nz2NNMmPctgSGtWDxnCjmX0+usX1FRhq29I70fewqvgEZ11nlmyhxmLFmnOV5dsBQTE1Nahfesx5ncOUobay4ej2XVuLeMPZR68USYL4+39WHBzvOM+fEI2cVlfDy4FVbmupdDQrzsefv+ELbGpjN6eRRbY9OZeX8Iza56P1wuLOObPRd44ccjvPDjEY4k5/DuQ80JcLG+E9O6roTo3USv/Y4W/Ycw8I35uDUKYeeXsyjKvlxn/cLMNHYumoVboxAGvjGf5v0fJ3rNtyQd3aepc+FwJEc3/EDLAcN4YPoXdPq/8SRG7+HYrz/U6i8r8Rzn923B0Tugvqaol8jtW1k0fx5PjBjJomU/0iK0DW9OGk9GWmqd9U8cPULbDh15d95Cvli6nNC2Ybz12kTOn6n53Pv+60Vs/GUdL016ncU//swDjzzKzDcma9W5ZyhMDHP8C90Ts1q2bBlmZmbs3buX999/nx49etC6dWuioqLYvHkz6enpDBkyBIDU1FSeeOIJRo4cSWxsLJGRkQwePBi1Wq3p788//yQ2NpadO3eycuVK1q9fz6xZs3S+5tdffw2AiYkJCxcu5OTJkyxbtowdO3bw+uuvAxAeHs78+fOxt7cnNTWV1NRUJk+eDMCzzz7L3r17WbVqFX///TePP/449913H+fOnbsTpw+AiKbu7I7PYnd8Fqn5KlYdTSG7uJyeQa511u8Z5EpWUTmrjqaQmq9id3wWey5k07+ph6bOmYxCjqbkkZqv4nJhGdvPXuZibgmN3Wy1+qpUq8kvrdAchaqKep3rrdr160+07zOQjv0ewMM3gIdHjcfRxY39mzfUWd/Z3YuHR79MWK/7sLS2rbOOtZ099k4umuPc8SjMlUpC/yUB1qnNkfw6Yx7H1m8x9lDqxWNtvVl+KInd5zO5kFXM3C2nsTQzpW9Td91t2ngTlZjDj4eTScop4cfDyRxJzuWxNj6aOvvjsziYkM3F3BIu5paweF8CJeWVhHja34lpXdfpHRto1LkvQeEROHj6EvbYc1g7uXJ296Y665/bsxkbJzfCHnsOB09fgsIjaNipL7F/rtfUybxwGreGzQhs3wNbFw+8mrXBP6wbWUnntfoqV5Wwd+k8Oj4xDgurut9TxrJ25XLue/BhBj70CP4BgYydOBk3dw9+W7emzvpjJ05m6FPPEBzSHB9fP0a9OA5vXz/279mlqbN980aeeGYkHcO74uXtw4ODHyesUyfWrFx+p6Yl7oB7IsAKCgriww8/JDg4mD/++IO2bdvy3nvv0bRpU9q0acOSJUvYuXMnZ8+eJTU1lYqKCgYPHkxAQAAtW7Zk7Nix2NrWvGktLCxYsmQJzZs35/7772f27NksXLiQqqqqOl+zadOmAEyYMIFevXoRGBhI7969mTNnDj/99JOmTwcHBxQKBZ6ennh6emJra0tcXBwrV67k559/plu3bjRq1IjJkyfTtWtXvv/++zty/kxNFPg7WXMqrUCrPCYtnyBXmzrbNHKxISYtX6vsZGo+/s7WmCrqfp1mHrZ42is5m1GoVe5hp2Tewy14/4EQXugcgKvN3bPcWlFeTkrcWZq0bq9V3qR1exJPG24Z89D2jbTu2hsLSyuD9Snqh5eDJS42Sg4n1ix1l1eqOZaSS/MGugOh5l72HE7UXuo7lJCts42JAno3ccPSzJRTqfl11rlTKivKyU4+j1cz7WUvr2ZtyLxQd1bl8oXTteo3CGlDVtJ5qiqrv0S5NQwhOzmOzISzABRkpnHpVDTezcO02h1e/RXeLcLwatraQDMyjPLycs6eOU27Dp20ytt17MSpE3/fVB9VVVUUFxdhZ1+T/S8vK6+17cRCacnJ48due8x3mlphYpDj3+ie2IMVFlbzZoyOjmbnzp1aAdMVcXFxRERE0KdPH1q2bEn//v2JiIjgsccew8nJSVMvNDQUa+ualHznzp0pLCwkOTkZf3//Wq95xc6dO3nvvfeIiYkhPz+fiooKSktLKSoqwsam7kDlyJEjqNVqmjRpolWuUqlwcXHROWeVSoVKpdIqUyqVOutfj52FKaYmCvJLtTNHeaoKWljWvURob2lG3jWZpvzSCsxMFNgqzcj7py8rcxM+fqgFZqYmqNVqlkclE5NeE8jFZxWz+EAiaQUq7C3NeaC5B2/2bcKMP2IpKqvUaz6GVFSQR1VVJXaOzlrlto5OFOQaZl9M0tlY0pIu8PhLUwzSn6hfztbVv/hyisu0ynOKy/Cws9TdzsaCnOLya9qUa/q7ItDFhi+HtcHCzISSskpm/HaKxOzr7+2qb6rCfNRVVVjaOWqVW9o5UJKfW2eb0vxcLO0crqnviLqqElVhPlYOzgSEdUdVmM+2T99ArVajrqqkcbcBNI94TNMmIWoX2cnxDHh9nqGnddvycnOpqqzEyVn7s9rJyYWc7CwdrbSt+XE5pSWl9OjTT1MW1rETa1etoGWbtjTw9uFo1CH274rU+pJ/z/iXBkeGcE8EWFcHL1VVVTz44IN88EHtjcJeXl6Ympqybds29u3bx9atW/nss8+YNm0aBw8eJDAw8Lqvo1DUpGauDZgSExMZOHAgY8aMYc6cOTg7O7Nnzx5GjRpFeXn5tV1pjdfU1JTo6GhMr/mbTXUFiVfMnTu31rLl22+/DU0fvu4crk+t9UhRq+S61blyeq4uLi2vYtaW0yjNTGnmYcfQNt5cLirjzD9ZrJNXfTNPySslLrOI9x8IoUugM1vP1L23466gpmbCt+nQnxvx9AvEr0kzg/QnDKtvU3de7VPzBeiNX04Atd8bCm7886C+9j1WR5PknGJGL4/C1tKM7kFuTO0fzCs/Hzd6kFXtmgGrb/Q20H5SsxXjn0bpZ09wcstPtB86Bhf/JhRmphK15ltO2DvRcsAwinIuE732W3q/NLvOTe93C8U1J0GN+iZ+GmDH1s38b/HXzPrgE5yca77EjZ34Gp++P4dRwx4FhYIG3j5E3P8QWzf+auCRC2O6JwKsq7Vt25a1a9cSEBCAmVndw1coFHTp0oUuXbrw1ltv4e/vz/r165k0aRIAx48fp6SkBCur6uWaAwcOYGtri4+PT539AURFRVFRUcG8efMw+efGaleWB6+wsLCgslI7K9OmTRsqKyvJyMigW7duNz3PqVOnasZ7hVKpZOz6mJvu44qCskoqq9TYX5OtsleakV9ad3CYX1qBg6X2+bVTmlFRpaboqsyWGsgorP6mn5xbgpe9koHNPDQB1rXKKqu4mFeCu61+2ThDs7FzwMTEtFa2qjAvBzsHJx2tbl6ZqpTje3YQMWzkbfcl6sfeuCxiU6M0j83Nqt/fztYWZBfVZLEcrc3JviardbXsorJa2SpHq9ptKqrUpOSVQh6cSS+kqacdj7bx5pM/79yezGspbe1RmJhQWpCjVV5amFcrq3WFpb0jpQW5WmWqwjwUJqYobao39h/fuILADr0ICo8AwMk7gApVKQdXfkGL/kPIToqjtCCPPz6cqOlDXVVFRtwpzu7ayLD5azEx4n2WHBwdMTE1JTsrU6s8NycbR2fdKxBQvTn+k/dmM+PdD2jboaPWc45OTsz64BPKVCry8/JwcXPjuy8/w7OBt8HnUO8M9EX03+iey+299NJLZGdn88QTT3Do0CHi4+PZunUrI0eOpLKykoMHD/Lee+8RFRVFUlIS69at4/LlyzRrVpM9KCsrY9SoUcTExPDHH3/w9ttvM27cOE3gVJdGjRpRUVHBZ599Rnx8PP/73//46quvtOoEBARQWFjIn3/+SWZmJsXFxTRp0oT/+7//Y/jw4axbt44LFy5w+PBhPvjgAzZtqnvzKFQHU/b29lqHvkuElVVqEnOKa13dF+Jpx/nMum/TEJdVRMg19Zt72pGYXUzlddJeCoUCM12btAAzEwVe9paaJUZjMzM3x7tRE84dj9IqP3s8Cv+mLW67/+N7d1JRXk7bHv1uXFkYRUl5JSl5pZojIauYrCIVYf41AbaZiYLW3o6cuqR7r9Sp1HytNgDt/Z2v2+YKC1PjfhSbmpnj7BtE6uljWuWpp4/hGti0zjZugU1r1489iotfECam1V/OKstUtbI/V+78rUaNZ3Ar7n/zMwa+sUBzOPsFERDWg4FvLDBqcAVgbm5Ok+CmHDl8UKv8yKGDNG/ZSme7HVs389GcmUyd9S4du+j+Ym2hVOLq7k5lZQV7dv5J5249DDb2O8bExDDHv9A9N6sGDRqwd+9eKisr6d+/Py1atOCVV17BwcEBExMT7O3t2bVrFwMHDqRJkyZMnz6defPmMWBAzb1Z+vTpQ+PGjenevTtDhgzhwQcf1NxSQZfWrVvzySef8MEHH9CiRQtWrFjB3LlzteqEh4czZswYhg4dipubGx9++CEA33//PcOHD+fVV18lODiYhx56iIMHD+Lr62vw86PL1tMZdGvoQtdAZ7zslQxt442ztQV/na/+Zja4lRejOvpr6keez8TFxoKhrb3xslfSNdCZbg1d2HK65tYFA5t5EOJhh6uNBZ52SiKC3egc4MyBq+6DNaR1A5q42eJqY0GgszUvdgnEytyUfRdubv/CndD9oSEc2r6RQ9s3kp6cwK9LPic3M4PO/R8CYNP/vmHlgne12qRcOEfKhXOUlZZQlJ9LyoVzpCcn1Or78PaNNO/YFRt7h1rP3cuUNtb4hIbgExoCgGugLz6hITj5NjDyyAxjzZEUnmrvR9dGLgS6WPNG/2BKKyrZfjpDU2dq/2Ce61Kz7WDt0RTa+zvzRJgvfk5WPBHmSzs/R9YcvaipM7pLIC29HfC0VxLoYsOo8ABa+ziy7ap+jaVp74eJ27eNuP3byEtLJnrtdxRnX6bxP/e1OrphGft++FRTv3HX+yjKziB67WLy0pKJ27+NuP3badbnEU0d7xbtObvnDxKidlGYmUZq7FGO/74C75YdMDExxdzSGscG/lqHmYUlShs7HBv41xqjMTz6xFP88esvbP5tA4kJF1g0fx4Z6Wk88Ej1PrLFX37GB7NqbleyY+tmPpz9Fi+8PIFmLVqSnZVJdlYmRYU1e1NjT51gd+QOUlMucuLYUaZOGE+VWs3Qp5654/MT9eeuXyKs635RjRs3Zt26dXXWb9asGZs3b75hv7Nmzaq1x+l6rwkwceJEJk6cqFX29NNPaz1etGgRixYt0iozNze/7uvdCYeTc7FVmvFgC08cLKtvNLpgVxxZ/2zKdbQyx9mmZgkxs6iM+X/FM6yNN70au5JbUs6PRy4SfTFPU0dpZsJTYT44WVlQXllFakEp3+1P4HByrqaOk5UFL4QHYGthSoGqgvisYt7ddlbzuneD1l17U1yQx/affiA/JwtPv0BGTf8AJ3dPAPJzssi9rP0LcP6k0Zp/X4w7w9Fd23Fy8+TNb1Zryi+nJHMh9gTPvf3xnZnIHeQf1opJkas0jx//dAYA+5euYdmzk401LINZGZWM0syEiX0aY6c0JyYtn9fW/U1Jec0WAA87S666+wunUvOZvSmGUeGBjAwP4FJuCbM2xRJ71dW7TtbmTOvfFGcbC4rKKojPLOL19SeITtJemjOGgHbdKCsq4MQfqynJz8bRy5+eY9/C1rn61hSl+Tla98SydfWk14tvE732O87u3oiVgzPtHnsOvzY1N8tscd9QUCg4/vtySvKyUdra492iA60ffOqOz09fPftGkJ+Xy/Il35KdlUlAw0a8O28hHl7VN1DOysokIz1NU3/jL+uorKzks48/4LOPa/YK9xv4AK/PqP4dUKYqY+nXX5J6KQUrKys6dO7KlLfnYGunvWpwL/i3XgFoCAr11TeI+g8YMWIEubm5/PLLL8Yeil5GrTpq7CEY3eJhbfg1Ju3GFf/lHgrxZIwiwNjDuCt8pU6g56d/GXsYRhc5sQezt50x9jCM7q1+wSRl170P9L/Ez7n+7ylWkRJrkH7MvP99FwHd9RksIYQQQtylJIOl038uwFq6dKmxhyCEEEKIf7n/XIAlhBBCCAORDJZOEmAJIYQQQi+yyV03OTNCCCGEEAYmGSwhhBBC6EcyWDpJgCWEEEII/cifytFJQk8hhBBCCAOTDJYQQggh9CNLhDpJgCWEEEIIvchVhLrJmRFCCCHEPefLL78kMDAQS0tL2rVrx+7du2+q3d69ezEzM6N169b1Oj4JsIQQQgihHxMTwxy3aPXq1UyYMIFp06Zx9OhRunXrxoABA0hKSrpuu7y8PIYPH06fPn30nfFNkwBLCCGEEPpRmBjmuEWffPIJo0aNYvTo0TRr1oz58+fj6+vLokWLrtvuhRde4Mknn6Rz5876zvimSYAlhBBCCP0YKMBSqVTk5+drHSqVqs6XLCsrIzo6moiICK3yiIgI9u3bp3Oo33//PXFxcbz99tsGPQW6SIAlhBBCCKOaO3cuDg4OWsfcuXPrrJuZmUllZSUeHh5a5R4eHqSlpdXZ5ty5c7zxxhusWLECM7M7c32fXEUohBBCCP0Y6CrCqVOnMmnSJK0ypVJ5/Ze+5ianarW6VhlAZWUlTz75JLNmzaJJkya3P9ibJAGWEEIIIfRiqNs0KJXKGwZUV7i6umJqalorW5WRkVErqwVQUFBAVFQUR48eZdy4cQBUVVWhVqsxMzNj69at9O7d+/YncQ1ZIhRCCCHEPcPCwoJ27dqxbds2rfJt27YRHh5eq769vT0nTpzg2LFjmmPMmDEEBwdz7NgxOnbsWC/jlAyWEEIIIfRjpBuNTpo0iaeffpqwsDA6d+7MN998Q1JSEmPGjAGqlxxTUlL44YcfMDExoUWLFlrt3d3dsbS0rFVuSBJgCSGEEEI/Rvpjz0OHDiUrK4vZs2eTmppKixYt2LRpE/7+/gCkpqbe8J5Y9U0CLCGEEELcc8aOHcvYsWPrfG7p0qXXbTtz5kxmzpxp+EFdRQIsIYQQQuhH/hahThJgCSGEEEIv8seedZMzI4QQQghhYJLBEkIIIYR+JIOlk0KtVquNPQghhBBC3HtKSksN0o+VpaVB+rmbSAbrHtP38z3GHoLRbR/XlT4Ldxt7GEb358vd6PnpX8Yexl0hcmIPxigCjD0Mo/tKnUD/L/caexhGt2VsF+x7TjH2MIwuP/KDen8NSdHoJrk9IYQQQggDkwyWEEIIIfRSJSksnSTAEkIIIYReJLzSTZYIhRBCCCEMTDJYQgghhNBLlaSwdJIASwghhBB6kTs96SZLhEIIIYQQBiYZLCGEEELoRZYIdZMASwghhBB6kfhKN1kiFEIIIYQwMMlgCSGEEEIvskSomwRYQgghhNCLXEWomwRYQgghhNBLlbEHcBeTPVhCCCGEEAYmGSwhhBBC6EVWCHWTAEsIIYQQepFN7rrJEqEQQgghhIFJBksIIYQQepGrCHWTAEsIIYQQepGrCHWTJUIhhBBCCAOTDJYQQggh9CIrhLpJgCWEEEIIvVRJhKWTLBEKIYQQQhiYZLCucfz4cd5//3327NlDZmYmAQEBjBkzhldeecVgrzFixAhyc3P55ZdfDNbnrRjewY+BzT2wU5pxOr2QhX/FkZhdfN023Rq5MKKjP14OlqTmlbLkQCJ747M0z7dsYM+QNj40drfB1UbJWxtj2Hchu1Y/fk5WjA4PILSBAwoFJGYXM2fzGTIKVQaf540M7+jH/c09sbM0IzatgIWRN3cenu0coDkPi/cl1DoPQ9v50NjNFldbJW/9HqP1PICTlTnPdQmknZ8jtkoz/r6Ux+eRcaTkldbLPG/ViE7+PNDSq/q8pBYwf+c5ErKuf166B7kyMjyABg5WXMor4bu9F9gTVzPvh1p58XCrBnjaWwKQkFXMsoOJHEqo/TNyLwnq1oGI157Hr11LHBt4sGjQ8xzfsNXYw7otT7X3ZWCIJ7ZKU06nF/LFrjgSc0qu26ZrQxeGd/DTvC+WHkzUev+38LLn8TbeNHazxcXGgpl/xLL/ms+Hp9r70jPIFTdbJeWVas5fLuT7g4mcySisl3nqY+qIvox4oCOOdlZExSbx6vwNnE5I11n/mfs78ET/tjQL9ADg2NkUZn+7mejTF+usP+nJnsx8fgBfrtnDG5//Vi9zMDTJX+kmGaxrREdH4+bmxvLlyzl16hTTpk1j6tSpfP7558YemkEMbevNo60b8Plf8bz003Gyi8r44OHmWJmb6mzTzNOO6f2bsv1MBi+sPMr2MxnM6B9MUw9bTR1LM1PiMwv5/K94nf142Vsy/9FWJOeU8Or6E7yw6ijLDydTVnnnr0MZ1s6Hx9p489lfcYxddYyc4nI+HNTiuuchxNOOGQOase10Os//eIRtp9N5a0BTmnrYaepYmZsSd7mIz/6K09nP7AdC8HKw5K3fY3hh5VEy8lV89EhLLM2M/3Z8IsyXx9v6sGDnecb8eITs4jI+Htzq+ufFy5637w9ha2w6o5dHsTU2nZn3h9DMs+a8XC4s45s9F3jhxyO88OMRjiTn8O5DzQlwsb4T06o3ShtrLh6PZdW4t4w9FIMY0sabwaEN+GJ3HOPX/E1OcRlzH7r++6KZhx1vRgTz59kMxq4+xp9nM5gWEUyw+1WfD+YmxGcW8cVu3e+LlNwSvtgdzwurj/Lq+r9JK1Ax98HmOFjeHXmACU/04KXHuzF5wS/0HPMZGdmFbPh4NLZWFjrbdGvdkDV/HuOBid/Q96UvuZiey/qPR+Plal+rbttgH0Y82JET5y/V5zQMrkptmOPfyPif6EagUql4+eWXcXd3x9LSkq5du3L48GEARo4cycKFC+nRowcNGzbkqaee4tlnn2XdunWa9omJiTz44IM4OTlhY2ND8+bN2bRpEwCVlZWMGjWKwMBArKysCA4OZsGCBZq2M2fOZNmyZWzYsAGFQoFCoSAyMvKOzX1wqDc/RiWzJz6LhOxiPtx+FkszU3o3cdPZ5tHQBkQn57Ay+iLJuSWsjL7I0Yt5DA711tQ5nJTD9weT2HNNtuZqIzv5czAhh2/3JXA+s4jUfBUHE3PILSk36BxvxuDW3vx4OJk9cdXn4YNtZ7A0N6VPsO7zMLi1N9FJOayMukhyTgkroy5y5GIuj7ZuoKlzKDGH7w8kamVvrubjaEWIlz3zd57nTEYhF3NLWBB5HitzU3pf57XvlMfaerP8UBK7z2dyIauYuVtOY2lmSt+m7rrbtPEmKjGHHw8nk5RTwo+HkzmSnMtjbXw0dfbHZ3EwIZuLuSVczC1h8b4ESsorCfGs/YvmXnJqcyS/zpjHsfVbjD0UgxjUqgGroi+yNz6bxOxiPv7zHEozE3o1dtXZ5pHQBhxJzmX1kRSSc0tYfSSFYyl5PBJa876ISspl2aEk9sbrzljuPJfJ0Yt5pOWrSMwp4Zu9F7BRmhHoYmPQOepr7GNd+Xj5Dn7bfYrYC+m8MHc1VpbmPN63jc42o99dxXcbDnDifCrnki4z/uO1mCgU9GwbpFXPxsqC76YP4+WP15JbeP1s4d1GrTbM8W/0nwywXn/9ddauXcuyZcs4cuQIQUFB9O/fn+zsut/8eXl5ODs7ax6/9NJLqFQqdu3axYkTJ/jggw+wta3+tlZVVYWPjw8//fQTMTExvPXWW7z55pv89NNPAEyePJkhQ4Zw3333kZqaSmpqKuHh4fU/acDLXomLjQXRSbmasvIqNX+n5NHcy05nuxBPO602AFFJOTT31N3mWgqgY4ATF3NLeP+h5vw8sgOfPRZKeKDzDdsampe9JS42FkQl5WjKyivVHE/Jo7mX7l/4IV52Wm0AohJzrtvmWuamCgCtrF2Vuvr/oYWXw033Ux+8HCxxsVFyOFH7vBxLyaV5A91zbO5lz+FE7ffOoYRsnW1MFNC7iRuWZqacSs03zODFbfO88vmQnKspK69Sc+JS3nUD4WYedlptoDqgCrmFz4drmZkoGNjcg0JVBfFZRXr3YygBXs54utiz4/A5TVlZeSV7j8XTsbn/TfdjrTTH3MyUnALtJfd5rwxiy4HTREafN9iYhfHdHbnXO6ioqIhFixaxdOlSBgwYAMC3337Ltm3bWLx4Ma+99ppW/f379/PTTz+xceNGTVlSUhKPPvooLVu2BKBhw4aa58zNzZk1a5bmcWBgIPv27eOnn35iyJAh2NraYmVlhUqlwtPTsz6nWouTdXUqO+eajFFOSTkedsrrtqurjZON7tT4tRytzbG2MGNYOx+WHkjk230JtPdzYubAZkxef4K/L925X7RO1uYA5BRfM6fiMjzsLHW2c7a2qKPNrZ2HpJwS0vJLGR0ewKc7zlNaXsljbbxxsbHA+Rb6qQ/OV34+isu0ym94XmzqPi9X+rsi0MWGL4e1wcLMhJKySmb8duqGe97EnVPz/1/7/9L9up8P5uSWaP/M5JaUaT5vbkVHfyemRgSjNDMhu6iMqb+dIr+04pb7MTR35+pgMSOnQKs8I6cQPw+nm+5n1vMDSM3MY+dVgdSjvUMJbdKAnmPuzW0oVbILS6f/XIAVFxdHeXk5Xbp00ZSZm5vToUMHYmNjteqeOnWKhx9+mLfeeot+/fppyl9++WVefPFFtm7dSt++fXn00Udp1aqV5vmvvvqK7777jsTEREpKSigrK6N169a3NE6VSoVKpb3xW6nU/SFXl95N3JjYsyYVPe33U0DtP22g4MYp2jr/HMItvK9MFNWZm/0Xslh7vHqPQVxmESFedjzQwqteA6w+wW5M7NVY8/jN365zHm4wqWufVSjqKLyOyio1MzfGMrlvYza80JnKKjXRyTkcNMJm775N3Xm1TxPN4zd+OQHUMUcUN+zr2vOmqKNJck4xo5dHYWtpRvcgN6b2D+aVn49LkGUkvRq78UrPRprHMzbG/POvG/9fXqv2x4NCr93Px1LyGLv6GPZWZgwI8WRaRDAvr/2bvDu8jWBI39bMf3Ww5vHjb3wP1J6nQqG44WfGFa8M68FjfVozcMLXqMqqg0ZvNwc+GPcgg15brCm71/xbl/cM4T8XYF35paq45lNDrVZrlcXExNC7d2+ee+45pk+frlV39OjR9O/fn40bN7J161bmzp3LvHnzGD9+PD/99BMTJ05k3rx5dO7cGTs7Oz766CMOHjx4S+OcO3euViYM4O233wbXvjfdx/4L2ZxOP6p5bG5avSLsbG1B9lXfUh2tzMm55hvo1XKKy2plI5yszGtlOq4nr6ScisoqErO19xckZZfQ4jrLT4awLz6b2LQjmsea82BzzXmwtiC3WPcHeXZxGc7/ZL80bW7xPACcu1zICyuPYmNhipmpCXkl5Xw+JJSzd/hqqb1xWcSmRmkem5td9fNRVDMnR2tzsq8zx+yi2j8fjla121RUqauvlMyDM+mFNPW049E23nzy5znEnXcgIZszq2syMleWr53q+ny4zvsip7i8VrbqRp8puqgqqriUX8qlfDidfp4lT7blvmburD6Scst93Y5Ne2OIik3WPLYwr/5V6eFsR3p2zTlzc7QhI/vG79vxQ7vz6lO9ePjVbzkVn6Ypbx3sjbuzHbu+Ga8pMzM1pUurQJ5/pDOu/aZR9W/dAf4f8J/bgxUUFISFhQV79uzRlJWXlxMVFUWzZs2A6sxVr169eOaZZ3j33Xfr7MfX15cxY8awbt06Xn31Vb799lsAdu/eTXh4OGPHjqVNmzYEBQURF6d95YyFhQWVlZXXHefUqVPJy8vTOqZOnXpLcy0pr+RSXqnmSMwuJquojLa+jpo6ZiYKWnk7cCq1QGc/MWkFWm0A2vk5cipNd5trVVSpOZNRiI+jlVa5j6MVGQX1e3sCXeehnW9Nat/MREGot8N19wTFpBbQzk97OSDMz0nvfURFZZXklZTj7WBJE3e7WrdzqG8l5ZWk5JVqjoSsYrKKVIT5a5+X1t6OnLpOhvFUar5WG4D2/s7XbXOFhel/7iPorlFSXvlPMFN9JOaUVH8++Dhq6piZKGjZwIGYNN3/l7HpBbT11d4/2M7XkZhb+HzQRaGo+UJ0JxWWlBGfkqU5Tiekk5aVT6+wmky4uZkpXVo35OCpxOv29fLQ7rz+dB8efX0JR89oB4p/RZ+n47Of0GX0As1x5HQyP20/RpfRC+6J4EquItTtP5fBsrGx4cUXX+S1117D2dkZPz8/PvzwQ4qLixk1apQmuIqIiGDSpEmkpVV/2zA1NcXNrfoqrwkTJjBgwACaNGlCTk4OO3bs0ARnQUFB/PDDD2zZsoXAwED+97//cfjwYQIDAzVjCAgIYMuWLZw5cwYXFxccHBwwN9fOjCiVylteErwZ646n8GSYb/Uv1dwSngzzobSikh1nL2vqTOnbhMwiFYv3J/7T5hKfDm7F0Lbe7IvPJryhM219HJmw7m9NG0tzE7wdaoInL3tLGrnaUFBaobnH1U9HU5jeP5gTl/I4lpJHez8nOgc68+r6Ewaf542sO5bCk+19uZhbUn0e2vtSWl7Jn2euOg/9mpBZVMbifQmaNvMfC2VYOx/2xmfRpaELbX0deWWN7vPgaa+sdR66B7mSV1JORoGKQFdrXureiL3xWbUuJDCGNUdSeKq9HxdziknJLeH/OvhRWlHJ9tMZmjpT+weTWVjGt3svALD2aAoLh7TmiTBf9sZl0qWRK+38HBn/0zFNm9FdAjmYkM3lglKszM3oHexGax9HXjfC/70hKW2scQsK0Dx2DfTFJzSEouxccpLvrcvtAX75+xLD2vmQkldCSl4pT7T1QVVRxc5zmZo6r/VpTGZRGd8fSNS0+XhQS4a08Wb/hWw6BzrTxseBSVf931qamdDg6veFnSUNXWwoUJVzubAMpZkJT7bzYX9CNtlF5dhbmvFACy9cbZTsPl/z2sb05Zo9vPpUL+IuZhKXksnk/+tFSWk5P2+vWSX4euoQLmXmM+vbzUD1suD0kRGMemcliWnZuDtXXwxVVFJGUUkZhSVlxF7Qvo9WUWkZ2fnFtcrvVrJEqNt/LsACeP/996mqquLpp5+moKCAsLAwtmzZgpOTEwsWLODy5cusWLGCFStWaNr4+/uTkJAAVN+K4aWXXuLixYvY29tz33338emnnwIwZswYjh07xtChQ1EoFDzxxBOMHTuWP/74Q9PXc889R2RkJGFhYRQWFrJz50569ux5R+a++kgKSjNTXu7RCDulGbHpBbyx4RQl5TUZNXc7pdafP4hJK+CdLad5tpM/Izr6cymvlHe2nOF0ek1qPNjdjnmPtNQ8frFb9cb/LbHpfPTPEtDe+CwWRMYxrJ0PL3VvSHJOCbP+iOWkEa4kWxV9EQszE17pFaQ5D1N+OVnrPFz94RGTVsA7m/85D52qz8Oczac5nV7zTT3Y3Y5PHq3Zjze2e/Uely0x6Xy4/SwALjYWvNitIU7W5mQXlbH1dAbLDyXV84xvzsqoZJRmJkzs0xg7pTkxafm8tu5vrfPiYWepdV5OpeYze1MMo8IDGRkewKXcEmZtiiX2qgyGk7U50/o3xdnGgqKyCuIzi3h9/Qmir7kq817jH9aKSZGrNI8f/3QGAPuXrmHZs5ONNSy9/XQ0BQszE8Z1b/TPjYgLmPqb9ueDm23tz4f3tp5hREc/hnfwIzW/lPe2ndG6QWgTd1s+GlTz+TCma/UXzq2n05m34zxVajU+TtbMCHbH3sqcgtIKzmYU8OovJ254k9M7Zf7Kv7BSmvPJxEHVNxqNSWbQa99ReNVSqI+Ho9a5GT2oE0oLM5bPflqrr7lLtzF36fY7NnZhHAp1nbuXxd2q7+d7blzpX277uK70Wbjb2MMwuj9f7kbPT/8y9jDuCpETezBGEWDsYRjdV+oE+n+519jDMLotY7tg33OKsYdhdPmRH9T7a5xIzTNIPy2NfJua+vCfzGAJIYQQ4vZJikY32WEqhBBCCGFgksESQgghhF6qJIWlkwRYQgghhNDLVX/1S1xDAiwhhBBC6EUyWLrJHiwhhBBCCAOTDJYQQggh9FIpGSydJIMlhBBCCL1UqdUGOfTx5ZdfEhgYiKWlJe3atWP3bt33R1y3bh39+vXDzc0Ne3t7OnfuzJYtW/Sd9k2RAEsIIYQQ95TVq1czYcIEpk2bxtGjR+nWrRsDBgwgKanuv4qxa9cu+vXrx6ZNm4iOjqZXr148+OCDHD16tM76hiBLhEIIIYTQi7GuIvzkk08YNWoUo0ePBmD+/Pls2bKFRYsWMXfu3Fr158+fr/X4vffeY8OGDfz222+0adOmXsYoGSwhhBBC6MUYS4RlZWVER0cTERGhVR4REcG+fftubtxVVRQUFODs7HxLr30rJIMlhBBCCKNSqVSoVCqtMqVSiVKprFU3MzOTyspKPDw8tMo9PDxIS0u7qdebN28eRUVFDBkyRP9B34BksIQQQgihl0q12iDH3LlzcXBw0DrqWuq7mkKh0HqsVqtrldVl5cqVzJw5k9WrV+Pu7n5b878eyWAJIYQQQi9VBrpLw9SpU5k0aZJWWV3ZKwBXV1dMTU1rZasyMjJqZbWutXr1akaNGsXPP/9M3759b2/QNyAZLCGEEEIYlVKpxN7eXuvQFWBZWFjQrl07tm3bplW+bds2wsPDdb7GypUrGTFiBD/++CP333+/QcdfF8lgCSGEEEIvlYZKYd2iSZMm8fTTTxMWFkbnzp355ptvSEpKYsyYMUB1RiwlJYUffvgBqA6uhg8fzoIFC+jUqZMm+2VlZYWDg0O9jFECLCGEEELoxVh/i3Do0KFkZWUxe/ZsUlNTadGiBZs2bcLf3x+A1NRUrXtiff3111RUVPDSSy/x0ksvacqfeeYZli5dWi9jlABLCCGEEHqpNOJfyhk7dixjx46t87lrg6bIyMj6H9A1ZA+WEEIIIYSBSQZLCCGEEHox1hLhvUACLCGEEELoxVib3O8FskQohBBCCGFgksESQgghhF5kiVA3CbCEEEIIoRdjXkV4t5MlQiGEEEIIA5MMlhBCCCH0IkuEuinUajk7QgghhLh1Px69aJB+nmzjY5B+7iaSwbrHdP1gp7GHYHR7pvTilfUnjD0Mo1vwSEtmbztj7GHcFd7qF0z/L/caexhGt2VsF8YoAow9DKP7Sp2ARZuRxh6G0ZUdXWLsIfynSYAlhBBCCL3IJnfdJMASQgghhF5kD5ZuEmAJIYQQQi+VEmDpJLdpEEIIIYQwMMlgCSGEEEIvVfK3CHWSAEsIIYQQepFN7rrJEqEQQgghhIFJBksIIYQQepGrCHWTAEsIIYQQepGrCHWTJUIhhBBCCAOTDJYQQggh9FIpVxHqJAGWEEIIIfQiAZZuEmAJIYQQQi8SYOkme7CEEEIIIQxMMlhCCCGE0ItksHSTAEsIIYQQepEASzdZIhRCCCGEMDDJYAkhhBBCL5LB0k0CLCGEEELoRQIs3WSJUAghhBDCwCSDJYQQQgi9SAZLNwmwhBBCCKEXCbB0kyXC2zRixAgGDRpk7GEIIYQQ4i4iGaz/oJFdAngotAF2lmbEpObzybazXMgsvm6bHk3cGN0tEG9HK1JyS/h2Vzy7zmXWWfepTn6M6dGIn6KSWfjneU35nim96qz/xc7zrDyUrP+EblHXQGd6N3bD3tKMtHwV605cIj5L9/wbudjwSEsvPO2V5JVWsOPsZfYmZGue7+DnyP+1863V7tUNJ6n459tdIxdrejd2w9fRCgcrc747kMiJ1HzDT+4WnN21iZg/11GSl4Ojlx/tHh2Ne1BznfXTz53kyLrF5KYmYe3gTLO+g2nSbYBWndM7N3B292aKcy6jtLHHr004rR8ajqm5Ra3+Tm75meO//Y/gng8S9thzBp/frXqqvS8DQzyxVZpyOr2QL3bFkZhTct02XRu6MLyDH14OlqTmlbL0YCL7LtT8bLTwsufxNt40drPFxcaCmX/Esv+q56+8bs8gV9xslZRXqjl/uZDvDyZyJqOwXuZZH4K6dSDitefxa9cSxwYeLBr0PMc3bDX2sAxuxgsPM+rRHjjZWXPoZDyvzF1OTPwlnfVHPtKdpx4Ip3mQNwBHYhOZ8dlaok5d0NQ5u/FDAhq41mq7aPUOXnl/ueEnYWCSwdJNAqz/mP/r6MfQ9r68uymW5OwSngn359MhrXniu4OUlFXW2aZ5A3tmPRzCd7svsOtsJt2buDL74eaMXXGUmGuChKaedjwU2oDzdfxyeOjzvVqPOzV05o0BTfnrzGXDTfAG2ng78EgrL34+dokL2cWEBzgzJjyAudvPkVNSXqu+s7U5L4QHsD8hm/9FJxPobM3jrRtQWFbB8Us1cy8pr+TdbWe12lZc9cFjYWZCSl4pB5NyGNXRv/4meJMSoncTvfY72g8dg1vDZpzbs5mdX87igelfYOPsVqt+YWYaOxfNIig8gvBnJnE5PpbDq7/C0tYBvzbhAFw4HMnRDT/Q6f9exq1hUwoyLrH/fwsAaPfoaK3+shLPcX7fFhy9A+p9rjdjSBtvBoc2YN6Oc1zMLeXJdj7MfagFo348Qkl53e+LZh52vBkRzLJDieyLzya8oTPTIoKZtP6EJjiyNDchPrOIrafTeeu+ZnX2k5Jbwhe740nNL0VpasIjod7MfbA5z66IJq+0ot7mbEhKG2suHo9l3/c/M2bd18YeTr2YPGIArzwVwei3F3MuMZ2pzz3Apq8m02LQmxQWl9bZpkdYMKs3H+TA8fOUlpXz6jMD2LToVVo/Op1Ll3MBCH9qDqYmCk2b5kE+bP5qMmu3Hb4T07ptFRJg6SRLhDdpzZo1tGzZEisrK1xcXOjbty9FRUWa52fNmoW7uzv29va88MILlJWVaZ7r2bMn48aNY9y4cTg6OuLi4sL06dNRq+/8D+bjYT78sD+RXWczuZBZxLsbY1GamxDRzENnmyFhPkQl5LD8QBJJ2cUsP5BEdGIOQ8J8tOpZmZvy9oMhfLj5DAWltYOV7KIyraNrkCtHEnO5lFf3h1N96BnkyoGEHA4k5pBeoGL9iVRySsrpEuhcZ/0ugS7klJSx/kQq6QUqDiTmcDAxh16NtYMQtRoKVBVax9Vi0wvZFJvO35eMm7W64vSODTTq3Jeg8AgcPH0Je+w5rJ1cObt7U531z+3ZjI2TG2GPPYeDpy9B4RE07NSX2D/Xa+pkXjiNW8NmBLbvga2LB17N2uAf1o2spPNafZWrSti7dB4dnxiHhZVtvc7zZg1q1YBV0RfZG59NYnYxH/95DqWZCb0a184sXPFIaAOOJOey+kgKybklrD6SwrGUPB4JbaCpE5WUy7JDSeyNz9bZz85zmRy9mEdavorEnBK+2XsBG6UZgS42Bp1jfTq1OZJfZ8zj2Potxh5KvRn/ZD/eX/w7v+w4wqm4FEbOWIy1pQXDBnTU2eaZad/y9c87OX42mTMJaYyZsxQThYLeHUM0dTJzCkjPytccA7uFcj4pnV3RZ+7EtG5bZZXaIMe/kQRYNyE1NZUnnniCkSNHEhsbS2RkJIMHD9YESH/++SexsbHs3LmTlStXsn79embNmqXVx7JlyzAzM+PgwYMsXLiQTz/9lO++++6OzqOBgyWutkoOXbVEUV6p5lhyLi287XW2a+HtoNUG4OCFbFp4O2iVTerXmH1xWUQl5txwLE7W5oQ3cmHj37rT64ZmqlDg62hVa+nlTHohgS7WdbYJcLbmTLp2/dPphfg5WnHVl06UZia83T+YWfc15fnO/ng7WBp8/IZSWVFOdvJ5vJq10Sr3ataGzAun62xz+cLpWvUbhLQhK+k8VZXVwaRbwxCyk+PITKjO5BVkpnHpVDTezcO02h1e/RXeLcLwatraQDO6PZ72SlxsLIhOztWUlVepOXEpjxBP3e+LZh52Wm2gOqAK8bTTeyxmJgoGNvegUFVBfFbRjRuIOyLQ2w0vN0e27z+lKSsrr2B39Bk6hwbddD/WlkrMzUzJzqv7/9bczJQnB3Zi2YY9tz1mYXyyRHgTUlNTqaioYPDgwfj7Vy/vtGzZUvO8hYUFS5YswdramubNmzN79mxee+015syZg4lJdQzr6+vLp59+ikKhIDg4mBMnTvDpp5/y3HN17z1RqVSoVCqtMqVSeVvzcLat3geTXVymVZ5TVI7HdQICZxsLcoqubVOGs03Nvpo+zdxp4mnHc8uib2osA1p4UVxWyV9n697HVR9slKaYmijIvya7VKCqwE5pXmcbe0szTl9TP19VgamJAlsLM/JVFaQXqPjxyEUu5ZViaW5Cj0auTOjeiA93nOPyNeftbqAqzEddVYWlnaNWuaWdAyX5uXW2Kc3PxdLO4Zr6jqirKlEV5mPl4ExAWHdUhfls+/QN1Go16qpKGncbQPOIxzRtEqJ2kZ0cz4DX5xl6Wnpztq7+Oc4p1s665hSX426n+z3nZG1Obon2/29uSRlO1rX3m91IR38npkYEozQzIbuojKm/nSL/Hlke/C/wcK0OtNOztTPQ6Vn5+Hm53HQ/7778GCkZOfx58FSdzz/cqy2Odtb88NveOp+/G/1bs0+GIBmsmxAaGkqfPn1o2bIljz/+ON9++y05OTlaz1tb12RAOnfuTGFhIcnJNRu3O3XqhEKh0Kpz7tw5Kivr3t8xd+5cHBwctI65c+fe0rj7hXiwdWI3zWH2T7DHte8HBdVrXNdR61mFAvU/pe52Sl7p05g5v8VQVll1U2O7v5UnW2PSb7p+/dM9/7pO19XliTklRCXncim/lPisYpYeSiKjUEW3Rjf/wWscCu2HalAo6q5ZV33NEvc/jdLPnuDklp9oP3QMA6Z8SvfnppJy8jAn/lgFQFHOZaLXfkuXZybVuen9TunV2I1fnuukOWr2v2j/T1//XPzTovYb43o/SjodS8lj7OpjTFz3N1HJuUyLCMbBqu6gX9S/JwZ0Invvl5rD3MwUoNa2DoWidpkurz5zH0Pv68DQyV+gKqs7eB4xqBtb9p4g9Z/9WfeCSrXaIMe/kWSwboKpqSnbtm1j3759bN26lc8++4xp06Zx8ODB67ZT3MwntA5Tp05l0qRJWmVKpZLt8/fddB97zmcSc9WeHwuz6vE421iQdVVmxcnanOzrZFqyr8lWXWmTU1T9jT/Y0w5nGwu+G1GzFGRmYkKoryOD23rT++O/uPpLTisfB/xdbHh7Q93f4upLkaqSyio19krtH3s7pVmtPVNX5JdW1Fm/skpNkY4PSTWQlFuCm83tZRzri9LWHoWJCaUF2ku5pYV5tbJaV1jaO1JakKtVpirMQ2FiitKmekns+MYVBHboRVB4BABO3gFUqEo5uPILWvQfQnZSHKUFefzx4URNH+qqKjLiTnF210aGzV+LiYmp4Saqw4GEbM6sLtA8Njetfl84WVuQfVUWy9HKvFZW62o5xeW1slWOVubklNx61lJVUcWl/FIu5cPp9PMsebIt9zVzZ/WRlFvuS9y+3/46xqGT8ZrHSvPqzwBPFwfSMvM05e7O9mRk33hf5cSn+zNl1APcN+ZjTpy7WGcdPy8X+nQMYcjkz29z9OJuIQHWTVIoFHTp0oUuXbrw1ltv4e/vz/r11Rt8jx8/TklJCVZWVgAcOHAAW1tbfHxqNoEfOHBAq78DBw7QuHFjTE3r/oWiVCpve0mwpKySlDLty8wzC1W0D3Dm3D/7kMxMFLT2deSryPi6ugDgZEoe7QOc+Smq5oOhQ6AzJ1OqP2iiEnN4evEhrTZvDmxKYlYxKw4mcW0G+YFWXpxOzef85Tu7x6RSrSY5t4Rgd1v+vurqx2B3W523TEjILqbFNXtqgt1tScotqTWvq/k4WHIp/85t3r8VpmbmOPsGkXr6GL6hnTXlqaeP4dOyQ51t3AKbcvGk9lVNqbFHcfELwsS0+mOkskxV60uF4p+sqRo1nsGtuP/Nz7Se3798AfYePjTv9+gdCa6g+orPa68MzCoqo62PI3GZ1T+TZiYKWjZwYPH+BJ39xKYX0NbXgfVX7SNs5+tITFqBzjY3S6EAc1NZYDCWwuLSWlcGpl7OpU+nEI6dSQKq90t1axfMmwt+vm5fk4bfx9TRD3D/S59wJCZBZ71nHupKRnY+m3b/fdvjv5NkiVA3CbBuwsGDB/nzzz+JiIjA3d2dgwcPcvnyZZo1a8bff/9NWVkZo0aNYvr06SQmJvL2228zbtw4zf4rgOTkZCZNmsQLL7zAkSNH+Oyzz5g3787vQ/k56iJPd/bjYk4xyTklDO/sj6q8iq2x6Zo60+9vxuUCFV/vqg66fo6+yOdPtuH/Ovqx+1wm3Rq7EubvxNgVR4HqQO5CpnawVFpeSX5pea1yawtTegW78/lO7SvL7pTI85k8FeZDUm4JCf/cpsHJ2py9/2zifyDEAwcrc1ZEVweTey9k0a2hC4NaerE/IZsAZ2s6BTjxw+Ga5d/7mrqTkF3M5UIVluamdG/kgreDFT8fr/nFa2FqgpttTbbDxdocbwdLissq67w9RH1r2vth9v/wKS5+QbgGNuX83i0UZ1+m8T/3tTq6YRkledmED6/ONjXueh9ndm0keu1igrpEkHnhNHH7t9NlxGRNn94t2hO7cwNOPg1xDWhCweVUjv++Au+WHTAxMcXE0hrHBtq3qDCzsERpY1er/E775e9LDGvnQ0peCSl5pTzR1gdVRRU7r7rX22t9GpNZVMb3BxI1bT4e1JIhbbzZfyGbzoHOtPFxYNL6E5o2lmYmNHCw0jz2tLOkoYsNBapyLheWoTQz4cl2PuxPyCa7qBx7SzMeaOGFq42S3efv3P7E26W0scYtKEDz2DXQF5/QEIqyc8lJvnMXstSnz37cxpRRD3A+KYPzSelMGXU/xaVlrPqjZiVjyZzRXMrIYfpna4HqZcGZYx9h+JvfkHgpEw+X6r1chcUqikpq9tgqFAqGP9yF5b/vo/Ku2TZxcyTA0k0CrJtgb2/Prl27mD9/Pvn5+fj7+zNv3jwGDBjA6tWr6dOnD40bN6Z79+6oVCqGDRvGzJkztfoYPnw4JSUldOjQAVNTU8aPH8/zzz9/x+ey4mASSjMTJkU0qb7R6KUCJv50XOseWB72SqquWhM/mZLPzF9jeK5bIKO7BZKSW8Jbv56qdQ+sm9G3mTsKBWyPSb9x5XpwNCUPGwtT+ge742BpRmq+iq/3JWiCHHtLc5yu2vuSXVzO1/sSeKSVF90CnckrrWDd8VSte2BZmZsytI039kozSiqquJhbwsLd8SRddZNKPycrxndrqHn8SKvqS/kPJubw45G6lwzqU0C7bpQVFXDij9WU5Gfj6OVPz7FvYevsDkBpfg5F2TX3J7N19aTXi28TvfY7zu7eiJWDM+0ee05zDyyAFvcNBYWC478vpyQvG6WtPd4tOtD6wafu+Pxu1U9HU7AwM2Fc90bYKc04nV7A1N9OaWW63Gy13xcxaQW8t/UMIzr6MbyDH6n5pby37YzWVapN3G35aFDNBTFjugYCsPV0OvN2nKdKrcbHyZoZwe7YW5lTUFrB2YwCXv3lxA1vcno38Q9rxaTIVZrHj386A4D9S9ew7NnJuprdUz5e+gdWSgsWTn0KJ3sbDp2M5/4X52llunw9namqqgmQXhjSG6WFOas/fkmrrzlfbWDO1xs0j/t0DMHfy5Wlv+yu/4mIO0ahNsbNmP5jevbsSevWrZk/f/5t99X1g523P6B73J4pvXjlqizBf9WCR1oye9u9ca+c+vZWv2D6f3nvXHlVX7aM7cIYRYCxh2F0X6kTsGgz0tjDMLqyo0vq/TVG/HjEIP0sfbKtQfq5m0gGSwghhBB6qay6t5Y07yTZRSmEEEIIYWCSwboDIiMjjT0EIYQQwuBkk7tuEmAJIYQQQi8SYOkmS4RCCCGE0EtFldoghz6+/PJLAgMDsbS0pF27duzeff2rMP/66y/atWuHpaUlDRs25KuvvtLrdW+WBFhCCCGEuKesXr2aCRMmMG3aNI4ePUq3bt0YMGAASUlJdda/cOECAwcOpFu3bhw9epQ333yTl19+mbVr19bbGCXAEkIIIYReKqvUBjlu1SeffMKoUaMYPXo0zZo1Y/78+fj6+rJo0aI663/11Vf4+fkxf/58mjVrxujRoxk5ciQff/zx7Z4CnSTAEkIIIYReDBVgqVQq8vPztQ6VSlXna5aVlREdHU1ERIRWeUREBPv21f33evfv31+rfv/+/YmKiqK8vH7+moYEWEIIIYQwqrlz5+Lg4KB1zJ07t866mZmZVFZW4uHhoVXu4eFBWlpanW3S0tLqrF9RUUFmZv38WSq5ilAIIYQQejHUVYRTp05l0qRJWmVKpfK6ba794/JqtbpW2Y3q11VuKBJgCSGEEEIvhgqwlErlDQOqK1xdXTE1Na2VrcrIyKiVpbrC09OzzvpmZma4uLjoN+gbkCVCIYQQQtwzLCwsaNeuHdu2bdMq37ZtG+Hh4XW26dy5c636W7duJSwsDHNz83oZpwRYQgghhNCLsa4inDRpEt999x1LliwhNjaWiRMnkpSUxJgxY4DqJcfhw4dr6o8ZM4bExEQmTZpEbGwsS5YsYfHixUyePNlg5+JaskQohBBCCL2ojXQn96FDh5KVlcXs2bNJTU2lRYsWbNq0CX9/fwBSU1O17okVGBjIpk2bmDhxIl988QUNGjRg4cKFPProo/U2RgmwhBBCCHHPGTt2LGPHjq3zuaVLl9Yq69GjB0eOHKnnUdWQAEsIIYQQeqmSv0WokwRYQgghhNDLlVsdiNokwBJCCCGEXoy1B+teIFcRCiGEEEIYmGSwhBBCCKEX2YOlmwRYQgghhNCLusrYI7h7yRKhEEIIIYSBSQZLCCGEEHqRqwh1kwBLCCGEEHqRPVi6yRKhEEIIIYSBKdSS3xNCCCGEHjq9s90g/RyY3tcg/dxNZInwHjP9j1hjD8Ho3hnQjLKcNGMPw+gsnDxJyi409jDuCn7Ottj3nGLsYRhdfuQHWLQZaexhGF3Z0SWMUQQYexhG95U6od5fQ240qpssEQohhBBCGJhksIQQQgihlyrZZaSTBFhCCCGE0IssEeomAZYQQggh9CIBlm6yB0sIIYQQwsAkgyWEEEIIvciNRnWTAEsIIYQQepFbaeomS4RCCCGEEAYmGSwhhBBC6EVdZewR3L0kwBJCCCGEXmQPlm6yRCiEEEIIYWCSwRJCCCGEXuQ+WLpJgCWEEEIIvUiApZssEQohhBBCGJhksIQQQgihF/ljz7pJgCWEEEIIvcgSoW4SYAkhhBBCLxJg6SZ7sIQQQgghDEwyWEIIIYTQi9xoVDcJsIQQQgihF/ljz7rJEqEQQgghhIFJBksIIYQQepFN7rpJgCWEEEIIvcgeLN0kwLrGiBEjWLZsWa3y/v374+joSF5eHn/88Yem/I8//mDgwIFMnz6dOXPmaMrnzJnDokWLuHTpEgBr167lww8/5PTp01RVVeHn58d9993HvHnz6n9S/zi/ZxNndvxCaX4O9p6+tH5kFG6Nmuusf/n8SY79soT8tGSsHJwJ7v0Ijbrcp3k+8rNpXI47VaudZ0g7uj0/o1Z57LY1nNy4nMbdH6D14NGGmZSBrFqznqUrVnE5K5tGgQFMmTiOdq1Db9ju6PETPDv2FYIaBrLmf4s15efjL/DFN0uIOX2WS2lpvD5hHE8Pe7w+p2AQv679iZ9X/I+srEwCAhvy4oTJtGzdps66uyN38Pu6NcSdO0N5WTn+DRvy9Kjnad8pXFOnoqKclcu+Z9sfv5N5+TK+fv6MHvsy7TuH19nn3WTqiL6MeKAjjnZWRMUm8er8DZxOSNdZ/5n7O/BE/7Y0C/QA4NjZFGZ/u5no0xfrrD/pyZ7MfH4AX67Zwxuf/1YvczCUGS88zKhHe+BkZ82hk/G8Mnc5MfGXdNYf+Uh3nnognOZB3gAciU1kxmdriTp1QVPn7MYPCWjgWqvtotU7eOX95YafxB0Q1K0DEa89j1+7ljg28GDRoOc5vmGrsYcljET2YNXhvvvuIzU1VetYuXIlvXr1Ys+ePVRUVGjqRkZG4uvry86dO7X6iIyMpFevXgBs376dYcOG8dhjj3Ho0CGio6N59913KSsru2NzSj6yh2Prl9Cs3+P0m/wJbg1D2P31HIpzLtdZvygrnd3fzMGtYQj9Jn9C076PcXTdd1w8vk9TJ3zkGzw4+3vNETFlIQoTE3xDa//yzE46R/z+rTg0CKivKept87YdfDD/c54b8TQ/L/uWdq1b8eLEKaSm6f5lClBQWMibs9+jY1jbWs+Vlpbi492ACS89j6uLc30N3aAit29l0fx5PDFiJIuW/UiL0Da8OWk8GWmpddY/cfQIbTt05N15C/li6XJC24bx1msTOX/mtKbO918vYuMv63hp0uss/vFnHnjkUWa+MVmrzt1owhM9eOnxbkxe8As9x3xGRnYhGz4eja2Vhc423Vo3ZM2fx3hg4jf0felLLqbnsv7j0Xi52teq2zbYhxEPduTEed1Byt1i8ogBvPJUBBPeX074U3NIz8pj01eTsbW21NmmR1gwqzcfJOK5D+n+zLskp2axadGrNHBz1NQJf2oOvn0naI77xnwMwNpth+t7SvVGaWPNxeOxrBr3lrGHcseoqyoNcvwbSYBVB6VSiaenp9bh5OREr169KCwsJCoqSlM3MjKSN954g8OHD1NcXAxAWVkZ+/fv1wRYv//+O127duW1114jODiYJk2aMGjQID777LM7NqezkRsI7NiXhp37VWevBo/G2tGVuD2b66wft3cz1o5utB48GntPXxp27kdgxz6c2bFBU8fCxg5LeyfNkX7mGKbmSnxad9Hqq0JVwsH/fUrY0JewsLKp13nq44eVPzH4wYE8+vADNAwMYMrE8Xi6u7F63Ybrtpv9/jwGRvQltEXtLGCLkGa8Ov5FBvTrg4W57l/Kd5O1K5dz34MPM/ChR/APCGTsxMm4uXvw27o1ddYfO3EyQ596huCQ5vj4+jHqxXF4+/qxf88uTZ3tmzfyxDMj6RjeFS9vHx4c/DhhnTqxZuXdnaEY+1hXPl6+g992nyL2QjovzF2NlaU5j/etO5sHMPrdVXy34QAnzqdyLuky4z9ei4lCQc+2QVr1bKws+G76MF7+eC25hSX1PZXbNv7Jfry/+Hd+2XGEU3EpjJyxGGtLC4YN6KizzTPTvuXrn3dy/GwyZxLSGDNnKSYKBb07hmjqZOYUkJ6VrzkGdgvlfFI6u6LP3Ilp1YtTmyP5dcY8jq3fYuyh3DESYOkmAdYtaNKkCQ0aNNBkqwoKCjhy5AiPP/44jRo1Yu/evQAcOHCAkpISTYDl6enJqVOnOHnypFHGXVVRTs7FODybttYq92jamsyEujMJWQln8LimvmfTNuQkn6eqsqLONhcObse3bVfMlNrfbI+s+QavkHZ4BN94ye1OKy8vJ+bMWcI7ttcqD+/YnmMndP9/rf99E8kpKbw46pn6HuIdUV5eztkzp2nXoZNWebuOnTh14u+b6qOqqori4iLs7B1q+i0rx8JCO8C0UFpy8vix2x5zfQnwcsbTxZ4dh89pysrKK9l7LJ6Ozf1vuh9rpTnmZqbkFBRrlc97ZRBbDpwmMvq8wcZcXwK93fByc2T7/pqtAGXlFeyOPkPn0KDrtNRmbanE3MyU7LyiOp83NzPlyYGdWLZhz22PWYi7hQRYdfj999+xtbXVOq7sr+rZsyeRkZEA7N69myZNmuDm5kaPHj005VeWDRs1agTA+PHjad++PS1btiQgIIBhw4axZMkSVCrVHZmPqqgAdVUVSjtHrXJLOwdK83PqbFNakIulnYNWmdLOEXVVJarC/Fr1sxPPkp+aRMNO/bTKk47sJudiHC0fePr2JlFPcnLzqKysxMVZexnPxdmJrKzsOtskJl1k/hff8P6sGZiZ/Tu2Mebl5lJVWYmTs4tWuZOTCznZWTfVx5ofl1NaUkqPPjU/A2EdO7F21QouJidRVVVF9KED7N8VSXZWpkHHb0juznYAZOQUaJVn5BTi8c9zN2PW8wNIzcxj51WB1KO9Qwlt0oCZ39adOb7bePyzvJmerf2eT8/Kx8PFoa4mdXr35cdIycjhz4O192wCPNyrLY521vzw2179ByuMQjJYuv07fjsYWK9evVi0aJFWmfM/v4B79erFhAkTKC8vJzIykp49ewLQo0cPzZJfZGQkvXv31rS1sbFh48aNxMXFsXPnTg4cOMCrr77KggUL2L9/P9bW1rXGoFKpagVgSqXytualuLZADQpFrdL/b+/Ow6qq9jeAvwdlEGQQlUEQCHDCKEdCUxHRnAqV63yTcshrOaWixjVUUEO9qak5JZp5s5+a09VUwGToSkAiKA5EpghqIjMqh/Fwfn8Q247ASb14FnHez/PwPJ21N/CencqXtdf+rro/4/eGcrV9Tlrc9zCxtoO5fXtpTJ6fjYtHQtDv/eVo0tBvk9X2Vmt5nwqFAouXBWHme5PhYNdWM9k06Mn/t0ooa/65qUVEeCj+vWsHAtesR4s/FKsfzFuIDatXYOr4vwEyGdrY2OKN4d4IP3m8npM/v7EDu+CzBT7S6zEffQlA+uMukclkUOLpnpiaO94Do726YNiHO1BaVjXja9PaFGtmvYWRC3dJYw3NhKHu2PKxr/R6xJzPANRsJimTPX2DyQXvDMG4IW4Y9N7aOt/3uyP7IizmMu5lFzxXbhJHqWicxVF9YIFVCyMjIzg71z797enpiaKiIpw/fx6RkZFYuHAhgKoCy9fXF3l5eYiNjcU779S8deTk5AQnJydMmzYNS5YsQfv27XHgwAFMnjy5xrnBwcEIDAxUGVu2bBnw2rhnfj/6RsaQ6eig5GGBynjJo8Ias1rVDIzNapxf+qgQMp0m0DNS/S2+oqwUGUnn8PLQCSrj+bdvoPRRIb5ft0AaU1ZWIvvmNfx67hT+9um3kOk0eeb3U59amJmiSZMmNWar8vLz0dK8RY3zi+RyXE1Jxc+//IpP1m0EUHVrTKlUosvrA7Bj46e1Lnpv6EzNzKDTpEmNmaWC/DyYPTGr9aSo78Ox/pMgBKxag25uqutyzFq0QOCa9SgrLcWDwkK0bN0aIVs3w6qNTb2/h+d1KuYaElJuS6/1dKv+WbQ0N8b9vMezWK3NjJCV9+hPv97scf2w4G1PjFiwE1dvZkrjXTrYwMLcGD98MVsaa9qkCV5/5SVMH9ULrQYtEf7I+4noi/jpyk3ptf7v18KqpSkycwqlcQtzE2Tl1ZzJftK8SYOxeOqbGDLjU1y+XvvTlHbWLeH1mgvG+n3+P6YnERrr7FN9YIH1jJycnNC2bVscP34cFy9ehIeHBwDA2toaDg4OWLduHUpKSqT1V3VxcHCAoaEhiopqX5Pg7++P+fPnq4zp6+tjRcTNWs9XR6epLlrYOuF+6kXYvPJ4jc391Iuwebn2haotHTrgt6uqT/Nk/nwRLdo6Q6eJ6h+bO0nnUFlRDrseHirjFu1fxRuLN6qMnf9mM4wtbdDRy0d4cQUAurq6cOnQHrE/JcCrfz9pPPanBHj261Pj/OZGRjiy70uVsQOHjyH+QhLWfxIImzbWLzzzi6Crq4v2HToi8Xw8+vR/PPua+FM8evf1qPPzIsJDsW5VEP4ZtAqvvd63zvP09PXRysICFRXlOBd5Fv28BtV5rqY9Ki7Do7uqt0Ezcx/As0c7JP/+lJ9u0yZ4vYsjlu04XduXkMwZ1w8LJ3nBZ9EuJKXeVTkWfeFXvDZ5vcrYtsVj8EtGNjb8X5Tw4goAHslL8EheojJ2L7sAXu4uuJiaAaDqWvTt3gH/3Pit2q8133cI/Ke9ieEz1yPx2q06z3vHuw+y8h7g1H+fbq0f0V8FC6xalJaWIjMzU2WsadOmaNWqqmeLp6cntm7dCmdnZ1haWkrnVN8mdHR0hJ2dnTS+fPlyyOVyDBs2DPb29igoKMCmTZtQXl6OQYNq/0Gjr6//P98S/KP2/Ucgft9naNHWGS0dOuBmbDjk+TlwfH0wAODyiX+juDAXbm9/CABwen0Ifj13CheP7oZjr0HIvZWKtPjv4e47v8bXTov/Hjaur0HfSPVxdF2DZjC1Vl0U3FRPH/qGxjXGRfKdMBb+gavQuVMHvPpyZ3z7n+9w734Wxo7yBgB8tvULZGVn45NlS6Cjo4N2To4qn2/eogX09fRUxsvLy3Ej7VbVf1eUIys7Bz//ch2GzZrBrq2txt7bs/jbhLexJjAA7Tu6oJPrKzh17Aiy7mfizVGjAQC7tm5GTnY2Fi8LAlBVXK0NWooP5vmh08uu0uyXvr4+jJpXzXKmXL2MnOxsOLdrj5zsbOwN2YFKpRLj3m7YDwdsPXQOC972xI07ObhxNwd+f/dEcUk5vv0+STpnh/9Y/JbzAIG/r6eaO94DH095A1NX/h/SM/NgYd4cAFBUXIai4jI8Ki5DSppq64+ikjLkPZDXGG9INn9zBounvolfM7Lwa8Z9LJ46HPKSMuw/HS+ds3vFNPyWlY+PNx8GUHVbcPkHo+D7zy+Q/lsOLFtW/dvwSF6KouLHSx9kMhl8R7yOr7/7EQpFpWbf2Augb2SI1s4O0utWL7WF7asuKMorQP7tht+S43lwBqtuLLBqERoaCmtr1ZmIDh064Oefq5648/T0xN69e6X1V9U8PDwQEhKCsWPH1hjfsmULfH19cf/+fbRo0QJdu3ZFeHg4OnTo8ELfS7W23fqgVP4A18IOVDUatbZD338EwMjcAgBQ/CBPpSeWUUtL9J0egIvHduPGuVMwMDVHV59psH2ix9XDrLvIuZmCfu8v18j7eBGGDBqAgsJCbN+1F9m5uXB2fAlb169BG2srAEB2Ti7uZWY909fMys7BGN/HzVT37NuPPfv2o0fXLvhy20Y1nylO/4Fv4EFhAb7evRN5uTlwcHTCqnWbYPn734Xc3Bxk3X/8i8fJY0egUCiw+dM12PzpGml80LA3sSig6vZ2WWkZ9uzYinu/3UWzZs3g1qsPFi9bgebGT79YXITP/i8azfR1sX7eyKpGo9duY+TCEDwqfty7ztbSDJV/WIc0baQ79PWa4usg1Qc6gvecQfCe7zWWvb59uuc0munrYZP/22hhYoSfrtzE8PfXqcx0tbUyR2Xl4wLpH2MHQF9PFwc+nanytVZs/w9W7Hjc/sTrNRfYW7fCnmP/ffFvRAPse7yC+VH7pddjNlQ1XI7dcwhfTfYTFeuFYoFVN5mSW2H/pXx8OkV0BOFWDu2EsvzMPz+xkdNrYYWMp1gTpA3szJvDpP9i0TGEexC1Bnpdp4iOIVxZ0m7MkDmIjiHcduWtF/492ozb9ucnPYXfDrxfL1+nIeEMFhERET0XzmDVjX2wiIiI6Ln8Ffpg5efnY9KkSTA1NYWpqSkmTZqEgoKCOs8vLy/H4sWL4erqCiMjI7Rp0wa+vr7S3sJPiwUWERERNVoTJ07ExYsXERoaitDQUFy8eBGTJtXd/FoulyMxMREBAQFITEzEkSNH8Msvv8Db2/uZvi9vERIREdFzqWzgtwhTUlIQGhqKuLg4vPZaVVuinTt3olevXkhNTa31QTNTU1OcOXNGZWzz5s1wc3NDRkaGSpcAdVhgERER0XOpr9t7de1e8r+2K4qNjYWpqalUXAGAu7s7TE1N8eOPPz71k/yFhYWQyWQwMzN76u/NW4REREQkVHBwsLRGqvojODj4f/66mZmZsLCwqDFuYWFRo99lXUpKSvDRRx9h4sSJMDEx+fNP+B0LLCIiInou9bXI3d/fH4WFhSof/v7+dX7f5cuXQyaTqf1ISEgAUPv+uUql8k/24q1SXl6O8ePHo7KyElu3bn2ma8NbhERERPRc6muz52e9HThr1iyMHz9e7TkODg5ITk7G/fs1d0rIzs5W2YmlNuXl5Rg7dizS0tIQERHxTLNXAAssIiIiek6i+mC1atVK2r5OnV69eqGwsBA//fQT3NzcAADx8fEoLCxE79696/y86uLq+vXriIyMRMuW6je9rw1vERIREVGj1KlTJwwZMgTvvfce4uLiEBcXh/feew9vvvmmygL3jh074ujRowCAiooKjB49GgkJCdi3bx8UCgUyMzORmZmJsrKyur5VDZzBIiIioufyV+jkvm/fPsyZMwdvvPEGAMDb2xuff/65yjmpqakoLCwEANy5cwfHjx8HAHTp0kXlvMjIyBr7ENeFBRYRERE9l79CgWVubo6vv/5a7Tl/3JbZwcEB9bFNM28REhEREdUzzmARERHRc1FWVoqO0GCxwCIiIqLn8le4RSgKbxESERER1TPOYBEREdFz4QxW3VhgERER0XOpZIFVJ94iJCIiIqpnnMEiIiKi51JfexE2RiywiIiI6LlwDVbdWGARERHRc2GBVTeuwSIiIiKqZ5zBIiIioufCGay6scAiIiKi58ICq268RUhERERUz2RKpVIpOgT9NZSWliI4OBj+/v7Q19cXHUcYXofHeC2q8DpU4XWowutAAAssegYPHjyAqakpCgsLYWJiIjqOMLwOj/FaVOF1qMLrUIXXgQDeIiQiIiKqdyywiIiIiOoZCywiIiKiesYCi56avr4+li1bpvWLNnkdHuO1qMLrUIXXoQqvAwFc5E5ERERU7ziDRURERFTPWGARERER1TMWWERERET1jAUWERERUT1jgUVE9Ix++OEHVFRU1BivqKjADz/8ICARETU0LLDoT/33v//F22+/jV69euHu3bsAgH//+984d+6c4GREYnh6eiIvL6/GeGFhITw9PQUkahiUSiX4YDpRFRZYpNbhw4cxePBgNGvWDElJSSgtLQUAPHz4EJ988ongdJrHYrPKjRs38PHHH2PChAnIysoCAISGhuLq1auCk2mGUqmETCarMZ6bmwsjIyMBicTatWsXXn75ZRgYGMDAwAAvv/wyQkJCRMcSpqSkBF999RW2bt2K69evi45DgrDAIrVWrlyJ7du3Y+fOndDV1ZXGe/fujcTERIHJNI/FZpXo6Gi4uroiPj4eR44cwaNHjwAAycnJWLZsmeB0L5aPjw98fHwgk8nw7rvvSq99fHwwYsQIDB48GL179xYdU6MCAgIwd+5cvPXWW/j222/x7bff4q233sK8efPw8ccfi473wi1cuBBz586VXpeVlaFXr15477338M9//hNdu3ZFbGyswIQkCgssUis1NRX9+vWrMW5iYoKCggLNBxKIxWaVjz76CCtXrsSZM2egp6cnjXt6ejb6HySmpqYwNTWFUqmEsbGx9NrU1BRWVlaYPn06vv76a9ExNWrbtm3YuXMngoOD4e3tDW9vbwQHB+OLL77A9u3bRcd74U6fPg0vLy/p9b59+5Ceno7r168jPz8fY8aMwcqVKwUmJFGaig5ADZu1tTV+/fVXODg4qIyfO3cOjo6OYkIJwmKzyuXLl/HNN9/UGG/dujVyc3MFJNKcL7/8EgDg4OAAPz8/rbwd+CSFQoEePXrUGO/evXutDwI0NhkZGXBxcZFeh4eHY/To0bC3twcAzJ07F8OGDRMVjwTiDBap9Y9//ANz585FfHw8ZDIZfvvtN+zbtw9+fn744IMPRMfTqOpi80naVmyamZnh3r17NcaTkpJgY2MjIJHmLVu2jMXV795++21s27atxvgXX3yBv//97wISaZaOjo7Kwv64uDi4u7tLr83MzJCfny8iGgnGGSxSa9GiRdKTUSUlJejXrx/09fXh5+eHWbNmiY6nUdXF5u7du6ViMzY2Fn5+fli6dKnoeBozceJELF68GN9++y1kMhkqKysRExMDPz8/+Pr6io6nMYcOHcLBgweRkZGBsrIylWPadMsYqFrkHh4eLhUWcXFxuH37Nnx9fTF//nzpvPXr14uK+MJ07NgRJ06cwPz583H16lVkZGSoPEmanp4OS0tLgQlJFG72THVSKBQ4d+4cXF1dYWBggGvXrqGyshIuLi5o3ry56HhCLFmyBBs2bEBJSQkASMXmihUrBCfTnPLycrz77rvYv38/lEolmjZtCoVCgYkTJ2LPnj1o0qSJ6Igv3KZNm7BkyRK888472LlzJyZPnowbN27g/PnzmDlzJlatWiU6osY8bVsKmUyGiIiIF5xG8w4fPowJEyagb9++uHr1Knr27IkTJ05IxxcvXoy0tDQcPHhQYEoSgQUWqWVgYICUlBS89NJLoqM0GHK5nMUmgJs3byIxMRGVlZXo2rUr2rVrJzqSxnTs2BHLli3DhAkTYGxsjEuXLsHR0RFLly5FXl4ePv/8c9ERSYO+//57nDx5ElZWVpg9ezYMDQ2lY4GBgfDw8ED//v3FBSQhWGCRWj179sTq1atVnpLRVlOmTMHGjRthbGysMl5UVITZs2dj9+7dgpJpVlBQEPz8/FR+iABAcXEx/vWvf2nF7VJDQ0OkpKTA3t4eFhYWOHPmDF599VVcv34d7u7ujX6xf11u374NmUwGW1tb0VGIhOMid1Jr1apV8PPzw3fffYd79+7hwYMHKh/a5KuvvkJxcXGN8eLiYuzdu1dAIjECAwOl3ld/JJfLERgYKCCR5llZWUlFlL29PeLi4gAAaWlpWtfJvKKiAgEBATA1NYWDgwPs7e1hamqKjz/+GOXl5aLjvXA2NjaYNGkSvvzyS6SlpYmOQw0IF7mTWkOGDAEAeHt7q3Suru5krVAoREXTmAcPHkhbgDx8+BAGBgbSMYVCgVOnTsHCwkJgQs2qq4v5pUuXYG5uLiCR5g0YMAAnTpxAt27dMHXqVMybNw+HDh1CQkICfHx8RMfTqFmzZuHo0aNYu3YtevXqBQCIjY3F8uXLkZOT0+h7Yc2YMQPR0dGYNWsWSkpK0LZtWwwYMACenp7w9PTkbJ4W4y1CUis6OlrtcQ8PDw0lEUdHR6fWgqKaTCZDYGAglixZosFUmteiRQvIZDIUFhbCxMRE5ZooFAo8evQIM2bMwJYtWwSm1IzKykpUVlaiadOq31EPHjyIc+fOwdnZGTNmzFBpwNrYmZqaYv/+/Rg6dKjK+OnTpzF+/HgUFhYKSqZZ5eXliI2NRVRUFKKjoxEbG4vS0lI4OjpiwIAB2LFjh+iIpGEssIj+RHR0NJRKJQYMGIDDhw+rzNLo6enB3t4ebdq0EZhQM7766isolUpMmTIFn332GUxNTaVjenp6cHBwkGYwSHtYWloiKioKnTp1UhlPSUlBv379kJ2dLSiZWPn5+Vi3bh02b96MR48eacVsP6ligUVq/fDDD2qP19bZvLFKT09H27ZtoaOj3UsXo6Oj0bt3b5XtgrRNaGgomjdvjj59+gAAtmzZgp07d8LFxQVbtmxBixYtBCfUnKCgIPz888/48ssvoa+vDwAoLS3F1KlT0a5du0a/P2W1kpISxMTEICoqClFRUTh//jwcHBzQr18/eHh4aEXTVVLFAovUqq2YePLWkLaRy+W1Npd85ZVXBCUSp7i4uMZCZhMTE0FpNMfV1RVr1qzBsGHDcPnyZfTo0QMLFixAREQEOnXqJG2p01g9uc7s+++/h76+Pl599VUAVevxysrK4OXlhSNHjoiIqDHLli1DZGQkzp8/D0dHR3h4eEgfVlZWouORQFzkTmo9ucVDeXk5kpKSEBAQoFXNFAEgOzsbkydPxunTp2s9ri3Fplwux6JFi3Dw4MFa2xFow3VIS0uT9p87fPgw3nrrLXzyySdITEzUin3n/nh7GAD+9re/qbxu27atJuMItWLFCtjZ2WHDhg0YM2YMWrZsKToSNRAssEitJ/8hBYBBgwZBX18f8+bNw4ULFwSkEuPDDz9Efn4+4uLi4OnpiaNHj+L+/ftYuXIl1q1bJzqexixcuBCRkZHYunUrfH19sWXLFty9exc7duzA6tWrRcfTCD09PcjlcgBVszfVWwSZm5trRfuSP87QFRcXo7KyUtqb8datWzh27Bg6deqEwYMHi4qoMadOnUJUVBT27NmDuXPnon379ujfv780i9W6dWvREUkUJdFzuHbtmtLIyEh0DI2ysrJSxsfHK5VKpdLY2FiZmpqqVCqVyv/85z/K119/XWQ0jWrbtq0yMjJSqVRWXYfr168rlUqlcu/evcqhQ4cKTKY5b731lnLw4MHKoKAgpa6urvLOnTtKpVKpDAsLU7Zr105wOs0aNGiQctu2bUqlUqnMz89XWlpaKm1tbZUGBgbKrVu3Ck6nWQ8ePFCePHlSuWjRImXPnj2Venp6ShcXF+XMmTNFRyMBtHu1Lv2p5ORklY9Lly4hNDQU77//vrTeQlsUFRVJ/a7Mzc2lp6NcXV21anPfvLw8aeskExMT5OXlAQD69Onzpw9FNBaff/45mjZtikOHDmHbtm2wsbEBUNWaoLp3nLZITExE3759AVRtgG1paYn09HTs3bsXmzZtEpxOs4yNjTFs2DB88skn2LhxI+bPn487d+5g27ZtoqORALxFSGp16dIFMpmsRndqd3d3rdkaplqHDh2QmpoKBwcHdOnSBTt27ICDgwO2b98Oa2tr0fE0xtHREbdu3YK9vT1cXFxw8OBBuLm54cSJEzAzMxMdTyPs7Ozw3Xff1RjfsGGDgDRiyeVyafuo8PBw+Pj4QEdHB+7u7khPTxecTjMqKyuRkJCAyMhIREVFISYmBkVFRbC1tcWoUaOeekNsalxYYJFaT279oKOjg9atW6t0M9cWH374Ie7duweg6smhwYMHY9++fdDT08OePXvEhtOgyZMn49KlS/Dw8IC/vz+GDx+OzZs3o6KiAuvXrxcdjzTM2dkZx44dw6hRoxAWFoZ58+YBALKysrTiidJhw4YhJiYGDx8+RJs2bdC/f39s2LABnp6ecHR0FB2PBGKbBlJr7969GDdunNTfplpZWRn2798vLe7VRnK5HD///DPs7OzQqlUr0XGEycjIQEJCApycnLTitvHWrVtx5MgRmJubY8aMGRgwYIB0LCcnB25ubrh586bAhJp16NAhTJw4EQqFAl5eXggPDwcABAcH44cffqjzqdvGYsKECdK2OO3atRMdhxoQrsEitSZPnlzrVhcPHz7E5MmTBSQSo7y8HI6Ojrh27Zo0ZmhoiG7dumlVcVVeXg5PT0/88ssv0pidnR18fHy0orjatGkTFi5ciI4dO0JfXx/Dhg1DcHCwdFyhUGjNbbFqo0ePlors0NBQadzLy0srbpkWFhZi3LhxUnG1atUqFBQUSMdzc3Ollh6kXXiLkNRS1rGx7507d2pt4dBY6erqorS0VO2ehNpAV1cXV65c0drrsGPHDuzcuRMTJ04EAHzwwQcYOXIkiouLERQUJDidOFZWVjWaarq5uQlKo1lhYWEoLS2VXq9ZswYTJkyQ1iNWVFQgNTVVUDoSiQUW1apr166QyWSQyWTw8vKSNrUFqn5LT0tL07qnpWbPno01a9YgJCRE5XpoG19fX+zatUtrel79UVpaGnr37i297tWrFyIiIuDl5YXy8nJ8+OGH4sKREE+usuGqG6qmvT8lSK2RI0cCAC5evIjBgwejefPm0rHqjX2f7N7c2MXHx+Ps2bMIDw+Hq6ur1FixWmPfEqRaWVkZQkJCcObMGfTo0aPGdWjMC91btWqF27dvw8HBQRrr3LkzIiIiMGDAANy9e1dcOCJqUFhgUa2qN2h1cHDAuHHjtPKpwSeZmZlpXVFZmytXrqBbt24AoLIWC0Cjv3XYp08fHD58WOr7VM3FxQVnz57l4/haqHqm/8kxIj5FSET0lJKTk3HhwoU6H/C4evUqDh06JP2CQo2fjo4Ohg4dKj1pfeLECQwYMECa2S0tLUVoaKhW7NFJqlhgkVoKhQIbNmzAwYMHkZGRgbKyMpXj1V28tcGAAQNw5MiRGs00Hzx4gJEjRyIiIkJMMIFu374NmUwGW1tb0VE0rqCgALt27UJKSgpkMhlcXFwwZcoUrXr4g/DUT1P/cf9G0g4ssEitpUuXIiQkBPPnz0dAQACWLFkibea6dOlSzJkzR3REjdHR0UFmZqa0XU61rKws2NjYoLy8XFAyzaqoqEBgYCA2bdqER48eAQCaN2+O2bNnY9myZdDV1RWc8MVLSEjA4MGD0axZM7i5uUGpVCIhIQHFxcUIDw+XbqESkfZigUVqOTk5YdOmTRg+fDiMjY1x8eJFaSwuLg7ffPON6IgvXHJyMoCqbYMiIiJgbm4uHVMoFAgNDcWOHTtw69YtQQk1a8aMGTh69CiCgoLQq1cvAEBsbCyWL1+OESNGYPv27YITvnh9+/aFs7Mzdu7cKT1RWlFRgWnTpuHmzZtasycjEdWNBRapZWRkhJSUFNjZ2cHa2honT55Et27dcPPmTXTt2rXWJqSNjY6OjrRotba/Ls2aNcPmzZsxZcoUTUcTwtTUFPv378fQoUNVxk+fPo3x48drxZ+JZs2aISkpCR07dlQZv3btGnr06AG5XC4oGRE1FHyKkNSytbXFvXv3YGdnB2dnZ+n2x/nz52tsn9NYpaWlQalUwtHRET/99BNat24tHdPT04OFhQWaNGkiMKFmGRgYqLQpqObg4AA9PT3NBxLAxMQEGRkZNQqs27dvSxsfE5F241Y5pNaoUaNw9uxZAMDcuXMREBCAdu3awdfXV2tmbOzt7WFjYwNfX1+Ym5vD3t5e+rC2ttaq4goAZs6ciRUrVqh0ry4tLcWqVaswa9Ysgck0Z9y4cZg6dSoOHDiA27dv486dO9i/fz+mTZuGCRMmiI5HRA0AbxHSM4mPj0dMTAycnZ3h7e0tOo5GtWjRAhcuXICjo6PoKEJVF936+vrS/oOXLl1CWVkZvLy8VM5trM1Xy8rKsHDhQmzfvh0VFRUAqrYRev/997F69Wqtmd0lorqxwKI6lZeXY/r06QgICND6ogKoehzb1dUV8+fPFx1FqGfZ5LuxP5oul8tx48YNKJVKODs7w9DQUHQkImogWGCRWmZmZkhMTGSBBWDVqlX49NNP4eXlhe7du9fYIkabWlYQEZF6LLBILc7aPPbSSy/VeUwmk+HmzZsaTCNOcXExlEqlNFuTnp6Oo0ePwsXFBW+88YbgdEREDQOfIiS1nJ2dsWLFCvz4449aP2uTlpYmOkKDMGLECPj4+GDGjBkoKCiAm5sb9PT0kJOTg/Xr1+P9998XHZGISDjOYJFanLWhJ7Vq1QrR0dHo3LkzQkJCsHnzZiQlJeHw4cNYunQpUlJSREckIhKOM1ikFmdtVN25cwfHjx+vdV/G9evXC0qlWXK5XOr1FB4eDh8fH+jo6MDd3R3p6emC0xERNQwssOiplJWVIS0tDU5OTtLWINrm7Nmz8Pb2xksvvYTU1FS8/PLLuHXrFpRKpVbtPefs7Ixjx45h1KhRCAsLw7x58wBU7cloYmIiOB0RUcPARqOkllwux9SpU2FoaIjOnTsjIyMDQNXaq9WrVwtOp1n+/v5YsGABrly5AgMDAxw+fBi3b9+Gh4cHxowZIzqexixduhR+fn5wcHCAm5ubtB9heHg4unbtKjgdEVHDwAKL1PL398elS5cQFRUFAwMDaXzgwIE4cOCAwGSal5KSgnfeeQcA0LRpUxQXF6N58+YICgrCmjVrBKfTnNGjRyMjIwMJCQkICwuTxr28vLBhwwaByYiIGg4WWKTWsWPH8Pnnn6NPnz7ShscA4OLighs3bghMpnlGRkbS9jBt2rRRef85OTmiYglhZWUFY2NjnDlzBsXFxQCAnj171tibj4hIW2nnYhp6atnZ2bCwsKgxXlRUpFJwaQN3d3fExMTAxcUFw4cPx4IFC3D58mUcOXIE7u7uouNpTG5uLsaOHYvIyEjIZDJcv34djo6OmDZtGszMzLBu3TrREYmIhOMMFqnVs2dPnDx5UnpdXVTt3LlTWnujLdavX4/XXnsNALB8+XIMGjQIBw4cgL29PXbt2iU4nebMmzcPurq6yMjIUNkaZty4cQgNDRWYjIio4eAMFqkVHByMIUOG4Nq1a6ioqMDGjRtx9epVxMbGIjo6WnQ8jfrjdkGGhobYunWrwDTihIeHIywsDLa2tirj7dq1Y5sGIqLfcQaL1OrduzdiYmIgl8vh5OSE8PBwWFpaIjY2Ft27dxcdT+MKCgoQEhICf39/5OXlAQASExNx9+5dwck0p6ioqNZNjXNycqCvry8gERFRw8NO7kRPKTk5GQMHDoSpqSlu3bqF1NRUODo6IiAgAOnp6di7d6/oiBoxfPhwdOvWDStWrICxsTGSk5Nhb2+P8ePHo7KyEocOHRIdkYhIOBZY9KcUCgWOHj2KlJQUyGQydOrUCSNGjNC6hqMDBw5Et27dsHbtWhgbG+PSpUtwdHTEjz/+iIkTJ+LWrVuiI2pESkoKPDw80L17d0RERMDb2xtXr15FXl4eYmJi4OTkJDoiEZFw2vUTkp7ZlStXMGLECGRmZqJDhw4AgF9++QWtW7fG8ePH4erqKjih5pw/fx47duyoMW5jY4PMzEwBiTSvvLwcH3zwAY4fP47Tp0+jSZMmKCoqgo+PD2bOnAlra2vREYmIGgQWWKTWtGnT0LlzZyQkJKBFixYAgPz8fLz77ruYPn06YmNjBSfUHAMDAzx48KDGeGpqKlq3bi0gkebp6uriypUraNmyJQIDA0XHISJqsHiLkNRq1qwZEhIS0LlzZ5XxK1euoGfPnlKTSW0wffp0ZGdn4+DBgzA3N0dycjKaNGmCkSNHol+/fvjss89ER9SIBQsWQFdXV+u2SiIiehacwSK1OnTogPv379cosLKysuDs7CwolRiffvophg0bBgsLCxQXF8PDwwOZmZlwd3fHqlWrRMfTmLKyMoSEhODMmTPo0aMHjIyMVI6vX79eUDIiooaDM1ik1qlTp7Bo0SIsX75c6lYeFxeHoKAgrF69Gn369JHONTExERVToyIjI3HhwgVUVlaiW7duGDhwoOhIGuXp6VnnMZlMhoiICA2mISJqmFhgkVo6Oo9bpVV3ca/+I/PH1zKZDAqFQvMBNezs2bM4e/YssrKyUFlZqXJs9+7dglIREVFDw1uEpFZkZKToCA1GYGAggoKC0KNHD1hbW2vdXoxERPT0OINF9JSsra2xdu1aTJo0SXQUIiJq4DiDRX+qpKQEycnJtd4W8/b2FpRK88rKytC7d2/RMYiI6C+AM1ikVmhoKHx9fZGTk1PjmLasu6q2ePFiNG/eHAEBAaKjEBFRA8cCi9RydnbG4MGDsXTpUlhaWoqOo3Hz58+X/ruyshJfffUVXnnlFbzyyivQ1dVVOZftCYiIqBoLLFLLxMQESUlJWru/nLqWBH/E9gRERPRHXINFao0ePRpRUVFaW2DxKUoiInoenMEiteRyOcaMGYPWrVvD1dW1xm2xOXPmCEpGRETUcLHAIrVCQkIwY8YMNGvWDC1btlTp/SSTyXDz5k2B6YiIiBomFliklpWVFebMmYOPPvpIpas7ERER1Y0/MUmtsrIyjBs3jsUVERHRM+BPTVLrnXfewYEDB0THICIi+kvhU4SklkKhwNq1axEWFsbeT0RERE+Ja7BILXV9oNj7iYiIqHYssIiIiIjqGddg0VP59ddfERYWhuLiYgAA63IiIqK6scAitXJzc+Hl5YX27dtj2LBhuHfvHgBg2rRpWLBggeB0REREDRMLLFJr3rx50NXVRUZGBgwNDaXxcePGITQ0VGAyIiKihotPEZJa4eHhCAsLg62trcp4u3btkJ6eLigVERFRw8YZLFKrqKhIZeaqWk5ODvT19QUkIiIiavhYYJFa/fr1w969e6XXMpkMlZWV+Ne//qW2hQMREZE2Y5sGUuvatWvo378/unfvjoiICHh7e+Pq1avIy8tDTEwMnJycREckIiJqcDiDRWo1b94cFy9ehJubGwYNGoSioiL4+PggKSmpRld3IiIiqsIZLFKrSZMmuHfvHiwsLFTGc3NzYWFhAYVCISgZERFRw8UZLFKrrvr70aNHMDAw0HAaIiKivwa2aaBazZ8/H0DVovalS5eqPEmoUCgQHx+PLl26CEpHRETUsLHAololJSUBqJrBunz5MvT09KRjenp6ePXVV+Hn5ycqHhERUYPGNVik1uTJk7Fx40aYmJiIjkJERPSXwQKLiIiIqJ5xkTsRERFRPWOBRURERFTPWGARERER1TMWWERERET1jAUWERERUT1jgUVERERUz1hgEREREdUzFlhERERE9ez/AcCPBVlfWrtjAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_home)"]},{"cell_type":"markdown","metadata":{"id":"oy610pA6Bh4z"},"source":["## df_adm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WFbY8PcrBh4z","outputId":"e8273212-05a9-4ba2-a6d6-4c4635308760"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>22595853.0</td>\n"," <td>33258284</td>\n"," <td>2180-05-06 19:17:00</td>\n"," <td>2180-05-06 23:30:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-05-06 23:04:00</td>\n"," <td>97.7</td>\n"," <td>79.0</td>\n"," <td>16.0</td>\n"," <td>98.000000</td>\n"," <td>107.0</td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 18:42:00</td>\n"," <td>97.9</td>\n"," <td>76.0</td>\n"," <td>18.0</td>\n"," <td>95.000000</td>\n"," <td>95.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>22841357.0</td>\n"," <td>38112554</td>\n"," <td>2180-06-26 15:54:00</td>\n"," <td>2180-06-26 21:31:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-06-26 20:54:00</td>\n"," <td>97.9</td>\n"," <td>86.0</td>\n"," <td>17.0</td>\n"," <td>93.000000</td>\n"," <td>96.0</td>\n"," <td>4</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-05 23:50:00</td>\n"," <td>98.5</td>\n"," <td>96.0</td>\n"," <td>17.0</td>\n"," <td>100.000000</td>\n"," <td>102.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>25742920.0</td>\n"," <td>35968195</td>\n"," <td>2180-08-05 20:58:00</td>\n"," <td>2180-08-06 01:44:00</td>\n"," <td>ADMITTED</td>\n"," <td>2180-08-06 01:07:00</td>\n"," <td>98.1</td>\n"," <td>91.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>98.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>742327</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 09:01:00</td>\n"," <td>98.4</td>\n"," <td>67.0</td>\n"," <td>18.0</td>\n"," <td>99.000000</td>\n"," <td>95.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>742328</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:05:00</td>\n"," <td>98.6</td>\n"," <td>72.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>87.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>742329</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 13:50:00</td>\n"," <td>97.8</td>\n"," <td>72.0</td>\n"," <td>16.0</td>\n"," <td>100.000000</td>\n"," <td>93.0</td>\n"," <td>2</td>\n"," </tr>\n"," <tr>\n"," <th>742330</th>\n"," <td>19999828</td>\n"," <td>29734428.0</td>\n"," <td>30712109</td>\n"," <td>2147-07-17 17:18:00</td>\n"," <td>2147-07-18 17:34:00</td>\n"," <td>ADMITTED</td>\n"," <td>2147-07-18 16:35:00</td>\n"," <td>99.6</td>\n"," <td>78.0</td>\n"," <td>17.0</td>\n"," <td>99.000000</td>\n"," <td>108.0</td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>742331</th>\n"," <td>19999987</td>\n"," <td>23865745.0</td>\n"," <td>34731548</td>\n"," <td>2145-11-02 19:28:00</td>\n"," <td>2145-11-02 22:59:00</td>\n"," <td>ADMITTED</td>\n"," <td>2145-11-02 21:51:00</td>\n"," <td>99.3</td>\n"," <td>103.0</td>\n"," <td>20.0</td>\n"," <td>97.571799</td>\n"," <td>113.0</td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>737074 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 22595853.0 33258284 2180-05-06 19:17:00 \n","1 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","2 10000032 22841357.0 38112554 2180-06-26 15:54:00 \n","3 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","4 10000032 25742920.0 35968195 2180-08-05 20:58:00 \n","... ... ... ... ... \n","742327 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742328 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742329 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742330 19999828 29734428.0 30712109 2147-07-17 17:18:00 \n","742331 19999987 23865745.0 34731548 2145-11-02 19:28:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-05-06 23:30:00 ADMITTED 2180-05-06 23:04:00 97.7 \n","1 2180-06-26 21:31:00 ADMITTED 2180-06-26 18:42:00 97.9 \n","2 2180-06-26 21:31:00 ADMITTED 2180-06-26 20:54:00 97.9 \n","3 2180-08-06 01:44:00 ADMITTED 2180-08-05 23:50:00 98.5 \n","4 2180-08-06 01:44:00 ADMITTED 2180-08-06 01:07:00 98.1 \n","... ... ... ... ... \n","742327 2147-07-18 17:34:00 ADMITTED 2147-07-18 09:01:00 98.4 \n","742328 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:05:00 98.6 \n","742329 2147-07-18 17:34:00 ADMITTED 2147-07-18 13:50:00 97.8 \n","742330 2147-07-18 17:34:00 ADMITTED 2147-07-18 16:35:00 99.6 \n","742331 2145-11-02 22:59:00 ADMITTED 2145-11-02 21:51:00 99.3 \n","\n"," heartrate resprate o2sat sbp EWS \n","0 79.0 16.0 98.000000 107.0 1 \n","1 76.0 18.0 95.000000 95.0 3 \n","2 86.0 17.0 93.000000 96.0 4 \n","3 96.0 17.0 100.000000 102.0 2 \n","4 91.0 18.0 99.000000 98.0 3 \n","... ... ... ... ... ... \n","742327 67.0 18.0 99.000000 95.0 2 \n","742328 72.0 15.0 100.000000 87.0 3 \n","742329 72.0 16.0 100.000000 93.0 2 \n","742330 78.0 17.0 99.000000 108.0 1 \n","742331 103.0 20.0 97.571799 113.0 1 \n","\n","[737074 rows x 13 columns]"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["df_adm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"49VsvFCZBh40","outputId":"fa6c5beb-964c-473d-f3e1-386bfcdbbc79"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","----------- Mean of EWS -----------\n","\n","1.5004911311482971\n","\n","----------- Median of EWS -----------\n","\n","1.0\n","\n","----------- Mode of EWS -----------\n","\n","0 0\n","Name: EWS, dtype: int64\n"]}],"source":["stat(df_adm)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8hG7_jWFBh40","outputId":"b81cacb7-4b30-446f-fabe-286c5f4f7e43"},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'number of records')"]},"execution_count":38,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.hist(df_adm['EWS'])\n","plt.xlabel('EWS')\n","plt.title('EWS for cases admitted in hospital')\n","plt.ylabel('number of records')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iTlryAwEBh40","outputId":"32c43e90-d6df-42a2-e1f8-0f862e63784d"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":43,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_adm)"]},{"cell_type":"markdown","metadata":{"id":"apbXTXXjBh40"},"source":["## df_exp"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zhj8KX4iBh41","outputId":"8f369a29-cd13-44ad-dd25-a1b9ff96b38a"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 19:51:00</td>\n"," <td>90.0</td>\n"," <td>113.0</td>\n"," <td>18.0</td>\n"," <td>43.000000</td>\n"," <td>93.0</td>\n"," <td>10</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10056931</td>\n"," <td>NaN</td>\n"," <td>33768159</td>\n"," <td>2142-06-24 19:44:00</td>\n"," <td>2142-06-25 02:19:00</td>\n"," <td>EXPIRED</td>\n"," <td>2142-06-24 20:51:00</td>\n"," <td>90.0</td>\n"," <td>105.0</td>\n"," <td>18.0</td>\n"," <td>60.000000</td>\n"," <td>128.0</td>\n"," <td>7</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-05 23:58:00</td>\n"," <td>90.0</td>\n"," <td>59.0</td>\n"," <td>15.0</td>\n"," <td>100.000000</td>\n"," <td>121.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:09:00</td>\n"," <td>90.0</td>\n"," <td>51.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>115.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10092355</td>\n"," <td>NaN</td>\n"," <td>37194940</td>\n"," <td>2127-02-05 23:45:00</td>\n"," <td>2127-02-06 06:16:00</td>\n"," <td>EXPIRED</td>\n"," <td>2127-02-06 00:45:00</td>\n"," <td>90.0</td>\n"," <td>58.0</td>\n"," <td>14.0</td>\n"," <td>100.000000</td>\n"," <td>123.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.0</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.0</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," <td>9</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.0</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.0</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," <td>6</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.6</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>483 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime outtime \\\n","0 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","1 10056931 NaN 33768159 2142-06-24 19:44:00 2142-06-25 02:19:00 \n","2 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","3 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n","4 10092355 NaN 37194940 2127-02-05 23:45:00 2127-02-06 06:16:00 \n",".. ... ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 2120-01-07 02:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 2148-12-08 22:11:00 \n","\n"," disposition charttime temperature heartrate resprate \\\n","0 EXPIRED 2142-06-24 19:51:00 90.0 113.0 18.0 \n","1 EXPIRED 2142-06-24 20:51:00 90.0 105.0 18.0 \n","2 EXPIRED 2127-02-05 23:58:00 90.0 59.0 15.0 \n","3 EXPIRED 2127-02-06 00:09:00 90.0 51.0 14.0 \n","4 EXPIRED 2127-02-06 00:45:00 90.0 58.0 14.0 \n",".. ... ... ... ... ... \n","502 EXPIRED 2120-01-06 21:51:00 90.0 142.0 24.0 \n","503 EXPIRED 2120-01-06 21:53:00 90.0 131.0 18.0 \n","504 EXPIRED 2120-01-06 21:57:00 90.0 126.0 24.0 \n","505 EXPIRED 2148-12-08 17:26:00 90.0 95.0 22.0 \n","506 EXPIRED 2148-12-08 18:11:00 97.6 93.0 17.0 \n","\n"," o2sat sbp EWS \n","0 43.000000 93.0 10 \n","1 60.000000 128.0 7 \n","2 100.000000 121.0 3 \n","3 100.000000 115.0 3 \n","4 100.000000 123.0 3 \n",".. ... ... ... \n","502 85.000000 110.0 12 \n","503 89.000000 124.0 9 \n","504 88.000000 99.0 12 \n","505 98.000000 132.0 6 \n","506 96.334677 117.0 1 \n","\n","[483 rows x 13 columns]"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["df_exp"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2E4uAD4ABh41","outputId":"31ee1dbf-5a6b-4762-c24b-d4f4bd317c77"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","----------- Mean of EWS -----------\n","\n","5.267080745341615\n","\n","----------- Median of EWS -----------\n","\n","5.0\n","\n","----------- Mode of EWS -----------\n","\n","0 6\n","Name: EWS, dtype: int64\n"]}],"source":["stat(df_exp)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NineLEKLBh41","outputId":"cbe89df3-47b0-47ae-bd28-4ed97df49c9b"},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'number of records')"]},"execution_count":41,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAHFCAYAAAAUpjivAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA8cUlEQVR4nO3de1RVdeL+8eeIcLiEKF5AFBSVUhMvI2miCWRSjpfS6aJo2sW+lmaSd0cdyRSMRrNy1K+al+rrWGuNWlk5UuEtzVA0lRqdipRKhl9KgIqIsH9/uDzjCS8cPYcDu/drrb1W57P32Tz7qIenfbUYhmEIAADApGq5OwAAAIArUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAB61evVoWi+Wq09atWyVJ7dq1U5s2bSq8f8OGDbJYLOrWrVuFeW+99ZYsFovef/9929iePXs0cOBAhYWFyWq1KigoSN26ddOECRMqlff1119Xq1at5OXlJYvFol9//fWGtvtmJCUlyWKxuPRn7Nq1S0lJSVfcvubNm6tfv34u/fk1yWOPPabmzZs7bX0//PDDNf9NJCUl2f3sy+f5+fmpefPmGjBggFatWqWSkhKn5QIuqe3uAEBNtWrVKrVu3brCeNu2bSVJcXFxWrRokXJzcxUcHGybv3XrVvn5+Wnv3r0qKiqSv7+/3bxatWqpZ8+ekqQPP/xQAwYMUGxsrFJTU9W4cWOdOHFCe/fu1bp16zR//vxrZjxw4ICee+45jRw5UiNGjFDt2rXtfp6Z7Nq1Sy+88IIee+wx1a1b191xqrWZM2dq3LhxTl/v2LFjlZCQUGG8adOmdq99fHz02WefSZKKi4uVk5Ojjz/+WE899ZTmz5+vzZs3V3gPcDMoO8ANateunaKioq46/1LZ2bp1qwYPHmwb37p1q0aOHKnFixdr586d6tOnj928Tp062X5Zp6amKjw8XP/85z9Vu/Z//7kOHjxYqamp182YlZUlSXrqqafUpUsXRzfxis6ePStfX1+nrAvu0bJlS5esNywsTHfeeed1l6tVq1aF5YYPH67HH39c/fr104MPPqgvvvjCJRnx+8RhLMBFYmNj7Q5rSdLJkyd16NAh9e3bV507d1Z6erptXk5Ojr7//nvFxcXZLd+gQQO7onNJrVrX/ucbGxurYcOGSZK6du0qi8Wixx57zDZ/5cqV6tChg7y9vRUYGKiBAwfqm2++sVvHY489pltuuUWHDh1SfHy8/P391atXr2v+3A8//FAdO3aU1WpVeHi4/vrXv15xOcMwtHjxYnXs2FE+Pj6qV6+eHnzwQX3//fd2y6Wlpen+++9X06ZN5e3trVatWmnUqFH65ZdfbMskJSVp0qRJkqTw8PAKhxQv2bx5s/7whz/Ix8dHrVu31sqVK6+5LZeUlJRo9uzZatOmjby9vVW/fn3FxcVp165dtmX+9re/qWfPnmrUqJH8/PwUGRmp1NRUlZaW2q1r//796tevnxo1aiSr1aqQkBD17dtXP/74o8OfTWXWdSVXOoxlsVj07LPP6q233lKbNm3k6+urDh06aNOmTZX6jJwhPj5eTz31lPbs2aPt27dX2c+F+bFnB7hBZWVlunDhgt2YxWKRh4eHJCkwMFDt27e3KzTbtm2Th4eHoqOjFRMTY9uVL8m23OVlp1u3blqxYoWee+45DR06VH/4wx/k6elZqXyLFy/W3//+d82ZM8d2yK1hw4aSpJSUFP35z3/WkCFDlJKSopMnTyopKUndunVTRkaGIiIibOs5f/68BgwYoFGjRmnq1KkVtvlyn376qe6//35169ZN69atU1lZmVJTU/Wf//ynwrKjRo3S6tWr9dxzz+mll17SqVOnNHv2bEVHR+urr75SUFCQJOm7775Tt27dNHLkSAUEBOiHH37QggUL1KNHDx06dEienp4aOXKkTp06pddff13r169X48aNJf33kKIkffXVV5owYYKmTp2qoKAgrVixQk8++aRatWplO2x4JRcuXFCfPn20Y8cOJSYm6u6779aFCxf0xRdf6Pjx44qOjrblTEhIUHh4uLy8vPTVV19p7ty5+te//mUrVWfOnFHv3r0VHh6uv/3tbwoKClJubq7S09NVVFTk0GdT2XU54sMPP1RGRoZmz56tW265RampqRo4cKCOHDmiFi1aXPf95eXlV/z7caWyfjUDBgzQ4sWLtX379mv+uQAOMQA4ZNWqVYakK04eHh52yyYmJhqSjJ9//tkwDMMYO3asceeddxqGYRgfffSR4eHhYRQUFBiGYRiPP/644eHhYRQWFtre/8svvxg9evSwrd/T09OIjo42UlJSjKKiokpnzcjIsI3l5+cbPj4+xh//+Ee7ZY8fP25YrVYjISHBNjZixAhDkrFy5cpKfTZdu3Y1QkJCjOLiYttYYWGhERgYaFz+dbN7925DkjF//ny79+fk5Bg+Pj7G5MmTr7j+8vJyo7S01Dh27JghyXjvvfds815++WVDkpGdnV3hfc2aNTO8vb2NY8eO2caKi4uNwMBAY9SoUdfcpjfffNOQZCxfvvyay12urKzMKC0tNd58803Dw8PDOHXqlGEYhrF3715DkrFx48arvreyn01l1nU1I0aMMJo1a2Y3JskICgqy+/uXm5tr1KpVy0hJSbnm+rKzs6/6b0KSsWPHDruf7efnd9V1ffPNN4Yk45lnnnF4u4Cr4TAWcIPefPNNZWRk2E179uyxW+bSXppLh1O2bt2q2NhYSVKPHj0kyba7fuvWrYqKirI7gbh+/frasWOHMjIyNG/ePN1///06evSopk2bpsjISLtDOZW1e/duFRcX2x3SkqTQ0FDdfffd+vTTTyu8509/+tN113vmzBllZGRo0KBB8vb2to37+/urf//+dstu2rRJFotFw4YN04ULF2xTcHCwOnToYHf4KS8vT08//bRCQ0NVu3ZteXp6qlmzZpJU4bDbtXTs2FFhYWG2197e3rr11lt17Nixa77v448/lre3t5544olrLrd//34NGDBA9evXl4eHhzw9PTV8+HCVlZXp6NGjkqRWrVqpXr16mjJlipYuXaqvv/66wnoq+9lUZl2OiouLs/v7FxQUpEaNGl33M7pk3LhxFf5NZGRkqGPHjpXOYBiGo7GB6+IwFnCD2rRpc80TlCUpJiZGtWrVUnp6uuLj43X48GHbicX+/v7q1KmTtm7dqvbt2ys7O1uPPPLIFdcTFRVl+1mlpaWaMmWKXnnlFaWmplbqROXLnTx5UpJsh3ouFxISorS0NLsxX19f1alT57rrzc/PV3l5ud2VZ5f8duw///mPDMOwHar6rUuHTMrLyxUfH6+ff/5ZM2fOVGRkpPz8/FReXq4777xTxcXF1811Sf369SuMWa3W667j//2//6eQkJBrniN1/Phx3XXXXbrtttv06quvqnnz5vL29taXX36pMWPG2H5GQECAtm3bprlz5+rPf/6z8vPz1bhxYz311FOaMWOGPD09K/3ZVGZdjrrRz+iSpk2bXvffxPVcKlYhISE3tR7gcpQdwIUCAgJshebSZeXdu3e3zY+JiVF6eroiIyMl2Z+vczWenp6aNWuWXnnlFR0+fNjhTJd+oZ04caLCvJ9//lkNGjSwG6vs/XHq1asni8Wi3NzcCvN+O9agQQNZLBbt2LFDVqu1wvKXxg4fPqyvvvpKq1ev1ogRI2zzv/3220plcoaGDRtq586dKi8vv2rh2bhxo86cOaP169fb9jpJFy/9/63IyEitW7dOhmHo4MGDWr16tWbPni0fHx9NnTq10p9NZdZVE126x9SlPaCAM3AYC3CxuLg4/fvf/9batWvVuXNnu8MEMTExOnDggDZu3ChPT0+7IiRduZBI/z18cyP/99utWzf5+Pjo7bffthv/8ccf9dlnn133aqur8fPzU5cuXbR+/XqdO3fONl5UVKQPPvjAbtl+/frJMAz99NNPtr1Wl0+Xyt+lovXbX/r/+7//W+HnX1rGkb09ldGnTx+dO3dOq1evvuoyV8ppGIaWL19+zfd06NBBr7zyiurWravMzExJlf9sKrOumiYtLU0rVqxQdHS07TAv4Azs2QFu0OHDh6945UnLli1tVz1JF8vOX//6V23YsEETJ060W/auu+6SJL333nuKjo6Wn5+f3fx7771XTZs2Vf/+/dW6dWuVl5frwIEDmj9/vm655ZYbujFc3bp1NXPmTP35z3/W8OHDNWTIEJ08eVIvvPCCvL29NWvWLIfXecmLL76o++67T71799aECRNUVlaml156SX5+fjp16pRtue7du+t//ud/9Pjjj2vv3r3q2bOn/Pz8dOLECe3cuVORkZF65pln1Lp1a7Vs2VJTp06VYRgKDAzUBx98UOFQmyRbCXj11Vc1YsQIeXp66rbbbrvpmygOGTJEq1at0tNPP60jR44oLi5O5eXl2rNnj9q0aaPBgwerd+/e8vLy0pAhQzR58mSdO3dOS5YsUX5+vt26Nm3apMWLF+uBBx5QixYtZBiG1q9fr19//VW9e/d26LOpzLqq2vHjx694f5yGDRva3dunvLzctlxJSYmOHz+ujz/+WO+++67atGmjd999t8oy43fCTSdGAzXWta7G0hWu2iksLDRq165tSDI2bdpUYX0dO3Y0JBnTp0+vMO+dd94xEhISjIiICOOWW24xPD09jbCwMOPRRx81vv7660pnvfxqrEtWrFhhtG/f3vDy8jICAgKM+++/38jKyrJb5npXzlzJ+++/b1tvWFiYMW/ePGPWrFnGlb5uVq5caXTt2tXw8/MzfHx8jJYtWxrDhw839u7da1vm66+/Nnr37m34+/sb9erVMx566CHj+PHjhiRj1qxZduubNm2aERISYtSqVcuQZKSnpxuGcfFqrL59+1b4+TExMUZMTMx1t6m4uNj4y1/+YkRERBheXl5G/fr1jbvvvtvYtWuXbZkPPvjA6NChg+Ht7W00adLEmDRpkvHxxx/b5fjXv/5lDBkyxGjZsqXh4+NjBAQEGF26dDFWr17t8GfjyLp+62pXY40ZM6bCss2aNTNGjBhxzfVd72qsoUOH2v3sy+f5+PgYYWFhRv/+/Y2VK1caJSUl180POMpiGJz6DgAAzItzdgAAgKlRdgAAgKlRdgAAgKlRdgAAgKlRdgAAgKlRdgAAgKlxU0FdvMHVzz//LH9//0rfGh8AALiXYRgqKiq67vPrKDu6+Dyg0NBQd8cAAAA3ICcnR02bNr3qfMqOZLudfE5OTqWe7gwAANyvsLBQoaGh130sDGVH/32IX506dSg7AADUMNc7BYUTlAEAgKlRdgAAgKlRdgAAgKlRdgAAgKm5texs375d/fv3V0hIiCwWizZu3GibV1paqilTpigyMlJ+fn4KCQnR8OHD9fPPP9uto6SkRGPHjlWDBg3k5+enAQMG6Mcff6ziLQEAANWVW8vOmTNn1KFDBy1atKjCvLNnzyozM1MzZ85UZmam1q9fr6NHj2rAgAF2yyUmJmrDhg1at26ddu7cqdOnT6tfv34qKyurqs0AAADVmMUwDMPdIaSLl41t2LBBDzzwwFWXycjIUJcuXXTs2DGFhYWpoKBADRs21FtvvaVHHnlE0n9vEPjRRx/p3nvvrdTPLiwsVEBAgAoKCrj0HACAGqKyv79r1Dk7BQUFslgsqlu3riRp3759Ki0tVXx8vG2ZkJAQtWvXTrt27XJTSgAAUJ3UmJsKnjt3TlOnTlVCQoKtveXm5srLy0v16tWzWzYoKEi5ublXXVdJSYlKSkpsrwsLC10TGgAAuF2N2LNTWlqqwYMHq7y8XIsXL77u8oZhXPNuiikpKQoICLBNPBcLAADzqvZlp7S0VA8//LCys7OVlpZmd0wuODhY58+fV35+vt178vLyFBQUdNV1Tps2TQUFBbYpJyfHZfkBAIB7Veuyc6no/Pvf/9Ynn3yi+vXr283v3LmzPD09lZaWZhs7ceKEDh8+rOjo6Kuu12q12p6DxfOwAAAwN7ees3P69Gl9++23ttfZ2dk6cOCAAgMDFRISogcffFCZmZnatGmTysrKbOfhBAYGysvLSwEBAXryySc1YcIE1a9fX4GBgZo4caIiIyN1zz33uGuzAABANeLWS8+3bt2quLi4CuMjRoxQUlKSwsPDr/i+9PR0xcbGSrp44vKkSZO0du1aFRcXq1evXlq8eLFD5+Fw6TkAADVPZX9/V5v77LgTZQcAgJqnsr+/a8yl5wCqh+ZTP3R3BIf9MK+vuyMAcKNqfYIyAADAzaLsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU6PsAAAAU3Nr2dm+fbv69++vkJAQWSwWbdy40W6+YRhKSkpSSEiIfHx8FBsbq6ysLLtlSkpKNHbsWDVo0EB+fn4aMGCAfvzxxyrcCgAAUJ25teycOXNGHTp00KJFi644PzU1VQsWLNCiRYuUkZGh4OBg9e7dW0VFRbZlEhMTtWHDBq1bt047d+7U6dOn1a9fP5WVlVXVZgAAgGqstjt/eJ8+fdSnT58rzjMMQwsXLtT06dM1aNAgSdKaNWsUFBSktWvXatSoUSooKNAbb7yht956S/fcc48k6e2331ZoaKg++eQT3XvvvVW2LQAAoHqqtufsZGdnKzc3V/Hx8bYxq9WqmJgY7dq1S5K0b98+lZaW2i0TEhKidu3a2Za5kpKSEhUWFtpNAADAnKpt2cnNzZUkBQUF2Y0HBQXZ5uXm5srLy0v16tW76jJXkpKSooCAANsUGhrq5PQAAKC6qLZl5xKLxWL32jCMCmO/db1lpk2bpoKCAtuUk5PjlKwAAKD6qbZlJzg4WJIq7KHJy8uz7e0JDg7W+fPnlZ+ff9VlrsRqtapOnTp2EwAAMKdqW3bCw8MVHBystLQ029j58+e1bds2RUdHS5I6d+4sT09Pu2VOnDihw4cP25YBAAC/b269Guv06dP69ttvba+zs7N14MABBQYGKiwsTImJiUpOTlZERIQiIiKUnJwsX19fJSQkSJICAgL05JNPasKECapfv74CAwM1ceJERUZG2q7OAgAAv29uLTt79+5VXFyc7fX48eMlSSNGjNDq1as1efJkFRcXa/To0crPz1fXrl21ZcsW+fv7297zyiuvqHbt2nr44YdVXFysXr16afXq1fLw8Kjy7QEAANWPxTAMw90h3K2wsFABAQEqKCjg/B3gOppP/dDdERz2w7y+7o4AwAUq+/u72p6zAwAA4AyUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGqUHQAAYGpufRDo70FNfI6QxLOEAADmwZ4dAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgajdddgoLC7Vx40Z98803zsgDAADgVA6XnYcffliLFi2SJBUXFysqKkoPP/yw2rdvr3/84x9ODwgAAHAzHC4727dv11133SVJ2rBhgwzD0K+//qrXXntNc+bMcXpAAACAm1Hb0TcUFBQoMDBQkrR582b96U9/kq+vr/r27atJkyY5PSBQWc2nfujuCA77YV5fd0cAANNzeM9OaGiodu/erTNnzmjz5s2Kj4+XJOXn58vb29vpAQEAAG6Gw2UnMTFRQ4cOVdOmTRUSEqLY2FhJFw9vRUZGOjXchQsXNGPGDIWHh8vHx0ctWrTQ7NmzVV5eblvGMAwlJSUpJCREPj4+io2NVVZWllNzAACAmsvhw1ijR49Wly5dlJOTo969e6tWrYt9qUWLFk4/Z+ell17S0qVLtWbNGt1+++3au3evHn/8cQUEBGjcuHGSpNTUVC1YsECrV6/Wrbfeqjlz5qh37946cuSI/P39nZoHAADUPA6XHUmKiopSVFSU3Vjfvs4/92D37t26//77betu3ry5/v73v2vv3r2SLu7VWbhwoaZPn65BgwZJktasWaOgoCCtXbtWo0aNcnomAABQs1Sq7IwfP77SK1ywYMENh/mtHj16aOnSpTp69KhuvfVWffXVV9q5c6cWLlwoScrOzlZubq7tvCFJslqtiomJ0a5du65adkpKSlRSUmJ7XVhY6LTMAACgeqlU2dm/f7/d63379qmsrEy33XabJOno0aPy8PBQ586dnRpuypQpKigoUOvWreXh4aGysjLNnTtXQ4YMkSTl5uZKkoKCguzeFxQUpGPHjl11vSkpKXrhhRecmhUAAFRPlSo76enptv9esGCB/P39tWbNGtWrV0/SxSuxHn/8cdv9d5zlnXfe0dtvv621a9fq9ttv14EDB5SYmKiQkBCNGDHCtpzFYrF7n2EYFcYuN23aNLu9VYWFhQoNDXVqdgAAUD04fM7O/PnztWXLFlvRkaR69eppzpw5io+P14QJE5wWbtKkSZo6daoGDx4sSYqMjNSxY8eUkpKiESNGKDg4WNLFPTyNGze2vS8vL6/C3p7LWa1WWa1Wp+UEAADVl8OXnhcWFuo///lPhfG8vDwVFRU5JdQlZ8+etV3tdYmHh4ft0vPw8HAFBwcrLS3NNv/8+fPatm2boqOjnZoFAADUTA7v2Rk4cKAef/xxzZ8/X3feeack6YsvvtCkSZNsV0Q5S//+/TV37lyFhYXp9ttv1/79+7VgwQI98cQTki4evkpMTFRycrIiIiIUERGh5ORk+fr6KiEhwalZAABAzeRw2Vm6dKkmTpyoYcOGqbS09OJKatfWk08+qZdfftmp4V5//XXNnDlTo0ePVl5enkJCQjRq1Cj95S9/sS0zefJkFRcXa/To0crPz1fXrl21ZcsW7rEDAAAkSRbDMIzKLlxWVqadO3cqMjJSVqtV3333nQzDUKtWreTn5+fKnC5VWFiogIAAFRQUqE6dOk5dd018XpNUM5/ZVBM/az7nqlETP2cA11fZ398O7dnx8PDQvffeq2+++Ubh4eFq3779TQcFAABwJYdPUI6MjNT333/viiwAAABO53DZmTt3riZOnKhNmzbpxIkTKiwstJsAAACqE4dPUL7vvvskSQMGDLC7cd+lG/mVlZU5Lx0AAMBNcrjsXH43ZQAAgOrO4bITExPjihwAAAAu4XDZkaRff/1Vb7zxhr755htZLBa1bdtWTzzxhAICApydDwAA4KY4fILy3r171bJlS73yyis6deqUfvnlFy1YsEAtW7ZUZmamKzICAADcMIf37Dz//PMaMGCAli9frtq1L779woULGjlypBITE7V9+3anhwQAALhRDpedvXv32hUd6eLjIiZPnqyoqCinhgMAALhZDh/GqlOnjo4fP15hPCcnh+dRAQCAasfhsvPII4/oySef1DvvvKOcnBz9+OOPWrdunUaOHKkhQ4a4IiMAAMANc/gw1l//+ldZLBYNHz5cFy5ckCR5enrqmWee0bx585weEAAA4GY4XHa8vLz06quvKiUlxe6p576+vq7IBwAAcFMcLjsFBQUqKytTYGCgIiMjbeOnTp1S7dq1r/mIdQAAgKrm8Dk7gwcP1rp16yqMv/vuuxo8eLBTQgEAADiLw2Vnz549iouLqzAeGxurPXv2OCUUAACAszhcdkpKSmwnJl+utLRUxcXFTgkFAADgLA6XnTvuuEPLli2rML506VJ17tzZKaEAAACcxeETlOfOnat77rlHX331lXr16iVJ+vTTT5WRkaEtW7Y4PSAAAMDNcHjPTvfu3bV79241bdpU7777rj744AO1atVKBw8e1F133eWKjAAAADfM4T07ktSxY0etXbvW2VkAAACczuE9O5L03XffacaMGUpISFBeXp4kafPmzcrKynJqOAAAgJvlcNnZtm2bIiMjtWfPHv3jH//Q6dOnJUkHDx7UrFmznB4QAADgZjhcdqZOnao5c+YoLS1NXl5etvG4uDjt3r3bqeEAAABulsNl59ChQxo4cGCF8YYNG+rkyZNOCQUAAOAsDpedunXr6sSJExXG9+/fryZNmjglFAAAgLM4XHYSEhI0ZcoU5ebmymKxqLy8XJ9//rkmTpyo4cOHuyIjAADADXO47MydO1dhYWFq0qSJTp8+rbZt26pnz56Kjo7WjBkzXJERAADghjl0nx3DMPTzzz9r+fLlevHFF5WZmany8nJ16tRJERERrsoIAABwwxwuOxEREcrKylJERIRatGjhqlwAAABO4dBhrFq1aikiIoKrrgAAQI3h8Dk7qampmjRpkg4fPuyKPAAAAE7l8LOxhg0bprNnz6pDhw7y8vKSj4+P3fxTp045LRwAAMDNcrjsLFy40AUxAAAAXMPhsjNixAhX5AAAAHCJG3rqOQAAQE1B2QEAAKZG2QEAAKZWqbJz8OBBlZeXuzoLAACA01Wq7HTq1Em//PKLJKlFixbcVBAAANQYlSo7devWVXZ2tiTphx9+YC8PAACoMSp16fmf/vQnxcTEqHHjxrJYLIqKipKHh8cVl/3++++dGhAAAOBmVKrsLFu2TIMGDdK3336r5557Tk899ZT8/f1dnQ0AAOCmVfqmgvfdd58kad++fRo3bhxlBwAA1AgO30F51apVtv/+8ccfZbFY1KRJE6eGAgAAcBaH77NTXl6u2bNnKyAgQM2aNVNYWJjq1q2rF198kROXAQBAtePwnp3p06frjTfe0Lx589S9e3cZhqHPP/9cSUlJOnfunObOneuKnAAAADfE4T07a9as0YoVK/TMM8+offv26tChg0aPHq3ly5dr9erVTg/4008/adiwYapfv758fX3VsWNH7du3zzbfMAwlJSUpJCREPj4+io2NVVZWltNzAACAmsnhsnPq1Cm1bt26wnjr1q116tQpp4S6JD8/X927d5enp6c+/vhjff3115o/f77q1q1rWyY1NVULFizQokWLlJGRoeDgYPXu3VtFRUVOzQIAAGomh8tOhw4dtGjRogrjixYtUocOHZwS6pKXXnpJoaGhWrVqlbp06aLmzZurV69eatmypaSLe3UWLlyo6dOna9CgQWrXrp3WrFmjs2fPau3atU7NAgAAaiaHy05qaqpWrlyptm3b6sknn9TIkSPVtm1brV69Wi+//LJTw73//vuKiorSQw89pEaNGqlTp05avny5bX52drZyc3MVHx9vG7NarYqJidGuXbucmgUAANRMDpedmJgYHT16VAMHDtSvv/6qU6dOadCgQTpy5Ijuuusup4b7/vvvtWTJEkVEROif//ynnn76aT333HN68803JUm5ubmSpKCgILv3BQUF2eZdSUlJiQoLC+0mAABgTg5fjSVJISEhVXLVVXl5uaKiopScnCzp4gNJs7KytGTJEg0fPty2nMVisXufYRgVxi6XkpKiF154wTWhAQBAteLwnp2q1LhxY7Vt29ZurE2bNjp+/LgkKTg4WJIq7MXJy8ursLfnctOmTVNBQYFtysnJcXJyAABQXVTrstO9e3cdOXLEbuzo0aNq1qyZJCk8PFzBwcFKS0uzzT9//ry2bdum6Ojoq67XarWqTp06dhMAADCnGzqMVVWef/55RUdHKzk5WQ8//LC+/PJLLVu2TMuWLZN08fBVYmKikpOTFRERoYiICCUnJ8vX11cJCQluTg8AAKoDh8qOYRg6fvy4GjVqJB8fH1dlsrnjjju0YcMGTZs2TbNnz1Z4eLgWLlyooUOH2paZPHmyiouLNXr0aOXn56tr167asmULDyoFAACSbqDsREREKCsrSxEREa7KZKdfv37q16/fVedbLBYlJSUpKSmpSvIAAICaxaFzdmrVqqWIiAidPHnSVXkAAACc6oZuKjhp0iQdPnzYFXkAAACcyuETlIcNG6azZ8+qQ4cO8vLyqnDujrOfjwUAAHAzHC47CxcudEEMAAAA13C47IwYMcIVOQAAAFzihm4q+N1332nGjBkaMmSI8vLyJEmbN29WVlaWU8MBAADcLIfLzrZt2xQZGak9e/Zo/fr1On36tCTp4MGDmjVrltMDAgAA3AyHy87UqVM1Z84cpaWlycvLyzYeFxen3bt3OzUcAADAzXK47Bw6dEgDBw6sMN6wYUPuvwMAAKodh8tO3bp1deLEiQrj+/fvV5MmTZwSCgAAwFkcLjsJCQmaMmWKcnNzZbFYVF5ers8//1wTJ07U8OHDXZERAADghjlcdubOnauwsDA1adJEp0+fVtu2bdWzZ09FR0drxowZrsgIAABwwxy+z46np6f+7//+T7Nnz9b+/ftVXl6uTp06VdmDQQEAABzhcNm5pGXLlmrRooWki08eBwAAqI5u6KaCb7zxhtq1aydvb295e3urXbt2WrFihbOzAQAA3DSH9+zMnDlTr7zyisaOHatu3bpJknbv3q3nn39eP/zwg+bMmeP0kAAAADfK4bKzZMkSLV++XEOGDLGNDRgwQO3bt9fYsWMpOwAAoFpx+DBWWVmZoqKiKox37txZFy5ccEooAAAAZ3G47AwbNkxLliypML5s2TINHTrUKaEAAACcpVKHscaPH2/7b4vFohUrVmjLli268847JUlffPGFcnJyuKkgAACodipVdvbv32/3unPnzpKk7777TtLF52I1bNhQWVlZTo4HAABwcypVdtLT012dAwAAwCVu6D47AAAANYXDl56fO3dOr7/+utLT05WXl6fy8nK7+ZmZmU4LBwAAcLMcLjtPPPGE0tLS9OCDD6pLly48KgK4Cc2nfujuCABgeg6XnQ8//FAfffSRunfv7oo8AAAATuXwOTtNmjSRv7+/K7IAAAA4ncNlZ/78+ZoyZYqOHTvmijwAAABO5fBhrKioKJ07d04tWrSQr6+vPD097eafOnXKaeEAAABulsNlZ8iQIfrpp5+UnJysoKAgTlAGAADVmsNlZ9euXdq9e7c6dOjgijwAAABO5fA5O61bt1ZxcbErsgAAADidw2Vn3rx5mjBhgrZu3aqTJ0+qsLDQbgIAAKhOHD6Mdd9990mSevXqZTduGIYsFovKysqckwwAAMAJHC47PBQUAADUJA6XnZiYGFfkAAAAcAmHy8727duvOb9nz543HAYAcFFNfG7aD/P6ujsCcEUOl53Y2NgKY5ffa4dzdgAAQHXi8NVY+fn5dlNeXp42b96sO+64Q1u2bHFFRgAAgBvm8J6dgICACmO9e/eW1WrV888/r3379jklGAAAgDM4vGfnaho2bKgjR444a3UAAABO4fCenYMHD9q9NgxDJ06c0Lx583iEBAAAqHYcLjsdO3aUxWKRYRh243feeadWrlzptGAAAADO4HDZyc7Otntdq1YtNWzYUN7e3k4LBQAA4CwOl51mzZq5IgcAAIBLOFx2JOnTTz/Vp59+qry8PJWXl9vN41AWAACoThwuOy+88IJmz56tqKgoNW7c2O6GggAAANWNw2Vn6dKlWr16tR599FFX5AEAAHAqh++zc/78eUVHR7siCwAAgNM5XHZGjhyptWvXuiLLdaWkpMhisSgxMdE2ZhiGkpKSFBISIh8fH8XGxiorK8st+QAAQPXj8GGsc+fOadmyZfrkk0/Uvn17eXp62s1fsGCB08JdLiMjQ8uWLVP79u3txlNTU7VgwQKtXr1at956q+bMmaPevXvryJEj8vf3d0kWAABQc9zQHZQ7duwoSTp8+LDdPFedrHz69GkNHTpUy5cv15w5c2zjhmFo4cKFmj59ugYNGiRJWrNmjYKCgrR27VqNGjXKJXkAAEDN4XDZSU9Pd0WOaxozZoz69u2re+65x67sZGdnKzc3V/Hx8bYxq9WqmJgY7dq166plp6SkRCUlJbbXhYWFrgsPAADc6obus1OV1q1bp8zMTGVkZFSYl5ubK0kKCgqyGw8KCtKxY8euus6UlBS98MILzg0KoNpqPvVDd0cA4EZOe+q5K+Tk5GjcuHF6++23r/k4it8ePjMM45qH1KZNm6aCggLblJOT47TMAACgeqnWe3b27dunvLw8de7c2TZWVlam7du3a9GiRTpy5Iiki3t4GjdubFsmLy+vwt6ey1mtVlmtVtcFBwAA1Ua13rPTq1cvHTp0SAcOHLBNUVFRGjp0qA4cOKAWLVooODhYaWlptvecP39e27Zt415AAABAUjXfs+Pv76927drZjfn5+al+/fq28cTERCUnJysiIkIRERFKTk6Wr6+vEhIS3BEZAABUM9W67FTG5MmTVVxcrNGjRys/P19du3bVli1buMcOAACQVAPLztatW+1eWywWJSUlKSkpyS15AABA9Vatz9kBAAC4WZQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgatW67KSkpOiOO+6Qv7+/GjVqpAceeEBHjhyxW8YwDCUlJSkkJEQ+Pj6KjY1VVlaWmxIDAIDqplqXnW3btmnMmDH64osvlJaWpgsXLig+Pl5nzpyxLZOamqoFCxZo0aJFysjIUHBwsHr37q2ioiI3JgcAANVFbXcHuJbNmzfbvV61apUaNWqkffv2qWfPnjIMQwsXLtT06dM1aNAgSdKaNWsUFBSktWvXatSoUe6IDQAAqpFqvWfntwoKCiRJgYGBkqTs7Gzl5uYqPj7etozValVMTIx27dp11fWUlJSosLDQbgIAAOZUY8qOYRgaP368evTooXbt2kmScnNzJUlBQUF2ywYFBdnmXUlKSooCAgJsU2hoqOuCAwAAt6oxZefZZ5/VwYMH9fe//73CPIvFYvfaMIwKY5ebNm2aCgoKbFNOTo7T8wIAgOqhWp+zc8nYsWP1/vvva/v27WratKltPDg4WNLFPTyNGze2jefl5VXY23M5q9Uqq9XqusAAAKDaqNZ7dgzD0LPPPqv169frs88+U3h4uN388PBwBQcHKy0tzTZ2/vx5bdu2TdHR0VUdFwAAVEPVes/OmDFjtHbtWr333nvy9/e3nYcTEBAgHx8fWSwWJSYmKjk5WREREYqIiFBycrJ8fX2VkJDg5vQAAKA6qNZlZ8mSJZKk2NhYu/FVq1bpsccekyRNnjxZxcXFGj16tPLz89W1a1dt2bJF/v7+VZwWAABUR9W67BiGcd1lLBaLkpKSlJSU5PpAAACgxqnW5+wAAADcLMoOAAAwtWp9GAvu03zqh+6OAACAU7BnBwAAmBplBwAAmBqHsQAAv1s18ZD9D/P6ujtCjcOeHQAAYGqUHQAAYGocxgIAOEVNPCSE3wf27AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOr7e4AAACg8ppP/dDdERz2w7y+bv357NkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmZpqys3jxYoWHh8vb21udO3fWjh073B0JAABUA6YoO++8844SExM1ffp07d+/X3fddZf69Omj48ePuzsaAABwM1OUnQULFujJJ5/UyJEj1aZNGy1cuFChoaFasmSJu6MBAAA3q/Fl5/z589q3b5/i4+PtxuPj47Vr1y43pQIAANVFjX/q+S+//KKysjIFBQXZjQcFBSk3N/eK7ykpKVFJSYntdUFBgSSpsLDQ6fnKS846fZ0AANQkrvj9evl6DcO45nI1vuxcYrFY7F4bhlFh7JKUlBS98MILFcZDQ0Ndkg0AgN+zgIWuXX9RUZECAgKuOr/Gl50GDRrIw8Ojwl6cvLy8Cnt7Lpk2bZrGjx9ve11eXq5Tp06pfv36Vy1IN6KwsFChoaHKyclRnTp1nLbe6uz3ts1sr7mxvebG9tZ8hmGoqKhIISEh11yuxpcdLy8vde7cWWlpaRo4cKBtPC0tTffff/8V32O1WmW1Wu3G6tat67KMderUMc1frMr6vW0z22tubK+5sb0127X26FxS48uOJI0fP16PPvqooqKi1K1bNy1btkzHjx/X008/7e5oAADAzUxRdh555BGdPHlSs2fP1okTJ9SuXTt99NFHatasmbujAQAANzNF2ZGk0aNHa/To0e6OYcdqtWrWrFkVDpmZ2e9tm9lec2N7zY3t/f2wGNe7XgsAAKAGq/E3FQQAALgWyg4AADA1yg4AADA1yg4AADA1yo4LLV68WOHh4fL29lbnzp21Y8cOd0dyiZSUFN1xxx3y9/dXo0aN9MADD+jIkSPujlVlUlJSZLFYlJiY6O4oLvPTTz9p2LBhql+/vnx9fdWxY0ft27fP3bFc4sKFC5oxY4bCw8Pl4+OjFi1aaPbs2SovL3d3NKfZvn27+vfvr5CQEFksFm3cuNFuvmEYSkpKUkhIiHx8fBQbG6usrCz3hHWCa21vaWmppkyZosjISPn5+SkkJETDhw/Xzz//7L7AN+l6f76XGzVqlCwWixYuXFhl+dyBsuMi77zzjhITEzV9+nTt379fd911l/r06aPjx4+7O5rTbdu2TWPGjNEXX3yhtLQ0XbhwQfHx8Tpz5oy7o7lcRkaGli1bpvbt27s7isvk5+ere/fu8vT01Mcff6yvv/5a8+fPd+ldx93ppZde0tKlS7Vo0SJ98803Sk1N1csvv6zXX3/d3dGc5syZM+rQoYMWLVp0xfmpqalasGCBFi1apIyMDAUHB6t3794qKiqq4qTOca3tPXv2rDIzMzVz5kxlZmZq/fr1Onr0qAYMGOCGpM5xvT/fSzZu3Kg9e/Zc91ELpmDAJbp06WI8/fTTdmOtW7c2pk6d6qZEVScvL8+QZGzbts3dUVyqqKjIiIiIMNLS0oyYmBhj3Lhx7o7kElOmTDF69Ojh7hhVpm/fvsYTTzxhNzZo0CBj2LBhbkrkWpKMDRs22F6Xl5cbwcHBxrx582xj586dMwICAoylS5e6IaFz/XZ7r+TLL780JBnHjh2rmlAudLXt/fHHH40mTZoYhw8fNpo1a2a88sorVZ6tKrFnxwXOnz+vffv2KT4+3m48Pj5eu3btclOqqlNQUCBJCgwMdHMS1xozZoz69u2re+65x91RXOr9999XVFSUHnroITVq1EidOnXS8uXL3R3LZXr06KFPP/1UR48elSR99dVX2rlzp/74xz+6OVnVyM7OVm5urt33l9VqVUxMzO/i+0u6+B1msVhMu/eyvLxcjz76qCZNmqTbb7/d3XGqhGnuoFyd/PLLLyorK6vw1PWgoKAKT2c3G8MwNH78ePXo0UPt2rVzdxyXWbdunTIzM5WRkeHuKC73/fffa8mSJRo/frz+/Oc/68svv9Rzzz0nq9Wq4cOHuzue002ZMkUFBQVq3bq1PDw8VFZWprlz52rIkCHujlYlLn1HXen769ixY+6IVKXOnTunqVOnKiEhwVQPy7zcSy+9pNq1a+u5555zd5QqQ9lxIYvFYvfaMIwKY2bz7LPP6uDBg9q5c6e7o7hMTk6Oxo0bpy1btsjb29vdcVyuvLxcUVFRSk5OliR16tRJWVlZWrJkiSnLzjvvvKO3335ba9eu1e23364DBw4oMTFRISEhGjFihLvjVZnf4/dXaWmpBg8erPLyci1evNjdcVxi3759evXVV5WZmWn6P8/LcRjLBRo0aCAPD48Ke3Hy8vIq/N+SmYwdO1bvv/++0tPT1bRpU3fHcZl9+/YpLy9PnTt3Vu3atVW7dm1t27ZNr732mmrXrq2ysjJ3R3Sqxo0bq23btnZjbdq0MeXJ9pI0adIkTZ06VYMHD1ZkZKQeffRRPf/880pJSXF3tCoRHBwsSb+776/S0lI9/PDDys7OVlpammn36uzYsUN5eXkKCwuzfX8dO3ZMEyZMUPPmzd0dz2UoOy7g5eWlzp07Ky0tzW48LS1N0dHRbkrlOoZh6Nlnn9X69ev12WefKTw83N2RXKpXr146dOiQDhw4YJuioqI0dOhQHThwQB4eHu6O6FTdu3evcCuBo0ePqlmzZm5K5Fpnz55VrVr2X40eHh6muvT8WsLDwxUcHGz3/XX+/Hlt27bNlN9f0n+Lzr///W998sknql+/vrsjucyjjz6qgwcP2n1/hYSEaNKkSfrnP//p7nguw2EsFxk/frweffRRRUVFqVu3blq2bJmOHz+up59+2t3RnG7MmDFau3at3nvvPfn7+9v+jzAgIEA+Pj5uTud8/v7+Fc5H8vPzU/369U15ntLzzz+v6OhoJScn6+GHH9aXX36pZcuWadmyZe6O5hL9+/fX3LlzFRYWpttvv1379+/XggUL9MQTT7g7mtOcPn1a3377re11dna2Dhw4oMDAQIWFhSkxMVHJycmKiIhQRESEkpOT5evrq4SEBDemvnHX2t6QkBA9+OCDyszM1KZNm1RWVmb7DgsMDJSXl5e7Yt+w6/35/rbMeXp6Kjg4WLfddltVR6067r0YzNz+9re/Gc2aNTO8vLyMP/zhD6a9FFvSFadVq1a5O1qVMfOl54ZhGB988IHRrl07w2q1Gq1btzaWLVvm7kguU1hYaIwbN84ICwszvL29jRYtWhjTp083SkpK3B3NadLT06/4b3bEiBGGYVy8/HzWrFlGcHCwYbVajZ49exqHDh1yb+ibcK3tzc7Ovup3WHp6uruj35Dr/fn+1u/h0nOLYRhGFfUqAACAKsc5OwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwBqjMcee0wWi6XCdN9992nw4MHq06eP3fIff/yxLBaLZs6caTf+4osvKiQkxPb6H//4h7p27aqAgAD5+/vr9ttv14QJE6pkmwC4Hs/GAlCj3HfffVq1apXdmNVq1bvvvquJEyfqwoULql374lfb1q1bFRoaqvT0dLvlt27dqri4OEnSJ598osGDBys5OVkDBgyQxWLR119/rU8//bRqNgiAy1F2ANQoVqtVwcHBFcbj4uJ0+vRp7d27V3feeaeki6Vm6tSpev7553X27Fn5+vrq/Pnz2r17t1577TVJ0qZNm9SjRw9NmjTJtq5bb71VDzzwQJVsDwDX4zAWAFO49dZbFRISYtuLU1RUpMzMTD300ENq2bKlPv/8c0nSF198oeLiYtueneDgYGVlZenw4cNuyw7AtSg7AGqUTZs26ZZbbrGbXnzxRUlSbGystm7dKknasWOHbr31VjVs2FAxMTG28UuHtlq2bClJGjt2rO644w5FRkaqefPmGjx4sFauXKmSkhJ3bB4AF6DsAKhR4uLidODAAbtpzJgxtnmff/65SktLtXXrVsXGxkpShbJz991329bn5+enDz/8UN9++61mzJihW265RRMmTFCXLl109uzZqt48AC5A2QFQo/j5+alVq1Z2U2BgoKSLZefMmTPKyMhQenq6YmJiJF0sOxkZGTp16pR2795tO4R1uZYtW2rkyJFasWKFMjMz9fXXX+udd96p0m0D4BqUHQCm0bJlS4WGhur999/XgQMHbGWncePGat68uebPn69z585dsexcrnnz5vL19dWZM2eqIjYAF+NqLAA1SklJiXJzc+3GateurQYNGki6uHdn8eLFatWqlYKCgmzLxMTE6PXXX1eLFi0UFhZmG09KStLZs2f1xz/+Uc2aNdOvv/6q1157TaWlperdu3fVbBQAl2LPDoAaZfPmzWrcuLHd1KNHD9v8uLg4FRUV2c7XuSQmJkZFRUUV9urExMTo+++/1/Dhw9W6dWv16dNHubm52rJli2677baq2CQALmYxDMNwdwgAAABXYc8OAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwtf8PMG4LmwPXma4AAAAASUVORK5CYII=","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.hist(df_exp['EWS'])\n","plt.xlabel('EWS')\n","plt.title('EWS for death cases in ED')\n","plt.ylabel('number of records')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e_mVRWhqBh41","outputId":"49c14724-d18f-419e-a96d-9ee58b74bbbf"},"outputs":[{"data":{"text/plain":["<AxesSubplot: >"]},"execution_count":42,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["pearson(df_exp)"]},{"cell_type":"markdown","metadata":{"id":"FLwPjbGOBh41"},"source":["## plot for all groups"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NrIjYWHBBh41","outputId":"185e8135-0a78-4e8c-c53e-0fe794336736"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n","fig.suptitle('EWS')\n","fig.align_xlabels\n","ax1.hist(df_home['EWS'])\n","ax1.set_title('Home')\n","ax2.hist(df_adm['EWS'])\n","ax2.set_title('Admitted')\n","ax3.hist(df_exp['EWS'])\n","ax3.set_title('Expired')\n","ax4.hist(df['EWS'])\n","ax4.set_title('Total')\n","\n","for ax in fig.get_axes():\n"," ax.label_outer()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8qhidVphBh42","outputId":"ec419a2a-c534-49dc-9124-48b84ce42e8a"},"outputs":[{"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>subject_id</th>\n"," <th>hadm_id</th>\n"," <th>stay_id</th>\n"," <th>intime</th>\n"," <th>outtime</th>\n"," <th>disposition</th>\n"," <th>charttime</th>\n"," <th>temperature</th>\n"," <th>heartrate</th>\n"," <th>resprate</th>\n"," <th>o2sat</th>\n"," <th>sbp</th>\n"," <th>EWS</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:36:00</td>\n"," <td>98.1</td>\n"," <td>83.0</td>\n"," <td>24.0</td>\n"," <td>97.000000</td>\n"," <td>90.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:43:00</td>\n"," <td>98.1</td>\n"," <td>85.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>76.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 16:45:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>22.0</td>\n"," <td>97.000000</td>\n"," <td>75.0</td>\n"," <td>5</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 17:56:00</td>\n"," <td>98.1</td>\n"," <td>84.0</td>\n"," <td>20.0</td>\n"," <td>99.000000</td>\n"," <td>86.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>10000032</td>\n"," <td>29079034.0</td>\n"," <td>32952584</td>\n"," <td>2180-07-22 16:24:00</td>\n"," <td>2180-07-23 05:54:00</td>\n"," <td>HOME</td>\n"," <td>2180-07-22 18:37:00</td>\n"," <td>98.4</td>\n"," <td>86.0</td>\n"," <td>20.0</td>\n"," <td>98.000000</td>\n"," <td>65.0</td>\n"," <td>3</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>502</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:51:00</td>\n"," <td>90.0</td>\n"," <td>142.0</td>\n"," <td>24.0</td>\n"," <td>85.000000</td>\n"," <td>110.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>503</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:53:00</td>\n"," <td>90.0</td>\n"," <td>131.0</td>\n"," <td>18.0</td>\n"," <td>89.000000</td>\n"," <td>124.0</td>\n"," <td>9</td>\n"," </tr>\n"," <tr>\n"," <th>504</th>\n"," <td>19866442</td>\n"," <td>NaN</td>\n"," <td>30537148</td>\n"," <td>2120-01-06 17:26:00</td>\n"," <td>2120-01-07 02:26:00</td>\n"," <td>EXPIRED</td>\n"," <td>2120-01-06 21:57:00</td>\n"," <td>90.0</td>\n"," <td>126.0</td>\n"," <td>24.0</td>\n"," <td>88.000000</td>\n"," <td>99.0</td>\n"," <td>12</td>\n"," </tr>\n"," <tr>\n"," <th>505</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 17:26:00</td>\n"," <td>90.0</td>\n"," <td>95.0</td>\n"," <td>22.0</td>\n"," <td>98.000000</td>\n"," <td>132.0</td>\n"," <td>6</td>\n"," </tr>\n"," <tr>\n"," <th>506</th>\n"," <td>19993842</td>\n"," <td>NaN</td>\n"," <td>37776073</td>\n"," <td>2148-12-08 17:23:00</td>\n"," <td>2148-12-08 22:11:00</td>\n"," <td>EXPIRED</td>\n"," <td>2148-12-08 18:11:00</td>\n"," <td>97.6</td>\n"," <td>93.0</td>\n"," <td>17.0</td>\n"," <td>96.334677</td>\n"," <td>117.0</td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>1438962 rows × 13 columns</p>\n","</div>"],"text/plain":[" subject_id hadm_id stay_id intime \\\n","0 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","1 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","2 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","3 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n","4 10000032 29079034.0 32952584 2180-07-22 16:24:00 \n",".. ... ... ... ... \n","502 19866442 NaN 30537148 2120-01-06 17:26:00 \n","503 19866442 NaN 30537148 2120-01-06 17:26:00 \n","504 19866442 NaN 30537148 2120-01-06 17:26:00 \n","505 19993842 NaN 37776073 2148-12-08 17:23:00 \n","506 19993842 NaN 37776073 2148-12-08 17:23:00 \n","\n"," outtime disposition charttime temperature \\\n","0 2180-07-23 05:54:00 HOME 2180-07-22 16:36:00 98.1 \n","1 2180-07-23 05:54:00 HOME 2180-07-22 16:43:00 98.1 \n","2 2180-07-23 05:54:00 HOME 2180-07-22 16:45:00 98.1 \n","3 2180-07-23 05:54:00 HOME 2180-07-22 17:56:00 98.1 \n","4 2180-07-23 05:54:00 HOME 2180-07-22 18:37:00 98.4 \n",".. ... ... ... ... \n","502 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:51:00 90.0 \n","503 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:53:00 90.0 \n","504 2120-01-07 02:26:00 EXPIRED 2120-01-06 21:57:00 90.0 \n","505 2148-12-08 22:11:00 EXPIRED 2148-12-08 17:26:00 90.0 \n","506 2148-12-08 22:11:00 EXPIRED 2148-12-08 18:11:00 97.6 \n","\n"," heartrate resprate o2sat sbp EWS \n","0 83.0 24.0 97.000000 90.0 5 \n","1 85.0 22.0 98.000000 76.0 5 \n","2 84.0 22.0 97.000000 75.0 5 \n","3 84.0 20.0 99.000000 86.0 3 \n","4 86.0 20.0 98.000000 65.0 3 \n",".. ... ... ... ... ... \n","502 142.0 24.0 85.000000 110.0 12 \n","503 131.0 18.0 89.000000 124.0 9 \n","504 126.0 24.0 88.000000 99.0 12 \n","505 95.0 22.0 98.000000 132.0 6 \n","506 93.0 17.0 96.334677 117.0 1 \n","\n","[1438962 rows x 13 columns]"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UTUDLPxWBh42","outputId":"30d0b2a7-b8b0-49b6-ed49-add6c7ba92a4"},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/rd/v_nvbd811692jx_5dj1yjm4h0000gn/T/ipykernel_5512/2426855554.py:1: FutureWarning: ['intime', 'outtime', 'charttime'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n"," df_status = df.groupby(\"disposition\").agg([np.mean, np.std])\n"]},{"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 tr th {\n"," text-align: left;\n"," }\n","\n"," .dataframe thead tr:last-of-type th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr>\n"," <th></th>\n"," <th colspan=\"2\" halign=\"left\">subject_id</th>\n"," <th colspan=\"2\" halign=\"left\">hadm_id</th>\n"," <th colspan=\"2\" halign=\"left\">stay_id</th>\n"," <th colspan=\"2\" halign=\"left\">temperature</th>\n"," <th colspan=\"2\" halign=\"left\">heartrate</th>\n"," <th colspan=\"2\" halign=\"left\">resprate</th>\n"," <th colspan=\"2\" halign=\"left\">o2sat</th>\n"," <th colspan=\"2\" halign=\"left\">sbp</th>\n"," <th colspan=\"2\" halign=\"left\">EWS</th>\n"," </tr>\n"," <tr>\n"," <th></th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," <th>mean</th>\n"," <th>std</th>\n"," </tr>\n"," <tr>\n"," <th>disposition</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"," <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>ADMITTED</th>\n"," <td>1.500478e+07</td>\n"," <td>2.879283e+06</td>\n"," <td>2.499526e+07</td>\n"," <td>2.887104e+06</td>\n"," <td>3.499184e+07</td>\n"," <td>2.887976e+06</td>\n"," <td>98.114917</td>\n"," <td>0.814825</td>\n"," <td>83.810867</td>\n"," <td>19.070872</td>\n"," <td>18.267168</td>\n"," <td>3.670207</td>\n"," <td>97.535127</td>\n"," <td>3.130615</td>\n"," <td>128.015261</td>\n"," <td>23.794080</td>\n"," <td>1.500491</td>\n"," <td>1.758065</td>\n"," </tr>\n"," <tr>\n"," <th>EXPIRED</th>\n"," <td>1.490136e+07</td>\n"," <td>2.870441e+06</td>\n"," <td>2.385412e+07</td>\n"," <td>3.082696e+06</td>\n"," <td>3.458701e+07</td>\n"," <td>3.263392e+06</td>\n"," <td>92.681159</td>\n"," <td>3.846073</td>\n"," <td>87.101944</td>\n"," <td>22.934420</td>\n"," <td>20.933747</td>\n"," <td>5.953813</td>\n"," <td>96.299548</td>\n"," <td>6.611146</td>\n"," <td>113.752281</td>\n"," <td>31.192675</td>\n"," <td>5.267081</td>\n"," <td>3.446252</td>\n"," </tr>\n"," <tr>\n"," <th>HOME</th>\n"," <td>1.499945e+07</td>\n"," <td>2.872222e+06</td>\n"," <td>2.501045e+07</td>\n"," <td>2.897628e+06</td>\n"," <td>3.500601e+07</td>\n"," <td>2.886721e+06</td>\n"," <td>98.101491</td>\n"," <td>0.584803</td>\n"," <td>78.178436</td>\n"," <td>15.400932</td>\n"," <td>17.067479</td>\n"," <td>2.027926</td>\n"," <td>98.313515</td>\n"," <td>2.667620</td>\n"," <td>129.525437</td>\n"," <td>20.258312</td>\n"," <td>0.656317</td>\n"," <td>0.974598</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" subject_id hadm_id \\\n"," mean std mean std \n","disposition \n","ADMITTED 1.500478e+07 2.879283e+06 2.499526e+07 2.887104e+06 \n","EXPIRED 1.490136e+07 2.870441e+06 2.385412e+07 3.082696e+06 \n","HOME 1.499945e+07 2.872222e+06 2.501045e+07 2.897628e+06 \n","\n"," stay_id temperature heartrate \\\n"," mean std mean std mean \n","disposition \n","ADMITTED 3.499184e+07 2.887976e+06 98.114917 0.814825 83.810867 \n","EXPIRED 3.458701e+07 3.263392e+06 92.681159 3.846073 87.101944 \n","HOME 3.500601e+07 2.886721e+06 98.101491 0.584803 78.178436 \n","\n"," resprate o2sat sbp \\\n"," std mean std mean std mean \n","disposition \n","ADMITTED 19.070872 18.267168 3.670207 97.535127 3.130615 128.015261 \n","EXPIRED 22.934420 20.933747 5.953813 96.299548 6.611146 113.752281 \n","HOME 15.400932 17.067479 2.027926 98.313515 2.667620 129.525437 \n","\n"," EWS \n"," std mean std \n","disposition \n","ADMITTED 23.794080 1.500491 1.758065 \n","EXPIRED 31.192675 5.267081 3.446252 \n","HOME 20.258312 0.656317 0.974598 "]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["df_status = df.groupby(\"disposition\").agg([np.mean, np.std])\n","df_status"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"167mpeVpBh42"},"outputs":[],"source":["df_vt1 = df.groupby(\"EWS\").agg([np.mean, np.std])\n","df_vt1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hhDfTd1qBh42"},"outputs":[],"source":["# saving this dataframe as a csv file for furthure using in other notebooks\n","df_status.to_csv('df_status.csv',index= False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ORibkALtBh42","outputId":"72852295-3389-467d-b862-dbc70ab9b0c5"},"outputs":[{"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>mean</th>\n"," <th>std</th>\n"," </tr>\n"," <tr>\n"," <th>disposition</th>\n"," <th></th>\n"," <th></th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>ADMITTED</th>\n"," <td>1.500491</td>\n"," <td>1.758065</td>\n"," </tr>\n"," <tr>\n"," <th>EXPIRED</th>\n"," <td>5.267081</td>\n"," <td>3.446252</td>\n"," </tr>\n"," <tr>\n"," <th>HOME</th>\n"," <td>0.656317</td>\n"," <td>0.974598</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" mean std\n","disposition \n","ADMITTED 1.500491 1.758065\n","EXPIRED 5.267081 3.446252\n","HOME 0.656317 0.974598"]},"execution_count":138,"metadata":{},"output_type":"execute_result"}],"source":["status = df_status['EWS']\n","# checking for results\n","status.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"77x5h8bbBh43","outputId":"d652b370-cb20-42d0-b7b3-e5022c546383"},"outputs":[{"data":{"text/plain":["<AxesSubplot:title={'center':'Average EWS'}, ylabel='disposition'>"]},"execution_count":140,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["status.plot(kind = \"barh\", y = \"mean\", legend = False,\n"," title = \"Average EWS\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cqZPzRcWBh43","outputId":"1504ac06-f404-439c-eb79-4ff5e613159d"},"outputs":[{"data":{"text/plain":["<AxesSubplot:title={'center':'Average EWS'}, ylabel='disposition'>"]},"execution_count":132,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["status.plot(kind = \"barh\", y = \"mean\", legend = False,\n"," title = \"Average EWS\", xerr = \"std\")"]}],"metadata":{"kernelspec":{"display_name":"tensorflow-cpu","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"9f8e923212b91b54e42be08f690fbb06a9e9db9519c444a97c0172f8f7ec17ca"}},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0} |