{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":37941,"databundleVersionId":4046133,"sourceType":"competition"}],"dockerImageVersionId":30646,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-04-30T17:05:39.907171Z","iopub.execute_input":"2024-04-30T17:05:39.907809Z","iopub.status.idle":"2024-04-30T17:05:47.534725Z","shell.execute_reply.started":"2024-04-30T17:05:39.907776Z","shell.execute_reply":"2024-04-30T17:05:47.53341Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/eeg-based-biometric-competition/Calibration_Info.csv\n/kaggle/input/eeg-based-biometric-competition/Enrollment_Info.csv\n/kaggle/input/eeg-based-biometric-competition/Result_example_0615_000000.csv\n/kaggle/input/eeg-based-biometric-competition/Testing_Info.csv\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:06.210922Z","iopub.execute_input":"2024-04-30T20:28:06.21125Z","iopub.status.idle":"2024-04-30T20:28:06.214721Z","shell.execute_reply.started":"2024-04-30T20:28:06.211223Z","shell.execute_reply":"2024-04-30T20:28:06.214106Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"file_path = \"/kaggle/input/eeg-based-biometric-competition/Enrollment\"","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:06.480262Z","iopub.execute_input":"2024-04-30T20:28:06.480508Z","iopub.status.idle":"2024-04-30T20:28:06.483571Z","shell.execute_reply.started":"2024-04-30T20:28:06.480485Z","shell.execute_reply":"2024-04-30T20:28:06.482975Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"csv_data = pd.read_csv(\"/kaggle/input/eeg-based-biometric-competition/Enrollment_Info.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:06.770312Z","iopub.execute_input":"2024-04-30T20:28:06.770542Z","iopub.status.idle":"2024-04-30T20:28:06.835335Z","shell.execute_reply.started":"2024-04-30T20:28:06.770518Z","shell.execute_reply":"2024-04-30T20:28:06.834707Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"csv_data[csv_data[\"Task\"] == 1]","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:07.088835Z","iopub.execute_input":"2024-04-30T20:28:07.089443Z","iopub.status.idle":"2024-04-30T20:28:07.106029Z","shell.execute_reply.started":"2024-04-30T20:28:07.089411Z","shell.execute_reply":"2024-04-30T20:28:07.105372Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"           EpochID SubjectID  Session  Task  Usage\n45     epoch000046    sub001        1     1      1\n49     epoch000050    sub001        1     1      1\n63     epoch000064    sub001        1     1      1\n75     epoch000076    sub001        1     1      1\n77     epoch000078    sub001        1     1      1\n...            ...       ...      ...   ...    ...\n57825  epoch057826    sub095        1     1      1\n57835  epoch057836    sub095        1     1      1\n57839  epoch057840    sub095        1     1      1\n57845  epoch057846    sub095        1     1      1\n57850  epoch057851    sub095        1     1      1\n\n[2850 rows x 5 columns]","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>EpochID</th>\n      <th>SubjectID</th>\n      <th>Session</th>\n      <th>Task</th>\n      <th>Usage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45</th>\n      <td>epoch000046</td>\n      <td>sub001</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>epoch000050</td>\n      <td>sub001</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>epoch000064</td>\n      <td>sub001</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>epoch000076</td>\n      <td>sub001</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>epoch000078</td>\n      <td>sub001</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>57825</th>\n      <td>epoch057826</td>\n      <td>sub095</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>57835</th>\n      <td>epoch057836</td>\n      <td>sub095</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>57839</th>\n      <td>epoch057840</td>\n      <td>sub095</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>57845</th>\n      <td>epoch057846</td>\n      <td>sub095</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>57850</th>\n      <td>epoch057851</td>\n      <td>sub095</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2850 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from scipy.io import loadmat\nmat_data = loadmat('/kaggle/input/eeg-based-biometric-competition/Enrollment/epoch000046.mat')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:09.576773Z","iopub.execute_input":"2024-04-30T20:28:09.577902Z","iopub.status.idle":"2024-04-30T20:28:09.649746Z","shell.execute_reply.started":"2024-04-30T20:28:09.577866Z","shell.execute_reply":"2024-04-30T20:28:09.64876Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"eeg_data = mat_data['epoch_data']","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:09.84057Z","iopub.execute_input":"2024-04-30T20:28:09.841373Z","iopub.status.idle":"2024-04-30T20:28:09.844763Z","shell.execute_reply.started":"2024-04-30T20:28:09.84134Z","shell.execute_reply":"2024-04-30T20:28:09.844001Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"ica = FastICA(n_components=num_components)\nica.fit(eeg_data.T)\nica_components = ica.components_\nselected_components = [0, 1, 2]  # Example: Selecting the first 3 components\ncleaned_EEG = np.dot(ica_components[selected_components], eeg_data)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T07:52:00.121724Z","iopub.execute_input":"2024-04-30T07:52:00.122583Z","iopub.status.idle":"2024-04-30T07:52:00.183817Z","shell.execute_reply.started":"2024-04-30T07:52:00.12254Z","shell.execute_reply":"2024-04-30T07:52:00.182481Z"},"trusted":true},"execution_count":12,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ica \u001b[38;5;241m=\u001b[39m \u001b[43mFastICA\u001b[49m(n_components\u001b[38;5;241m=\u001b[39mnum_components)\n\u001b[1;32m      2\u001b[0m ica\u001b[38;5;241m.\u001b[39mfit(eeg_data\u001b[38;5;241m.\u001b[39mT)\n\u001b[1;32m      3\u001b[0m ica_components \u001b[38;5;241m=\u001b[39m ica\u001b[38;5;241m.\u001b[39mcomponents_\n","\u001b[0;31mNameError\u001b[0m: name 'FastICA' is not defined"],"ename":"NameError","evalue":"name 'FastICA' is not defined","output_type":"error"}]},{"cell_type":"code","source":"cleaned_EEG.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T06:19:14.826164Z","iopub.execute_input":"2024-04-30T06:19:14.826487Z","iopub.status.idle":"2024-04-30T06:19:14.832618Z","shell.execute_reply.started":"2024-04-30T06:19:14.826446Z","shell.execute_reply":"2024-04-30T06:19:14.831741Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"(3, 1000)"},"metadata":{}}]},{"cell_type":"code","source":"l = pd.DataFrame(eeg_data)\n# l.drop('64')\nl = l.drop([l.index[64]])","metadata":{"execution":{"iopub.status.busy":"2024-04-30T02:42:47.826999Z","iopub.execute_input":"2024-04-30T02:42:47.8274Z","iopub.status.idle":"2024-04-30T02:42:47.834077Z","shell.execute_reply.started":"2024-04-30T02:42:47.827369Z","shell.execute_reply":"2024-04-30T02:42:47.83294Z"},"trusted":true},"execution_count":106,"outputs":[]},{"cell_type":"code","source":"l","metadata":{"execution":{"iopub.status.busy":"2024-04-30T02:47:25.422181Z","iopub.execute_input":"2024-04-30T02:47:25.422509Z","iopub.status.idle":"2024-04-30T02:47:25.449814Z","shell.execute_reply.started":"2024-04-30T02:47:25.422485Z","shell.execute_reply":"2024-04-30T02:47:25.44874Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"          0          1          2          3          4          5    \\\n0   11.067101   5.449813  -9.878695  -8.957481  10.878105   8.200901   \n1   23.571569  24.194706   4.902365   3.897734  21.456177  21.771076   \n2   21.041195   6.718518  -1.423255  -2.492061  21.029360  18.689709   \n3   -0.579348  -7.577970 -22.499859 -18.979822   3.677352   2.988013   \n4   10.786279  -1.537357 -13.243974  -4.561080  14.919620  14.011766   \n..        ...        ...        ...        ...        ...        ...   \n59  16.276215   9.184290   2.532568  -7.804439  19.324432  17.046093   \n60 -21.043962 -33.143185 -44.005558 -36.239178 -15.784920 -15.160236   \n61  11.520661  -1.656450  -9.402312  -4.903343  12.546417  13.611138   \n62   4.403626  -8.360818 -17.392288 -10.209394   3.157320   1.306155   \n63   0.184157 -14.079184 -23.979727 -20.719372   4.072815   4.738183   \n\n          6          7          8          9    ...        990        991  \\\n0   11.822778  15.351372  22.351505  14.785765  ...  24.171293  42.157475   \n1   24.644402  26.282183  34.298164  27.311575  ...  28.112415  39.503418   \n2   18.902960  30.611538  31.257799  28.654346  ...  17.448954  42.207569   \n3    3.784144  13.122669  16.595703  11.628991  ...  10.035358  25.577948   \n4   16.412537  22.157660  26.396044  22.073084  ...   2.210635  22.112991   \n..        ...        ...        ...        ...  ...        ...        ...   \n59  17.947784  28.635700  28.898754  28.000944  ...  17.234285  41.062870   \n60 -12.409399  -3.384034  -0.420009  -5.434723  ...   3.208546  19.978798   \n61  16.134171  26.327578  28.236425  21.620211  ...  -9.170183   8.317041   \n62   4.104298  10.187404  12.129596   4.398243  ...  -9.433425   7.397158   \n63   3.554637  15.808437  14.541035  12.147223  ...  20.388050  42.410919   \n\n          992        993        994        995        996        997  \\\n0   31.241108  41.290405  47.881744  34.324558  54.364727  32.839165   \n1   33.809467  44.217003  53.945343  37.823864  60.191044  36.231365   \n2   28.103077  34.382862  48.351940  40.361595  40.199425  27.285892   \n3   17.457390  26.585176  36.956165  53.746475  18.211370  15.888704   \n4   10.545990  18.689150  25.932880  26.941788  18.511093   9.565126   \n..        ...        ...        ...        ...        ...        ...   \n59  28.572807  40.187950  50.291561  30.916607  50.195389  27.906116   \n60  10.585496  18.562460  30.432201  30.080788  20.662354  13.826987   \n61   1.048245   3.903092  17.065243  14.001833   7.862938   1.250756   \n62   0.218445   0.220602  11.435529   8.748920   3.152502  -2.648632   \n63  31.547115  38.594151  53.057278  49.420944  41.053452  31.796421   \n\n          998        999  \n0   26.422878  21.054577  \n1   25.991398  20.034035  \n2   19.185682  21.343958  \n3   10.542969   9.318177  \n4    2.861026   0.676787  \n..        ...        ...  \n59  20.776070  18.579132  \n60   5.676037   5.769497  \n61  -7.960996  -3.777391  \n62 -10.057514  -8.398685  \n63  23.224417  26.830217  \n\n[64 rows x 1000 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>990</th>\n      <th>991</th>\n      <th>992</th>\n      <th>993</th>\n      <th>994</th>\n      <th>995</th>\n      <th>996</th>\n      <th>997</th>\n      <th>998</th>\n      <th>999</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>11.067101</td>\n      <td>5.449813</td>\n      <td>-9.878695</td>\n      <td>-8.957481</td>\n      <td>10.878105</td>\n      <td>8.200901</td>\n      <td>11.822778</td>\n      <td>15.351372</td>\n      <td>22.351505</td>\n      <td>14.785765</td>\n      <td>...</td>\n      <td>24.171293</td>\n      <td>42.157475</td>\n      <td>31.241108</td>\n      <td>41.290405</td>\n      <td>47.881744</td>\n      <td>34.324558</td>\n      <td>54.364727</td>\n      <td>32.839165</td>\n      <td>26.422878</td>\n      <td>21.054577</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>23.571569</td>\n      <td>24.194706</td>\n      <td>4.902365</td>\n      <td>3.897734</td>\n      <td>21.456177</td>\n      <td>21.771076</td>\n      <td>24.644402</td>\n      <td>26.282183</td>\n      <td>34.298164</td>\n      <td>27.311575</td>\n      <td>...</td>\n      <td>28.112415</td>\n      <td>39.503418</td>\n      <td>33.809467</td>\n      <td>44.217003</td>\n      <td>53.945343</td>\n      <td>37.823864</td>\n      <td>60.191044</td>\n      <td>36.231365</td>\n      <td>25.991398</td>\n      <td>20.034035</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.041195</td>\n      <td>6.718518</td>\n      <td>-1.423255</td>\n      <td>-2.492061</td>\n      <td>21.029360</td>\n      <td>18.689709</td>\n      <td>18.902960</td>\n      <td>30.611538</td>\n      <td>31.257799</td>\n      <td>28.654346</td>\n      <td>...</td>\n      <td>17.448954</td>\n      <td>42.207569</td>\n      <td>28.103077</td>\n      <td>34.382862</td>\n      <td>48.351940</td>\n      <td>40.361595</td>\n      <td>40.199425</td>\n      <td>27.285892</td>\n      <td>19.185682</td>\n      <td>21.343958</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.579348</td>\n      <td>-7.577970</td>\n      <td>-22.499859</td>\n      <td>-18.979822</td>\n      <td>3.677352</td>\n      <td>2.988013</td>\n      <td>3.784144</td>\n      <td>13.122669</td>\n      <td>16.595703</td>\n      <td>11.628991</td>\n      <td>...</td>\n      <td>10.035358</td>\n      <td>25.577948</td>\n      <td>17.457390</td>\n      <td>26.585176</td>\n      <td>36.956165</td>\n      <td>53.746475</td>\n      <td>18.211370</td>\n      <td>15.888704</td>\n      <td>10.542969</td>\n      <td>9.318177</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10.786279</td>\n      <td>-1.537357</td>\n      <td>-13.243974</td>\n      <td>-4.561080</td>\n      <td>14.919620</td>\n      <td>14.011766</td>\n      <td>16.412537</td>\n      <td>22.157660</td>\n      <td>26.396044</td>\n      <td>22.073084</td>\n      <td>...</td>\n      <td>2.210635</td>\n      <td>22.112991</td>\n      <td>10.545990</td>\n      <td>18.689150</td>\n      <td>25.932880</td>\n      <td>26.941788</td>\n      <td>18.511093</td>\n      <td>9.565126</td>\n      <td>2.861026</td>\n      <td>0.676787</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      <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>59</th>\n      <td>16.276215</td>\n      <td>9.184290</td>\n      <td>2.532568</td>\n      <td>-7.804439</td>\n      <td>19.324432</td>\n      <td>17.046093</td>\n      <td>17.947784</td>\n      <td>28.635700</td>\n      <td>28.898754</td>\n      <td>28.000944</td>\n      <td>...</td>\n      <td>17.234285</td>\n      <td>41.062870</td>\n      <td>28.572807</td>\n      <td>40.187950</td>\n      <td>50.291561</td>\n      <td>30.916607</td>\n      <td>50.195389</td>\n      <td>27.906116</td>\n      <td>20.776070</td>\n      <td>18.579132</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>-21.043962</td>\n      <td>-33.143185</td>\n      <td>-44.005558</td>\n      <td>-36.239178</td>\n      <td>-15.784920</td>\n      <td>-15.160236</td>\n      <td>-12.409399</td>\n      <td>-3.384034</td>\n      <td>-0.420009</td>\n      <td>-5.434723</td>\n      <td>...</td>\n      <td>3.208546</td>\n      <td>19.978798</td>\n      <td>10.585496</td>\n      <td>18.562460</td>\n      <td>30.432201</td>\n      <td>30.080788</td>\n      <td>20.662354</td>\n      <td>13.826987</td>\n      <td>5.676037</td>\n      <td>5.769497</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>11.520661</td>\n      <td>-1.656450</td>\n      <td>-9.402312</td>\n      <td>-4.903343</td>\n      <td>12.546417</td>\n      <td>13.611138</td>\n      <td>16.134171</td>\n      <td>26.327578</td>\n      <td>28.236425</td>\n      <td>21.620211</td>\n      <td>...</td>\n      <td>-9.170183</td>\n      <td>8.317041</td>\n      <td>1.048245</td>\n      <td>3.903092</td>\n      <td>17.065243</td>\n      <td>14.001833</td>\n      <td>7.862938</td>\n      <td>1.250756</td>\n      <td>-7.960996</td>\n      <td>-3.777391</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>4.403626</td>\n      <td>-8.360818</td>\n      <td>-17.392288</td>\n      <td>-10.209394</td>\n      <td>3.157320</td>\n      <td>1.306155</td>\n      <td>4.104298</td>\n      <td>10.187404</td>\n      <td>12.129596</td>\n      <td>4.398243</td>\n      <td>...</td>\n      <td>-9.433425</td>\n      <td>7.397158</td>\n      <td>0.218445</td>\n      <td>0.220602</td>\n      <td>11.435529</td>\n      <td>8.748920</td>\n      <td>3.152502</td>\n      <td>-2.648632</td>\n      <td>-10.057514</td>\n      <td>-8.398685</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>0.184157</td>\n      <td>-14.079184</td>\n      <td>-23.979727</td>\n      <td>-20.719372</td>\n      <td>4.072815</td>\n      <td>4.738183</td>\n      <td>3.554637</td>\n      <td>15.808437</td>\n      <td>14.541035</td>\n      <td>12.147223</td>\n      <td>...</td>\n      <td>20.388050</td>\n      <td>42.410919</td>\n      <td>31.547115</td>\n      <td>38.594151</td>\n      <td>53.057278</td>\n      <td>49.420944</td>\n      <td>41.053452</td>\n      <td>31.796421</td>\n      <td>23.224417</td>\n      <td>26.830217</td>\n    </tr>\n  </tbody>\n</table>\n<p>64 rows × 1000 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(10, 4))\nplt.plot(eeg_data.T.flatten())\nplt.xlabel('Time (samples)')\nplt.ylabel('Amplitude')\nplt.title('Waveform')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T01:44:24.99182Z","iopub.execute_input":"2024-04-30T01:44:24.992469Z","iopub.status.idle":"2024-04-30T01:44:25.358128Z","shell.execute_reply.started":"2024-04-30T01:44:24.992438Z","shell.execute_reply":"2024-04-30T01:44:25.35689Z"},"trusted":true},"execution_count":9,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(eeg_data\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mflatten())\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime (samples)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"],"ename":"NameError","evalue":"name 'plt' is not defined","output_type":"error"}]},{"cell_type":"code","source":"import librosa\ndef preprocess_eeg_data_signal(EEG_data, sample_rate=250):\n#     mel_spec_features = []\n        # Compute mel-spectrogram features\n    psd = np.abs(librosa.stft(np.array(EEG_data).flatten(), n_fft=2028))\n#     EEG_data = EEG_data.flatten()\n#     EEG_data = np.array(EEG_data)\n    mel_spec = librosa.feature.melspectrogram(S=psd,sr=200)\n    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n#     zero_crossing_rate = librosa.feature.zero_crossing_rate(EEG_data)[0]\n#     spectral_centroid = librosa.feature.spectral_centroid(y=EEG_data, sr=sample_rate)[0]\n#     spectral_bandwidth = librosa.feature.spectral_bandwidth(y=EEG_data, sr=sample_rate)[0]\n#     mel_spec_flattened = mel_spec_db.T.reshape(-1, mel_spec_db.shape[1] * mel_spec_db.shape[0])\n#     mel_spec_features.append(mel_spec_flattened)\n#     print(mel_spec.shape)\n#     print(zero_crossing_rate.shape)\n#     print(spectral_centroid.shape)\n#     print(spectral_bandwidth.shape)\n#     combined_features = np.vstack((mel_spec.reshape(-1,mel_spec.shape[2]), zero_crossing_rate, spectral_centroid, spectral_bandwidth))\n       \n#     print(combined_features.shape)\n    return mel_spec","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:16.17401Z","iopub.execute_input":"2024-04-30T20:28:16.17438Z","iopub.status.idle":"2024-04-30T20:28:16.190111Z","shell.execute_reply.started":"2024-04-30T20:28:16.174352Z","shell.execute_reply":"2024-04-30T20:28:16.18937Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"from mne.preprocessing import ICA\n\n\nica = ICA(n_components=20, random_state=97)  # Specify the number of components to decompose into\n\n# Fit ICA to the data\nica.fit(raw_eeg_data_1)\n\n# Plot ICA components\nica.plot_components()\n\n# Remove artifact components (manually or programmatically)\nica.exclude = [0, 1]  # Specify the indices of components to remove\n\n# Apply ICA inverse transform to reconstruct the cleaned EEG signals\ncleaned_raw = raw_eeg_data_1.copy()  # Create a copy of the original raw object\nica.apply(cleaned_raw)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T07:52:06.396677Z","iopub.execute_input":"2024-04-30T07:52:06.39706Z","iopub.status.idle":"2024-04-30T07:52:06.427785Z","shell.execute_reply.started":"2024-04-30T07:52:06.397029Z","shell.execute_reply":"2024-04-30T07:52:06.426562Z"},"trusted":true},"execution_count":14,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[14], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m ica \u001b[38;5;241m=\u001b[39m ICA(n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m97\u001b[39m)  \u001b[38;5;66;03m# Specify the number of components to decompose into\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Fit ICA to the data\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m ica\u001b[38;5;241m.\u001b[39mfit(\u001b[43mraw_eeg_data_1\u001b[49m)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Plot ICA components\u001b[39;00m\n\u001b[1;32m     10\u001b[0m ica\u001b[38;5;241m.\u001b[39mplot_components()\n","\u001b[0;31mNameError\u001b[0m: name 'raw_eeg_data_1' is not defined"],"ename":"NameError","evalue":"name 'raw_eeg_data_1' is not defined","output_type":"error"}]},{"cell_type":"code","source":"raw_eeg_data_1.get_data()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T03:06:06.091506Z","iopub.execute_input":"2024-04-30T03:06:06.092152Z","iopub.status.idle":"2024-04-30T03:06:06.100109Z","shell.execute_reply.started":"2024-04-30T03:06:06.09212Z","shell.execute_reply":"2024-04-30T03:06:06.099164Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"array([[ 11.06710052,   5.44981337,  -9.87869549, ...,  32.83916473,\n         26.42287827,  21.05457687],\n       [ 23.57156944,  24.19470596,   4.90236521, ...,  36.2313652 ,\n         25.99139786,  20.03403473],\n       [ 21.04119492,   6.71851826,  -1.42325521, ...,  27.28589249,\n         19.1856823 ,  21.3439579 ],\n       ...,\n       [ 11.52066135,  -1.65645015,  -9.40231228, ...,   1.25075603,\n         -7.96099567,  -3.77739096],\n       [  4.40362644,  -8.36081791, -17.39228821, ...,  -2.64863229,\n        -10.05751419,  -8.3986845 ],\n       [  0.18415685, -14.07918358, -23.97972679, ...,  31.79642105,\n         23.22441673,  26.83021736]])"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_raw.filter(20,124)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T03:12:29.851685Z","iopub.execute_input":"2024-04-30T03:12:29.852533Z","iopub.status.idle":"2024-04-30T03:12:29.895744Z","shell.execute_reply.started":"2024-04-30T03:12:29.852499Z","shell.execute_reply":"2024-04-30T03:12:29.894953Z"},"trusted":true},"execution_count":173,"outputs":[{"name":"stdout","text":"Filtering raw data in 1 contiguous segment\nSetting up band-pass filter from 20 - 1.2e+02 Hz\n\nFIR filter parameters\n---------------------\nDesigning a one-pass, zero-phase, non-causal bandpass filter:\n- Windowed time-domain design (firwin) method\n- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n- Lower passband edge: 20.00\n- Lower transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 17.50 Hz)\n- Upper passband edge: 124.00 Hz\n- Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 124.50 Hz)\n- Filter length: 825 samples (3.300 s)\n\n","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s\n","output_type":"stream"},{"execution_count":173,"output_type":"execute_result","data":{"text/plain":"<RawArray | 64 x 1000 (4.0 s), ~591 kB, data loaded>","text/html":"<details open>\n    <summary><strong>General</strong></summary>\n    <table class=\"table table-hover table-striped table-sm table-responsive small\">\n        <tr>\n            <th>Measurement date</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n        <tr>\n            <th>Experimenter</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n        <tr>\n            <th>Participant</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n    </table>\n    </details>\n    <details open>\n        <summary><strong>Channels</strong></summary>\n        <table class=\"table table-hover table-striped table-sm table-responsive small\">\n            <tr>\n                <th>Digitized points</th>\n                \n                <td>67 points</td>\n                \n            </tr>\n            <tr>\n                <th>Good channels</th>\n                <td>64 EEG</td>\n            </tr>\n            <tr>\n                <th>Bad channels</th>\n                <td>None</td>\n            </tr>\n            <tr>\n                <th>EOG channels</th>\n                <td>Not available</td>\n            </tr>\n            <tr>\n                <th>ECG channels</th>\n                <td>Not available</td>\n            </tr>\n        </table>\n        </details>\n        <details open>\n            <summary><strong>Data</strong></summary>\n            <table class=\"table table-hover table-striped table-sm table-responsive small\">\n                \n                <tr>\n                    <th>Sampling frequency</th>\n                    <td>250.00 Hz</td>\n                </tr>\n                \n                \n                <tr>\n                    <th>Highpass</th>\n                    <td>20.00 Hz</td>\n                </tr>\n                \n                \n                <tr>\n                    <th>Lowpass</th>\n                    <td>10.00 Hz</td>\n                </tr>\n                \n                \n                \n                \n                <tr>\n                    <th>Duration</th>\n                    <td>00:00:04 (HH:MM:SS)</td>\n                </tr>\n                \n            </table>\n            </details>"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_raw.get_data()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T03:10:26.517199Z","iopub.execute_input":"2024-04-30T03:10:26.517693Z","iopub.status.idle":"2024-04-30T03:10:26.527018Z","shell.execute_reply.started":"2024-04-30T03:10:26.517657Z","shell.execute_reply":"2024-04-30T03:10:26.526031Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":"array([[-1.22124533e-15, -9.38625857e-01, -1.82745994e+00, ...,\n         2.27922717e+00,  1.14823752e+00, -1.66533454e-15],\n       [ 0.00000000e+00, -8.82369016e-01, -1.72699875e+00, ...,\n         3.89847281e+00,  1.97126375e+00, -9.43689571e-16],\n       [-5.55111512e-16, -7.07484164e-01, -1.42943777e+00, ...,\n         6.46828907e-01,  3.32254640e-01, -1.44328993e-15],\n       ...,\n       [-1.77635684e-15, -3.11287049e-01, -6.24193483e-01, ...,\n        -2.25397344e+00, -1.15167127e+00, -2.22044605e-16],\n       [-2.05391260e-15, -1.56546447e+00, -3.06972145e+00, ...,\n        -1.25504438e+00, -6.45478231e-01,  6.10622664e-16],\n       [ 8.32667268e-16,  8.83231815e-02,  1.68868835e-01, ...,\n         1.01820557e+00,  5.15282130e-01, -3.33066907e-16]])"},"metadata":{}}]},{"cell_type":"code","source":"import mne \n# channel_types = ['eeg'] * 64\nchannel_name = ['Fp1','Fp2','F3','F4','C3','C4','P3','P4','O1','O2','F7','F8','T7','T8'\n,'P7'\n,'P8'\n,'Fz'\n,'Cz'\n,'Pz'\n,'FC1'\n,'FC2'\n,'CP1'\n,'CP2'\n,'FC5'\n,'FC6'\n,'CP5'\n,'CP6'\n,'FT9','FT10','TP9','TP10','F1','F2','C1','C2','P1','P2','AF3','AF4','FC3','FC4','CP3','CP4','PO3','PO4','F5','F6','C5','C6','P5','P6','AF7','AF8','FT7','FT8','TP7','TP8','PO7','PO8','Fpz','CPz','POz','Oz','FCz']\n\nsfreq = 250\ninfo = mne.create_info(channel_name, sfreq, ch_types='eeg')\nraw_eeg_data_1 = mne.io.RawArray(l,info)\n\nmontage = mne.channels.make_standard_montage('standard_1005')\n\nraw_eeg_data_1.set_montage(montage)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T02:58:02.167744Z","iopub.execute_input":"2024-04-30T02:58:02.168193Z","iopub.status.idle":"2024-04-30T02:58:02.219556Z","shell.execute_reply.started":"2024-04-30T02:58:02.168159Z","shell.execute_reply":"2024-04-30T02:58:02.218639Z"},"trusted":true},"execution_count":122,"outputs":[{"name":"stdout","text":"Creating RawArray with float64 data, n_channels=64, n_times=1000\n    Range : 0 ... 999 =      0.000 ...     3.996 secs\nReady.\n","output_type":"stream"},{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"<RawArray | 64 x 1000 (4.0 s), ~591 kB, data loaded>","text/html":"<details open>\n    <summary><strong>General</strong></summary>\n    <table class=\"table table-hover table-striped table-sm table-responsive small\">\n        <tr>\n            <th>Measurement date</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n        <tr>\n            <th>Experimenter</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n        <tr>\n            <th>Participant</th>\n            \n            <td>Unknown</td>\n            \n        </tr>\n    </table>\n    </details>\n    <details open>\n        <summary><strong>Channels</strong></summary>\n        <table class=\"table table-hover table-striped table-sm table-responsive small\">\n            <tr>\n                <th>Digitized points</th>\n                \n                <td>67 points</td>\n                \n            </tr>\n            <tr>\n                <th>Good channels</th>\n                <td>64 EEG</td>\n            </tr>\n            <tr>\n                <th>Bad channels</th>\n                <td>None</td>\n            </tr>\n            <tr>\n                <th>EOG channels</th>\n                <td>Not available</td>\n            </tr>\n            <tr>\n                <th>ECG channels</th>\n                <td>Not available</td>\n            </tr>\n        </table>\n        </details>\n        <details open>\n            <summary><strong>Data</strong></summary>\n            <table class=\"table table-hover table-striped table-sm table-responsive small\">\n                \n                <tr>\n                    <th>Sampling frequency</th>\n                    <td>250.00 Hz</td>\n                </tr>\n                \n                \n                <tr>\n                    <th>Highpass</th>\n                    <td>0.00 Hz</td>\n                </tr>\n                \n                \n                <tr>\n                    <th>Lowpass</th>\n                    <td>125.00 Hz</td>\n                </tr>\n                \n                \n                \n                \n                <tr>\n                    <th>Duration</th>\n                    <td>00:00:04 (HH:MM:SS)</td>\n                </tr>\n                \n            </table>\n            </details>"},"metadata":{}}]},{"cell_type":"code","source":"raw_eeg_data_1","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"v = preprocess_eeg_data_signal(eeg_data.T,250)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T17:06:33.267091Z","iopub.execute_input":"2024-04-30T17:06:33.267736Z","iopub.status.idle":"2024-04-30T17:06:43.148091Z","shell.execute_reply.started":"2024-04-30T17:06:33.267705Z","shell.execute_reply":"2024-04-30T17:06:43.146643Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"psd = np.abs(librosa.stft(np.array(cleaned_raw.get_data()), n_fft=2024))\nmel_spectrogram = librosa.feature.melspectrogram(S=psd, sr=40)\nmel_spectrogram_db = librosa.power_to_db(mel_spectrogram, ref=np.max)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T17:06:43.150503Z","iopub.execute_input":"2024-04-30T17:06:43.151515Z","iopub.status.idle":"2024-04-30T17:06:43.236856Z","shell.execute_reply.started":"2024-04-30T17:06:43.151461Z","shell.execute_reply":"2024-04-30T17:06:43.234843Z"},"trusted":true},"execution_count":12,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m psd \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(librosa\u001b[38;5;241m.\u001b[39mstft(np\u001b[38;5;241m.\u001b[39marray(\u001b[43mcleaned_raw\u001b[49m\u001b[38;5;241m.\u001b[39mget_data()), n_fft\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2024\u001b[39m))\n\u001b[1;32m      2\u001b[0m mel_spectrogram \u001b[38;5;241m=\u001b[39m librosa\u001b[38;5;241m.\u001b[39mfeature\u001b[38;5;241m.\u001b[39mmelspectrogram(S\u001b[38;5;241m=\u001b[39mpsd, sr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m40\u001b[39m)\n\u001b[1;32m      3\u001b[0m mel_spectrogram_db \u001b[38;5;241m=\u001b[39m librosa\u001b[38;5;241m.\u001b[39mpower_to_db(mel_spectrogram, ref\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mmax)\n","\u001b[0;31mNameError\u001b[0m: name 'cleaned_raw' is not defined"],"ename":"NameError","evalue":"name 'cleaned_raw' is not defined","output_type":"error"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# mel_spec_db = librosa.power_to_db(img, ref=np.max)\nmel_spec = librosa.display.specshow(mel_spectrogram_db.reshape(mel_spectrogram_db.shape[0],-1),\n                         y_axis='mel', x_axis='time')\n\nplt.colorbar(format='%+2.0f dB')\nplt.title('Mel Spectrogram')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T03:12:56.061475Z","iopub.execute_input":"2024-04-30T03:12:56.06178Z","iopub.status.idle":"2024-04-30T03:12:56.402364Z","shell.execute_reply.started":"2024-04-30T03:12:56.061756Z","shell.execute_reply":"2024-04-30T03:12:56.401413Z"},"trusted":true},"execution_count":179,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAm4AAAHWCAYAAADO2QWWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAADyoElEQVR4nOz9eZxdRbX3j3/2dMY+Q09Jd+aJkBDmcIEIAmLEMOoF5YJcrygiPhdwwOfK5fKo6GVQ4Ssoo175xSmAoqASBQSCgDLIDXPICJk7nZ5Pd58+4971+6NW1d77dCckpJsQWO/Xq19n1V5Vq1bV3qe7eu9aexlCCAGGYRiGYRjmXY+5tx1gGIZhGIZhdg1euDEMwzAMw+wj8MKNYRiGYRhmH4EXbgzDMAzDMPsIvHBjGIZhGIbZR+CFG8MwDMMwzD4CL9wYhmEYhmH2EXjhxjAMwzAMs4/ACzeGYRiGYZh9BF64Mcx7mBNOOAEnnHDC3naDYRiGGSV44cYwe4mf/exnMAwDhmHgb3/72zC9EAKTJ0+GYRg47bTTxtSXcrmMH/7whzjssMOQTqeRzWYxb948fOELX8CqVavGtO9d4c9//jOuuuqqve0GwzDMXocXbgyzl4nFYrjrrruGHX/iiSewZcsWRKPRMffhrLPOwte+9jUceOCB+O53v4tvf/vbOO644/Dggw/i2WefHfP+34o///nP+Pa3v7233WAYhtnr2HvbAYZ5v3PKKafg3nvvxY9+9CPYtv+VvOuuuzB//nx0dXWNaf/PP/88li5dimuuuQb/9V//FdLdcsst6OvrG9P+R5tqtQrP8xCJRMa8L8/zUC6XEYvFxrwvhmEYgO+4Mcxe59xzz0V3dzceeeQRfaxcLuO3v/0tPvWpT43YxvM83HTTTZg3bx5isRjGjx+Piy66CL29vbvd/xtvvAEAOOaYY4bpLMtCY2OjLl911VUwDAOrVq3C2WefjXQ6jcbGRnz5y19GsVgc1v5Xv/oV5s+fj3g8joaGBpxzzjnYvHnzsHrPPfccTjnlFNTX1yOZTOLggw/GD3/4QwDA+eefj1tvvRUA9KNlwzAAABs2bIBhGLjhhhtw0003YebMmYhGo3j99dcBAMuWLcMHP/hBJJNJZLNZfOxjH8PKlSuH9f/Xv/4VRxxxBGKxGGbOnIkf//jHeqxBDMPAJZdcgiVLlmDevHmIRqN46KGHAAA33HADPvCBD6CxsRHxeBzz58/Hb3/722F9KRv33nsvDjjgAMTjcSxYsACvvvoqAODHP/4xZs2ahVgshhNOOAEbNmwYZoNhmPcvfMeNYfYy06ZNw4IFC3D33Xfj5JNPBgA8+OCDyOVyOOecc/CjH/1oWJuLLroIP/vZz/DZz34WX/rSl7B+/XrccsstePHFF/H3v/8djuPscv9Tp04FACxZsgTHHHNM6K7fjjj77LMxbdo0XHfddXj22Wfxox/9CL29vfjFL36h61xzzTX4xje+gbPPPhuf//zn0dnZiZtvvhnHHXccXnzxRWSzWQDAI488gtNOOw2tra348pe/jJaWFqxcuRJLly7Fl7/8ZVx00UVoa2vDI488gl/+8pcj+rN48WIUi0V84QtfQDQaRUNDAx599FGcfPLJmDFjBq666ioUCgXcfPPNOOaYY/DCCy9g2rRpAIAXX3wRixYtQmtrK7797W/DdV185zvfQXNz84h9LVu2DL/5zW9wySWXoKmpSdv54Q9/iDPOOAPnnXceyuUy7rnnHnzyk5/E0qVLceqpp4ZsPPXUU/jjH/+Iiy++GABw3XXX4bTTTsPXv/513Hbbbfj3f/939Pb24vvf/z4+97nPYdmyZW95ThiGeZ8gGIbZKyxevFgAEM8//7y45ZZbRCqVEkNDQ0IIIT75yU+KD33oQ0IIIaZOnSpOPfVU3e6pp54SAMSSJUtC9h566KFhx48//nhx/PHH79QPz/PE8ccfLwCI8ePHi3PPPVfceuutYuPGjcPqfutb3xIAxBlnnBE6/u///u8CgHj55ZeFEEJs2LBBWJYlrrnmmlC9V199Vdi2rY9Xq1Uxffp0MXXqVNHb2zvML8XFF18sRvp1tX79egFApNNp0dHREdIdeuihYty4caK7u1sfe/nll4VpmuLf/u3f9LHTTz9dJBIJsXXrVn1s7dq1wrbtYX0CEKZpihUrVgzzRZ07RblcFgceeKA48cQTh9mIRqNi/fr1+tiPf/xjAUC0tLSI/v5+ffyKK64QAEJ1GYZ5f8OPShnmXcDZZ5+NQqGApUuXYmBgAEuXLt3hY9J7770XmUwGH/nIR9DV1aV/5s+fj7q6Ojz++OO71bdhGHj44Ydx9dVXo76+HnfffTcuvvhiTJ06Ff/yL/8y4h43dadIcemllwKQQQQAcN9998HzPJx99tkhH1taWrDffvtpH1988UWsX78eX/nKV/QduKBfu8pZZ50VukO2bds2vPTSSzj//PPR0NCgjx988MH4yEc+ov10XRePPvooPv7xj2PChAm63qxZs/Tdz1qOP/54HHDAAcOOx+NxLff29iKXy+GDH/wgXnjhhWF1P/zhD+s7dQBw1FFH6XGkUqlhx998882djp9hmPcP/KiUYd4FNDc3Y+HChbjrrrswNDQE13XxiU98YsS6a9euRS6Xw7hx40bUd3R07Hb/0WgUV155Ja688kps27YNTzzxBH74wx/iN7/5DRzHwa9+9atQ/f322y9UnjlzJkzT1Pux1q5dCyHEsHoK9ShX7a878MADd9vnINOnTw+VN27cCADYf//9h9WdO3cuHn74YeTzefT396NQKGDWrFnD6o10bKS+FEuXLsXVV1+Nl156CaVSSR8faQE6ZcqUUDmTyQAAJk+ePOLxt7N3kWGY9ya8cGOYdwmf+tSncOGFF6K9vR0nn3zysDtQCs/zMG7cOCxZsmRE/Y72Zu0qra2tOOecc3DWWWdh3rx5+M1vfoOf/exnO937Vrs48TwPhmHgwQcfhGVZw+rX1dXtkY+1BO92jTUj9fXUU0/hjDPOwHHHHYfbbrsNra2tcBwHixcvHvFVLyPNyc6OCyH2zGmGYd4z8MKNYd4l/PM//zMuuugiPPvss/j1r3+9w3ozZ87Eo48+imOOOWZMFyyO4+Dggw/G2rVr9WNOxdq1a0N3ntatWwfP8/Tjv5kzZ0IIgenTp2P27Nk7HQsAvPbaa1i4cOEO6+3OY1PAD7hYvXr1MN2qVavQ1NSEZDKJWCyGWCyGdevWDas30rEd8bvf/Q6xWAwPP/xw6L17ixcv3i2/GYZh3gre48Yw7xLq6upw++2346qrrsLpp5++w3pnn302XNfFf//3fw/TVavV3X7v2tq1a7Fp06Zhx/v6+vDMM8+gvr5+2F089XoOxc033wwAel/YmWeeCcuy8O1vf3vY3SIhBLq7uwEAhx9+OKZPn46bbrppmN/BdslkUvu0K7S2tuLQQw/Fz3/+81Cb1157DX/5y19wyimnAJB3uBYuXIjf//73aGtr0/XWrVuHBx98cJf6UnYMw4DruvrYhg0b8Pvf/36XbTAMw+wKfMeNYd5FfOYzn3nLOscffzwuuugiXHfddXjppZdw0kknwXEcrF27Fvfeey9++MMf7nB/3Ei8/PLL+NSnPoWTTz4ZH/zgB9HQ0ICtW7fi5z//Odra2nDTTTcNe4S3fv16nHHGGVi0aBGeeeYZ/OpXv8KnPvUpHHLIIQDknbSrr74aV1xxBTZs2ICPf/zjSKVSWL9+Pe6//3584QtfwP/9v/8Xpmni9ttvx+mnn45DDz0Un/3sZ9Ha2opVq1ZhxYoVePjhhwEA8+fPBwB86Utfwkc/+lFYloVzzjlnp+O6/vrrcfLJJ2PBggW44IIL9OtAMplMKH3WVVddhb/85S845phj8H/+z/+B67q45ZZbcOCBB+Kll17apTk89dRT8YMf/ACLFi3Cpz71KXR0dODWW2/FrFmz8Morr+zimWAYhtkF9mZIK8O8nwm+DmRn1L4ORPGTn/xEzJ8/X8TjcZFKpcRBBx0kvv71r4u2tjZdZ1deB7J9+3bx3e9+Vxx//PGitbVV2LYt6uvrxYknnih++9vfhuqq14G8/vrr4hOf+IRIpVKivr5eXHLJJaJQKAyz/bvf/U4ce+yxIplMimQyKebMmSMuvvhisXr16lC9v/3tb+IjH/mISKVSIplMioMPPljcfPPNWl+tVsWll14qmpubhWEY+jUd6nUg119//Yhje/TRR8Uxxxwj4vG4SKfT4vTTTxevv/76sHqPPfaYOOyww0QkEhEzZ84UP/3pT8XXvvY1EYvFQvUAiIsvvnjEvu68806x3377iWg0KubMmSMWL16s5+utbOxoHI8//rgAIO69994R+2QY5v2HIQTvemUYZte46qqr8O1vfxudnZ1oamra2+6MKR//+MexYsUKrF27dm+7wjAMo+E9bgzDvO8pFAqh8tq1a/HnP/8ZJ5xwwt5xiGEYZgfwHjeGYd73zJgxA+effz5mzJiBjRs34vbbb0ckEsHXv/71ve0awzBMCF64MQzzvmfRokW4++670d7ejmg0igULFuDaa6/d4QuEGYZhdoRhGLj//vvx8Y9/fGzs8x43hmEYhmHe7/T09ODSSy/FAw88ANM0cdZZZ+GHP/zhbr8wvHbhFnwPpWVZmDBhAj7xiU/guuuuC733cVfhPW4MwzAMw7znOeGEE/Czn/1sh/rzzjsPK1aswCOPPIKlS5fiySefxBe+8IVR6Xvx4sXYtm0b1q9fj9tuuw2//OUvcfXVV78tW7xwYxiGYRjmfc3KlSvx0EMP4ac//SmOOuooHHvssbj55ptxzz33hF7OXcvatWtx3HHHIRaL4YADDsAjjzwyYr1sNouWlhZMnjwZp512Gj72sY/hhRdeeFu+8h63XcTzPLS1tSGVSu12+h2GYRiGebcghMDAwAAmTJgA0xz7+zfFYhHlcnlUbAkhhv0Njkajb+uRY5BnnnkG2WwWRxxxhD62cOFCmKaJ5557Dv/8z/88rI3neTjzzDMxfvx4PPfcc8jlcvjKV77yln2tWbMGy5Ytw/nnn/+2fOWF2y7S1taGyZMn7203GIZhGGZU2Lx5MyZNmjSmfRSLRUyfPhHt7T2jYq+urg6Dg4OhY9/61rdC2VDeDu3t7Rg3blzomG3baGhoQHt7+4htHn30UaxatQoPP/wwJkyYAAC49tprdeq/IOeeey4sy0K1WkWpVMJpp52GK6644m35ygu3XSSVSgEANr54O9KpOIz1lNsxEQcG81L2KM6juQEAIKJRGJu3ymPqvwHDACifoZggk3aLRAIAYHZ3Q1jylBi9fbJ+PAqUq7JeQ1bqNm+FaJEXmPH0q7Jeg8zlWH5mK5xJ0l7n32Q/DQdW0fa/Mhl564HSV68oYKXkf1rWJDk2w6b/vBrqgAT5W6T/kiaOB97YIuWprfJz4zb52ZQGDGqbpk2c/YO+PFTw56qfvnDxGOmG5GcyCaynuaojXbkK2JRqKeLIT9sCiiUEEeu7pP+pCNAgxwKT2g0OARG6zHvoPGXl/KBvCBiXlnJHP40lBW/NdrJHc+AJGFFpQ+TLYZ1tADEpi+3ShtFch8rLHVLdIue9ul3OgdUQgTdYkS4mpM3yljLsBjl/5U5PDrfRRLlbyja5KzwasAcMtsv5ME15zW3ZnkHJlWMuutJub8VBvSP76i7L+o0RWe4qR9AUKWsZABqcspZrdY2RCjpL0kZzVNroLDnaXtC+b0/qeiqOLqt6DdSup+yEZACoj1TRW7a1LO3bO9T1lBw0kE9dJeWH365xF2z0lu2QDAAZR5ZzFVvLSpeNuOgtW2RDfs+6S7LcGHXRRXID6XrKVkhWOmUjG5Ent69s6nJQVjol95Iu43hariddN5UbIx66Sib15VHfppZ7K9TO8WicJjIBWdlXctqR11p/xdByX0Xe+chSubdsoD4idF/KvpJ3xY++iqHtBe3nqlLO2GFd2hHoD8i1Ywn2reajKTpcVzvfI81HmvoeqAIp+rXST36lbRGSZTsDGfKpu2zQeZHlrrKBpshwXS+Npd4ZbqPWfn/VCMlKlyMbql3QZnfZQNEt4eo3fqD/ro0l5XIZ7e092LDxN0inE3tkq79/CNOmno3NmzcjnU7r4zu623bttdfi2muv1eVCoYBnn30Wl1xyiT72+uuvY8qUKW/Ln5UrV2Ly5Ml60QYACxYsGLHujTfeiIULF8J1Xaxbtw6XXXYZPv3pT+Oee+7Z7X554baLqFuz6VQc6VQCRpIWF8k4ICixtFq41ck/1iIW9eupC8s0gCot3FJUTy3cSkMQNi3cKkVZPx4DypVQfSMZg6A+jLj8I6kWWuVoBA4dKzqyn3TMxIAjj6XpD5wnBKwoLdyovl64JaKA8ts0/DGpxZxaWKlyMuYv3MgvuFVfNoQ/V26V2pINw/NtKnvq07b8hVs04h+rubMvlP/xqN9W5db0XH/hVqiE7ZeqvhyYRy+m7NGx4MKN3NU625SL66AfiSgq5K9Nx6pReS6sWAQe/RI1Y7RwiwA2nYsy/dGIRE0t2+RicOFmOOGFW50dhW3IMVuGtFvyHCTpnBZcWV+Vh9wIkrahZakzQnJYZyJfDdvIV0e2H2wDAEUvqAvbKLjDbSQsC0X6ByZB53EoUK7VFSwHCUvaiFvOsHa7YqMYqFerK3sj6VwU6VjCcmvsVxEPyNJHOyQrXdAeAJQCNks19kuWpeVg38P98MvxGl0haMML68qeFZKVzpflBVjxTC2XPDOkK1q+TvkRt1zEAjIAxCxLywUvrCt5JuI19uOWh7Lw5VpdxavVWaG+duaHKhetWj+skBy0XxUG4pb87gX9GslHJcessC5mjawrDBuLX6/WflmMrCvV2AjaVH0BeEe3/aTrYkirvwlvF0+OJ51OhxZuO+KLX/wizj77bF0+77zzcNZZZ+HMM8/Ux9Siq6WlBR0dHaH21WoVPT09aGlp2TO/yf6sWbMAAPvvvz8GBgZw7rnn4uqrr9bHdxVeuDEMwzAMM7Z4nl547ZGN3aChoQENDQ26HI/HMW7cuBEXSgsWLEBfXx+WL1+O+fPnAwCWLVsGz/Nw1FFHjWh/7ty52Lx5M7Zt24bWVvkk6tlnn90l3yz1j2dN1pZdgRduDMMwDMO8r5k7dy4WLVqECy+8EHfccQcqlQouueQSnHPOOaFHoUEWLlyI2bNn4zOf+Qyuv/569Pf348orrxyxbl9fH9rb2+F5HtauXYvvfOc7mD17NubOnbvbvvLrQBiGYRiGGVvUHbc9/RlDlixZgjlz5uDDH/4wTjnlFBx77LH4yU9+ssP6pmni/vvvR6FQwJFHHonPf/7zuOaaa0as+9nPfhatra2YNGkSzj33XMybNw8PPvggbHv375/xHbfdpVCUG9JTMhgAjgO8Ts/F1b4npTNNfxO+2uPW1ePvhaO9a4Y3IMt9/TBU/ZUb5WcmAUyf5NsDgGwGRoX2a9WR3bzcsG9PisPLSbn5KFnfGxSYeJS8HSso1sCMGXIcAODSl6Ge/C5XARLV/i14HtBIewoStE9hXL38jEWAXtrcbwb2TFRpP1tJdWoCHb3hesqGaUo7gL9fzjT1fkCYNN6qq4M11L43UZX+GwBQpHpm1Z8XlRxE9Vmq+D4qW4mIPmQ2J0PHvG39MJLUl9onR7aMZASoVMP2hYCgc6zqV/tVgpIyzDorVN9ptWHE5VfR7Ka9jYF/qYwo2a34SU5s2r9oR2mzdMVBNirneXtRXkOTE0PYmJf7Jycn5fnfMCjL0+qGtDw5Qbp8AlOp3huD8hxPp/KbgwnMqJOBJOuo3YxkAeuo3sy6IulimJGU8pv5GNkoad2sQD1poxiQZb21g1HsV+fLADCLymsGY5hNNtZQu1nJEtbloyEbb1C7mXUl30ayTO0imF3ny9J+GWtJ3o90q/pleXaqjDUDvgwAawYi2C8lz+3Kfrmvbm7aL+9PutUDUrd/qoJVJM8h3aoBB/unqtTGJht+Wcmv9/vtgrLSHZCuDKunykEZAA5IV0IyAKzQ5WpIVjolv5aTPh6YqWq5VndAerhuRb+FeWlXy3Kcbkiu1b2ak/K8jNS9mrNCclD3Ws7CvLSnZQCYl/ZC8s7q1epe6ZO6gzJeSJZ9m9S3F5KVbt4I9V7pk/KBWWXfL4+k29V6u2sjqCu4eyFhkhD+7+I9sbEH/PWvf92pvqGhAXfddddu2Zw9ezaeeuqp0LHahFSjnaCK77gxDMMwDMPsI/AdN4ZhGIZhxhZPjEJwAqdWB3jhxjAMwzDMWLMXokrfq/CjUoZhGIZhmH0EQ4z2rrn3KP39/chkMuhZ/yukUwmY22TWAJFJw3jmJVmpKUOflDkhVQejq1seU8EJm7f5L4edNF7bAACjvcOv19MnPx0bSNVRPcpwsPpNYILMnCCefV0ey8hN4p2/6UH9XHpT/Ap6S/3sMvrWSTkzTW6uzrdZqJsu/3uxGmWfZiO91TpiA/SyXzh0U7alWfoO+MES6ymTQjYF5OldNCq4wrb8l+YWaMN9LAq0d4XtJuN+O5WJIUZZEjzhz5XCNPzgAnpxq9igbFowWrPh+t0D2p7YLoNAdNYDwA/IyFEGh0wCoj0n6zmyb6+3ACMjxyV65TiNtLRhxB1t39vcJ11sTWPob50AgEgTvVi3Q86102TAoKAEFaTg9lVhpuV8FN+UY4s0Gqjk5FfTppebC4qBgAfk2+lFzfQC3rbtGbietOvSiziLVQtR9VLWqqwfUy9fdS0ta53tIl+hF8LasjP/pbsV5CgjQooyIvSVI8ioTAwlyrgQLaOj6MsA0EPtsk4FfZRFIUs2+isOUpSVYID6TjlV5JRs+9kLAJnNIJhhQdr3sy90FB3q28+k0FSTVaEhUkUf2ctS332B7AjBvoBwVgXVd9apooeyKDSQritQ7lYZHAJZJlTGie6AH7U2eiuUjcFxtZyhQJS+so0s1evTGRyq6CqFbagMEY2R6ojZLrQfuu/h2R2CWSFqM0T0lC1klU/koyr3lO0d+GGTH1Xyw0ZTdHgmid31Q/U93A/fRhfVa4q46KSMFs3R4bqgXGtjJB9HmoNhflQsNJDcTTYayUZn2UaznitfV1svaKOH7DcE+s6OoKutF7TZWbZRdIu4fNV1yOVyu/Qi2z1B/+3cdM+oZE5omHLOO+L3uxl+VMowDMMwzNjCj0pHDX5UyjAMwzAMs4/Ad9wYhmEYhhlbxCjccRN8xw3ghRvDMAzDMGOMITwYe7jw2tP27xX4USnDMAzDMMw+At9xe7sM5AEAhmn6LwVUqZ1KMu0O4jGd1gpOIFLSCaSFAgAnousIiso00jKSFBHHT3WlKJT8VFAqKJjSVjUc4sKIUGTTFPLDMpBqKYdMpPYHUKW0TSrKUo3Dsf30TXmKCK1U/D7VmFTEp23LKFLAT1vlCXlc6dUcqBRaKvpUpe4Swk8dpWx5XtgnpSvQuMpe2G8hfNkOzJlXk/JK6cquriJINjwBMUQ+xYTfvuLXDdkKzoNZc14BGBF5zKLgWTNhobKtGnLDzQuYiZr/JC0/89dIeBRBqqq4noGia2sZkGmwFHmKHLUMP0WWcneAdLYpdD3HlP4MVuXYbMND3pWyjkZ1TSQ86UGF+qx4JqrC1DIAlOnTFQaKrpSrNPgh10SCUq8NVk2yb2CI5IQldQXqO2F52l6V+iy5hpZdEf6sCEP7UQnolB8u9V10TSQDMgC/7JnaRinQT6mmz0qgXFVfS91n0Cepq4qwHOy7artarrM93beyUQz1JWWB4XPg7syPEfxXcjmgK9bUK3mG7mvYHITs+32P5If6Wqp6lLkOFc+XywFduaae6tsTYVn6YWg5OB/eCHOk57FmLOUdzIcqqzlQOgEjJEtfjWHnRZWFGNmP2mPVgB878zGoq61Xqbkm9kbGKw5OGD144cYwDMMwzNjiiT3PfMCZEwDwo1KGYRiGYZh9Br7jxjAMwzDM2MKPSkcNXrgxDMMwDDO28MJt1OCF225iFAowbAPokWmR4HlAi0xxpTfvUzCB4Xl+KqgIbRRPxvxgA/VZoDquC0MFNmztkJ8Tmn27cdrhPmm8nybLMn27AMrtLmJzaeM/5CZ7s86GV5CylZGbvEVZ6JRLIJ2ISx+NRASoS6oBy89oxE9PVQoHOqBa9QMKVOCCafqb9YNfNlUvGIAAyA3+aeozQQEM+YK/pyESuFSVH2TDKJA/DSk/HZYKGIjYgJojHaRA5Zip03IZiYiur4I7QCmvzPF1vr9qZ7RBY4s5OsrASNA5jkfgZCjwIymPORGqk4zAGpDn00xIm1ZJQFTD6a2AQEBDlFJ7OWq3NVChFDY2bVy3TIGsLa+d13tl6rXWeBEb8zLFzIS4DDLZXpRz2xCpoJfSN9XZKr2QgwTJKrAhQYEIBdcKyVLn6Q30UUq9VXRNRCiwQQURBMsxS8pqY3zM8rScsJUuWM/U9QC5uVrZUxuvHVPozdcKtWHfBKCuPoo1GNFGzPIgAnJIZ3rahhqnVyMH7XsCUDtx1CVnBGQr4KqKU1Ib1lXfAoaWFao/AIiovgVgG76s+toRpuH3ZdVUdEawDwAxM7yvKGYK3Ve0RheMu6q1H8TYST0nsIEnsgO51q/hfvhlk86GgeH7o8zAMbvGRmgOrJo5sEaeq0iNjaBNY4T5UL8ilc40/AAiP5ZKaLnW/kh9m8bwsQTnwzJ2fm6Ydz+8cGMYhmEYZmwRYs9foMup1QHwwo1hGIZhmLGGH5WOGhxVyjAMwzAMs4/Ad9wYhmEYhhlb+D1uowYv3HYX25Y/TfWynEkBT79KstwIjnoZOCBME4YKSlCb5T0BvZ2ZbvvqgITuPj/zQJTa5YcgZs+S9qK0aT9VBxSpzX4T5WdBbj6PHzceGJS6yDy5SR0VF0685lQPlGFQMAJiUqc36Jer/l6EDGVwcBx/gz5t6EffoPwUHjBAQQwNcuyI2Dq7hA5miDhAW7eUs2RXjbdS8TMiKBzH36EbDPxQ9jzpt6DxGqbh7/JV9YfKMoAAgFGvdvtTnWCGiGCQBAUn6CAFwE9zUKMT/UUYKuuBfvV71d8hTr9oyptl0IRd7+pABK8o65tJE2ZGnltRlfNoxkxUBlS2BrJr+sVyWY49EpXnPVeKYkq9DJjJU7aD5uQQnumW5+PQRmn3Hz1y3ufU5/D0pmYAwOmT5Dn5++ZGnDaxBwCwdKsMuDl1Qi8A4I9b63HaxD4AwJ/bsgCAk1v78Ccty77/vC2LRUpuy4R0f2lPY+H4AQDAw+3Sr5NaBvCX9pSWAeDR7SmcOE7Kyzqk7oRmea39tbMOxzfL6+qx7TKY5cPj81r+0Dipe7xDlk8YN4QnO+X38rjmIWmjI6HlJ0h3fLNf79gmOVdPd8vrZUFjAX/vkvIxpPt7V1zLz1C9oxqKVI5hQaMvA8BRjUU8S/LRpHu2O4ajGuS1+1xPlGz45VrdPzWU8HxABoDne6IhGQDm18vy8t5oSFY6Ve+IBvk9+t+eiC4HZaU7vF7KL/RK3eH15ZC8I91h9fKaf7HXCclKF5SlDZtsVLV8aFZmGXmpzw7Ju6JT8sGZt7bxSs7W9V7J+e1G6qvW/s78OCTr4uU+S8sAQuWRdLtab3dtBHVD7l545MiPSkeNvfqo1HVdfOMb38D06dMRj8cxc+ZM/Pd//zdEYAPifffdh5NOOgmNjY0wDAMvvfTSMDtvvPEG/vmf/xnNzc1Ip9M4++yzsX37dq3fsGEDLrjgglA/3/rWt1Aul4fZYhiGYRiGebeyVxdu3/ve93D77bfjlltuwcqVK/G9730P3//+93HzzTfrOvl8Hsceeyy+973vjWgjn8/jpJNOgmEYWLZsGf7+97+jXC7j9NNPh0er81WrVsHzPPz4xz/GihUrcOONN+KOO+7Af/3Xf70j42QYhmGY9zXC8++6vd2fPY1KfY+wVx+VPv300/jYxz6GU089FQAwbdo03H333fjHP/6h63z6058GIO+ajcTf//53bNiwAS+++CLSafn45ec//znq6+uxbNkyLFy4EIsWLcKiRYt0mxkzZmD16tW4/fbbccMNN4zR6BiGYRiGAeR7TY09fNS5p+3fK+zVO24f+MAH8Nhjj2HNmjUAgJdffhl/+9vfcPLJJ++yjVKpBMMwEFX7vwDEYjGYpom//e1vO2yXy+XQ0NDw9p1nGIZhGIZ5h9mrd9z+8z//E/39/ZgzZw4sy4Lrurjmmmtw3nnn7bKNo48+GslkEpdffjmuvfZaCCHwn//5n3BdF9u2bRuxzbp163DzzTfv9G5bqVRCqeRvlu/v79/1gTEMwzAM4yPEnr9Al1/AC2AvL9x+85vfYMmSJbjrrrswb948vPTSS/jKV76CCRMm4DOf+cwu2Whubsa9996L//N//g9+9KMfwTRNnHvuuTj88MNhmsNvKG7duhWLFi3CJz/5SVx44YU7tHvdddfh29/+9nDF4CAA10/tNFQAGilCsiwjikQDRZw6DjCuUR6jO4LG9i55PIBIymg2Ix7T9dHTN7zvGKWy6tgOOIFoTECn1BL9RUBFDFGUoyi7fhi1q44JeHlKiaUiJnWEpdBj0VQqfkSPipRVqb6yaSBK/ir/O7r9tFkqCrRc8dvEY/4xwE9DFaxvRqFvCit9NOKPuUI+Vlx/vCoaV6XIilg6dZXISbtGgmyVKjri1J8fFyhLe+pXhBgsw8zEtBysb2TiQJLu9vYO+WNQw6E5NcgdM2XDHQgHxXhDHoyIHFO1T9p1mgQEDcuI1uSncYFMvezLojRYFc/EYDESqjZQjCLryHM2SOmtMqpciqA5KuU8pbdqiAgtZyPSbr6qykC+IgeRorEMVhykVPAz1UvaQtdL2mEbjgnkqxQNSyl5Biu2TvEzSO084ddTp0WVqx4wVA1E+wIYrFpwa+vRL/ihqoUCKQepXcEVvlz1dYMk5ymlV1eRbLgWekphG10l38aWvJzHAymdXHvB0zbahmiOMxa25F0ty3YuDsrIC2X9gLyW56X9spLX9UvdAWkLbwZkAFibq2h5dZ+8huakZHllX1nLL/cUSWfj5R4ZDTsnLc/LSz1DVI6EZAB4oWdIy//bIyN290/F8Fz3AMnyd1ew/HR3H8kyIviv3V3YLyV/Jz7WI4PG9ks1hWQAeKpbRjDPTmXwaE87yeMAAMt6OjCrTtb7U+8GAMCsuikAgN/1vYZZdQcAAP7Yu5Z0M/GH3tUk7wcA+ENuJWan9tey7FuWf9v3CmbWHQgAWNL7NABgZt3R+E3fP6jeEQCAe/uWU/kw/KLnUZJPBAD8oudRLf+q9wnSfRA/6/kLAOCa1EIAwP+v+08AgOtSJ+POrgcAAN9Nnarbzaw7LlTvmrpTdL1r604DANzWvgQA8P/VfQqLux8EAFxdJ7cD3dP3LGbWHQUA+EnHbwEA36s7S7f7/+o+hYK7F3JecVTpqLFXF27/8R//gf/8z//EOeecAwA46KCDsHHjRlx33XW7vHADgJNOOglvvPEGurq6YNs2stksWlpaMGPGjFC9trY2fOhDH8IHPvAB/OQnP9mpzSuuuAKXXXaZLvf392Py5Mm7MTqGYRiGYZjRZa8u3IaGhobdFbMsS0eD7i5NTfI/smXLlqGjowNnnHGG1m3duhUf+tCHMH/+fCxevHjEu3FBotFoaN8cwzAMwzBvE77jNmrs1YXb6aefjmuuuQZTpkzBvHnz8OKLL+IHP/gBPve5z+k6PT092LRpE9ra2gAAq1fL298tLS1oaWkBACxevBhz585Fc3MznnnmGXz5y1/GV7/6Vey/v7wNvnXrVpxwwgmYOnUqbrjhBnR2dmr7ygbDMAzDMGMEZ04YNfbqwu3mm2/GN77xDfz7v/87Ojo6MGHCBFx00UX45je/qev88Y9/xGc/+1ldVo9Vv/Wtb+Gqq64CIBdzV1xxBXp6ejBt2jRceeWV+OpXv6rbPPLII1i3bh3WrVuHSZMmhXwQvNmRYRiGYZh9BEPwymWX6O/vRyaTQe8LNyNdF4cRDB7Y0hGurDbgp+qAzRTZqtMsOUCv3MiLaa3yU6Vnyg36NoQfYCAOmBUyb1QqQJU25iv7RbkxWXQNQPQWQvXd/ioEba4OvgDGoo3LZr3ceK9TYNUngfoU+Utr+8Z6IC83LqvgC2ML9R1x/PRWSUorZVv+uCgdF7JpoKvX1wN+IEJDFtgkNySjjgIXBgp+Cit1DPDThykb3RTxG4sAeeorThv1C2UgKdt6q6R9lfrKiFhAfV1IZ46vg7tF2jOb5cZrMVSBkaC0WRZNYDCgIyXtic4Bsp+Et0WO00zJx+2VdTLtk5m2/cAGSkNWeqMIO0XjpCEZtqFTY6mMV8L1v6oDG2VbJyaVxUEbxZL0MRqR10Z+KIJYVF4XBQpcULpS2YbjuFoGgIjtokQBArYldWXaZO+YHooqsIB0hYqNmC1lFfxQF6loOUEBF0NUjjvVYTbKrhWSVV9l16Rp9obpSp6Uo6ZsN1T1/dCBEU5VlxN2VdcDgIRdRdFVQRLUt2eF5GDfVc8M+GH6fpBPUcsN2Y9Zrg6SSFLf+aqt5YLr1yuRvagl7QfLpUBfgAxAUbLyMWK6KLpK9si+LMd34EeC5qpI9mOBvnffDzPUd9G1EFPzof3wdECJ6nuoamm5QPbjb8MPfS5Iju7Ej4Tl6oCSOnXdBsqqXiwwNjWuipDfT4cCaSrCCMlKN2w+PFPbGyL/EzS2vGshqX30dUWyMZIftfZ3pBvJDzU/Q66FIbeET794PXK5nH7/6Vih/nb2PfU9pOvie2ZrsIDsBy9/R/x+N8O5ShmGYRiGGVs8MQp73Pg+E7CXX8DLMAzDMAzD7Dp8x41hGIZhmLGFX8A7avDCjWEYhmGYsYVfBzJq8KNShmEYhmGYfQS+47abiKZmiHQCiNHLeYslGCpqkiJHxSSKFrVsGOo/BBUB2dsPNGdlvXHyhcF++qoyjCLlR+2nyFPH0W1FnYz0NNa9CdFEkaubuuWx6TI1TPc9HWg8QfpWWiWjVCNTohAV6YdhSR/N1hREQUb9GQfI1DF642cipn0zhmSEqjduHIx+GRkp0hnfNwAiHodBeV1FRurMzZv9NF8FaUMkEzDoxcciJaM5ja4ef35m0qtaVCqrhqw/bx3dfp95ipqldDsoUQqsupgfBauYPkHbMCm6VKcc6xsAsnJOzRnUZyoOK0N2KULWaOvWkaOI03mP0TmzLG3fiEa0zsxQui9KveUkItq+nmfyPz6pD0hIu2KLnA8jGYHXLaN4DYpg9XJ0bXgChSFpTwg5dtc1YVEEmWVTWjNhwKEUV+oSjUblOMsVC/EYpdmqUkRboozqgJRTdbKv/oEYlYvwBuQcZFJ0PvsTWvbocs2kCnD75TWWSRa1HwCQrSugb5BsJGW7XD4ekpWud1DOTZZ0faTLjqAzBuNoqJOyOSB19ao8GNdyL/VdX1fQcjYhfewbioXkoK5nKI76ONkokI14AT1DUm5ISF1XPqHLXUNSbkrIc9g1lAjJANAYL6Cb7DWS/WBZyfUx6UdvMablnoL0sSFeDMnSvrJRRDfpGpWuEEcT9dVF9pt03zHUU71ealcfL4Zk2XccDdSmh2yoctdQXPcV7LuTfGqmueociofkoK67EBtmoz5WQm8xqmU5H365dpxBG8Fx1o5ZlRtjRXQXY1oGgO5iTMs91FcD9d1TjGo52G5nNjrJx+a4Xw7KSldbL2hjZ/Z35ketfceg35nvJGIU3uPGj0oB8MKNYRiGYZixhh+Vjhr8qJRhGIZhGGYfge+4MQzDMAwztvAdt1GDF24MwzAMw4wtnKt01OCF225iVMswKjaMtu3ygGP7m+UpOMFopyT20QiwZqOUm2hDf6kMlOUGcYNSSMELpLoq06bRXkrjFHF0PaN+yK9H/3mIQZkyyqAN+sWCA3e7rPfaSzJg4UCvA5tWZwEArRNlgEGsp0enVHKMzdKmSwEM49IwVDBAWgYRmPkhoF/6aURp7H3SR6MuqefAUAEDA0MwxlEARQdtuE9EgSL5q4IvaC4QjQA9OV8GgPZeQG3qV0EH5SqQ9zfpA4DXKdNtmWXXr08pXrCpXQcSiDaZhkqn9gr2XyCbtgnR1kcyjc+xgByl+yJdyEaMgjR66TyNS8F9Q86f2Sg3P1fflOM1kya8vJxnk1KOSXtyfG6H9MOIGihupXoqw1fK79KitDmeK6+5Nzrr4Qo55m0FOd6yZyLSIev1VKSRFAUu9FUsZEjuKst2WcdDV0nKjVGp66ZyNiLQVZJ9NUTkvHeXDdQrOaDrKVMwAp2KfrqkkzYwRNMdozFVPUBlEaPLD4bh70Gu/f/aE372OPU73DQAlQ2MVHCFTMtmGUBVyOswYspKRbcBMUvKQ1XZImEL5ElO2lI3UJHllCMwUKnXshxTFnV0SeYqWQBAxlHljJZ7yzItT30kLANATymNBop16SpJXVOg3FSja4wCnUUpN1OcTUchjXEUN9NekLoWXU5hHNXbXpAXz/g4sL1Qp2UA2Ebl1jiwjeq1al1Ky21Dst6EhIG2oaSWAWBLXpYnJQ0tT0yaWjdJywmqZ4ZkANhEQSdT6kxsGkxqWeqSWt4wIHXTUrK8fiCBqXXWMBsbKQBF6TYOxkLyjnTrKSBnesoKyVIX1+U3+6U8Iy11b/bHtBxs92Z/jPy1yH+/HJSVbmf1gn3tio03B+RFNCNl6/KMlK3TxjH7JrxwYxiGYRhmbBGen4N7T2wwvHBjGIZhGGaM4UelowZHlTIMwzAMw+wj8B03hmEYhmHGFo4qHTV44cYwDMMwzNjCj0pHDV64vV3iFK4VjfgXk0qHlJJRTyIeh9FCkZVpCgnc1qGjJoVKvWTTaahWAZUmqiSjLxFx/NRSKk1U1dVtjDoKUaO0SE3Th3QY4sQGGRXpVYCILcP5qhRBKDzXH0tFykaSbJUqfuie8sM0gRxFv05oDs9BJuVvGlX1k3HpOwDUp/1jXb3htmWyGYvoFFM6qjQ5JPsF/PRWnqfTSCnM8TS3UcdPTaVSZQ0M+WNRKJ1p6IhQuK4ep5EhG1V5zOstwIhYJMsUMkaVIj7r47LfIK4nwyUBfW1Y9dJnsykJY4AiRxOyXWVdP8xy+D9JI2LCjlEfNFWi6utjCUpXZkj7SacKE1LORqT9fNVBnM57X0nOaTZa1uVMhORyVLfrKSlZ6lS5IVpCp06jI+13FqNoikq5i+o1BeuRrpt0jdESOnW9MrWLoCHiy0qn6jWTDVVujJTRSfWayUZnKYKmaEXLtbrGSHmYrlP3VaG+HS13FJ0d6kay0RhROod0FS3Pr/dtKLkjoFPyYVl5nrrKti53ap1v/1CSu0l3aNbv69Bs2I+gTtnvKNkhuVZ3iPKDdIdkqyFZ2rdDcq3uoIyUu2ksB2f8sRyc8X0MyrU6ZUP1fVCmGpKDfR+UcdFJUc1hH12Slc6vd1DGJR/9clBW7YLyruh2pV7Qr535GNQdnKUIcNIdmPHLtbqDs15AFqTzdLmr5KHg8p2rfRleuDEMwzAMM7Z4YhQelfIdN4AXbgzDMAzDjDX8qHTU4KhShmEYhmGYfQReuDEMwzAMM8Z4/kt43+7PsFwq726mTZuGm266adTt8qPS3UQk6yDqkjDsTv+gF96IjtUbAADGzMlAu0z3pAIMUPUAj1Je0YZ4YfrrZ6NEqZcigQ3varO+E/GPBTfYA0C3TKlk1/u26qfIjfRmDGiZSemqyISVsWHYlKIraodtxgOBAopU0h+nZYX9qlb1Rn5tI1KzYV9BKa+0DR24kAAKxXDbiO1v8td5jgJtVb1OSg9mm34wQ/BT1S+7fj1Ani+n5itgGn4aLAqCMFNRP50Zbfw3YrZfR9mL+PPnFSl9GKUkq3bLjddmcQBGRNY3lE3T0OdCpcPyhlxYmXBQhUHDFRWB3p4ETZu0X6jaaIjLtGMdlEooarnYSnLakf2/MSADXBojZazIUXqjmLTxYm8ak+JSfq5bnpdpSVl+ojOD/eqk/Ei71B2QruAPW6WsNt7ftSGNY5rl/P3xTdnXiePlvC/ZkMAZk+QYblojvysXTLe0fP40Oac/XefhE1PlXP52o7wOP9wq5+L3m+M4fry08fh2GQR0SL3AXzvk5BxEm7Gf6pDjnpcVeELJGal7uiuB2Wkp/6Nb9jm9ToRkAPhHjyoD/0vyFNkllvfEtfx8jxXSvdRn6TRRL/ZKXWsCeCUnZZWG6rWcpeVX+obrVMqrFf0qoMOXG+mr92ouikZVL6eCQPyySq+1IieF+giwst+XAb+cjYRlpcuOUG8VyWm6JoNlJaccg3SmltcM+DolJ+2wLmkbWDPg1OgcLa/ul7o6ZbPfCckAkLCNkKx0cTtcL74TXcI2sG4gbCNYVnI8oIvX1IsH6kWtsC5qjayrrSdtGDV9GTvVKRtrlM4K60reXrhn8y55VHrffffhjjvuwPLly9HT04MXX3wRhx56aKhOsVjE1772Ndxzzz0olUr46Ec/ittuuw3jx4/fo76nTZuGjRtlGkzTNDF+/HicfPLJuOGGG1BfX7/LdviOG8MwDMMw7wvy+TyOPfZYfO9739thna9+9at44IEHcO+99+KJJ55AW1sbzjzzzFHp/zvf+Q62bduGTZs2YcmSJXjyySfxpS99abds8B03hmEYhmHGlnfJHbdPf/rTAIANGzaMqM/lcrjzzjtx11134cQTTwQALF68GHPnzsWzzz6Lo48+esR2HR0duOCCC/Doo4+ipaUFV1999Yj1UqkUWlpaAAATJ07EZz7zGdx99927NQZeuDEMwzAMM7aMYuaE/v7+0OFoNIoovQN1T1m+fDkqlQoWLlyoj82ZMwdTpkzBM888s8OF2/nnn4+2tjY8/vjjcBwHX/rSl9DR0bHTvrZu3YoHHngARx111G75yI9KGYZhGIbZZ5g8eTIymYz+ue6660bNdnt7OyKRCLLZbOj4+PHj0d7ePmKbNWvW4MEHH8T//M//4Oijj8b8+fNx5513olAoDKt7+eWXo66uDvF4HJMmTYJhGPjBD36wWz7yHbfdZWgIsOBvpI9G/A35KmvA9Im+rikj5eBmfbXx36DN59Te6B30bbVR8EMm6WcZUEQcHahgpEjXIDeal5/uRnye3CXt9uYByEAEFRBhpqR9UfRg1FE2gBwFMagAANv0s0CUaKN+uQJU1KZ92uRfkRvSEY/5wQnK11LJD15QlCtApi48H0M0j5YF9MoAC6TIlif8oAQVbBAxgTx9GVS2AxXAEHV8n9Q8Fkr6vzRB9QwVfGBb/piUjaob6DPwSRkWjEwsrFN+An7wQ6niBz+Rzm6i85VwUN0yJGUakhE1dMCCoHk0bMAry7ZmVPYlXFkWgX9aTVPQtPiZMPor0tcJTgVbCvTm/Jic53WD8is/sbmAlf1yjqYnZds3Bk3MqpPGB6qyz4RF/gRktSE9abmYkrCpnmw3LyuQtGW9f2qU9utsqTuxxUHSlufnvKnyGk05VS2nHXkuzpwSQcaRNj7cKm2o8vHjLaQdae9wSkpSZ3s4jIJylB+HUDluCRxWb2gZAA7OGojSvB1AASBRU4RkAJhH5YgpkAjIgNw0T/vhMTctBUtlsUgZMElOkc40BNK2LwNA2g7Us8M2Mo4RkpVOyerqywa+Yg0kq8sx6/iXodbVyEEd4Ac9jFRWwQxmQNb16NeFEL5MlyuaokJ/vZqi8mDVAxojIuRHc9QvKxmUDaQ5WiuH29XacAVgxaRcIVPNMaBCk6OCQIJlV1CWE5WpRAgYARlAqDySrtaGK4SWVV8Ofe/LnkAkIAPy11ttvaANZd/UN5cEoOfD71vXo1pVKtsGUBECRXVy3klG8VHp5s2bkU6n9eGR7rYtWbIEF110kS4/+OCD+OAHP7hn/e+AlStXwrZtzJ8/Xx+bM2fOsMUfAPzHf/wHzj//fAghsHnzZvzXf/0XTj31VDz55JOwVCDdW8ALN4ZhGIZhxpZRXLil0+nQwm0kzjjjjNAjyIkTJ+5SFy0tLSiXy+jr6wstvLZv3673pu0JTU1NmDVrFgBgv/32w0033YQFCxbg8ccfDz2e3Rn8qJRhGIZhmPcUqVQKs2bN0j/xePytGwGYP38+HMfBY489po+tXr0amzZtwoIFC0ZsM2fOHFSrVSxfvjzUpq+v7y37U3fZRnqsuiP4jhvDMAzDMGPLKAYn7Ak9PT3YtGkT2traAMgFFiDvtLW0tCCTyeCCCy7AZZddhoaGBqTTaVx66aVYsGDBDgMT9t9/fyxatAgXXXQRbr/9dti2ja985SsjLhYHBgbQ3t6uH5V+/etfR3NzMz7wgQ/s8hj4jhvDMAzDMGOLEKPzs4f88Y9/xGGHHYZTTz0VAHDOOefgsMMOwx133KHr3HjjjTjttNNw1lln4bjjjkNLSwvuu+++ndpdvHgxJkyYgOOPPx5nnnkmvvCFL2DcuHHD6n3zm99Ea2srJkyYgNNOOw3JZBJ/+ctf0NjYuMtj4DtuDMMwDMO8Lzj//PNx/vnn77ROLBbDrbfeiltvvXWX7ba0tGDp0qWhY+qdcYodvTtud+GF225iVCowKhWgo1seaB0HsUnKRooiW8ZRuFss6kdW5mUkIaIOMEDyoIz6NNR/EZvbgSSFD6kozaESRIoiMSlSUkSjgHoeniUdRUfG5ybgdkqdVS/ru7kqXKouPGm3OgBEJ8hj1kR5O1dUKIIwGfOjQ5MUv5ZMAB2UvitLm0JXb5KfTSUgQWMPRs+qSFB1e9s0gbVbpJyiW8gZyhNUKstoTDlo+REL2FKRpKYBFCshvSjIslEo+32q+sWKvyGWPkWRUo4lzEAqLd9HkZfRwSpyT5SqMIJpsgAYcZXCzAWssE5Gw6o5kFbKm2SqLKfZhVB+lCnKNWIClHbMVWOxhQ6bExQWp9JiwRZI1Ul70aQcS77qIBmRbYuUzsYyBJqirpYBYFadrO+YHg7O+DIAHJyp6ujUyXH5GaUozeaoixhFlWYpwjNmuYhZKspS2rINB3V2uE+lMw1b69qLlLbIdkMyAHSUHKSozfainJcU6baXbEzSOnnNTYxXsb0kr9fWmJyDzrJN5RK6yrLe+Jics85SFONp/tYOSt1+dSUtz6I0X535iC6vGSSZ0n6tG4yEZACYkaS0YvkIZgZkAJiZrGCttlGldk5IDurWDjiYSfIbpJtZVw3IFa2boW3YNfbtkKx0Sp5B186beb+8MxtrSTczWcUbeV+W49y5bgZFLr+Zt6gvF+sGLerLpb788ki6keqpsrL/Vn6M5O/OdLsyzumkW5+3tfyGmqu6akjeFd2u1ttdG0Fdwd0LOT/fJS/gfS/ACzeGYRiGYcYWXriNGrzHjWEYhmEYZh+B77gxDMMwDDO2iFGIKhV74RHvuxBeuDEMwzAMM7bwo9JRgxduu4nR2QWjEPfzFVUqMBppgz1tuBctFALsRGBU5aZQQSk5jFfW+JvuKeWVoEAAoyEN1FOKrDylgsrUweiUwQ9ikowmMDZuAcZR6HBXjupJH/KvDCFxgLRXXC1tRCdZKPfIzbtOg/TbLQT+c6FUTUZ9QpaHSv4XxKFLxB7hUmmhIIymemDr9rDOsmTaK8APtKhUgCk0NypoQ+mcEewHU16p+qbhp6eiIAXtt9IH7QVSUxkJSltlB4ImqoHACUCm+3Isv39Azg9tyBeDlNZMpc+q99/To1NqAfAKlHKGUkjZGWnfiNswhjy/LwBubwUW+an+oTQcA9U8pb6JS51XUsEPgElBA5TJDEm7goor7Y2LynnPV52QHNQNVBxMiId1rfEy8lU5b5MSqp4sT0mUkKNUWpOoXV/FwdSEvMY6S/L8TEuWtDyFbPSUZbupiRJ6KWhgGulyFTskq3pKnp5UfVE5UUZPWW2ml+eiq+RgVlIFHjik88tKDrZTsgpE6Cnb2l5vxQrZD+r6Aja6SspHeR12a10l4IfUdZQcLat2M5IV9JStUD1drquir+LL2o+ArHRdJWWjSn355e3FsG570cK0hJS7qK9gWdXz/aoGZPkd6KlYITmo6y77uk49Hy46S6ov0pUC9UphG10BG12BvrW/NbppSRfdZZW+rar9UHJnQLe9FK4XLHfuxEZ3Oex/d9nScm9A113eyXzUjLMjMAcdAV1tvaCNndkP6mrrBW12lCwUXd4ltS/DCzeGYRiGYcYWD6Nwx21UPNnn4YUbwzAMwzBjCz8qHTX4finDMAzDMMw+At9xYxiGYRhmTBGe0C8f3xMbDC/cGIZhGIYZa0Yj1+go5Cp9L8ALt93EmzgRXjoBs4vSPzkOMK5eymWKcmzvkGXLArp65TEz8FQ6Rymdmij1UpGiL0tloEDRpPGoX1+lkVJRmsk4hEMRkioqkqIzI1lA5KUfJpnw8h5MMuFRNKkVhb/RM0KptHIyTZQxpVFGigbGhEzKfweP8lFRKvtRnOQXBvK+3yIQualSaan6OZrHRExH2SIlo0TF6jYYGYrabEjJT0r7JetJnbeF5jhmy3RdgL8Xor5O9yW2D8h64yhllzoPgB99WqzAoPlAxCa7DlCmc0VRpIaqb1vaJxW1CstEZIaM8jUy0h+vM6/7MeuoflR+2hNsbc/JyPkWFQGT3Kj0hH9ZCQ/o7JGpzjLJIpkViFFaKJtSZMWsKnKujPBMOhQdR2mispEy2grSN5UKavNQXEearhuUOhXV+UougbkpeX38b688Pwdninh0uxznsU3Sj99ujuPUCbLNTavkmL64n/Tn+ys9XDJbyr/ZJP04bWIFD7TJvk4cL8f+SHsMC5qk/GSnnO/D6mX56e4YDslWyA+VrqqKF/qkrFITvUzl6UkXr+akrNJ4vd4fwcSADAAT4y5WD0i5JSZ1qjw+6ul0VeOj8lpeOxhBM8kqXVVjRJbfzDuod3wZALKOhw1DvgwAG4ackAwAaVvOz8YhOyQrnZJTAZ2SNxekLmn55aAMAHFLYGvRl0Pt7OG6rUVby20UoRq3BLYUpJwgXbCs5Bhdv1sKlpaVjZgVlndFp+T2Gl170ULUDOuipi87AZ1TU0+VtxUs9WsQ28j/iOXLfl+mLis5EtBFaurZZljeFV1tvcgO+tqZbqS+VNniDVL7PLxwYxiGYRhmbOHghFGDF24MwzAMw4wtvHAbNfimKcMwDMMwzD4C33FjGIZhGGZs4TtuowYv3HYTY6AfBir+JnvTBLr7pUxpp0QTpaNyHD8oIUaRAj1rgCTJKapPOqOlGSJOm99Vh5blb/h3IvqY4VIKphJtmleb5U1AVOXF3b9V1o/VVbF9m9yQH6EN7C1zBoeFVhtNcsM7ylUgTxv3MxQU0JvzgxOaGsL+Z7MwalNWVSq+3yqtlWUBKhBDBRkkAkEYdbGwP/Mm+19UNb5gGqwBuVnebKSUV1HHD3qI0Vx19gExacOIkU6lE4tY/piCwQZxaluSm+C9zrwOWBBFyjGl0mclIv5YAum1vJwcp0ntjBSd47ooxIDs34hKf8oremCPk3pB5u1GCwAFG9TLa6ia88gmMGGCvOYqtPl4XDqPwSKlmqqXadB6BuPYr0kGbnQPyDmaN06mT+sdjOOI8V0AgL4hOe8faB1Az5C8/j48UdroLcjyqZNz6KNghrOmSV3XUAKf20/K3VTvqwfk0EP1bjxSjlOVbzmyhF4Kjvj6gWS/GMXFc1Rfst7nZ/dp+YB6VU+W59X3oZt0c7Ml6juGgxrIJ/LjwHo5P93FGA6qL2oZAA6qL6K35MsA0FuK4UCy11eWPh4cKB9ST2mzSlFqV0YvyUqnAj8OjZbQRX0dSvY7izEcRrLSHZItaXuHZMP2G6K+LhuldFuliJZ7Ka1YfbSs+22kIJNOKjdFS+gq+bLyozlGfqi+Iiq1V0TLfWVpPxsph2SlywbaAEB9hFJ2lSJoiKpUZBHqu4wOujbHxaSuoxjRsqrXMIL9t/JDlZUfykZXKYKGiEpFJnWNkXJIrtWp1GyqXU/Z0XJfRQWW+OWgrHS1NrrLvh9dlAatKeqXg7LS1droLduoj1S1LOe7ulPdSGNR5a6SgyGXftG8k/DCbdTgR6UMwzAMwzD7CHzHjWEYhmGYMUWIUXgBL7/HDQAv3BiGYRiGGWv4UemowY9KGYZhGIZh9hH4jhvDMAzDMGML33EbNQzBD413if7+fmQyGfS0/w7pdBJGW5tUpOpgLH8lXHliCwBAJBMw1m6Qx+opzdKmdj+ScuoEWS8lozmN7l4/+rRXRskhmdCRjyIjbRgbt0BMlm29e/8GALD2awIADPx5O+LT5Y3U3OsyyrFuYhWDWymtTbOMJrIyBowIpUOZ3Rj2PxkDxlHkaK+MzhMzp8DYTGOeMF5+5mQKKTi2Tm8lkpTqqbcPSFC6KkqbJZIJGFu3yWN1sh4qFN2USgJb2sN+UNSo1JMt0/TTgamUWqs3yc9MUqfL0tGigB8B3N5N/jfLz7ZOoJ6iZjt6qW4U3prtcgwUhWqkokCM+orSJ6XDQrHi+9PRJz8bU/BWybGYzRR5q6J/G+og2qgeRaG6nQUYKqcOpTAz4jbcLhn5ZkRlPVFW0bEmBtdIOVIn6z/10mTMbZbpw361Rl4b/zJ9O+5cK8/V+TM7AQC/3iDH/slpnfjTFikvmiDn5aG2Rny0VdpY1i7P/wnjZfmJjgYc1yzn6G+dWQDAB5pzeL47AwA4gqI6/7cng0Pr5XXxUq+c20OysvxibwqHke7lPqk7KDOIV3Py+j8wI9OCvZZL4oC0lF/vl/MXLM9JyYjnVRQpu3+qgDUD8vqYVSevmXWDfnmtlouki4VkAJiRLOLNvC8D0OVpiRI2DEW1LHVRnQ7sjTyl10rI87VxKIKpARkAJifKWp66k3pT4vK7sqngaHkjpcOamqhoeXJC6jYPOSE5qNs4FMHkuLS/tSB1E+OVkAwAmwuy70nxCraQbhLpthSckFxbb0Ks8pb2txYcTIjL67+N0mtNiFdD8q7oauu1xKhctDGB5Pair2sjecII9ZSuJUrtSnZIVrqgXKvbmf2d2VDtWmMutlHqrVZKs7ataIVkZX+kvnamq7VRa3/ILeHCV76HXC6HdJr+No0R+m/nd/4VaRXt/3ZtFcto+Oav3hG/383wo1KGYRiGYZh9BH5UyjAMwzDM2CKE/NlTGwwv3BiGYRiGGVuEJ3/21AbzLnpU+t3vfheGYeArX/mKPlYsFnHxxRejsbERdXV1OOuss7B9+/ZQu8ceewwf+MAHkEql0NLSgssvvxzVavit0EII3HDDDZg9ezai0SgmTpyIa6655p0YFsMwDMMwzKjxrrjj9vzzz+PHP/4xDj744NDxr371q/jTn/6Ee++9F5lMBpdccgnOPPNM/P3vfwcAvPzyyzjllFNw5ZVX4he/+AW2bt2KL37xi3BdFzfccIO28+Uvfxl/+ctfcMMNN+Cggw5CT08Penp63pavRl8fDLcMoyI33qKnF0jSxnlK8STUZviI42+WV6mYIrbc0A7o9FagtFiiPqs31RsUsADPkwEKkKmlNBSoYLVQPerHqRMwM3ID6LZOeWzOnF6s2SqDF+ZPlZvm3ZyAlaGgh85B6pM22XsCAM0PpfEy+nJ+RI8KSqCgA8RjQIfc4G44lP7LtrTfKFCKJ9MADPpfQd3yVovsckXPA6K0gbVvcHgUUdUFelQfNG8F6YfhDfrzrNJP5YtAQW4iFx2ynVGQG7bhWNo33Y/nwRxfF+rS6x6CQSlyVMorlQLLqE/oemJA9mPEIyhvkrJTqIb8MYtV39+4vF68vAeT/pOs9EjBTnn6ZZWGJ9t6BVkWeRcF2jhfKcn6MctDF23Wn52Sfa7qzeLoRun3Kz31AIDDKT3TK931OCAl5Vd7ZIDBAakSXu2V8v4pOS+qPLuuiFf65DU3q062e60vham0WX9FTgYbTE2UsJoCCtTG+DUDSdKVsbq/LqRbO5DU8jryf2K8jDcHfRkA1gXKb+bl90ZtjF+fj6E1IAMIlVtI3kBBBC2xSkgGgE1DUYyLqk39UqfKmwuRkAwA42NVbKJ6rbQpPKhTcjNtSN9aiGB8QAaA8dGqlsdF5ebxtqKjy0oeTxvLtxQcLbdRAEBz1NWBAs1Rv55vP6zbWnDQGPFlAGiO+Jv+lbyNNrw3R6ohWdVrIhsqGKA54H9zzViao64OKNC6gh2SAWi/thXtkKx0QRkAtpeUX672ozHgV3NE+eHr2mrrlfxyUFY6JXfU6DpKvv9K1xzduY22mrnaVrTQFPG0rHRKVrr2kh2Sd0W3vRS2EbS5rWih6O6FezYcVTpq7PWF2+DgIM477zz8z//8D66++mp9PJfL4c4778Rdd92FE088EQCwePFizJ07F88++yyOPvpo/PrXv8bBBx+Mb37zmwCAWbNm4fvf/z7OPvtsfOtb30IqlcLKlStx++2347XXXsP+++8PAJg+ffo7P1CGYRiGeb/CC7dRY68/Kr344otx6qmnYuHChaHjy5cvR6VSCR2fM2cOpkyZgmeeeQYAUCqVEIuFE5PH43EUi0UsX74cAPDAAw9gxowZWLp0KaZPn45p06bh85///Nu+48YwDMMwDLO32KsLt3vuuQcvvPACrrvuumG69vZ2RCIRZIOPBwGMHz8e7e3ycd9HP/pRPP3007j77rvhui62bt2K73znOwCAbdvk+8LefPNNbNy4Effeey9+8Ytf4Gc/+xmWL1+OT3ziEzv1rVQqob+/P/TDMAzDMMzuo4IT9vSH2YsLt82bN+PLX/4ylixZMuyu2a5y0kkn4frrr8cXv/hFRKNRzJ49G6eccgoAwKT9Up7noVQq4Re/+AU++MEP4oQTTsCdd96Jxx9/HKtXr96h7euuuw6ZTEb/TJ48+W35yDAMwzDve4TwH5e+3R9+HQiAvbhwW758OTo6OnD44YfDtm3Yto0nnngCP/rRj2DbNsaPH49yuYy+vr5Qu+3bt6OlpUWXL7vsMvT19WHTpk3o6urCxz72MQDAjBkzAACtra2wbRuzZ8/WbebOnQsA2LRp0w79u+KKK5DL5fTP5s2bR2voDMMwDMMwb4u9Fpzw4Q9/GK+++mro2Gc/+1nMmTMHl19+OSZPngzHcfDYY4/hrLPOAgCsXr0amzZtwoIFC0LtDMPAhAkyzc/dd9+NyZMn4/DDDwcAHHPMMahWq3jjjTcwc+ZMAMCaNWsAAFOnTt2hf9FoFNFodLjCtuRPXqbdEdkMjHWUxqmeIjBtS+s06j+F+hRQpshEiqgUdHfQ2LxVR6aiq09+pvyoRWNIpv0x8kPQ/3dkSJ+XUYCRKRF4ORmJN+d4ip6sAocfKR8vGw6lWSoJOAlKs2TXrN/jEZn2Kkgy4aeMGkcpsrp6fb3aNEpjh2nqOQpFjqrUUiqFVDoQPdtL0aoqnVQq7kehujISC5al5w8m2aBIT2EYMCrhV8GgNw9QBCjKZEO1r7qASsFSVBGykUB0qLRvRCwYSVlPR5WSDuWqjhgVKl1VxQXolr5KU9W3Wo6jbtwQrAxFmFalzspYMOJyzF47RcB6Ah4FvCq76t8sIzA8myLWSq6Jxpist3FI2vrQuEH8akMWAHDOVPmo/zcbZWToJ6f0Y2mbvD5PbpXz/setdThtgowwXtomz8sprfKa+/O2JE5uHSJZXnOLWobwyHYpLxwvU0093B7HR0h+ZLuM/vzwuCKVY1g4XsqPd8TIxyIe3S7lE8eVyEYUH2mR8iPt8juoyn/ZFsVHWuR1vaxDnpMTxpVDsmwnyx8aX8Zftkl5YYuvO3F8JVTvxPEVPKrbSN2f2+Q5/mhrFQ+S/JFWOd8Pttk4ieTfb5bX16kTZfmBLTYWTZDynyjV3EmtbkgGgKVbLXy0VbWRJ3fRBE+XlfzHzVJ38kRPyx8l3R82Gzhloi8D0OX7A7r76H/UUyd5+P1mXw7qTp/safm0SULrgrLS/W6TbHvGJNmnKp82ycBvN3pkT+p+s9HFxwIyAHxsshGSpX2P/DLw201SdzrZ/+0mV8u/3ii/gx+ndndvqODMKcN1Slb2791YDclB3a83VPDPZOOeDfI6OXNKNCSregDwz1OMYbp7NpRDsvQjFpIB4C7VbnIMSzbK78MnJsd1O1Xv7o0lXS8ov5Xu1xtL2kat/SUbi/jE5DhsYy/cufKgfy/ukQ1m7y3cUqkUDjzwwNCxZDKJxsZGffyCCy7AZZddhoaGBqTTaVx66aVYsGABjj76aN3m+uuvx6JFi2CaJu677z5897vfxW9+8xtYlvxlunDhQhx++OH43Oc+h5tuugme5+Hiiy/GRz7ykdBdOIZhGIZhxgbhCf2Koz2xwbwLokp3xo033ojTTjsNZ511Fo477ji0tLTgvvvuC9V58MEH8cEPfhBHHHEE/vSnP+EPf/gDPv7xj2u9aZp44IEH0NTUhOOOOw6nnnoq5s6di3vuuecdHg3DMAzDMMyesdff4xbkr3/9a6gci8Vw66234tZbb91hm2XLlr2l3QkTJuB3v/vdnrrHMAzDMMzbgR+VjhrvqoUbwzAMwzDvQQT97KkNBoYQHF+7K/T39yOTyaD39f9BOpWAsU1u9hfZDIznKMiiSW72FvtNk+WIA6M3p2UAwOr1/ub71nGyfr1sZ7RtBxqyUrd+i/xMRIG0TCckJshoWmPjZogGmcLIWL5C1kvJzadDD2xAbKbcIFveQGmXxlsobZGbfSPj5NPxcoeHyAS5D9BqlBtZjWYKFKh6ftAD7RVESxOwlfLETpWBIGjroHHX+4EKqSTZCG78pyCJWATopffhqbRgKjVVMgG8QWNWgQvB/nXQgwGUyJ7a79AZSIFFKbp0wEX3gG4reuXmehVogKqnA0owIDfUo74OYot8ObMRpYCB7iEY9XJ+RU/YhpGKymAOAN4mOQdmaxqFpzsBAE6T9MPNUSqrRgteXspmyiJdFVZG9lVYLzdNR5sMVHJyfE6Wgh9clZYLyLfJtqYlj72xqREWbTjOV/35q90S4gkDtYhAuINLeoN+Q1Y86b9jeii6ss+oJf0fqlpI2PK66q9IXZ3tYqAqx5K0pC5P7RKWi6GADAAF10TUlH0Vqa+Y6WGIUvLEqK8CtYtbLvJVZUPqBqoWUuRHT1n2naX0TH1lW8s58jFl+zaS1C5ftZCw1bhk36o8WDV1X8F2A1U1Zo/sy3YZx0OXSjlE6Y26ShYaKJBE+ZFxXAxQXylbjcWkufOQd31ZzqOp5WDftTZ6yY96x0NXWdZrcGh+KpaW+6he1iH/qyYytX7YHvIBWc6BqeXavvsrJtJkr5f6zjoeusqynkrB1FU2tbxzPwzqWyAfkGv7rvWjr2Jqez3Ud0PEQ2dJys1Rqesu+3NV68dg1dTndpDsB8sj6fpJTo/gR/C8AEBXyURTjR+NkeF+9FdNba+/Zpw7Ohe18xG8NnvKJopuEd9aex1yuRzSaRmwNFaov52dX/0XpFU6w7drq1RG842/fkf8fjfDd9wYhmEYhhlTODhh9OCFG8MwDMMwYwvvcRs13tVRpQzDMAzDMIwP33FjGIZhGGZMGY1co5yrVMILt91FCHn1qGwHpZIOSkBCBgUY7WrTfgOwVQYx6KCDVMJ/c7/acK+Ix/wMAZPGy09PQKRU0ADpcoMAZWXQG+4NuXG30GMjOkHa37Ra9jk9lcOWN6U8LdUHAHDqDYDe3K8yC2hbDUk/KEAFGJim77cKDjADN2yDGRMAIGYBym8M0tjr/CAGNU5QO8/zgzbUBtaeft9+lDbcR2wgRxkZKCBD78Avu0A86ttT7WgMYrvMEGCkaDO+bfp+axuBTAg6G4TpZ75wZH0d4BDMn6cCLUwDFrlmJuWYyu1yzqyy0NkrVH0zacGww0EDwhOwZUyK1nkFQTqgkJfzEY3Jc9JZjGFiUs7LG4Oy8ymJElYPSLklJt/4vr0o2zVEqnqTfCKw+T1iqqCE4UEMKnDBpCCIkmcgZsrxBTdN+5urpX1VTts2+mmDudLlKiZStNlc6VK2QF+Z6kWkrqfst1NyQ0DXEJF9dJNuyJXj7CwZGCS5izamN0VNLTfqdiYaIzUb1sl+d8lAY1TQPBsBG+RH1AzpxsdMdJKuSPPYXTK0rOo1xwwtN5KNLhlPRPZ9Wemaauo1Ri10UoaN5phF9mU5H7XQTfWKUUu3C8oAkKfvf3cZaAzIcn4sdFK9ZmrXWQrLtbpGklXfQ1ETXeRTmQJQuophGQCGYn5ZzWkP2WiIGiF5V+ZqqGauCq6pfVJ9+/Pj6wr6fALNMV8GwuWRdCPVK9TUU351l4BSzRwUAz7mA/Uao2F/G99CV1tP2W+KmTT3eOfhR6WjBj8qZRiGYRiG2UfgO24MwzAMw4wp/Kh09OCFG8MwDMMwY4vAnj/q5LeBAOBHpQzDMAzDMPsMfMeNYRiGYZgxRQTiuPbEBsMLt7dPueLLeQrZSVJapHFNVE7CmCjTVOlIyfYuP3rSpk+HdBEHwpERcEaJwoJMQ0dIiihFrWbq/OhNS0VxShupqVUYFFmZTVOUqG3AphRDao+AO+BHLaJYJbuUhqpQBlyqmKXI0L6c7zdFsOpyLBqIlCW/HQfIU/SnioYdKsjjwfmoBOZR1VOfyZivU9GiABCnvoaorzrSVT0/mlTNT8UFUKZ5MP16gIwWVfW9QGSoig5Vc1Bx9RyJvPTXUxGh9XF/PhQjvN07Os2h+jGIfum3EaOI0+1DMJrJHk2P4Rjw8hRFavnRpNI+EKFUSoIiPVsTQzo91cFZGY3bU4riqMY+AEB3Sc7l1MY8ABmFOis1qGUA2D9WQndRzmVjTPrYWZC6plgRXUVfVu0aoyWyT+2iJW1P2Qja7CS5idp1laIhGQAaIuWQvaD9hmgZXaUI2ZDntaMY0XKtrrMURWNEyt1lqWuMlEOy0jVE5LntKavIW7+cdUiO+TplQ9XrIl1TtIKuki9LHx0td0V9XdBebd9KzjpV0tloiPiyrFdFd42NoP0O8mOc8qPkaFn5GLQ5kv2grHTZEerV2ugm+43RCjrJx2bquzPmoEm1idXYiPk2einyud5xQ/KOfMyO4EdXyab5qIbkt9L1xAPzEa/xcQc6JetzFhhLN42zUZXLti/Hfd3O5rSvErbfV7FDstLV2gja747bKLgC2Ip3FN7jNnrwo1KGYRiGYZh9BF64MQzDMAwztnij9LMPYRgGfv/734+6XV64MQzDMAwzpqhHpXv6sydUKhVcfvnlOOigg5BMJjFhwgT827/9G9ra2kL1enp6cN555yGdTiObzeKCCy7A4ODgnnUOuZBTP7ZtY8qUKbjssstQUlujdhFeuDEMwzAM855naGgIL7zwAr7xjW/ghRdewH333YfVq1fjjDPOCNU777zzsGLFCjzyyCNYunQpnnzySXzhC18YFR8WL16Mbdu2Yf369bjtttvwy1/+EldfffVu2eDghN0lHgPicT9VUjIBtDRIuUKb/AsUrOC6wEZayWeS8jOV8DfOD9vU7sFQqaB6+rR9Q2+cp3bFsm7itsn/AuxGGUTQtTqGFoqHWLdd+nXkgdtRqFDQA51xbwB+6iXaNCvKsm+jOSX9BPzAi6ZWIF8I+6v8yA0AGQpiyFDEQ3efnBsal+wn6gclRGgXfskfiw5KUHPQ2Q8kAqmlAJnCSgVFUDCIt2KL9DvhwMhQ4IIKMEj6fRoF6mtcVn72DPhBD2ZetxODFMxQR+0aEjqwwYzTPMYCXx2d7stPpaXSVCkblTXyPDlxG9WuMtmi68UGRHV4QIOgobgUpGCof7NMoFySfcaTcrP35nwS0yjY4M9bGwEAxzX34/GOegDAEfVS90yXHPvcdAHLe2TatGlJ+d/ey71pTIhJ317tTQPwN5OvyKX0pvAVOXmOU7aLtQPyvEdMeY57ynWwKCVW34C85g2o1FRJreuvJPU4+2gjtZo+VQaAXEVeQyrdVq5ioULyAG1WL3oG8lUp5105Sao8UDUxSOm4CqQbrJohGZCphmrrKftDrhGSlf0BStGl2vVTaq8h10RP2Zfl2E0UKXhEpQAreqZuo2wEP0c6puypFGNDrqntKd9U30XPQDel9irr1FumloP1lF++fUPbD8pKp+S+iqHnDwB6y4aWVfqxohfR6cHKnvw+dJUMlD2H/FD1ZLmvbIRkdU6CclA35DraD5XqrKdsoEiy8qPsOToVmeo7qAvKtTb8cfrlolvjv+uEZGVfjaWb+i4FyqURdL0189E7wnyocfaVjZCs52MEG8p+d8lA0R2e0m6seTdElWYyGTzyyCOhY7fccguOPPJIbNq0CVOmTMHKlSvx0EMP4fnnn8cRRxwBALj55ptxyimn4IYbbsCECRNGtL127VpccMEF+Mc//oEZM2bghz/84Yj1stksWuiP9OTJk/Gxj30ML7zwwm6Ng++4MQzDMAwztnjG6PyMMrlcDoZhIJvNAgCeeeYZZLNZvWgDgIULF8I0TTz33HMjD83zcOaZZyISieC5557DHXfcgcsvv/wt+16zZg2WLVuGo446ard85jtuDMMwDMPsM/T394fK0WgU0Wh0B7V3TLFYxOWXX45zzz0X6bR8ytDe3o5x48aF6tm2jYaGBrS3t49o59FHH8WqVavw8MMP6zty1157LU4++eRhdc8991xYloVqtYpSqYTTTjsNV1xxxW75zXfcGIZhGIYZU0YzOGHy5MnIZDL657rrrhvW35IlS1BXV6d/nnrqqZC+Uqng7LPPhhACt99++x6NbeXKlZg8eXLoMeqCBQtGrHvjjTfipZdewssvv4ylS5dizZo1+PSnP71b/fEdN4ZhGIZhxhQhDP3C8D2xAQCbN2/Wd8gAjHi37Ywzzgg9gpw4caKW1aJt48aNWLZsWchWS0sLOjo6Qraq1Sp6enr03rQ9oaWlBbNmzQIA7L///hgYGMC5556Lq6++Wh9/K3jhxjAMwzDMPkM6nQ4ttkYilUohlUoNO64WbWvXrsXjjz+OxsbGkH7BggXo6+vD8uXLMX/+fADAsmXL4HneDveizZ07F5s3b8a2bdvQ2toKAHj22Wd3aSyWJQOfCoXCW9T04YXb7pIbALyqn7qpWtXRpCp6UcQpVVM8DmNcQ0iHbR065ZKYGgmZFsmkDq0zxjfTQaHtiTq6CCd4QFJG5dkzsvIYRVqOP6ICeFI+7JBtsl3ZwNSpvQAAj7JQRcYZfiQjRWwalBYFhbIfxdlIX46BvJ8qSkV9ppL+Z5e0j96c/LQtP3JURdl6nh+BqSJqHerTNP3I21ggklRFmgZTXpVUmiz5aTYmfJ1Tc0mXi1oUJYri7BkYbkuFNHoCpkr9Rce8XBGISL9VxClUxGkqOmKKK68sQvXtRtneiFiwGykylSJUq71DMKs1kaMADJoGMy79cAfU+QKiMTmWalnanZzMo+xK+aQWeS56SlF8aJyUVRqqBU19ujy/IRfSHVLfr1NcHUhps1Saq3mZAV1vblr+gukuRTEpUQjZmJwsDE+bFUhzpdJaBdNcNVDaqR6dQqocSnElxxLRZZXWSrXrKkVCKa6AcCqrnaW8Cqaaqk07pdJc9VUCKa8CKamGpbwKpLmKmeGUVzHTCclK12OF01oF0xbtLL2RSmlUH6kiboVTXgXtR5RMqY8iph2Sg7qYOTzlVTZSRR35kaa+E4GUVwkrnGoqZvk2oqafUqu2r131I275aa6UnKHo5mB5Z35o+9EqHJKbKa1VsKzqNQRsqPmOWeFzkbBsPR9BXW29iBmcj3DKK8dwdNR2UBer9SNgIzGCH0FZ+1FjI2jfMRyZ8uod5t2Q8qpSqeATn/gEXnjhBSxduhSu6+p9aw0NDYhEIpg7dy4WLVqECy+8EHfccQcqlQouueQSnHPOOTuMKF24cCFmz56Nz3zmM7j++uvR39+PK6+8csS6fX19aG9vh+d5WLt2Lb7zne9g9uzZmDt37i6Pg/e4MQzDMAwzpggxCnvc9nC9uXXrVvzxj3/Eli1bcOihh6K1tVX/PP3007rekiVLMGfOHHz4wx/GKaecgmOPPRY/+clPdmjXNE3cf//9KBQKOPLII/H5z38e11xzzYh1P/vZz6K1tRWTJk3Cueeei3nz5uHBBx+Ebe/6fTS+48YwDMMwzHueadOmQezC6q+hoQF33XXXbtmePXv2sACI2r52pe9dgRduDMMwDMOMKaMZnPB+hxduDMMwDMOMLZ4Bsacv0B2DF/Dui/DCbXdJJuRPH23CVxvwAbmBH4Ch0zmV/NRVdbTx3nGAqNoIH95paQQTzW6iVFnJOIz6TLheRxdQLze1et0y2sCMyj7z6wTqDpB23YLsx876mzpNlYUqL/SGeYXIyY38xrQmIEkb9FVwwPi0H2Sggi82b6NxBtJWqbRclcqw8YUwa7ZX2hYwUAjrimU/9Zbyw7Zk2quAH2Kr3IBvpGN+sIEKZojYOnDDsMhuknSeCNiK+PVj4WNmzNFBD0ay6NcDZAADyTqoIRmDM54CELLyWHWNemFkAW5OjsWm+BM7Y8Ksp8CWLjoHpgHDDt9Wt1KG1pk9UudQkEIs58IwpNw2KNNQNURLGKBUZw0UDJCv0mb4SBlDVel3ijbeF6q2louu1CVsaXOoaiNmeaST141jCi2rVFbFqgUzIAOArcquHZKVruzV1rO0vRKlNzICOoVqJ2BoueKptEWmLitZpc0qe6aOJ1H2a+WgDU/4ssCObSj7JdfUabkqyg9hoFTjR8kztazqB30MykpXO5aKZ0LFGJUCfamymg/fH2NYXyXPLwdlXd/zZaWrtTvSfFfIr4owdJqtkf1Q/styVfiy6qc0gh/BT92XN9zvasDHka4PVVb1qsLXKVn5qHUiXE/pgrLqe6R6tTZKAV25xsdq4Lzv7PoeaSwj6SrCPzfMvgkv3BiGYRiGGVPeDblK3yvwwo1hGIZhmDGF97iNHvw6EIZhGIZhmH0EvuPGMAzDMMyYIkYhOGGPgxveI/DCjWEYhmGYMYX3uI0evHDbXTwvHC1ZrgCUegVx+kzKSEjhODCScXlMpXHqH/QjJFX0JNkTlgVD2VbtIk4g7RMdq89AZLPShIpkpFRSpiWAqDytGzfKFFn7T81hoFdGE8YmyuhCryh0pKkoUiqoJPlYLPvRlipFVaksjwNAnqI/VRqvZFxH1Oq5cQLRtupY1fXHotJmKaoukIqH7UZsIE9RnCodVtX1U4wpGwWKJK2LAnZ4TuEJnZJKUD2jQOMwDT9dlRuoXyR79Clcz0/RpfqiFFhGJu6fv4r0xyhXUdku66lZsDJUPxWBZZINRx5z8xWIqjwvlgp4LXkQNEzDUsf8FGX5AXmNRSilTcdQHDFL9r9uUF4TzVEbbQWVdkjqihQB6ZhCR26qiDPHFBgifZ0tx9RfkeWM4+o0SCqNTnfZ1qmDOkt+6qCgvCu62nqNkSq6y+H0QMGyb8PQ7dRp7KEUYCqDWU/Z0nJHyY9IVbKKEu0oWWiKSLmLbKhyZ8kK9CV11QjQTfVU36qdAYG+ioqUlbq+iqXlnorvY2cpHNmtyp4Ybr+7bIVkRVCWfclzZhmWlh3T0jol95SlzqQ/A11lEwjJAGCH5Np6vg1Jb8XU8+37AeQq8mBE+2EM88MyhtvoJRum4cv+uP2ysqHux/RVDFj0xekrU3S9YaJf+yHrB8vBegDQWzZgktxTDvcdLPdSO9MwQ7Lq21JyRX3P/L6jNX5ETd9HVS9oo9Z+0MegTo1lpL77KwaKLt+52pfhhRvDMAzDMGMKByeMHrxwYxiGYRhmTPE8A94e7lHb0/bvFTiqlGEYhmEYZh+B77gxDMMwDDOmcHDC6MELt93FNOWP2mRflwRWbZBygwwGQGeP/Gxp9ncWqwAG09Cb71WKK0Eb+Y3+AX9Tfyzq96kCBHKUNunVtTD2mwLAT3ll0KZ5Myrgbpcb+luaKC3WgIu6rPTDy8tPu97UvqmgBEPtCDbN4cEDnucfS1GggApIUHOiZOWzChAIHusflHKFNuirQATP81NeBftWwQaqvmUF0l+ZIf9RrABZFZxA854vApTmSQdhDAWCE1QQRpkiAeKeH2RAwQOitwAjFQ3boOAElKt6J7wYLJM/ZXiUvcwboqCArXIuos1FGLRL3bClzowbMOvkV7HSQ0ENyUDghLovroJJPD8owbL9QBnLlPUnxaUfcctF1JR69YDBtdUGZgGP9osoCyag01qpwAXbkGXHFKinAIco9ZO2XThULx5oFyO9VaMzIEIyACQt33+lMw1oG4pIoKxkFVhgGb5ci4cRplH4QQNV6t4OTLfSqbRPlhGWg30H66k/Kq4wdIokHfsioFNTqXrVwBCVbAT6Nmr8CMpq1irCCPQRTiEV9MNPV+XLqnvtV42sdEquBHSVwLiCOk8E+vf8vnfmxzCdF5a1DRXjVDPf1cA4q4E58FNY+bpKTb1guVqjq+7Mx4BOnwvPt+Gnt9qxjYoXlpWuNn1X0MbO7Ad1tfWCfct+8Y7De9xGD35UyjAMwzAMs4/Ad9wYhmEYhhlT+I7b6MELN4ZhGIZhxhRPGHp7xp7YYPhRKcMwDMMwzD4D33HbTYTjyIwIeRkUEHr7vsqO0NwgP+NxfyO/CjYoVoA4yWXacK9SGDiOzJSg7AKAJ3TwAlIU/NCc1fUMypxgpOVnpKUAMylPa7xP7pA3kybMHumjGacNtQUPZpzW7SobgMrCUCj5/UdobKWyf0wHB9AGfcvysyqoIAzbkm2AcOaEoJ7GpwlmOwAo6IH6NMzhx1RAQYr8dgM7bs3Af2a0i1kHFARRfVUDfatMC+qz6m9FF7TL16iOsLtX2fI8fUrVzmUnTW4lTIianeii4s9BsJ1HSSMse7jOtMKb99ORMqqenKPmmGw4UIloua8sr7kGp0TlCBqjUu4pSV02WkZvSV7D2Ug5pGuIltBZlPOs2nmIoiFaJlm2a4qW9TQ0kA1XkI1IRW/qb4hQVgoYWvaEo3Vqo32wXq2ukex7IqLrqdMSLCs5aNOXqwE/qsPqAUBVOCFZ6Xy5tl1Vy9mAbmf16h13mK4qwpkqqsLW9oI6t6aeuxOdgB2oV+u/M0xXH3EDPvk+BuVaXXaEsYzk40j+7kgn/bBCfXmBcrVGVxVWSN5ZPQDIOm5IrrXhoaZvWIEx+7paH10xcr2d6XbXhj/fw23U6rKO1BXc8O+PdwLOVTp68MKNYRiGYZgxhV8HMnrwo1KGYRiGYZh9BL7jxjAMwzDMmOJhFIITdvC+xvcbvHBjGIZhGGZM4deBjB78qJRhGIZhGGYfge+47SZGqSSjE1W0aDwGjMtKWYXTDci0TgYAdPbKY/UUVtiY9o2pdE9RijI1DQiKujSKlDMpFoVBKaBEicIMq66ONBU9lPIqEwcAFDd6SBwo/ytpXy+jUKeMy6O7Xfo7YZxMUyWq8HO1xORlIMqU6qk+CSQDEaZqnEWKPlXpp8qBsoqoTcapXRHIpLS/AGQkaaxmzMqGaYajVAEgn/dzACX8OdJEpN/ehm7pd8SCkUlInRuo49BlrqJKVRTqQNHvU6f7MvzQREp5ZWaiWjai9Bl3fD8MI2zDtnQQrJGQ9b0ePwq12itl26NIvyogyvKY3nwb2MirolCVTeEBlbK061B0WX85gpgl5W6KBK0KA0VXjnXIlfVVCpzBqqWj/nIVOT+uMNBHsnqk0VP2f0Uo2aBUVj1lW6e1UjoT0DZU6qhe8tUyHPQEZNnO0umv+ipKJ0Jtam2oeo7p6HbKj/4q6cq+TZU1LVcxdbteLUtfeysmLEPKuWpYN1A14ZCuX7UzbC3bI7SrtdFXMbU8kg3HsGp0Fga1DakbrJpa7g/oBkiOkC4XsB+UtR9qLFWjZpxGSPbt+7L0w9B9KV2wbBvKD4Pmx0J/JVyvv2KE5FobTo19xzRDcq1ukGxESTdYMbTcH9Ap2Z9vv1yrG6wYIXlHunxgDvIjzId/XsK6XGVkXe18B+cjX6PLjzhXw20o+45pIVcxUHTf+TtXYhTe48Z33CS8cGMYhmEYZkzhR6WjBz8qZRiGYRiG2UfgO24MwzAMw4wpHvzdOXtig+GFG8MwDMMwYww/Kh09eOG2u1QqQKXsb7ivuv4G/hRtjFeBA9EojMaMPKY24w/kh9sMpXgKpHYCZBooJavcR8k4DEoPZbRQAABtso9NNbW9lqkD0o+qgfpmGcTgDlHqq1gg1RJtxjeiZL9SBQYKZJf6zg/5fqrN+MF0VSrIQKW5UnOjxqDKwSCA0Bx4w3XxqC+rAAMVTADolFcGBVdo/wE/wCGQwkqjUmOZxvA0W57w+6xQUEjZhaHmQaXBIhuGHTxn/phMOt2GRRu0s/LTSDqwKdjApMCFSo8Hk4IT1NTC8FNc1X4CflCCChTIRsso0sZ8lZKquxTVMihgQaerErGATCmkYiX9H22DTmvll/0URvIcC2EE0lqRLloOtJG6aiAFVm06LE/4qaUUwXI9pT4K6pSNrOOnsqpNa1UfSjWlUmrt2MZIfmSd6oiytFEdlr4raL8qwu2qIiwrG9p+zTizI+jMEerVO67+Y7aztFnZEdJmeQinmkIgHZZJurRTDclKl3b8NsFxeoF0VQLBdFWGlpWPSlbfTlUOykagrOSME9apsvRDpcMannor67g6OKfWR3k+h8+H8sOkyKCM4+lyUPZ1Vsh/ASsw5rCuIkbWiZr0WkbAhlFjf0e62noI2PT2UsorZvTghRvDMAzDMGOK/B96D1/Ay+tNABycwDAMwzAMs8/Ad9wYhmEYhhlTeI/b6LFX77hdd911+Kd/+iekUimMGzcOH//4x7F69epQnWKxiIsvvhiNjY2oq6vDWWedhe3bt49or7u7G5MmTYJhGOjr6wvplixZgkMOOQSJRAKtra343Oc+h+7u7rEaGsMwDMMwhNpuvKc/zF5euD3xxBO4+OKL8eyzz+KRRx5BpVLBSSedhHze38D/1a9+FQ888ADuvfdePPHEE2hra8OZZ545or0LLrgABx988LDjf//73/Fv//ZvuOCCC7BixQrce++9+Mc//oELL7xwzMbGMAzDMAwz2uzVR6UPPfRQqPyzn/0M48aNw/Lly3Hcccchl8vhzjvvxF133YUTTzwRALB48WLMnTsXzz77LI4++mjd9vbbb0dfXx+++c1v4sEHHwzZfeaZZzBt2jR86UtfAgBMnz4dF110Eb73ve/tvtOOAzgRnfJKxOMwWhqlTkVRRigdUrUK9PZLWUVDxqN+ZGIyGdKJeNzvp0qRTZ7n21Of8ZjftkSRlY0qbZUHo1nWcynCM1pnozgobzEnplG03YAHq57W7RQxqSMlHRvIKPsUJZpJAe01dygpFRfKFT8FWJIiawcG/TGrz4iDYQRDJYORnQBQLA9Pg+UJOYeAH82JHJUNPy+UshF1dGosHXWq0nlVXT/tmMI0/NRfKlo14cCglFdIOGFbpqGjSQ0VVWqZcCkoV43Yy0t/rLQLUaLzH6HoUgcwY9Jvw6Y5FYBFl4NR+y31gHJJ+hONyfPZUYijjiIkV/fLc5FxXLw5mAxNxyBFnhZdExUhO8hRCqmKMHTqKrcm5ZUQBjpKYUeC5WDKq66SLwd1thGWlc40amwYYVnqKNUYRCg1lvTf1Gmnhqd/suCYQssA4JgiJO+onm9/hJRaptB9BVNjAUDUsrUctyytC8oAEA2kq/LTFg1PZWUZvi4oA4BjiEDaKXlMpUWKmSbyLvlBfuddU8tBP1S76E7SJ/l9GyP44duPqr5IFzMF8pRiKUbH8q6hZf+cyTmWqb18Wc23GsuIuhobedfQfgzq+bAwRH7kR9AFZd+GmlM1TuhyUFY6NU7VbrBqIGb6YwaABOkKrqHHEJyfwdrz6RpwauoFyyPpausFbebdvZfyih+Vjg7vquCEXE7+AW5oaAAALF++HJVKBQsXLtR15syZgylTpuCZZ57Rx15//XV85zvfwS9+8QuY5vAhLViwAJs3b8af//xnCCGwfft2/Pa3v8Upp5yyQ19KpRL6+/tDPwzDMAzD7D4ejFH5Yd5FCzfP8/CVr3wFxxxzDA488EAAQHt7OyKRCLLZbKju+PHj0d7eDkAusM4991xcf/31mDJlyoi2jznmGCxZsgT/8i//gkgkgpaWFmQyGdx666079Oe6665DJpPRP5MnTx6dgTIMwzAMw7xN3jULt4svvhivvfYa7rnnnt1qd8UVV2Du3Ln413/91x3Wef311/HlL38Z3/zmN7F8+XI89NBD2LBhA774xS/u1G4ul9M/mzdv3i2/GIZhGIaRCDE6P8y75HUgl1xyCZYuXYonn3wSkyZN0sdbWlpQLpfR19cXuuu2fft2tLS0AACWLVuGV199Fb/97W8BAILObFNTE6688kp8+9vfxnXXXYdjjjkG//Ef/wEAOPjgg5FMJvHBD34QV199NVpbW4f5FI1GEVXZDhiGYRiGedt4whiFF/Dyo1JgLy/chBC49NJLcf/99+Ovf/0rpk+fHtLPnz8fjuPgsccew1lnnQUAWL16NTZt2oQFCxYAAH73u9+hUCjoNs8//zw+97nP4amnnsLMmTMBAENDQ7Dt8FAt2igsdncJr1JeFYoAAKPqDquigwwcx9+0rzbm54f8IAaXAhCq5FswvVWQaiAIAJAb9oUKJKBN8yr9kx3YXE+mRNmDQ2tQtTHejBoQZSkbVjhIIZQKSgUFlCu+H5Vw2h0AfhBDMABBp+1SQQeeb692nELoIIJhn2rMChUUoezSeJGM+DrVTyDNlqAUVkYxkJZL1Ve74D0xLL0V4AduqPRgeo4TgeCGiD82J2OEjlkpCj6IOzAGqS3t0Bce4BVr0icbgCA3hwUnAHAo3Y7arJuNlFDxZB+T4jJdVV/FQUuM0l+VpZ+NlGqquxwJpKuSF0fWqeiUQCoVlCqnnQpKJKcovVHRM0MyACTtKtIBGQDqXL+s5AQFYdS5JupILiqd5SJh+TIAJHXZQ8ESWgaAgiW0nKfN6XEqJyxP24gFbCo5Zvr1lByzhn9qmezHLM+XdTvfptqQrnWmiUhAVjYiARnwN6RHzBF0run74apACIEYzUeUPiN0+UYtgWjAXwCIBvyIBvpSfQflnfmh+oq64b6jrgj44esi+rz4OuWbrmfuXBcdoZ4qj2TDH6famO+F5LfSRUxDy3aNzt6BLjKCfccMn5dgOVqjG2mcjrH7c+UYNTYC9iMmv1ZjX2evLtwuvvhi3HXXXfjDH/6AVCql961lMhnE43FkMhlccMEFuOyyy9DQ0IB0Oo1LL70UCxYs0BGlanGm6OrqAgDMnTtX36U7/fTTceGFF+L222/HRz/6UWzbtg1f+cpXcOSRR2LChAnv3IAZhmEY5n2IGIXgAsHBCQD28sLt9ttvBwCccMIJoeOLFy/G+eefDwC48cYbYZomzjrrLJRKJXz0ox/Fbbfdtlv9nH/++RgYGMAtt9yCr33ta8hmszjxxBPf3utAGIZhGIbZLUZjjxrvcZPs9Uelb0UsFsOtt9660wjQICeccMKIdi+99FJceumlu+0jwzAMwzDMu4V3RXACwzAMwzDvXTg4YfTghRvDMAzDMGOKgLHHe9R4j5uEF267iVEqwXBMYIDyqToOsLVTyhR1qS+taATo6pNyNiU/32z3UySpKM0YRSbmC0AdRaFu7pCf47JAQ0bW7yNbHd0wKKpVhwf1Sn/6V5tINMkoW7ciQ4n6VxqwozLKqX+9jHJMz/CjYb0BGXmooikNT/iRo1GKEu3qBcbXh+ZCzKYoYNuGsX4TGaPoyIEhIFUnZe2r5/vrkF31yhXHGZ5+qqXJT/2lsG2go0fKKZm6ypiQleVMEthE56JR9u1t6YM5a5ys10D+ULSqyBVgDMmxu+2DAACz4uroWiND0cHFCpChVF65Ifk5QWb3EBs7dWSvKFJkb74I56Cm0HyYE+Q5hOvBUlGrakj1LswERaRSpLFZZ6HSJ2VvQKrK/ZSaTBhINsuIUMrOg0yuhFhURoL+ef1EAMAHxvXgvk0yHdtpE/sAAI9vl3N1wrg+/K1Tyh9okhlL/tGTxhENsrPnu+X1qsrLe1M4LCvn6MU+OY+HZQe1fGhWXn8v9iZxCMmv5OS1fGBaztnLuSQOyeS1DAAHZ4bwSi4RqvdqLoGDMr4MAAdm5DX9Wi6OA9JSfr1fnp8D0gW8puSUvNZeH4jpspLnkm7lQEzLK/qVroSVA1EtSxuyPCdVwqqADACrBqLYv65M9iLUzi/vnwrr9k+VsTogA8Cq/ghmB2QAurxmIBKSAWC/VAWrtA1KbzbgYL86XwYQKit5Zb+j26l6s1PVULvZqeqIutp6s+qqWD1gU19K55dH0s1MVsmPHddbOyjLM5Mj26jta1adq8tBWemUPaVbO2iH5KBuzaCjfVwz6Gg/gvKu6EaqFxxX7ThH0u1qvR3pgnOldNMDupl1VRTMmih2Zp+CF24MwzAMw4wpntjz15Dwa0wkvHBjGIZhGGZM4T1uo8e7JuUVwzAMwzAMs3P4jhvDMAzDMGMKByeMHrxwYxiGYRhmTOE9bqPH23pUumnTJpRKpWHHPc/Dpk2b9tgphmEYhmGYfRnDMPD73/9+1O2+rYXbtGnTcPjhh+ONN94IHe/s7ByWKJ5hGIZhmPc36lHpnv7sKVdddRXmzJmDZDKJ+vp6LFy4EM8991yoTk9PD8477zyk02lks1lccMEFGBwc3OO+DcPQP7ZtY8qUKbjssstGvBG2M952cMLcuXNx5JFH4rHHHgsd35U0VgzDMAzDvH9Qj0r39GdPmT17Nm655Ra8+uqr+Nvf/oZp06bhpJNOQmdnp65z3nnnYcWKFXjkkUewdOlSPPnkk/jCF76w551D5mLftm0b1q9fj9tuuw2//OUvcfXVV++Wjbe1cDMMA7fddhv+3//7fzj11FPxox/9KKRjGIZhGIZ5t/GpT30KCxcuxIwZMzBv3jz84Ac/QH9/P1555RUAwMqVK/HQQw/hpz/9KY466igce+yxuPnmm3HPPfegra1th3bXrl2L4447DrFYDAcccAAeeeSREetls1m0tLRg8uTJOO200/Cxj30ML7zwwm6N4W0t3NRdta9+9au4//778c1vfhMXXnghyuXy2zHHMAzDMMx7GPUetz39GU3K5TJ+8pOfIJPJ4JBDDgEAPPPMM8hmszjiiCN0vYULF8I0zWGPVPXYPA9nnnkmIpEInnvuOdxxxx24/PLL37L/NWvWYNmyZTjqqKN2y+89jio9+eST8fTTT+OMM87AP/7xjz01965HRKMQsSiMCePlgYFBnepK38ftkSmEkEoAhZpn15mEX78xG9bVJf1UU3FK/+R5QIkWxDadrqYGiLhM1SMoXZWZkil5oqkqrDp5cVtD0p94axV9b0h79fvJFDjugEBkikzLYkRk3iSjntI6WSbQHPZNTGyB0aVSTcl0RUY73VqORfQxmPS/wPRJECQbDdKWSCZgvLExPOaEHAcijk5FpdNhDeT9sccpNVah5Kfh0vNO6VtKZWDaOJorOSZzYgUoShsiJ1MlGXXSlpGJA/UytZM1Sc4LMkmILT0h+95ACSalKRMFWc/okufYaEoBSUq9lSyQjQTKT2+RboyLUjtKZdVSh+pmmfbJysrxugMejAjNG/1eEmUP0UlSb9jyoNNP14YrsPkl6XeGUkHlyw4cR/q7oLlXTqnl4uRW6WfSkX4f09Qvp9Op4vD6QS0DwLx0AQlbynPTMiVUzJI2ZyVLiNlSnpmU11zUcjE1Iec2Ykrd1EQFEUuejwmxiq4HAJPiFS23xqq63fholerJdi2xqpbHRash+81RFxFK11MfcUnnocHxQjZU2TE9ZElW7dK2p+VMoF2mxkbalt+fqOmFZABI2SJgQ4Tsp2wRqqfaJUl2DPmZtAWipi/LesN1cfpaREwPKjOa6ithATHLl4M2ElZAtn1dwq6xESjHauzHrLC8s3o700UtEaoXtURIlvV3rIuaYTloP2paIVnpgjIAOIF6To0uYoqAH0L3HZR3Radk2/B1QXlXdHtqI2IKOGZNPTPgoyng7oUtTYJ+9tQGAPT394eOR6NRRFX6xF1g6dKlOOecczA0NITW1lY88sgjaGqSaQrb29sxbty4UH3bttHQ0ID29vYR7T366KNYtWoVHn74YUyYMAEAcO211+Lkk08eVvfcc8+FZVmoVqsolUo47bTTcMUVV+yy78DbvON2/PHHIxLx80oecMABePbZZ5HNZnmPG8MwDMMwY8bkyZORyWT0z3XXXTeszpIlS1BXV6d/nnrqKa370Ic+hJdeeglPP/00Fi1ahLPPPhsdHR1v25+VK1di8uTJetEGAAsWLBix7o033oiXXnoJL7/8MpYuXYo1a9bg05/+9G71t1t33NQq9w9/+EOoDACRSAQPPPDAbnXOMAzDMMx7H4E9f9Spoko3b96MdDqtj490t+2MM84IPYKcOHGilpPJJGbNmoVZs2bh6KOPxn777Yc777wTV1xxBVpaWoYt4qrVKnp6etDS0rJH/gNAS0sLZs2aBQDYf//9MTAwgHPPPRdXX321Pv5W7NbCLZvN7lLwgeu6u2OWYRiGYZj3MB797KkNAEin06GF20ikUimkUqlds+t5+pUcCxYsQF9fH5YvX4758+cDAJYtWwbP83a4F23u3LnYvHkztm3bhtbWVgDAs88+u0t9W5Z8bF8oFHapPrCbC7fHH39cy0IInHLKKfjpT38aWskyDMMwDMO828jn87jmmmtwxhlnoLW1FV1dXbj11luxdetWfPKTnwQgF2GLFi3ChRdeiDvuuAOVSgWXXHIJzjnnnNCj0CALFy7E7Nmz8ZnPfAbXX389+vv7ceWVV45Yt6+vD+3t7fA8D2vXrsV3vvMdzJ49G3Pnzt3lcezWwu34448PlS3LwtFHH40ZM2bsjpl9m3gMiMcBtVE/mfA3ztMmdaRok38mBfQOSLkpKz/bu4G6mN8WgKAVt1Gp+AEIykYyLvsEIDLyvwejUASSFCCQoI36zfK/j8i4HKxmWd8akCt4M24i2UAb9Gl/u11vwhuQBStN/g9RIEBdFMiHV/9GTx/Q1Reei21d8rO1CVB3WZNx+dnVA4P8VXNlFIrDX8QzJDfBw/OAnNy0D4fmoKMPiDnh+tEIUKqE6okOOcdGpuIHLERpD6Zh+EEPbs3/exXXDx4xR9juWfXri4q0K8pkPy/nyjBNwFQRBTQ2T8BUO6hVUENZ6sRACWba9n1T9ouyr0qfrGfFBURZHrNSsp5XUvb9oAQ7IusUXRvdg/Ka6SzI8x+1POQqsq++spyPvoqcz1Q5gq6SE9J1lW30lOWxzpJsp8odJQvdWmfRMQftRSlvL6p6Jhq1LP1uD+jaCjYdk/OzrWhjG11qbcU6eawAbC6QPCR1m4Zkua0AjIvJa38L6TbEk1g/IOdmGgXJrB+Q52lyMonNeQqOSCpdFVPq5Fy9SQEfU1NxrA/IALCRvh8TknG8SUFAM5RusIwp9H3fRNdCKwXarBvIYzp9t9fnpZPTkgm8mZfX97SE9GPlUA/2jzcAAN4oyCCSaTH5PX6tvBVzHfmf+4qKzEYzz5mIFyurAQAHWTOlDXcD5lqTAADPuvI//CNMeZdgpbcacwz5u/ll8SIA4AAcgOViGQDgMHwAALDKkLr9xRw87/1J6swPAQCWDdyC4+vk+6ueK/8aAPBPkY/jeZIPjZwm+x68HQBwZN2/YkVVvttzjnOC9L/0MOZFPwoAWD7wK9kudQ7WlJ8EAMyKHgsA6IYM6KnfPgFrC/ImwdT40QCA7dWVGG/LP2wd1TUAgCZHPlbaUvhfjIsdAADIe90AgJiZxmBVbiSPmPLaqYoSbIMCkwx5/bnt8hwL4cI2pa7k9tMxD1FLno+KJy9SVa62lyCEvK4KFfn7LRFpxlBZBmw1xOT5GdzWjqQtN7oPbZO+RSx5/vs3b0U6Km96FLZKG/WR6RigDfAJqxEAMNDWhjqnZUQb+a1dyETk+S9tl8FGaasF+Q75u9mC/O4Nbt0OAKhzxiO3Zav2/Z1ECANiTx+V7mF7y7KwatUq/PznP0dXVxcaGxvxT//0T3jqqacwb948XW/JkiW45JJL8OEPfximaeKss84KvfasFtM0cf/99+OCCy7AkUceiWnTpuFHP/oRFi1aNKzuZz/7WQDy1WktLS047rjjcO2118K2d305xrlKGYZhGIYZU0bzUenbJRaL4b777nvLeg0NDbjrrrt2y/bs2bNDARDA8IQEoxW8+bYzJzAMwzAMwzDvLHt8x40zJTAMwzAMszNGI2XVaKS8ei+wWwu3M888M1QuFov44he/iCTtHVHsyq1IhmEYhmHeH4xGkvjRSDL/XmC3Fm6ZTCZU/td//ddRdWafoOrKny1ysydamvwN9HnaaN9cLz9N09/0rrIp2BbQS5vws/T2fZV1YMt2Pyiho09+1lcAyjygN96XKzB6KDiiiQIAyhRokHEg8nLzvp2ljfFVAUO9mJ/ewl/u8BBpoYOW/BQlacNoTvt+KFJJoLffHxfgBxHUzo+qo7IeqECBaMTPDFGqSY8Wi/jzp7JGZBJ+gIAKAFDzDwA2+R0IGDAyNJeC2lX8TbhikAIKouS3J4AkZTYYUrpAMITKKJGMwEiQT9SXEQ9kb6Axif4S1Y9CVPxABUCeAwAQJRflLeHzYzfaMJLSnmingJKIAZfaqNeFq3MoAJSKFJhBm4x7SxFMqpPX1f/2yk3yi1r7cM9GuZn+/Blybh/YKsvnTC3gyU4pn9wq+3yqK4JTSX6+R/qzf0rOy4u9ScwZL2283i+vjQPSZbxBARGHZGS9TUNxTEnIelsL0v7UhJyX9mIMM5KyXndZ+jgjWUIvybMoI0NPKYr96qQ8UJHnZ05algerURxActmTugMzZQjI83MQ+eGJYFnKh2TpHCOCg7N0Dgw5zoOzFURNh+xJXYTKB2UqSNoxLQNA0o5qOW4r+/JcZCNJHJSRcmNOXo/z0i4acnVkQ+qaco1aHpfLynpUbs1N0vLE3BSy4WFi//5aBoCp/dMCstzIP5fK03P7Y16G5L7D5NiyHmb3nQgAOIB0r+cO0+W5uVNDuoNyl2n50NyntU7Jyv4/5b6syytyZ4d0K3Kfxty0vIiPzl1GOoEVuXO0DAAr+yeR/wIrcp8J6Vbk9g/J0g8q983FvKyS5XdqXlaEZKUbqd6OdHMzAitzvgwgVF5Bsu+XEZL9cQ6vBwDzJo2CjV30I+j/3IxAwS3iP1Z+F8y+yW4t3BYvXjxWfjAMwzAM8x6FH5WOHhxVyjAMwzDMmMKPSkcPjiplGIZhGIbZR+A7bgzDMAzDjCn8qHT04IUbwzAMwzBjCi/cRg9DjNarfN/j9Pf3I5PJoPfVO5BOxWG0y9QmiEaA9Vul3CjToYgZ02Q54sDo6ZXHVFqr19f5KZhmyGgxodJXDQ5CODKSzaCUJYg4OipTNDVJ3RvrgSaZKgd/f4X6lhFr/fe3oe4A2de2v8t+mucUMLhJyrF6GQFZHTIRrZdRX5FZ1H8jRWR6AqgnWaWwqgZSpIyTqViQU+m8GoCevvCEeZ70HQCKFEEai4RlAChQJGldElgjU/voqNJI4P+KusArZ/oGwnZVCizTBJoo8bDqu7NP2xHtFMUbD0SO1st5QzfZrE8C3TJ1jE5zNVjWqcV0ZCqlCTPGpf2o4m192oa3tkPqGyg6V0XsJiM6eldF85ZX5uBMklGLlU1yPuzxDqrb5bjMpNzX4eYoMlUAxV55jj1X6rp76lDxpD3HlOe1txhFQ1za6xqS57GRyr3FKOpjJS0DQCZWQi4gA0Avpc/KxkroLUq5PiZtdBdi2kY31WuMF9FF0aSNqh61a4wV0UP2s1HZrq8UDckAkImU0VemehHSUTkdKaOX6tVTu+5iFA0kd1FfTdR3ZzGGxqgfrQoADdGSlrNReT77yw7SkYqWASDlyHKuHEEmUtay8lHbIF0P6RoiZXSTTvXdWYqiifrqKVG9aBm91Fc99a3KGaeCXMWXASBXcbSs0pSlnYpOY5YlnUpT1hCpoJP6aqa+O0sRNEWH15N926iPVKkvm/quajllS91A1dZysB4gU6opP7rJx8ZIWfuh+u4qOW/bD9VX0K+d+bGz+VBp35qilWF+BG34fbk0B1ZIVrr+qqyXtpUfNrLkk0oZ10j2O0sOmqOVYbo+6ku166/a2l6t/R3pauv1lG0aWxXdZQdDbgkXvvI95HK5t0zWvqeov50/PfjrSFjRPbI15Jbw+Ve+/474/W6G77gxDMMwDDOmcHDC6MELN4ZhGIZhxhQxCo9K+fmghKNKGYZhGIZh9hH4jhvDMAzDMGOKRz97aoPhhdvu4ziAEwGqtME8GffTQ9FmeWPrNqpr6w33xrisPBax/fvFFdpUXyhQ+xKMMh0byPv2KbABA7SB3rYhbNpw30spkjLSh3LB0q5WKhQQ4Rho65DpymZP6pb1LQ9WRt5wVZv1xSClbGqs84MS8uTb1InAlnYpq3RVBVkfff0ylRcAxOXmcPT2A1HaiKrGG4/5AQWK4L1zlW5Kpcjqyvl2KSUVIraf/qpepvsS67fTOC3fnvK7LqbtGSrVmDoX/Xk/bZcKMPAERF4FUEidkY4ChuzTqJfzYpAO+aKf1oraGakYvKL8FaPORnmd7NuZ5qHaRucsLuffSpoQBUqbRZeV213R98N1+izCdIDikPQ3SSmeyq6FBG2S3z4oAzlSkQq2BWQA2DIogzGykRLW98v5q6eN2m/k0mggG6t65fWiNtS/3pvRm8lX9Mr5a4yU8UqPlNVm7xe6sxhP9f63W+paY1L3j64sWmNSt3pAXq8T4mWsUjLpXu9PYEJcyq/l5HxPpPIruQQmaVnqJscreDmn0mvJCXx5u+x7erKKl/vkNTktKXX/2xvDDJKfp/RgM5NVPNMtbcyqk5vOn+5OaN3TpNuvTrb7W1cc+6Wk/FyPSt8l2z3bHcN+2oa89manPDwbkKWNiJaf7JTflf2p/NeOqE5d9dcOlfbLw+PbpTyX0kkt64jodFLLtkv7Kt3RXzsiOi3UU53yejkgI/Bkh5RViqdl2x3d7rEaG3/ZFsGBVO+VPqk7MCvwWkAGgIe3UXqweuAv22zSAQDwyLY4DqYsgI+1y2/EwfXAoyQfQrqH2qjcYOBhsnFQvfze/WmrjUMafFnVA4AXu20c1ijlpVuljcMaTPyZ5EOp3kNtlpb/si2q60n/TS0/2GZq3QNbpDy/UX4u7yb7jSZeDMjSD0vLL3TJz8ObTCzd4ssA8OBWQ5f/ss0I2Xhwq6HrBdsF7dXar9X9b5eBwxulby91y2OHkv0/bzVxaKOJovvOP2wTwoAQe7jHbQ/bv1fgR6UMwzAMwzD7CHzHjWEYhmGYMYUflY4evHBjGIZhGGZM4Rfwjh78qJRhGIZhGGYfge+4MQzDMAwzpgj62VMbDKe82mV0yqvX/wfpVAJGl4zOhG0BmymKVEVijm8GAIhYFEabjHjU0Zbrt/pRiIcfII9RmisdZQrAyPVrWSQpajUjU3wYPb0QdZSq6cG/y2MTZGhW5W+bYKZkRFHuVXKrpYrt62R0YcO4IdllVsCgZbszhVJeqVRQMcdPO5VJ+j72y1RQGEfptlQEbCYFdMnUXjoC1jSAehmZqFNjJRPAANmIUcTpEKW8SsaBjh7SUd+9A/69ceUH4Ee1KjppriI20JDy+wdk1CdFpor2PjnOVMxvmyQ5l/fLuaGQea8zr6NIvRxF3qakj2ZjAojLsYg2sj8+heprMiWaWU/pykoulWMQOTlmNd+FlUNwspTWakCO1643Ue2VOzpMOv2Chi2qQL4z/D/Xpu31+uWUHkVe9VciOmXUSKmgmgMyINMzKXmYLlZCJ6WraqY0V53FKJpUqilK8dQUsKHSPQXTP3XqeirlUETLHcWI1nWVIsPqATIdUTCVEiBTKym5NrVSZykSSvek2u3MRnfZ70v27YRSNSldMF1S0H5ztBzoy09pFEx1JOtVtFxrvyk63H5XyUFDZHgKoy6Sm0g3kv1GlVqp5ITkt9I1RKqhvlTfSq7tu6tsa7mjRLpoFV0BWfpoo5nkWl1XyQ7JO/NDlbtJboyo9FK2lkeaj+bozs6Fsm+hIeJqWfbl7lS3s7F0kq55hDkI6nZlPt5KN9J8K/udJRsFt4ivvv7ddzTl1U0H/CfiVuytG+yEglvEV94hv9/N8KNShmEYhmGYfQR+VMowDMMwzJjCj0pHD164MQzDMAwzpnBU6ejBj0oZhmEYhmH2EfiO224isvUQ6QSMNzfIA+mU/2/AAG1qT8qN7gbgBxyo1E2phJaNkty8LeKURmnrNiBLGy57+uRnQxZGXtoVakN/bw6GR68ijAZSLwEobDOQTEp/DFqWmzEDtiU30jqUoqbUbSI+SYRtKOqTfqCF2rQ/vdEPTlABCB0d8tN1/WMZCg7IDwFFSomlfK1WgQrldKKUXXClXxAC6KAxq4AB25QBB0EbEQcgs2revW45P2Z93A9cUGmzCmXAsUL1FWKgCIOCMERR+mVkrLA9yJRXRpLSZpENpQvZK8uxGFUP1Zz0N0Kpj9xeP/Ckul3KdiMFH1iAWSd9LG2X9a2UgEvjNGk6DBpS8N8tysSFuFNBqSLnqioolZYhUPGk7Jiyr5Ir+4mYnpYdU46p7Fm6XpF0piF1xaqfSq1Qlf24wkDRtam+7GeoaqNIfRZIV/IMXXYpcGKI7LnC0LKAoW2oevlqeEwF10LJNbSs+lb1hrQfFn2aGKRrs0DthgI2VLuSa4RkABgkGyXP0LIa52DV0uNUuqFAu8Gq1MWo78GqqeUizUfetbQc7EuVSzW6omcg76q+pP2oa6FAsq+T7QaqFgZJjpGNQdcIyQAQqfjtlOz7ZYZkXY/kojtcN0D28+RXPDAfcZqDfNVEwgrXi1b88zRQCZ+zfNWfq3x1BJ0b1g25ph5nPjAH+Zr5CJYLw8ZiIlIVofkOlkfS1drIuwZiVXWdUF+mpctBWemUTzubj4G30NXWGwqUB6uGnq93En4B7+jBCzeGYRiGYcYUIYA9fYcFvwNDwo9KGYZhGIZh9hH4jhvDMAzDMGOKgAEPe/aIVuxh+/cKvHBjGIZhGGZM4Uelowc/KmUYhmEYhtlH4Dtuu4nRn4MhKjJ9EyCjHIco/K+lUX5SRKNIxGHUUaqmBEUhdvX6EZIqJFClZ8qkICja0mhp1n2KKEWTqk/ThKA0WUaVbDmyXWIaYEQpWjBBEZaWgWSaZKoea/YAitJCiSI9Vcqr7gE/7ZNOCRVIP2XSer+eIkjTKaC9KzxREcevpyJIbdsfu/pU0aiGAWRoTlVEqOsOf3FP1ZU/AT/M8ZT+yxN+25FQtsoUQepYw+0Xyzq9lajIflSKKtmWjhVkZKiRjPhjsVUYr6HnWZSlYFiG/rQysp46TwalzpFtlT8GzJooUk+54QEeRbIZVD9H6agAP3J0sGrBMmT/OYo4VVGjfRUHzWZYZxseBiiyMkI6FdEYt1wdUVlnS3/Lnv9/30iPMAx6XaaKEAWACl1z6pKveEZIBgDTFlq2KKpVXeZGoK9gjyr61awpG4aAbYTPcfC/VSugM2rqBXVWwB4A2IZvxzZUHdW3Xz/4qW2o/gKzpnSm4ZeDsmq3M7v+p9/OrrFhG2EZ8KOKLcO/hJVfthmob/7/2zvzOKmKc+//6pzT2/R092zMBsO+uhEDSnCLClHUuOYaQ3iJEC5mwdxLyKZJrqgxkkRvNPEaTYzBLLjc5EazIUZF1MQdxS2IDIJsM8zaPT090+up94+qOkvPADMyzdDyfP30p586T52nnqrTPRbndP0KfeqptlTZ2E/fDc19zND65u3MQ9keq52+fXH69Dyfc1w8jndVz5M3Vv2Ni7NNT/6Y7cen+tlvPU2923Xy++nRuJ2vZo+V0x6IL7+eM6bB7PwOJ7SqdOigiRtBEARBEAWFBHiHDnpUShAEQRAEUSTQHTeCIAiCIAoK7VU6dNDEjSAIgiCIgkKPSocOmrgNllwOyGUBueiAe71gY+v6r5vOiMUIAOCXW0eFS61PH5cLFrguL4PH8YN+9W6alq3qsYAfCMr25Q/omdwaiqc5WI1cHJEVP6DXSg2YOfGzTj0kY2U4mF+2Ibe84jIv5vOIrbnEQfEeCQFtUTsnwF4kkMsBEblAICQXY8QTgNqiS21r5fXYW3+pd7UlmOZ4aq98bTH7WETGNTS7rk8spuCdvaLsN8DUL7rVNzzos7fNisoFFyG5UCTea/ms87S+vx5gAY/1q3G1cIGpbcL6+0ui2b/8ZV4Rz+yUW1k5Fi6ote2aj1nnGH4VA9Blmmphg/Pfm+m03LpKLmyIpr0Y4RerFxrjYqxCnhx29YggassbtcCgK6tbiwbaUiJWxmRoS9tbUTl9HMxl2z6vqx7gddkAEM3Y22XFHDYAxLMaAI/DBgCPtQ0SUsJnldOGtZ0Qk3++4lnNsmPSpzE7piHthPQZzLC2KdIyjq2y0nL7LrkwIirLaZNZthq/Tke9zrxthXTHlkPqvN6cI4Y8rytjIK3aythtqXK+L8uZNX7K15m2t8bqTLu3q4pl7K2g1HnJHLOuR8qKr2ICsYzoX0Z+1GIZzarn9Km25Nobq5wy7RyzjjzSpjuP9H7qifh2jhkZvyuru+w+Pvl5Vb6syex6Dp86Jz9+LKNZ9VQ/syZz9Dl/DPr3ZfuMlaOeoy01jv35DpRH1vrMiXKOM8t2tp1fT8XszGjIcPv6EsUJTdwIgiAIgigopOM2dAzr4oRnnnkGF154Ierr68EYwyOPPOLyc85x3XXXoa6uDoFAAHPnzsXWrVst/44dO7BkyRKMGzcOgUAAEyZMwMqVK5FOp/ttr7GxEaFQCGVlZQXsFUEQBEEQTswhehHDPHFLJBKYPn067rzzzn79P/rRj/DTn/4Ud999N1588UUEg0Gce+65SCbFI6F33nkHpmni5z//Od5++23cdtttuPvuu/Htb3+7T6xMJoP58+fj9NNPL2ifCIIgCIIgCsWwPio977zzcN555/Xr45zj9ttvx3e/+11cfPHFAIDf/OY3qKmpwSOPPILPfOYzmDdvHubNm2edM378eGzZsgV33XUXbr31Vle87373u5g6dSrmzJmD5557rnCdIgiCIAjCBS1OGDqOWB237du3o7m5GXPnzrWORSIRzJo1C88///x+z4vFYqioqHAdW79+PX7/+9/v985ef6RSKXR1dbleBEEQBEEMHj5EL+IIXpzQ3NwMAKipqXEdr6mpsXz5NDY24o477nDdbWtvb8eiRYvwu9/9DuFweMDtr1q1CjfccMP+KyTECkWWyYjVow54WUQYXo9YjQlYW1KhO2FtuaRWZ7KcLGuavSIx6zgmbaZWeBq6fa7apkrC/Bq43JaJGXL1ajIHLrcnyiWUD0BS7cskV5Pqjnl8Sv5OMBhwl/vDNO3Vob1J93HAXn2azthbXKnVm8yxelatJlVjUG6vwEXAZ/uyso2Me6su5jcc23LJGGqsnWQcx/L/CedYEepC7rnE1d5LajVvwF4JzJznqqGUY6oF7L2BrNW80mf2cmvbK1MOow57iytN6/vnSq0mVVteVQeSyMhrPDEkVjC3pXwYX2rbAFDlS1llZQd04avwphE0vJYtfHbZr8l6PuHzal5USdtgXhk/7bKdvgpvBl7NY9kA4M94EPFkpS2+IxFP1rJDhvDFHD5fRsQo84gYRtprxVOXoMontySDx7J1Zrft0dwxvJqBkNzKKy5XI6pyV9boN4/OtLJzsp+GjJmF+tNa5ZXfXWZYti7PK/Nm4ZHxwnIM4nK1Y8iTs+xSw7Ty6i9HtarVysMR38rDpz7zhmWrVa5W2/uJnz8eznpd6jrJtmMZ3cqjQ640rfDmgFReHik7D7UiVZ3njJ+QbQeNnMvO9+Xn6MyDyX6We3MwuS7z6JujWrkZ8ZiybxrCDluMldoKTrOui9OXXy+W0ax4rvEAYKZ0y4Yrj/zx0BAy7HYB+zPhzMPpUyu01XnOmB1pHRpNgYqaI/aO22DZs2cP5s2bh8svvxxLly61ji9duhSf/exnccYZZwwq3rXXXotYLGa9du3aNdQpEwRBEMRRgXpUeqgv4gieuNXW1gIA9u3b5zq+b98+y6fYu3cvzjrrLJxyyin4xS9+4fKtX78et956KwzDgGEYWLJkCWKxGAzDwK9+9av9tu/z+RAOh10vgiAIgiAGDwcbkhdxBD8qHTduHGpra/Hkk0/iIx/5CACgq6sLL774Ir70pS9Z9fbs2YOzzjoLM2bMwOrVq6HlCag+//zzyCkBWAB/+tOf8MMf/hDPPfccRo4ceVj6QhAEQRAEMRQM68Stu7sbjY2NVnn79u3YtGkTKioqMHr0aCxfvhw33XQTJk2ahHHjxuG//uu/UF9fj0suuQSAmLSdeeaZGDNmDG699Va0trZasdRduWnTprnafOWVV6BpGo477rjCd5AgCIIgCHAc+qNOelIqGNaJ2yuvvIKzzjrLKq9YsQIAcOWVV+K+++7DN7/5TSQSCVx11VWIRqM47bTTsG7dOvj9Yl+gxx9/HI2NjWhsbMSoUaNcsTlJLBMEQRDEEQHJgQwdwzpxO/PMMw84wWKM4cYbb8SNN97Yr3/RokVYtGjRoNr8IOe4CPiBQMAqcp8PbPu7olBfCQBg8W7hMxzDq+xYwtpjk8mVqdwjVt2xWJe9v2fa3sPT2qkyIfc73bIDbFS1ONYr6+0Ve6LytAmzU6wW5FlxppkwockFm1wu6mL2FpGAXIWKoKrkuCZqRShj9p6fKkcnavWmXAkFQweSKdkHx+8S4nK/UPX4mjkebSfdq3NFHMeqUwDQ9D4rP5lX5u38Vis7lbH7k1Q5pu06Wfsxeh/kqk+eTIGFZZ8zahWYfPcZVl+s1bzprLU61IwJI9Ohckta8t+a2v/UsPc0tZwmrF+gqtWqagUpN22bMfHu0WxNcdXdkJGFLv0lusxNDl1ANy3bJ8/VGIeHub+Pzh8eMDawv5rqdyim3B/R+bsUZat3tScqIPZKFefZtqqX5ZpV7uMz7cud/87BrDxUWya3Y6hjyZyGUrkiUe1HWqKLcenNMQTlfrEp046Rlqt4TeTcPkdc57uy1b6RnDNrT9OgId7VHqolhumyrTzkV1DtixrQ7X1LrY+8o3ygPFJ549ibs1co9jrGoLfPeGiWrfZFDaqvWI4hJHN0jnFO+u0xcIy9rBeGGgOGUjke3bKfJQazbL/u9vl1Zo1Hia76YsdQ+6mK8bBtkaNdVvu6qj+DvTmGEoct4otyIsugFoc7ffn1enIagnKs1Hirhek5zuw8uH3N1HiEeN8YibxrkXBcC6dPfXaCedcpxEVbWcf3jig+jtjfuBEEQRAE8eFgKHTY6IabgCZuBEEQBEEUFHpUOnQcsXIgBEEQBEEQhBuauBEEQRAEUVD4EP1XTDDG8Mgjjwx5XJq4EQRBEARRUI7EnRO++MUvgjGG22+/3XW8o6MDCxYsQDgcRllZGZYsWYLu7u5Dbo8xZr0Mw8Do0aOxYsUKpFKpQcWhiRtBEARBEEcVDz/8MF544QXU19f38S1YsABvv/02Hn/8cfz1r3/FM888g6uuumpI2l29ejWampqwfft2/OxnP8Nvf/tb3HTTTYOKQYsTBks2J15ex+bu1WXiPVgi3l2bmNsyDQCAqjJLgoL7pMSEihXwg3ulNAizl2tbsiJKhmRUNXiVkB6Btk3UrxKb2bM93dBCIp4RFLN4LaDBG5aSD155WlCzlCfQn5xG3g4U4NyW68iX+fB4bNtwxArK/sUTdn2/TMCXJ3sC2NIfHtnfRNLOqcRr11PSI1KexJJEgWOjd3Wez2PLmKj4fjnePWkrby7rM+c/65T0B+f2Gn6P7J/PsPuk6+74XvtrxbwivqdCvOvlXpgJufG4lC4w04CmNAuUBIjBoNZQ8X7+mZmTm0jrUr4hmvJakiCtSaFz2JPTEJSfndaU6LPa6LwtbWCET8Rol76KnI42uSF3lZS6UOUMZ+iQtpJxaEvZ/VQ+YYvxYFJvRm0iDnitza4V8YxuDbfaNDzfBuwNtAGPZWty0/hEToMhN57vkuepj0E8q0GXm7+r8xgMSz5CbUCeMpm1abySbYjKDdQzJrNsJb3RkTbsevI8JScRTdvx1QbwPTlm2UmHT8kyRPPGNpo2XLby5V+DaMawZDfsHGGVlQyEugZJk1m2qqfKWW7b6ppEHdcnmrF9ys7m+XIOX9rRdn4eWUceqi+qzLnbVj6e15azbI+HHVPZGYfPvrZ9ffkxTDCXPRBffj3uGI+MdV3s8e/Pl+unn9G8742z3J8vf6ycMTOm3d7h5EhaVbpnzx585StfwWOPPYYLLrjA5du8eTPWrVuHl19+GTNnzgQA3HHHHTj//PNx66239jvRA4CtW7diyZIleOmllzB+/Hj85Cc/6bdeWVmZtUFAQ0MDLr74Yrz66quDyp8mbgRBEARBFJShXFXa1dXlOu7z+eDz9aMv2l8M08TChQvxjW98A8cee2wf//PPP4+ysjJr0gYAc+fOhaZpePHFF3HppZf2G/Oyyy5DTU0NXnzxRcRiMSxfvvygubz77rtYv379oLVl6VEpQRAEQRBFQ0NDAyKRiPVatWrVgM/94Q9/CMMw8B//8R/9+pubm1FdXe06ZhgGKioq0Nzc3O85TzzxBN555x385je/wfTp03HGGWfg5ptv7rfu/PnzUVpaCr/fjylTpuDYY4/FtddeO+D8AbrjRhAEQRBEgeHcvSnPB40BALt27UI4HLaO93e3bc2aNfjCF75glR999FGUlJTgJz/5CV599VXXz5EOlc2bN6OhocH1GHX27Nn91r3tttswd+5c5HI5NDY2YsWKFVi4cCEefPDBAbdHEzeCIAiCIAqKCftn1YcSAwDC4bBr4tYfF110EWbNmmWVR44ciZ///OdoaWnB6NGjreO5XA5f+9rXcPvtt2PHjh2ora1FS0uLK1Y2m0VHR4f127RDoba2FhMnTgQATJkyBfF4HPPnz8dNN91kHT8YNHEjCIIgCOJDRSgUQigUch1buHAh5s6d6zp27rnnYuHChVi8eDEAcacsGo1i48aNmDFjBgBg/fr1ME3TNRF0Mm3aNOzatQtNTU2oq6sDALzwwgsDylOXi9t6e3sH3DeauBEEQRAEUVCOhC2vKisrUVlZ6Trm8XhQW1uLKVOmABCTsHnz5mHp0qW4++67kclkcPXVV+Mzn/nMfleUzp07F5MnT8aVV16JW265BV1dXfjOd77Tb91oNIrm5maYpomtW7fixhtvxOTJkzFt2rQB94MmboNFY+KVELNjBgAROatX0hbVlXZdKf0Bp8Ce0irIey5vyX4AwL5W8R4OgeXy5EMcn16mu9eXZNtN+CrFDH731ggAYNyp3XjnXyMAAMef0gYAyMVN6CHNFcOSxAj4gJCUNulNyly9drvqhwbtcmWPpgk/AASEFAU6u+y+K0zukM7Q3fGdkhuqHUN3S444z+sHVl5iS7GodjTNkjZhSsIjIHNNZy3pDktGxGuAJ6VcR9gn4wbAlAwI0uKYkv7QNCtHpmRVGIMRlGZItpVM2okqDQVLusRhKzTAlB8nTQ4pM2wfY1w2L979es6SIlD4ddNafRQyxLVQkiEhIwePjFEiJUU8mokS3XTV88n4Ps206vs0VYe7bFXfoz7e8pihhlYzoUG3bIEOv2wzLqU8fBpHHO4YMTlkfs1El+yVisGgWbbONKvvAKBnNVdbyqfacrbt00UjXVlmjanwGfBqtq188ayWF0OUfTqHJj+GARkznnXbVj+lnT8GEY/pspUvJK+VkksJG6aVU5nKUf5Z92s5xOVYqbYTOdtW8igB2XZ3VkPAY7p8IcNEwmGLtjWXDQARwx6DMjne6hr6dROJXN/xzu9zmaOfYU/O5Qt7ci5b5KhbeXXLaxYxVN8Y/Joab3k9NY44bLuPT9plcnxa0wwRr20DcJVD0k5IX8jLXbYYb4awnteWo+zvx2fl4ekbo0/8/eUhJWfy2y7ziPhKHuSwMgS/cTtcGyesWbMGV199NebMmQNN0/CpT30KP/3pT/dbX9M0PPzww1iyZAlOPvlkjB07Fj/96U8xb968PnXVnT3GGGpra62FDIYx8OkYTdwIgiAIgjgq2bFjR59jFRUVuP/++wcVZ/LkyXj22Wddx3jeTDW//EGhiRtBEARBEAVlKBcnHO3QxI0gCIIgiIIylHIgRzskwEsQBEEQBFEk0B03giAIgiAKCj0qHTpo4kYQBEEQREHhnB/yj/OH6sf9xQ49KiUIgiAIgigS6I7bYFG6YEp3K5UGtuwUttIHKxHCWwwAuLy5mxT6X8jlbA24cqmDFpV1trwPKH2wqjLxHk+AT5sEAOABqa3m9diacROFSjN6hE6cf/YI8PaEcF0kRKLMuIbpcztEDFPG782BeaV+W0boIlk6Z6YJeKTtE9uKcJ8PrLxUHFMaafI8JHpt7TX1rjEg3u0+5vMCze3CLvW7Y6Uzol01RoDIwTnOAJDVbVtpuqVl/e6UQydOHov12GOqiPfaOUosDTvncaXPpmtAiby2qaz73Gyur14d59ACmqvvPCPfe7NIt0mNNL/UVqvRoUWEZpzW2iPqZTk02ZVsTHZJiYCbQDIldO1KPfK6GzmUeMW4hOS7oZlIS/0sXWprpaX2lUc3LXuk1NNK53SMDCpb5F9X0mP5qgNCiy5nir6P8CeR5aJepU/kkeMMlf6Uq16F5dNQ5bABoMqXQsa0bQDImJoVT/kqfGmrXCb7p2KUeTLIcamz5ck6hx0hIwsOt4+DodyblfFsn4pR5sm42i4rzVi2Oi9rMiueOk9p5XHZrshD+CYEsy5b+VSMrNW2zKsfX8STtfKISC2zHGeIeOyxB4CQLHPOUGqIsVKPmEJGzmXvzxf22ONT1mescpYUocpDlUOenJXHWGs8mGNs7LFy2vvzqfgZk2FiqW0DwPigPQbh/GvhyVmfgaBhfz+d9v58qu3xjr6Md4x3vs99Ldz1nH0pNdwP+pxlpz2Q8XC23V8e/cVQ5VLDRE8u72/WYeBIEOD9sEATN4IgCIIgCgrHoevn0rxNQI9KCYIgCIIgigS640YQBEEQREGhR6VDB03cCIIgCIIoKDRxGzroUSlBEARBEESRQHfcCIIgCIIoKGJxwiHquA1NKkUPTdwGCevoBEsngbiQ3EBVBVBdJmwp/cGrKkTZ4wVT0hU+KSfxznsOGQshO8BDQmaD1VYA0sbeFvEeDNiyGkzcIGV7m4GAlNOI98i2xKXk7QlLqoInxRJ5pjNwuYRehoAe6udmq19ITMDklmQJNNlmoMeyrfzH1Ij3ynKgpU3Y5UI+BIleu88JmWMqDdRXybZ8bp/XA/i9fXNSBHyufAAAGdm/iN/Oyys/0krOpNTnGJtuOQYyhke3xzQsY/g80MoDMp6US0mkwZSkSFZeYykLwoI+KyfeI64nK+fIJUQ9LSL+1Gg+2Y5Ph+5zL8XnaW5dK+c9cKU16amS0iPyNGYApaVC8sFbIg429wQwShf2P/ZVAgBmVXXiH63lAICPVQpNkefbIsJX2YXXOsW1OqEsDgDY2BnCiWVijF6Lis/hCRHxOX89GsTxEXGtXo8Fpa8Hb8XEWB0TFp+XN2MlOC4i7HzfG7EAjgsLSZG3usR4HxtOWvYxoZSM4cPxEdsGgGMd5ePC4jv1ry7xeZkaTlv2MdL3Vswuv235MvI8j2W/FfNYPlXvWBnj9ahXjk8Gb0Q9lg0Ab0Q9OC6SkfWE73hZfjPmsew3Yob05fBmTLdsUU+37E1RXY5pTp6nu2zlU/ZxyhfVcUKZsF/vlPXKTOnTLHtTVHywPlJmWrbl65S+ctNlA8BrHRqmS/t16ZtebrpsAHhNlk8sNy3bGePECtsGgBMrTJft9E0v53itk8l4XMZnlv2qUDbCR+Wf2Vc73LbyKftE6Xutw207fRvbgRmVtg2IstPO970i7ZnS90r7gfPoz5cf42B9UT7neQeK0V+OMyuB3tzhf9hGj0qHDnpUShAEQRAEUSTQHTeCIAiCIAoK5/ZThEOJQdDEjSAIgiCIAsPBYR7yb9xo5gbQo1KCIAiCIIiige64EQRBEARRUOhR6dBBEzeCIAiCIAqKKV+HGoMAGOc0hx0IXV1diEQi6PzXPQiHSsASUg4klQa27XJXrpVrr0NB4I1GYSvJEI2JcwBg/GjxnpM6D6YJZKUd7RLvug4+bRIAgJcJaQe2axd4hVjvrf3pCVGvMgQA6Pnb+/BPEvIKya1CesE3ygBPi48884qn41plwJYN6RJSC9poER+jqoEKkS9Xsh2BAJAS9XipaEvbvkOUR1SCKTmQYIk4VlFu90WOFa+qAuuQ69q9QkKBtXcKXyQMtmuPKwaa24CQtJWOSSRkS4gouZF9su2AHwhLORVD/psk3m1LpzS1us/L5oCIrL9byq9EgrbkSEjIXqA3aY2VlYeSLmmPAZVCYgM7muR5JUBCjD0qheQGbxQ+Vh60fRE5Vrs77fgeKbUC2P+8lGvgLcmQrInkVnEtmOxmLgXwrJR8MUT9nk4vAiEhSxHvFNextCxtlUulxEY8KsanNJxEl5TwCEnZjq6oXe6K+V2+aLQEESn90RkVfSkL9yLaFbBsAOiMKV8Pol3CjoSkLx5AJCjixRIifiSYRLQ7IG1Zr1vGCPZadnmp+By0x0tQUSrqdcjzVLk9XoLyUhUjIM/rteyyEtF2R08AFSW9lg0A5QF5Xm/AspWvoqQXbYkSy+7j6xG+Snlee2/AsttkvaqSXrT3yrb8Mo9eMQYVgaRll/vFdepM+iy7XfoqA0mXDQBtMmZFoBcdDlu1XVXizkOd197rd9n7i9/e63fl5MzRGUPlURXoddnKVyn73J6U8R3l/nz91cv3lfsOHKMtKfPwyzwc5YHmocoVPtHnjpQYgwpfymUDQHvKj0qVU0rGkOXWpB8j/LYNACP8yT71DhRjf77+6qlya9KPRDaFS1+5DbFYDOGwlHAqEOr/nZdVfgsezXdIsTJmCn9s/+FhyftIhu64EQRBEARRUDjnONT7RHSfSUATN4IgCIIgCgoJ8A4dtKqUIAiCIAiiSKA7bgRBEARBFBRzCHTcDvX8Dws0cSMIgiAIoqBwDIEcyJBkUvzQo1KCIAiCIIgige64EQRBEARRUOhR6dBBE7fBomnilcnIMgO8chil5hivrRZlnw/suAnCNqQ+194WQBc2V1pjQaUX1gvmkRKDSttN14G0aIu1tVltst5eea7UKAsIfZzA9JCly+YfJ7TGcp0Z6GWirWy7iKVVArxbaHppIyPuHLM5oCMq2owIzTbEu608kBTx4Xdo8vQkXcPEEj1Cyw0AU7FSKaGJBtjaal1x4ctkbI20rKPvCjUeHVGhdwcAsW53vXQGaOuUuUmdtZ4UkFH6ZzIG0nbchBzHUpmPptnjEJdafbEEYIjc+F7Zl3KpL+cxAC7z6ZVxcybg88hjor+sVI5VidfORy6RYgGPpeOWaxZ9Yj7d0uEz5CVISwk8XxVDd5von9cvYvV0e2EYIo9YXGhTpbIGTHlOp9RxCkVFjtG0D6XRjPSJWGXRNNqkDlW59LVJX0UsjVbpq4yJGK0pL6riGcsGgIp4Bi1J0feqLrevsiuE9rTwVXSp+B5UxEQf2tLiM1oVy6I15ZHniHrtKXVeyK4XFxp8LSkDVd2lsi3hq4yrsgcjuoOWDQBV3aVok/Uq4qLt9rSBynjQsgGgwlsqc9RR5bNtcV4p2tK6jKfi67LtIFpljBFe8TlpTRuo8gZlP+3zlF3hEb6OjF227ZzMS0e5V9id8rxybwnapV2p2pJ5VHlL0JrW8vLQUCl18NqlT53XkdZQ4bDFGJRYdplH+KIZzbI7M8JX7rFjVnoDcqxkfJ8f7Q5b5KhhhLTzfR1pDWWegGyLybYDLjvf15lmcjwCMg+GCisP4avyBRy2P8/nR4eMoc7rcMSISl+ZoxweQI4iht/KSYy3Xa7sx2fn4e8Tw45vl5228uXHcMZvTzMkc1I38jDC+aE/6iQ1EAE9KiUIgiAIgigS6I4bQRAEQRAFhR6VDh00cSMIgiAIoqCYfAgmbvSsFAA9KiUIgiAIgiga6I4bQRAEQRAFhcv/DjUGQRM3giAIgiAKDAdgDkEMAmCc00PjgdDV1YVIJIKO7b9DOFQCbcf7wuHxAK+8I+y6MvGelLIZY+qAbbuFXVspA3UDaSkHMWm0eNccMhjZrF1PURYGAPByIdvBOmPgEXnsz/8UISrFcvz0m+3QAiJeull8TfQgkJOqF5pUqTDKNTC/Ls8VS9eZT8mT+IVsBWDLdoRLgWiXsH1e9+D4fbYciFc2YJpASMqcKMmNYADY1WTbABAssdtpaXfHb+mwx6o8ZI+RlM5Qkh/83b0i//KgLY+iJD1SaSHZAYBv2yfqVcq80lmgUsaN9Yj3SAmwLyZjOH5JIK+R2SrlOoKibRb2W9IfvEWMD6svR+b5nSLESNFWbp+IrwUNmL05ywaAXGcaml/Ez8WUTwPPyq9m3l87nuFo2yr66Q+I8WncW4lyv7gGbT1ibD2aia6MyDOgi3rRtCiHPBl0SDvikXIcKQ/KvcJWkh4Vstya8rhs5dun5DfUeSkPRviEvS8pJTFkuTlpoMYv+re7V/jq/VmXDQB7kwbqrHq69Inynl4ddQFh7+wRvpEB07JH5flGl+Qsu6FE+N5P6GgoEYO6IyHGfWzQxPYEkzaXPiZjcMtWvu0JhjHS3il9DSX2eaPlR+x9+TUeUwpsE8o3GC+URfBet9sGgLGyvC0OTAjZNiDKjfIrOF76tncD41QMWW+c8sVtW8UYVwq8F+cyBpPn2eXGLi7bEr7GOMdEhw0AE0PMslUMdd7EMHPZyjeQelu7xDWZFNbsPKwYJiaGNcsW52l9fO/GbN+B6m2V9SZF+vqc5+XXU+WJER2N8rs6MSI+X42xnGW/KyVuJkeMPvUO5pscMfrUc9oH822JZjFJ2o1dWdkXuzwxbCBlJvHD925GLBZDOCz+X1Io1P8750S+CoP5Dn7CAcjyFJ6M3XZY8j6SoTtuBEEQBEEUFFpVOnTQxI0gCIIgiILC+RD8xo0eEAKgVaUEQRAEQRBFA91xIwiCIAiioNCj0qGDJm4EQRAEQRQUmrgNHfSolCAIgiAIokggOZABYsmB7Lgf4XAJtPeF3AMCfuD9PcJWsh7VQvqDB/xgu4RUhZL0wJ4WW55igpQDkRIaLJmyG1TSGF6PJcnBq0RcFo2Cl5UJ/9+eFcdGVQAAev60A76xIt7r60SdY09sxcsv1wEAPnqckOPQPIAmlTiMeikH4hd5sYAHCEhJjpCsVB4G2jqFXVUu3lW5tgqISb0BJeVhcnEOYPuCJUA8IewSKduhZESCAWBfmztGc6ctyVFWao9NJgsnfHeHyDvoBcplPTnGiPcCXjnezVFZTy5J93ls2ZO4kizxA+0yXyk7Yrb3gHnFcn0zlpJjJGJqlSVAQMSz5ECqSpHbKvqihUR8M54W5YivT4xcawpaqYif2i365okw5HrEV1MvEXkoeRBuAl07RVyPlNp4a0cNwl7RxrtdYtwbSnrQ1CvGuconfEoOJOzJIJ4V7ZfoQpKgJ6fDpwm5g4wpxl1nos0cZ9Z4K3USzpnlT8r6Xs1EMidsvy5qOsvK9mnivJTJXLbyOePlx0hkxVgFDZF3LKMj4hF2m5QnUdIlHWnDsqMZ4Yt4cohnRIyQPK87q6HUEG0pn4qfyOqWHZdth4wcorJexGPKtnTZdg5tKV2Ou8pLR4U3J/MQvjJPDjErht0XVVZ2yNG2srtkHmFHHmUyRruVh4k2aVfJttvSumV3pDWrniqXyb7EssIXMUyXrXzKjmY02baMkdGtPDpl2+XenJVTpdfOUdnOeqpc3s9YWeNt9M2xbx4aKqTdJvtZ5TXRKu0Rss/OclveeEQz9njkx+/KaAj34+uTR1qz4rXL+JWq7ZSGEb6+vv6uizMnZ/z95Zgfwxm/NaUhmUti5dZVh1UO5NTwsiGRA/ln150kBzLcCRAEQRAE8eGGHpUOHUf8o9Lrr78ejDHXa+rUqZb/F7/4Bc4880yEw2EwxhCNRl3n79ixA0uWLMG4ceMQCAQwYcIErFy5Eul0+jD3hCAIgiAI4tAoijtuxx57LJ544gmrbBh22j09PZg3bx7mzZuHa6+9ts+577zzDkzTxM9//nNMnDgRb731FpYuXYpEIoFbb731sORPEARBEEczdMdt6CiKiZthGKitre3Xt3z5cgDAhg0b+vWrSZ1i/Pjx2LJlC+666y6auBEEQRDEYcCU/x1qDKIIHpUCwNatW1FfX4/x48djwYIF2Llz5yHFi8ViqKioOGCdVCqFrq4u14sgCIIgCGI4OeInbrNmzcJ9992HdevW4a677sL27dtx+umnIx6Pf6B4jY2NuOOOO/CFL3zhgPVWrVqFSCRivRoaGj5QewRBEARxtMMZB2fmIb7oUSlQBBO38847D5dffjlOOOEEnHvuuVi7di2i0Sj+93//d9Cx9uzZg3nz5uHyyy/H0qVLD1j32muvRSwWs167du36oF0gCIIgiKMaLn/jdiivQ93r9MNCUfzGzUlZWRkmT56MxsbGQZ23d+9enHXWWTjllFPwi1/84qD1fT4ffL6+mjMs2QvmYbZmGyD0xgDAEDpDqIiId00T2mUAoBZUeAxLV4xlMgAArmI1tViabcgJHSP05mwNuJzQo+KhUiAltcDGjJD1RLnkY+UwW7oBACde3gMAMDt1nHKp0DozY6It5rV1uVhQaHspHTdUhoSuGgBkZR7BANApHxcrnTVd9rcnCaTSbh8ApDPuvqTSwPvNMp7sZ1WZo77UZ8vK3zFUO3wqvq7b9ZQ+W7e9QpgFMq68eWcCTGrBMV/exz2VAfxC8w7dUkPPY4CnRHzmEf3TQj5LT471iPhqzJDOAVrGHZdz5DpljlILLr1XlL0AzG6Rm/UJ0gBmKK02GcLkyHTJa2SKP1ZKd49pAJPaZ0xTVZhqCvuSIu/xpSZ+tU3Uu+540ebaJpH3ovFJrH5PXOMvThJ9v3ebD8smi8/Mne+Kxr48Wejb3b7Zj+XThObez7cK39KJPfifLX5ZT8T4RaMfSydKe6v4/vy7LN/3nh+LxqsYIo+rJmVwT6NXxhPX8Xfb/fjsWFFvzQ4RY/4Y4Xvgfa9l/267OG/BuDTue88j+yV8v90uygvGZvDg+8L+zBhxnR7Y4cEVY4V9/3bxmfjsuCwe2iHqfXqMuAiP7BblS0Zl8H+7RL1LG4Tvz7s9uGiUiPHXPaLeJ0eK8sO7DFwsfX+WMS4alcGfdosYyven3QYuGiXi/UX6LnSUL8zzXTAyi7/u0WVbwve3PQYukLbyXTBSXus9Os6XvrXSd/7IrGXPqxf11u21y05b+ZT96F7xYTu3LofHmmwbgKOcddkA8PcmHec4bAD4RF0Of28SH9hzZAxnOd+3bi+z8sj3Pd6kYU6tsJ9oFr65tTmXrXxz+6l3IF9/9QDg7Noc1jtsAFjfzFz2geoVMsYTTQxz6/rWc8bvzdFvxYqZI/6OWz7d3d3Ytm0b6urqBnzOnj17cOaZZ2LGjBlYvXo1NK3ouk0QBEEQRYs5RP8VE4wxPPLII0Me94ifwXz961/H008/jR07duC5557DpZdeCl3XMX/+fABAc3MzNm3aZN2Be/PNN7Fp0yZ0dIg7TGrSNnr0aNx6661obW1Fc3Mzmpubh61PBEEQBHE0cegPSsXrUFm0aFEfbVin8gQAdHR0YMGCBQiHwygrK8OSJUvQ3d19yG072zQMA6NHj8aKFSuQSqUOfrKDI/5R6e7duzF//ny0t7djxIgROO200/DCCy9gxAjxiPDuu+/GDTfcYNU/44wzAACrV6/GokWL8Pjjj6OxsRGNjY0YNWqUKzbt9kUQBEEQRxfz5s3D6tWrrXL+z6IWLFiApqYmPP7448hkMli8eDGuuuoq3H///Yfc9urVqzFv3jxkMhm8/vrrWLx4MYLBIL73ve8NOMYRP3F78MEHD+i//vrrcf311+/Xv2jRIixatGhokyIIgiAIYsCYzARjR4aOm8/n26827ObNm7Fu3Tq8/PLLmDlzJgDgjjvuwPnnn49bb70V9fX1/Z63detWLFmyBC+99BLGjx+Pn/zkJ/3WKysrs9puaGjAxRdfjFdffXVQ+R/xj0oJgiAIgihujqTfuG3YsAHV1dWYMmUKvvSlL6G9vd3yPf/88ygrK7MmbQAwd+5caJqGF198sf++mSYuu+wyeL1evPjii7j77rvxrW9966B5vPvuu1i/fj1mzZo1qPyP+DtuBEEQBEEQinxB/P2pQPTHvHnzcNlll2HcuHHYtm0bvv3tb+O8887D888/D13X0dzcjOrqatc5hmGgoqJiv7+Nf+KJJ/DOO+/gscces+7I3XzzzTjvvPP61J0/fz50XUc2m0UqlcInP/nJfrfrPBA0cRssmiZftpwGAlIaokR+cJQEiNdjy2N4peyEoVvyDpachvLpOhAKCnuL3B1iRNiW05AfTNbRCa4+pL3uHzXyhC2NwZNZ2+6VtpSdMHtMaCXyhqv6rZ+S6OhJ23kriRMA8MtjftXPgP0upU0sX0/SPk9JinhMICL75/OgD2q1r8ew+2bKf2GpfDRmyYCoeqxExGJVIUu2Q+XNSn12W50JOwbE+DDlK1HXyQAYs20A5p4YtEpxTXlG5MN6ZX8rg4CUDeFKKiRigqt/GEppE6NMxGQGA/MreRI5tt058Kxb3kPzaTCC8twRMn6v/a9NwyPGVPeJY8mcDibFKWv80sc4rhgj+qBL+ZCTK2VMZuKsGlj1AODjNZpV74waTfpE/TPrdDCpoXRyFbOG8dQaQ9pJ6bPrzajSncON48o1y55RJa8d0phe4fiMAZgQtqVNJoY1GUPEHBey606MyGsMYFqZ+lMmrsH4kMorg9GlusvXUKpb9hgrXhb1QfcDiDKHZE6lz+0LO3w+XX6n5Nco7LHrBj12vaD8bHKIYwFDAyCulSfv2YdHs+MZmvM4c8Vw2sxxTBy3f8OblcFMbtuKtGl/rlJSJkJV6c2Zlp3M2ef1Zt13PpzlhPyOqLy6Mllw+XAnpqR8oKErnbNsAIhncrJtDVFZz+S6FUPZ7amM9IlrvC+ZhBDaAVqT6u+fx7JNeCyf03b6WpIpmDLG3mRS+nxoTqakLXyqDHixp7dX2uLvpij7XTE491v1OBd/L3f19shyCd7vTUg7aMXor57TBuA6Lz9GU9KOke97vzcBzoPg3P1ZORyYMMGGaMurfEH8lStX9vnJ1Jo1a1xC+48++ihOP/10fOYzn7GOHX/88TjhhBMwYcIEbNiwAXPmzPlAeW3evBkNDQ2ux6izZ8/ut+5tt92GuXPnIpfLobGxEStWrMDChQsP+rMwJzRxIwiCIAiioAzFqlB1/q5duxAOh63j/d1tu+iii1yPIEeOHNlvzPHjx6OqqgqNjY2YM2cOamtr0dLS4qqTzWbR0dGx39/FDYba2lpMnDgRADBlyhTE43HMnz8fN910k3X8YNDEjSAIgiCIoiEcDrsmbv0RCoUQCoUOGmv37t1ob2+3tGFnz56NaDSKjRs3YsaMGQCA9evXwzTN/f4Wbdq0adi1axeampqsOC+88MKA+qLLJ2+91h3cg0MTN4IgCIIgCoqJHBhyB694kBiHQnd3N2644QZ86lOfQm1tLbZt24ZvfvObmDhxIs4991wAYhI2b948LF26FHfffTcymQyuvvpqfOYzn9nvitK5c+di8uTJuPLKK3HLLbegq6sL3/nOd/qtG41G0dzcDNM0sXXrVtx4442YPHkypk2bNuB+0KpSgiAIgiAKCh+i3UoPBV3X8cYbb+Ciiy7C5MmTsWTJEsyYMQPPPvus63HrmjVrMHXqVMyZMwfnn38+TjvttANulalpGh5++GH09vbi5JNPxr//+7/j+9//fr91Fy9ejLq6OowaNQrz58/Hsccei0cffRSGMfD7aHTHjSAIgiCIDz2BQACPPfbYQetVVFQMWmx38uTJePbZZ13H8kX+h0r0nyZuBEEQBEEUlCNJgLfYoYnbYMlmxWv3PlGud+i9KNmLrjgAgHEOxKUERa+Ux9AYkLIlO1znGbpdr77SPqaIy73S2jvByiPymPxBo1yzn34vAc0nlnqnZIreCo6mf5UCAOqOETG4CTCp/qHkL3ibzLu2zNYDUO+aBsTFknRUySX9Cdm21wN48uQ9vB5b3kPJowT8QEfMjqcSAYQkiZI2yZcAcdaDDiQz7tykHAcSSaC6zJ1H2pZEMeMivhaSsiqGZmkt8E7hYz4PIKU+rH8beXVL00JJeLCg146v9BqUhoVpQv19UTIfsa3iqxYckYEeUAkJn1aigQWk9EhO5MszHEx2S8mAmGl1TYB0Uow35+Kz49dz1hJ/Je+R4wweKe+RzokcfbKczBmWrydryBgmeqUd0EWbiaxop1TnSEhf0BDndWcMhAxVT/hKDdOyIx7h686KjpR5OOIZw7LVeRVe7qpX6eXoytg2AMRlzCqfiUROt2zh0x22qFfjF+VYxkC1n1v1AKA2wBGX8WvldyCe0VEvr0t3VozVKKlcE8/qlp2QMUaVwIoxttSuBwCjS21b+boyBsaoevK8caW2PSHEZL52WcWYKH3xrG7ZXRmR4+QIc9lO36Swhpj8qkwtk5IbWbcNANNkOZbhOKZctSXG7Lhy5rKV71hpd0mfs3x8hTvG9AodsYy0Kw3ZlumyAeCECkOeZ+LEStsGgBMrDcv+aKXf5TupqgQxac+sEhcxljUtO+7wxfPqOWMoe1Zl0PKdJGWArHqO8seqSuV4C9/Hqkot2xlD1VMxTqkKWeVTq8Iu38eqSvut57QBuM47UAzli8lrcWpVGLEsRzI3HHIgObBD/HXWof7G7cMC/caNIAiCIAiiSKA7bgRBEARBFJhD13EDPSoFQBM3giAIgiAKjMlzONSHfCanR6UAPSolCIIgCIIoGuiOG0EQBEEQBWUot7w62qGJG0EQBEEQBYUjB36ID/k4rSoFQI9KCYIgCIIgiga64zZYMlkgkwHK5Qa3GhMaZAAQEPpgvKJclH0+sLQUUlKaZG81An6peZaQumh+udWGoQutMwBIpmyf0nmrUMJSPUBEbp4r9XoQFDHiuz2oEPviouNN0WZdXQ903X2LuXefhqBXHOMJoSunlUshq0TSbjPod+cKuHXnVN9iQgPO0lvz+YCcrKd02TIZa4ysfmbk+DAGyM12rbjcBJj8t4XSidOYPX4yD1YqY5oOVWqlqWY4NNjy9dYcuHzKVtc1nQPPiLZ4Sr5LrTcW8Vs6eBaGbg0D84pYZVPEeVrEBzMuztWC4uuX2Zu2vohWKAYc6He4hsd9PUPeDJJS92tkidDXi6Z9GFsqrltXWozZ+FKhKxhLe102AEws7UHUYQNAhyqHetCWEvbkkPC1JL1WPaevNSWux6RSkUer5etFW8pj2eI8j1WvXbY1JdTrOCdp1QOAqaEkWqSG3eRS26dsdd6k0pSM6cEUyyfOm1KatG0ZvyXlcdmiXso6z2mL81Jot3Jy+6aGUo620jKmgWkh2waAaaG0ZU8Np2UMu9zaj2+yjNGelr5Q2tGuqifK08IZtKm2whkrhtMGgGMc5amhTF78jJ1HyK53zAFiTAtn5XXRZdtZtEhb1WtJ6S4736faakvrVtvKttuyy62pvr78elNDGbQ74ol+2uXWdF5f0obLHohv8DGyrn5OC2ddtvJNy6vnPO9AMZTPHp8sWlM6hkjAf1AI8VwS4B0KaOJGEARBEERBUXuVHmoMgh6VEgRBEARBFA10x40gCIIgiILCeQ4ch7bVFicdNwA0cSMIgiAIosDQb9yGDnpUShAEQRAEUSTQHTeCIAiCIAqK0HE7xEelpOMGgCZug8fQ3bIdhgFURVxVWFuHMEJBYOtOYZeXivfKMJAUy/YRLBHvPhnL5wM0eRNUxdc0wCvlLzLyvFAJeEBKd6TEcm8m5UBC9RkAon71WCH3wDQNZTVSzkOG95aagC5lMvziY6BkQZjfY8uAKHw+W9bDKQ0CCNkPv5Q7KZWSJb1J0X/Alg9Rkij7Q8lwKMmQ3W22xEcoYNdJiz6jRMqvtCdk3xhYRcjOCQACXsArP+aqnhwrpDK29Igia1ptMl3mU+q1dDpYQMbyOb46Wp7MiMagB4WtRURbvTtF2z5/DukWMY7eKtEPPaJZsiGQf5h4jlvSIDwr8jGlQgw3gZ6EGEt/QEgL7EmUYFxYSLI83lQJAJhVEcf6fUK25uQKIbnxYrsoTwkl8XpUjFW9X8R4M1aKap+w34qJz2uFV+S4uasEIcO0bAAo0Tkau4Wtsu9I2+PSmRbXTK0Di2V05LjoVFxKl2RMhlhG2Fnp68r4kTaF3Z0VkZOmOk9DT04cS8j3eFaz7I60eO/JMausYnRlmfRpiDtsEYMh6bABoNfpM72yD0zm40VHWsUQvnZZTua8Dlt8F1tTDEnTI8dFxfAg6rABWOWU0ydjRDPMZSufyiMtY7SnNStme8odvzXJkFL1UnZbqqzid6T3H78jzSy7LeX2taWYde1arbaZ1ZblS9r18n3tqf59Vl/kte20xoq5bHWestvk9yZlGi57f76M9LWnmMsWeYhyR4q5bOVrlzHS1nlu2+lrTQJpU7ds4dMd9XRHDD0vxoF9+fVU/AzX0ZoEUsPwxJHzIdg5gdOjUoAelRIEQRAEQRQNdMeNIAiCIIiCQosThg6auBEEQRAEUVBIDmTooEelBEEQBEEQRQLdcSMIgiAIoqDQlldDB03cCIIgCIIoKGJV6aE+KqXfuAE0cRs8ug7ohi2Nkc0CcSmPUVUm3iNCZoGXloKNqxfH/FKC4v09VihuuIefB/xCXgQAM033cQC8RMhrsMpySyKEKVkKKcdhVNryFjm5JFzrNZHulnGZkHfwRACekf96yYq2WEjm2JMG0C37JKVOojFbVkP1RclgBEuAuJTaSAjZCRi6PUbWOwc8HtsP2FIhznhKAqQyZPuDUg5EY/Z4Sx+rDNrnWZIcsp3upH1MyY2kMnb9bN/fTLCAx1XmPRkwqdzCe6X8iorldfRTYXKYvaIPSmLFN1bE1Mr98Puk7EqJOJZ6pxseQ8qNyCFmHgbNL2JoAdGWmZbXSQdKgiKGKSUPJkS6kJYSG+fWtwEA2nv9uLhB2K09Yvzm1LVbvlOrhWxNW6/wnRLqRntSfNYmhsX1b+0V5UnhuOWbGhHaAm3JACp9wm6VvhH+pGVX+lOuGFX+JNqSti1i+FHhE/U6UuJzVeFLoV3a5XKsOlNeq9yetOsBQFvKhyppO/NQ5UrpUzErHfErZPyOlNdlA0CZV5Sjaa9ld6SFr8KbRltK2eLz1C59ld40WqWvSsqrjEh5LLst5bF8HWlhl3mEL5qxy05btO2x2lLnlXuzlq18zvgtSdsGgBFeRx5et6/D1zd+mSeLaMawbJGjYdkd8u+Pko3p8BmWXemIn9/WgPPw2X3rlG1FPDkrj/3l6MyjTZ5X5c2iVbY1QrblLLfl9SXqM1DmsAG4yv358us582iXbVXKtiu9HiuPSkce+fWcMQaaR/51GeGIWen1oDdHd66KGZq4EQRBEARRYHJD8KCTFicANHEjCIIgCKLAiMec9Kh0KKBVpQRBEARBEEUC3XEjCIIgCKKg0B23oYMmbgRBEARBFBQTJtghbzJPEzeAHpUSBEEQBEEUDXTHbbBomnhJOQseDIJVVwhfSkgGoEdIYrBsFtgn5BggJT2g67bsRR7MNMGzYvk2Ej1WfUsaRJ2XzgBSAYPHhdQBKxcHsp05eEYLKYJ0QpznGwUke8WlDo0Vq3LMJIcecUhaAOApKXURCQAVYeFT0huTxgCJPXYfACCVsPsbkgkFpW5GLG53zOeV7ThkNvLHwDBsaY6MlOvoSfWVDTF0IFTiisGbRV4s6LXjqfolPqt95lOyLVLipC0GBIQsBG8V+TJDg9ku6im5DhbxW/IfuiW/4uiLlElhSnZE1+x/Esn6vF1qs5QDZkz0j6WknIkB8KxYb6VL1RNmMEuuRb0rqRCY9vUMBEWsbbEwxoVFH/6yqwoAcNqIbvz325UAgIXjxGfywR3Cd1Z1Dx5vEr5pISGd8dS+CowPijw3t5QDAOoDIn5jWzmqfOLz8V5CfJYjHhM7e8T4+TXxWdvb64POuGUDAJPl5qR9ffZJyQrOmWXnOLPqyeFAi/SlpezJvqQXKWm3pcUYJLIaOqQdzYjPS6d6T+uWL54V53WkDZcNAN055rKdvnjW9sWymuWLZjTZpvC1p0U5ltEtOy4lWtrTmstW9VSMMo8uY9nl/nwxq3+qbR0dVjzx3p5SbWtolXYiJ95bU5pl2/XUWDE7flqMQblXd9nK57Td/WSW3ZpiVtttDhsA2lLMZef74tY4q/iaZVfIttV5FV7bV+VT/bRjtEtfIqtZOfXkvC5fT87rqqd8lXkxKg/iy6/XmmJWPNV2t2o7xew8HHm191NP9cW6Fo5yeT8+FcMaA0fM9hRDMndod74+CPSodOigiRtBEARBEAVlKPYZpb1KBfSolCAIgiAIokigO24EQRAEQRQUsc8o7VU6FNDEjSAIgiCIgjIUv0+j37gJ6FEpQRAEQRBEkUB33AiCIAiCKCh0x23ooIkbQRAEQRAFZSjEc0mAV0ATt8Fi6IChgweE4BZLp22f1GrjlUIDCx4vmNJ280jdr5Z2W+9NaZRJnTbu99n6Y0r3LZ2xzuUlUistmQJ8QiMLfnkJlS6aCSAr4sW7RIzSdA/2xUIAgMp0Uta3tcNgSp0wqRcFxuwcy8V56E1a9dArYyg9N8MAOqJ2vgrV54TQEEM2CyitMybfVTmbBYJ+d9+TGSAjde2UL5uzj3mkflrA4x4DwB7bVBrQRE68V+qndSXs+jIW86prwcHK/O4cezPgUseNJ2X9jNRgC/ntcdFsjSJTyraZMXWtpS+dg1Yq25Iab6wz2+c8zcvB5WHmU07xxk07tUxKxAp5suhKCd2nkQGR2/uJAObVC3tLXHxeP1ou8tnW7cf4oLD39IrzxpSksS8pxnKU1G9TWml1/oxl10g9t5aUgWqHDQDVvqxlK923NkdZ6aFVeERe0bSBkCHsLqmHFvZkLTvfF/LkYEqtsVJDDEjGZJadlFpgznJQxshwcV7QyCHrsMXQ6iiR55jyFySqnOUa/LqyxbiX6CZyDhsA0vIa+zUTAd22ASCgM8sOyXoiBpN+ESzHuVV22sqn7Kz0+TWOkGHbok92uUR+1LyWz2nLejL/sEdz2cqnbJ88L+yx7VL51VPlkOHwWePB4ZfjodouMZhlhzxuX8Rj22GPnb/TBoAyr12OSJ/B1HnM0U87fonhbivgyCuU53PGKMtv2wN48nwejSMs++Kx+sIsW+Xhc4yBrx+fGg+f1U/mGHu3L+zp31fmdddzxgwaDPrhl3EjhhCauBEEQRAEUVDoUenQQRM3giAIgiAKCk3chg5aVUoQBEEQBFEk0B03giAIgiAKzFDcLaM7bgBN3AiCIAiCKDD0qHTooEelBEEQBEEQRQLdcRskXDPAdQPa3n3iQHkYiHYJW8lqSHkPBEuA9qiwq6REiKEDmtB3YEmh/aB2X2PxbiGtAQDv7xXv1RVWfOYV699ZV9w+R5dz74SQ6Mh0A0avkGGoahDxzV5g6rGtoi0pMaGXMDCvlLjokTIZEakd4PcCoRJh90h9iooy+5iS61D9zmZtGRMlXZHN2bIhSibD5LZkh3r3ixN4JAym4nnl+nrVHgAEfLady9ltAEBE1jN0O65PagV0dFnyKNa7IpuzJU0Uhl22JEIiAdvfEhfvatw1ZvdP1fca8NaL66iFRB5cfTaCXphxIcOhBZQkih1encezHLqHu/y6lAzgWY54QlyDYEBcn6pAL7y6GI+wT8RPZDzQ5CelWkqPGJaEgS3bUiLPA4CRASmJIWUqRvhEvRxnKPOID09G+iKeHHry5DfiWd2SrIhK6Q8lSdCWMiy7WcqOBHTTskvkeXt6PZZUxY4eMX5hGf+9hAdlHmFviYvzKr0cm7uEXeUT570V88j8Od7u8kqfOG9T1Is62c93O0S9hhKOzV0i39ElIsbbMUP6gLdiop9jpSLPG1EN40uFvalT+CZI5ZzXOnRMCosxerpTfCYmhRn+3ibsY8uF758tGk6oEPaz+8T78bK8vhn4aKWwH98r8plZxfC33eJanVItYj3amsOsEcL+y25x3U+vEZ/XtXvSlv23HeJz8vHqEvxxdzcA4BM1EQDAw7uFXM/ZNaX41W7xd+Li6loAwI/3vo0rq04AANzW9jYA4HNVx+Hmln8AAJaOOB0A8O29vwcAfK3+0/je7scBAMuqzwEA3Ljrz/jP2ksAAF/f/H0AwPWTrsP3dj4CAPjPuk8BAG7f+xoA4MqqmVi1ZwMA4KoRZwIAft32ChZWzgQA/KrtGQDA56vOAAD8sm0D/r1K1Lu/43UAwGcrpuPu9q0AgMsrp4p6Tdvwb5WTAQC/ad4p+lkxDgBwX/MOXFIxQdbbAQC4pGIC7usQf4cvqGgAAKxrbwIAzKusw986drl8j7bvwXmVI2W9FgDAuZV1+O3eZnmO8D3YZPsectgA8L9NbfhEZY2ot7cNAPCJyho82d4OAJhTWQ0ArnK+75m2Lpwh/3+T7/vfve2YU1mNlHn49UBIx23ooIkbQRAEQRAFhfMh2GSe0ybzAD0qJQiCIAiCKBrojhtBEARBEAUmB+BQH9HSHTeAJm4EQRAEQRQYsSL00CZu9KhUQI9KCYIgCIIgigS640YQBEEQRIE59Dtu9KhUwPhRdO/xzjvvxC233ILm5mZMnz4dd9xxB04++eQBndvV1YVIJILO1+5EOBQAe2OLcAT9QK+UzPDKeXCDWNrNg0EwuRQcQSlZsasZMOSNzpFiyT2X8hqsrcOut2WHeB9VbUlb8BGVol5rO3h5mbBf/ZeoJ2Mmn9gN73gRL7NTyHEYlTqy7UJGQI/YN1m1oMiXlQhJBBaSkhvlpUCZ1DZICKkAjK4D2jqEXSZkBNARFe+hIBBPCFtJhaQz4jgAJHpsn5IIUfVUORICdkuJlWDA9inpj7DUXsjlbBkQKbGBTinRUeKz5T1KZF/2dVoSH+Z7Ynm9Vhe2xgAVsp/RbtmXAPjeTjEeHhnL0KwYvF32U8prsEjAvu7tMsaIMMzNQjaABeW1S4ucWciH3F5RTwuJcc/uTUILyusiF11xk4NnRP+0gPCZCVP6gMbXKwAAZUFxfVq6ShGQEh9dKSmxwoFEVrRhMHFuIityLTFyaEsJX7lXyHy0JD2okPbepMi7RsqBvN/jxaiAsN9LCN/YYBr/klIbU0NCimJT1IfjI8J+SUptnFQhYm7YZ+DMGmGv2S7ymT9Ww6/fE3EXjhP1f7s9g/83TsR9eKcYg4sbxB/8vzcxzK0Tx15q12V8Exs7xBjNqBBxX5USHR8pNy25jhPKhG9zl4ZpYSkHEhe+SSET27qlrEep8G2NqzLH9oRof1xQtL09wTBW2jukT8mI7OrR0FBiWjYAjCwxscdhA0BTr2bJkihfvSw3JzXU+m0bAGr9pmVX+0VbLUnmsgEhgQIArSnmsgGg0sfR7rABoFWd5+/ra08xlw0A5V6OzrRtA3CVlV3mVbIwzGUrX7SfegfyhT196+0vfsTDEcvYNgDEMsxlD8TXX73BxA95OOIOG4Cr3J9voPUGG0OVgwZHMpfETdtWIRaLIRx2/D0sAOr/nQx+MHboj0o5kocl76Fg7NixWL58OZYvXz6kcY+aR6UPPfQQVqxYgZUrV+LVV1/F9OnTce6556KlpWW4UyMIgiAI4jCxefNmXHTRRYhEIggGgzjppJOwc+dOy59MJrFs2TJUVlaitLQUn/rUp7Bv375Dbnfs2LFgjIExBl3XUV9fjyVLlqCzs3NQcY6aiduPf/xjLF26FIsXL8YxxxyDu+++GyUlJfjVr3413KkRBEEQxIcaPkT/HSrbtm3DaaedhqlTp2LDhg1444038F//9V/w+/1Wna9+9av4y1/+gt///vd4+umnsXfvXlx22WWH3DYA3HjjjWhqasLOnTuxZs0aPPPMM/iP//iPQcU4Kn7jlk6nsXHjRlx77bXWMU3TMHfuXDz//PP9npNKpZBKpaxyV1dXwfMkCIIgiA8nR8Zv3L7zne/g/PPPx49+9CPr2IQJEyw7Fovh3nvvxf3334+zzz4bALB69WpMmzYNL7zwAj72sY/1G7elpQVLlizBE088gdraWtx000391guFQqitFT+TGjlyJK688ko88MADg+rDUXHHra2tDblcDjU1Na7jNTU1aG5u7vecVatWIRKJWK+GhobDkSpBEARBEAXANE387W9/w+TJk3Huueeiuroas2bNwiOPPGLV2bhxIzKZDObOnWsdmzp1KkaPHr3fGz0AsGjRIuzatQtPPfUU/vCHP+BnP/vZQX+KtWfPHvzlL3/BrFmzBtWPo2Li9kG49tprEYvFrNeuXbuGOyWCIAiCKFK4WDF1KC95x62rq8v1cj4dOxAtLS3o7u7GD37wA8ybNw9///vfcemll+Kyyy7D008/DQBobm6G1+tFWVmZ69wD3eh599138eijj+Kee+7Bxz72McyYMQP33nsvent7+9T91re+hdLSUgQCAYwaNQqMMfz4xz8e+DDiKJm4VVVVQdf1Pj8u3Ldvn3XLMh+fz4dwOOx6EQRBEATxQRiK37eJiVtDQ4PridiqVav6tLZmzRqUlpZar2effRamKVZqX3zxxfjqV7+Kj3zkI7jmmmvwyU9+EnffffcH7tnmzZthGAZmzJhhHZs6dWqfyR8AfOMb38CmTZvwxhtv4MknnwQAXHDBBcgpBYUBcFT8xs3r9WLGjBl48skncckllwAQt0yffPJJXH311QOKoVRTurrFDJr1qBk+A5LSVjIVsg43NbCElLvg8tl+T8qWA1H1ciI2SyTd9QAgkQQyUgbCL9vuToIbeXkoOZB0Gl4pGZBJCVkGI6kjm5JyICmHHIgu4jIm21efBp8H8Hhk+yk7V9UXw2vnBgBMt221h3A6AzDZVo/05QAkHTYcZd2w21K/g+hNAWbO9gP9y4H0OP61pUkJD+7wKTmQpBgPzVlfSq1YMXQNvFfUY1klB8IsmRHlg/wDwDyanY/y9aSsttQQ8IyUAzEYcioPuUQ/m0pDMw4gB6JJOZCULQfSnRX56hkRqzubQo4JqY1E1h6CRNaUXRDvPfKPA0cOPTlxzJvNSp8Jn7R75WeyJ5eRZRM9OeFL5kzL57QBIGVyh51ztZk2s5ad4eo8DRmesdoXvgx6ZPsZeY17c0zGYFZuaVOzck2bzJV3ylFO9eMbaL3BxhBlDb2yLykrR9NlH6jeQGIkHW0n8/I4mK+/egfyHckxBhv/cI+Vx1HPM0DfQOsNNoYq6zmOlCn+fhx+NbChaW/Xrl2umyk+n69PnYsuusj1CHLkyJHQdR2GYeCYY45x1Z02bRr+8Y9/AABqa2uRTqcRjUZdE68D3egZDFVVVZg4cSIAYNKkSbj99tsxe/ZsPPXUU67HswfiqJi4AcCKFStw5ZVXYubMmTj55JNx++23I5FIYPHixQM6Px4XWmFjTv96IdMkiA8f79vmL/e6XWsdq+CfeKN/GwD+5Kj3UGte/D37sQFg935sgiAQj8cRiUQK2obX60Vtbe1+HzMOltraWlRVVblWgfZHKBRCKBTqc/ykk07Cli1bXMfeffddjBkzBgAwY8YMeDwePPnkk/jUpz4FANiyZQt27tyJ2bNn99vW1KlTkc1msXHjRpx00knWOdFo9KD90eVNgf4eq+6Po2bidsUVV6C1tRXXXXcdmpub8ZGPfATr1q3rs2Bhf9TX1+Nf//oXjjnmmD6z/SOVrq4uNDQ0UL4FgvItLJRvYaF8C8uRnC/nHPF4HPX19QVvy+/3Y/v27Uin00MSz+v1HnTSdiC+8Y1v4IorrsAZZ5yBs846C+vWrcNf/vIXbNiwAQAQiUSwZMkSrFixAhUVFQiHw/jKV76C2bNn73dF6ZQpUzBv3jx84QtfwF133QXDMLB8+XIEAoE+dePxOJqbm8E5x65du/DNb34TI0aMwCmnnDLwTnBiwMRiMQ6Ax2Kx4U5lQFC+hYXyLSyUb2GhfAtLseV7NHHvvffyiRMncr/fz6dPn84feeQRl7+3t5d/+ctf5uXl5bykpIRfeumlvKmp6YAxm5qa+AUXXMB9Ph8fPXo0/81vfsPHjBnDb7vtNqvOmDFj1A/1OAA+YsQIfv755/PXXnttUPkfVVteHSpq645i2W6D8i0slG9hoXwLC+VbWIotX6J4OCpWlRIEQRAEQXwYoInbIPD5fFi5cmW/K1iORCjfwkL5FhbKt7BQvoWl2PIligd6VEoQBEEQBFEk0B03giAIgiCIIoEmbgRBEARBEEUCTdwIgiAIgiCKBJq4DZA777wTY8eOhd/vx6xZs/DSSy8Nd0qDzikajWLZsmWoq6uDz+fD5MmTsXbt2sOS6zPPPIMLL7wQ9fX1YIzhkUceOWD9P/7xj/jEJz6BESNGIBwOY/bs2XjssccOS67A4PMFxN5406dPR0lJCerq6vD5z38e7e3thU8WwKpVq3DSSSchFAqhuroal1xySR918APx4IMPgjFmbQk3XNx111044YQTrP2BZ8+ejUcffXRYc/qgeQ3n9y2fH/zgB2CMYfny5futc8899+D0009HeXk5ysvLMXfu3GH5OzeQXAHg9ttvx5QpUxAIBNDQ0ICvfvWrSKot9ArM9ddfD8aY6zV16tQBnXukfNeI4oUmbgPgoYcewooVK7By5Uq8+uqrmD59Os4991y0tLQUTU7pdBqf+MQnsGPHDvzhD3/Ali1bcM8992DkyJGHJd9EIoHp06fjzjvvHFD9Z555Bp/4xCewdu1abNy4EWeddRYuvPBCvPbaawXOVDDYfP/5z3/ic5/7HJYsWYK3334bv//97/HSSy9h6dKlBc5U8PTTT2PZsmV44YUX8PjjjyOTyeCcc85BIpE46Lk7duzA17/+dZx++umHIdMDM2rUKPzgBz/Axo0b8corr+Dss8/GxRdfjLfffruo8hru75uTl19+GT//+c9xwgknHLDehg0bMH/+fDz11FN4/vnn0dDQgHPOOQd79uTvI1Y4Bprr/fffj2uuuQYrV67E5s2bce+99+Khhx7Ct7/97cOUKXDssceiqanJeqm9Lg/EkfRdI4qYQcn1HqWcfPLJfNmyZVY5l8vx+vp6vmrVqqLJ6a677uLjx4/n6XT6cKW4XwDwhx9+eNDnHXPMMfyGG24Y+oQOwkDyveWWW/j48eNdx37605/ykSNHFjCz/dPS0sIB8KeffvqA9bLZLD/llFP4L3/5S37llVfyiy+++PAkOAjKy8v5L3/5y+FOow8HyutI+b7F43E+adIk/vjjj/OPf/zj/D//8z8HfG42m+WhUIj/+te/LlyCDgaT67Jly/jZZ5/tOrZixQp+6qmnFjhLwcqVK/n06dMHdU4xfNeI4oDuuB2EdDqNjRs3Yu7cudYxTdMwd+5cPP/880WT05///GfMnj0by5YtQ01NDY477jjcfPPNyOVyhyvtQ8I0TcTjcVRUVAx3Kv0ye/Zs7Nq1C2vXrgXnHPv27cMf/vAHnH/++cOSTywWA4CDjteNN96I6upqLFmy5HCkNShyuRwefPBBJBKJ/W7uPBwMJK8j5fu2bNkyXHDBBa6/FQOlp6cHmUzmsH3nBpPrKaecgo0bN1qPct977z2sXbv2sH7ftm7divr6eowfPx4LFizAzp07D1j/SP6uEcXFUbPJ/Aelra0NuVyuz2b0NTU1eOedd4omp/feew/r16/HggULsHbtWjQ2NuLLX/4yMpkMVq5ceTjSPiRuvfVWdHd349Of/vRwp9Ivp556KtasWYMrrrgCyWQS2WwWF1544YAftQ4lpmli+fLlOPXUU3Hcccftt94//vEP3Hvvvdi0adPhS24AvPnmm5g9ezaSySRKS0vx8MMP45hjjhnutAaV15HwfXvwwQfx6quv4uWXX/5A53/rW99CfX39B5r0DZbB5vrZz34WbW1tOO2008A5RzabxRe/+MXD9qh01qxZuO+++zBlyhQ0NTXhhhtuwOmnn4633noLoVCoT/0j9btGFCnDfcvvSGfPnj0cAH/uuedcx7/xjW/wk08+uWhymjRpEm9oaODZbNY69t///d+8tra2oLn2Bwb5qHTNmjW8pKSEP/7444VL6gAMJN+3336b19XV8R/96Ef89ddf5+vWrePHH388//znP394knTwxS9+kY8ZM4bv2rVrv3W6urr42LFj+dq1a61jR8rjm1Qqxbdu3cpfeeUVfs011/Cqqir+9ttvD3dag8pruL9vO3fu5NXV1fz111+3jg3mUemqVat4eXm56/xC8UFyfeqpp3hNTQ2/5557+BtvvMH/+Mc/8oaGBn7jjTcWPN/+6Ozs5OFwuN9H50fyd40oTmjidhBSqRTXdb3P/7g/97nP8YsuuqhocjrjjDP4nDlzXMfWrl3LAfBUKlWoVPtlMBO3Bx54gAcCAf7Xv/61sEkdgIHk+//+3//j//Zv/+Y69uyzz3IAfO/evQXMzs2yZcv4qFGj+HvvvXfAeq+99hoHwHVdt16MMc4Y47qu88bGxsOU8cGZM2cOv+qqq4Y7jT4cKK/h/r49/PDDfa4vAOv6OieU+dxyyy08Eonwl19+ueB5ftBcTzvtNP71r3/ddey3v/0tDwQCPJfLHZa885k5cya/5ppr+hwvpu8aURzQb9wOgtfrxYwZM/Dkk09ax0zTxJNPPjlsv7v5IDmdeuqpaGxshGma1rF3330XdXV18Hq9Bc/5g/DAAw9g8eLFeOCBB3DBBRcMdzoHpKenB5rm/jrpug4A4IdhVznOOa6++mo8/PDDWL9+PcaNG3fA+lOnTsWbb76JTZs2Wa+LLroIZ511FjZt2oSGhoaC5zxQTNNEKpUa7jT6cKC8hvv7NmfOnD7Xd+bMmViwYAE2bdpkfTbz+dGPfoTvfe97WLduHWbOnFnwPD9orsP9fcunu7sb27ZtQ11dXR9fMX3XiCJhmCeORcGDDz7IfT4fv++++/i//vUvftVVV/GysjLe3Nx8xOa0cOFC17/+du7cyUOhEL/66qv5li1b+F//+ldeXV3Nb7rppsOSbzwe56+99pr1r88f//jH/LXXXuPvv/8+55zza665hi9cuNCqv2bNGm4YBr/zzjt5U1OT9YpGo0dkvqtXr+aGYfCf/exnfNu2bfwf//gHnzlz5mF7nP6lL32JRyIRvmHDBtd49fT0WHXyPxP5HAmPb6655hr+9NNP8+3bt/M33niDX3PNNZwxxv/+978f0Xkdad+3/sh//Jif8w9+8APu9Xr5H/7wB9dnKB6PH3G5rly5kodCIf7AAw/w9957j//973/nEyZM4J/+9KcPS35f+9rX+IYNG/j27dv5P//5Tz537lxeVVXFW1pa+s03nyPhu0YULzRxGyB33HEHHz16NPd6vfzkk0/mL7zwwnCndMCcPv7xj/Mrr7zSVf+5557js2bN4j6fj48fP55///vfP+Ajk6Hkqaee4gD6vFSOV155Jf/4xz/uyv9A9Y+0fDkX8h/HHHMMDwQCvK6uji9YsIDv3r37sOTbX64A+OrVq606/X0mnBwJ/zP5/Oc/z8eMGcO9Xi8fMWIEnzNnzrBP2gaS15H2feuP/MlQfs5jxozp9zO0cuXKIy7XTCbDr7/+ej5hwgTu9/t5Q0MD//KXv8w7OzsPS35XXHEFr6ur416vl48cOZJfccUVrkeexfBdI4oXxvkw3FcmCIIgCIIgBg39xo0gCIIgCKJIoIkbQRAEQRBEkUATN4IgCIIgiCKBJm4EQRAEQRBFAk3cCIIgCIIgigSauBEEQRAEQRQJNHEjCIIgCIIoEmjiRhAEQRAEUSTQxI0giMPKokWLcMkllwx3GgRBEEWJMdwJEATx4YExdkD/ypUr8ZOf/GRYNgInCIL4MEATN4IghoympibLfuihh3Dddddhy5Yt1rHS0lKUlpYOR2oEQRAfCuhRKUEQQ0Ztba31ikQiYIy5jpWWlvZ5VHrmmWfiK1/5CpYvX47y8nLU1NTgnnvuQSKRwOLFixEKhTBx4kQ8+uijrrbeeustnHfeeSgtLUVNTQ0WLlyItra2w9xjgiCIwwtN3AiCGHZ+/etfo6qqCi+99BK+8pWv4Etf+hIuv/xynHLKKXj11VdxzjnnYOHChejp6QEARKNRnH322TjxxBPxyiuvYN26ddi3bx8+/elPD3NPCIIgCgtN3AiCGHamT5+O7373u5g0aRKuvfZa+P1+VFVVYenSpZg0aRKuu+46tLe344033gAA/M///A9OPPFE3HzzzZg6dSpOPPFE/OpXv8JTTz2Fd999d5h7QxAEUTjoN24EQQw7J5xwgmXruo7Kykocf/zx1rGamhoAQEtLCwDg9ddfx1NPPdXv7+W2bduGyZMnFzhjgiCI4YEmbgRBDDsej8dVZoy5jqnVqqZpAgC6u7tx4YUX4oc//GGfWHV1dQXMlCAIYnihiRtBEEXHRz/6Ufzf//0fxo4dC8OgP2MEQRw90G/cCIIoOpYtW4aOjg7Mnz8fL7/8MrZt24bHHnsMixcvRi6XG+70CIIgCgZN3AiCKDrq6+vxz3/+E7lcDueccw6OP/54LF++HGVlZdA0+rNGEMSHF8ZJwpwgCIIgCKIooH+aEgRBEARBFAk0cSMIgiAIgigSaOJGEARBEARRJNDEjSAIgiAIokigiRtBEARBEESRQBM3giAIgiCIIoEmbgRBEARBEEUCTdwIgiAIgiCKBJq4EQRBEARBFAk0cSMIgiAIgigSaOJGEARBEARRJNDEjSAIgiAIokj4/3gYctO0BQmEAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# mel_spec_db = librosa.power_to_db(img, ref=np.max)\nmel_spec = librosa.display.specshow(v.reshape(v.shape[0],-1),\n                         y_axis='mel', x_axis='time')\n\nplt.colorbar(format='%+2.0f dB')\nplt.title('Mel Spectrogram')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T17:06:55.816992Z","iopub.execute_input":"2024-04-30T17:06:55.817635Z","iopub.status.idle":"2024-04-30T17:06:56.167455Z","shell.execute_reply.started":"2024-04-30T17:06:55.817603Z","shell.execute_reply":"2024-04-30T17:06:56.166454Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"csv_data['SubjectID'].unique()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:29.890839Z","iopub.execute_input":"2024-04-30T20:28:29.891239Z","iopub.status.idle":"2024-04-30T20:28:29.899262Z","shell.execute_reply.started":"2024-04-30T20:28:29.891207Z","shell.execute_reply":"2024-04-30T20:28:29.898388Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"array(['sub001', 'sub002', 'sub003', 'sub004', 'sub005', 'sub006',\n       'sub007', 'sub008', 'sub009', 'sub010', 'sub011', 'sub012',\n       'sub013', 'sub014', 'sub015', 'sub016', 'sub017', 'sub018',\n       'sub019', 'sub020', 'sub021', 'sub022', 'sub023', 'sub024',\n       'sub025', 'sub026', 'sub027', 'sub028', 'sub029', 'sub030',\n       'sub031', 'sub032', 'sub033', 'sub034', 'sub035', 'sub036',\n       'sub037', 'sub038', 'sub039', 'sub040', 'sub041', 'sub042',\n       'sub043', 'sub044', 'sub045', 'sub046', 'sub047', 'sub048',\n       'sub049', 'sub050', 'sub051', 'sub052', 'sub053', 'sub054',\n       'sub055', 'sub056', 'sub057', 'sub058', 'sub059', 'sub060',\n       'sub061', 'sub062', 'sub063', 'sub064', 'sub065', 'sub066',\n       'sub067', 'sub068', 'sub069', 'sub070', 'sub071', 'sub072',\n       'sub073', 'sub074', 'sub075', 'sub076', 'sub077', 'sub078',\n       'sub079', 'sub080', 'sub081', 'sub082', 'sub083', 'sub084',\n       'sub085', 'sub086', 'sub087', 'sub088', 'sub089', 'sub090',\n       'sub091', 'sub092', 'sub093', 'sub094', 'sub095'], dtype=object)"},"metadata":{}}]},{"cell_type":"markdown","source":"'sub001', 'sub002', 'sub003', 'sub004', 'sub005', 'sub006',\n       'sub007', 'sub008', 'sub009', 'sub010', 'sub011', 'sub012',\n       'sub013', 'sub014', 'sub015', 'sub016', 'sub017', 'sub018',\n       'sub019', 'sub020'","metadata":{}},{"cell_type":"code","source":"# first data collection \n# subject = ['sub001', 'sub002', 'sub003', 'sub004', 'sub005', 'sub006',\n#        'sub007', 'sub008', 'sub009', 'sub010', 'sub011', 'sub012',\n#        'sub013', 'sub014', 'sub015', 'sub016', 'sub017', 'sub018',\n#        'sub019', 'sub020']\n\nsubject = ['sub001', 'sub002', 'sub003','sub004']\n\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:31.410929Z","iopub.execute_input":"2024-04-30T20:28:31.411319Z","iopub.status.idle":"2024-04-30T20:28:31.414991Z","shell.execute_reply.started":"2024-04-30T20:28:31.41129Z","shell.execute_reply":"2024-04-30T20:28:31.414205Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"task = csv_data['Task'].unique()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:32.032555Z","iopub.execute_input":"2024-04-30T20:28:32.032808Z","iopub.status.idle":"2024-04-30T20:28:32.036895Z","shell.execute_reply.started":"2024-04-30T20:28:32.032784Z","shell.execute_reply":"2024-04-30T20:28:32.036066Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"task","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:32.642517Z","iopub.execute_input":"2024-04-30T20:28:32.642818Z","iopub.status.idle":"2024-04-30T20:28:32.648038Z","shell.execute_reply.started":"2024-04-30T20:28:32.642792Z","shell.execute_reply":"2024-04-30T20:28:32.647191Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"array([10, 11, 13,  5,  4,  3,  9, 12,  2,  8,  7,  1,  6])"},"metadata":{}}]},{"cell_type":"code","source":"csv_data[(csv_data['SubjectID'] == \"sub001\") & (csv_data['Task'] == 2)].shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:34.850772Z","iopub.execute_input":"2024-04-30T20:28:34.851145Z","iopub.status.idle":"2024-04-30T20:28:34.86276Z","shell.execute_reply.started":"2024-04-30T20:28:34.851096Z","shell.execute_reply":"2024-04-30T20:28:34.861994Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(30, 5)"},"metadata":{}}]},{"cell_type":"code","source":"list_epoch = []\nlabel = []\ndef findNumberEpochRead():\n    for sub in subject:\n        for ta in task:\n            list_epoch.append(csv_data[(csv_data['SubjectID'] == sub) & (csv_data['Task'] == ta)]['EpochID'])\n            label.append(ta)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:35.25069Z","iopub.execute_input":"2024-04-30T20:28:35.250994Z","iopub.status.idle":"2024-04-30T20:28:35.255416Z","shell.execute_reply.started":"2024-04-30T20:28:35.250965Z","shell.execute_reply":"2024-04-30T20:28:35.254612Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"findNumberEpochRead()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:35.728913Z","iopub.execute_input":"2024-04-30T20:28:35.729782Z","iopub.status.idle":"2024-04-30T20:28:35.971909Z","shell.execute_reply.started":"2024-04-30T20:28:35.729748Z","shell.execute_reply":"2024-04-30T20:28:35.971117Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"len(list_epoch)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:37.690914Z","iopub.execute_input":"2024-04-30T20:28:37.691792Z","iopub.status.idle":"2024-04-30T20:28:37.695808Z","shell.execute_reply.started":"2024-04-30T20:28:37.69176Z","shell.execute_reply":"2024-04-30T20:28:37.695228Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"52"},"metadata":{}}]},{"cell_type":"code","source":"\"12_epoch000241.png\".split(\"_\")[0]","metadata":{"execution":{"iopub.status.busy":"2024-04-30T01:51:42.926833Z","iopub.execute_input":"2024-04-30T01:51:42.927779Z","iopub.status.idle":"2024-04-30T01:51:42.933296Z","shell.execute_reply.started":"2024-04-30T01:51:42.927741Z","shell.execute_reply":"2024-04-30T01:51:42.932477Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"'12'"},"metadata":{}}]},{"cell_type":"code","source":"ica = FastICA(n_components=num_components)\nica.fit(eeg_data.T)\nica_components = ica.components_\nselected_components = [0, 1, 2]  # Example: Selecting the first 3 components\neeg_data = np.dot(ica_components[selected_components], eeg_data)\n\nscaler_standard = MinMaxScaler()\neeg_data = scaler_standard.fit_transform(eeg_data)\n\neeg_data = preprocess_eeg_data_signal(eeg_data.T.flatten(),250)\neeg_data = resize(eeg_data, (125, 125))\neeg_data = np.stack((eeg_data,) * 3, axis=-1)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T07:54:09.781042Z","iopub.execute_input":"2024-04-30T07:54:09.781434Z","iopub.status.idle":"2024-04-30T07:54:10.058222Z","shell.execute_reply.started":"2024-04-30T07:54:09.781404Z","shell.execute_reply":"2024-04-30T07:54:10.057245Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"eeg_data.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T07:54:36.101228Z","iopub.execute_input":"2024-04-30T07:54:36.101632Z","iopub.status.idle":"2024-04-30T07:54:36.107345Z","shell.execute_reply.started":"2024-04-30T07:54:36.101596Z","shell.execute_reply":"2024-04-30T07:54:36.106492Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"(125, 125, 3)"},"metadata":{}}]},{"cell_type":"code","source":"import numpy as np\nfrom scipy import signal\n\n# Define preprocessing parameters\nlow_cutoff = 0.5  # Low cutoff frequency for bandpass filter (Hz)\nhigh_cutoff = 50  # High cutoff frequency for bandpass filter (Hz)\n\n# Initialize a list to store preprocessed data\n# preprocessed_data = []\n\ndef preprocessing(game_data):\n    # Iterate over each subject's EEG data\n    # for subject_data in preprocessed_data_list:\n    # Apply bandpass filter to each channel\n    filtered_data = []\n    for channel in range(game_data.shape[1]):  # Assuming channels are along axis 1\n        channel_data = game_data[:, channel]\n        b, a = signal.butter(4, [low_cutoff, high_cutoff], btype='bandpass', fs=128)\n        filtered_channel_data = signal.filtfilt(b, a, channel_data)\n        filtered_data.append(filtered_channel_data)\n\n    # Convert filtered data to numpy array\n    filtered_data = np.array(filtered_data).T  # Transpose to original shape\n\n    # Normalize each channel\n    normalized_data = (filtered_data - np.mean(filtered_data, axis=0)) / np.std(filtered_data, axis=0)\n\n    return np.array(normalized_data)\n        # Store preprocessed data for the current subject\n    #     preprocessed_data.append(preprocessed_subject_data)\n\n    # Now, preprocessed_data contains preprocessed EEG data for each subject and each game\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:41.816924Z","iopub.execute_input":"2024-04-30T20:28:41.81726Z","iopub.status.idle":"2024-04-30T20:28:42.164575Z","shell.execute_reply.started":"2024-04-30T20:28:41.817233Z","shell.execute_reply":"2024-04-30T20:28:42.163809Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"preprocessing(eeg_data).shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:43.362749Z","iopub.execute_input":"2024-04-30T20:28:43.363289Z","iopub.status.idle":"2024-04-30T20:28:43.780697Z","shell.execute_reply.started":"2024-04-30T20:28:43.363256Z","shell.execute_reply":"2024-04-30T20:28:43.77993Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"(65, 1000)"},"metadata":{}}]},{"cell_type":"code","source":"from librosa.display import specshow\ndef melSpectrogram(wave):\n    wave = wave.flatten()\n\n    mel_spec = librosa.feature.melspectrogram(y=wave, n_mels=128)\n\n    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n\n    plt.figure(figsize=(14, 8))\n    specshow(mel_spec_db, y_axis='log', fmax=8000, x_axis='time')\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:28:46.040982Z","iopub.execute_input":"2024-04-30T20:28:46.041644Z","iopub.status.idle":"2024-04-30T20:28:46.853855Z","shell.execute_reply.started":"2024-04-30T20:28:46.041608Z","shell.execute_reply":"2024-04-30T20:28:46.853174Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"epoch_list = []\nlabel_list = []\nfrom tqdm import tqdm\n# from skimage.transform import resize\nfrom sklearn.decomposition import FastICA\nfrom tensorflow.keras.applications.vgg16 import preprocess_input\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nimport matplotlib.pyplot as plt\nnum_components = 20\ndef epochConvertToMelSpectrogram():\n    for index,data in tqdm(enumerate(list_epoch)):\n        for epoch in data:\n            mat_data = loadmat('/kaggle/input/eeg-based-biometric-competition/Enrollment/'+epoch+'.mat')\n            mat_data =np.array(mat_data['epoch_data'])\n      \n            file_array = mat_data\n            \n#             ica = FastICA(n_components=num_components)\n#             ica.fit(file_array.T)\n#             ica_components = ica.components_\n#             selected_components = [0, 1, 2]  # Example: Selecting the first 3 components\n#             file_array = np.dot(ica_components[selected_components], file_array)\n\n#             scaler_standard = MinMaxScaler()\n#             file_array = scaler_standard.fit_transform(file_array)\n#             file_array = preprocessing(file_array)\n#             file_array = preprocess_eeg_data_signal(file_array.T.flatten(),250)\n#             file_array = resize(file_array, (125, 125))\n#             file_array = np.stack((file_array,) * 3, axis=-1)\n#             label_list.append(label[index])\n#             epoch_list.append(file_array)\n#             librosa.display.specshow(mat_data.reshape(mat_data.shape[0],-1),\n#                                      y_axis='mel', x_axis='time')\n            \n#             label_list.append(label[index])\n            melSpectrogram(file_array)\n            plt.colorbar(format='%+2.0f dB')\n            plt.savefig('/kaggle/working/Spectrogram/'+str(label[index])+\"_\"+epoch+'.png')\n            plt.title('Mel spectrogram')\n            plt.tight_layout()\n#             plt.show()\n            plt.close()\n            ","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:29:52.376066Z","iopub.execute_input":"2024-04-30T20:29:52.376472Z","iopub.status.idle":"2024-04-30T20:29:52.38344Z","shell.execute_reply.started":"2024-04-30T20:29:52.376442Z","shell.execute_reply":"2024-04-30T20:29:52.382722Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"import os\nos.makedirs('/kaggle/working/Spectrogram')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:29:43.931117Z","iopub.execute_input":"2024-04-30T20:29:43.932307Z","iopub.status.idle":"2024-04-30T20:29:43.935809Z","shell.execute_reply.started":"2024-04-30T20:29:43.932264Z","shell.execute_reply":"2024-04-30T20:29:43.934907Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"import shutil\nshutil.rmtree('/kaggle/working/Spectrogram')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:08:46.421715Z","iopub.execute_input":"2024-04-30T19:08:46.422624Z","iopub.status.idle":"2024-04-30T19:08:46.427379Z","shell.execute_reply.started":"2024-04-30T19:08:46.422569Z","shell.execute_reply":"2024-04-30T19:08:46.426358Z"},"trusted":true},"execution_count":148,"outputs":[]},{"cell_type":"code","source":"epochConvertToMelSpectrogram()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:30:00.960633Z","iopub.execute_input":"2024-04-30T20:30:00.960945Z","iopub.status.idle":"2024-04-30T20:36:52.644218Z","shell.execute_reply.started":"2024-04-30T20:30:00.960919Z","shell.execute_reply":"2024-04-30T20:36:52.642759Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stderr","text":"30it [06:50, 13.68s/it]\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/PIL/ImageFile.py:536\u001b[0m, in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 536\u001b[0m     fh \u001b[38;5;241m=\u001b[39m \u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileno\u001b[49m()\n\u001b[1;32m    537\u001b[0m     fp\u001b[38;5;241m.\u001b[39mflush()\n","\u001b[0;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mepochConvertToMelSpectrogram\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[27], line 38\u001b[0m, in \u001b[0;36mepochConvertToMelSpectrogram\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m melSpectrogram(file_array)\n\u001b[1;32m     37\u001b[0m plt\u001b[38;5;241m.\u001b[39mcolorbar(\u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%+2.0f\u001b[39;00m\u001b[38;5;124m dB\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 38\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/kaggle/working/Spectrogram/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mepoch\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMel spectrogram\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     40\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/pyplot.py:1119\u001b[0m, in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1116\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m   1117\u001b[0m \u001b[38;5;66;03m# savefig default implementation has no return, so mypy is unhappy\u001b[39;00m\n\u001b[1;32m   1118\u001b[0m \u001b[38;5;66;03m# presumably this is here because subclasses can return?\u001b[39;00m\n\u001b[0;32m-> 1119\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore[func-returns-value]\u001b[39;00m\n\u001b[1;32m   1120\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()  \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[1;32m   1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/figure.py:3390\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3388\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m   3389\u001b[0m         _recursively_make_axes_transparent(stack, ax)\n\u001b[0;32m-> 3390\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2190\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m   2191\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m   2192\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2194\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2195\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2196\u001b[0m \u001b[43m            \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2197\u001b[0m \u001b[43m            \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2198\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2199\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   2201\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2039\u001b[0m     optional_kws \u001b[38;5;241m=\u001b[39m {  \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m   2040\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2041\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m   2042\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2044\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m   2046\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m meth\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    451\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:446\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 446\u001b[0m \u001b[43mmpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimsave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mupper\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/matplotlib/image.py:1656\u001b[0m, in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1654\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m   1655\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[0;32m-> 1656\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/PIL/Image.py:2439\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2436\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2438\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2439\u001b[0m     \u001b[43msave_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   2441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m open_fp:\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/PIL/PngImagePlugin.py:1402\u001b[0m, in \u001b[0;36m_save\u001b[0;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[1;32m   1398\u001b[0m     im \u001b[38;5;241m=\u001b[39m _write_multiple_frames(\n\u001b[1;32m   1399\u001b[0m         im, fp, chunk, rawmode, default_image, append_images\n\u001b[1;32m   1400\u001b[0m     )\n\u001b[1;32m   1401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m im:\n\u001b[0;32m-> 1402\u001b[0m     \u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_idat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info:\n\u001b[1;32m   1405\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m info_chunk \u001b[38;5;129;01min\u001b[39;00m info\u001b[38;5;241m.\u001b[39mchunks:\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/PIL/ImageFile.py:540\u001b[0m, in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m    538\u001b[0m     _encode_tile(im, fp, tile, bufsize, fh)\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, io\u001b[38;5;241m.\u001b[39mUnsupportedOperation) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 540\u001b[0m     \u001b[43m_encode_tile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflush\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    542\u001b[0m     fp\u001b[38;5;241m.\u001b[39mflush()\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/PIL/ImageFile.py:559\u001b[0m, in \u001b[0;36m_encode_tile\u001b[0;34m(im, fp, tile, bufsize, fh, exc)\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc:\n\u001b[1;32m    557\u001b[0m     \u001b[38;5;66;03m# compress to Python file-compatible object\u001b[39;00m\n\u001b[1;32m    558\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 559\u001b[0m         errcode, data \u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    560\u001b[0m         fp\u001b[38;5;241m.\u001b[39mwrite(data)\n\u001b[1;32m    561\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m errcode:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x800 with 2 Axes>","image/png":"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shutil\nshutil.make_archive('/kaggle/working/Spectrogram','zip','/kaggle/working/')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:52:10.357327Z","iopub.execute_input":"2024-04-30T19:52:10.357722Z","iopub.status.idle":"2024-04-30T19:52:24.04032Z","shell.execute_reply.started":"2024-04-30T19:52:10.357684Z","shell.execute_reply":"2024-04-30T19:52:24.039198Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/Spectrogram.zip'"},"metadata":{}}]},{"cell_type":"code","source":"folder_path = \"/kaggle/working/Spectrogram\"","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:36:59.433735Z","iopub.execute_input":"2024-04-30T20:36:59.434153Z","iopub.status.idle":"2024-04-30T20:36:59.438599Z","shell.execute_reply.started":"2024-04-30T20:36:59.434105Z","shell.execute_reply":"2024-04-30T20:36:59.437745Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"from PIL import Image\nimport os\nfrom tqdm import tqdm\nimages = []\nlabels = []\nfor filename in tqdm(os.listdir(folder_path)):\n    if filename.endswith('.png') or filename.endswith('.jpg'):  # Adjust file extensions as needed\n        # Read the image\n        image_path = os.path.join(folder_path, filename)\n#         img = image.load_img(img_path, target_size=(224, 224))  # Resize to VGG input size\n#         img_array = image.img_to_array(img)\n#         img_array = np.expand_dims(img_array, axis=0)\n        img = Image.open(image_path)\n        newsize = (124, 124)\n        img = img.resize(newsize)\n        img = img.convert('RGB')\n        # Convert the image to numpy array (optional)\n#         print(img_array.shape)\n        img_array = np.array(img)\n        img_array = img_array / 255.0\n        # Append the image and its corresponding label to the lists\n        images.append(img_array)\n        index = filename.split(\"_\")\n        gameId = index[0]\n        labels.append(int(gameId))","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:38:50.411512Z","iopub.execute_input":"2024-04-30T20:38:50.412776Z","iopub.status.idle":"2024-04-30T20:39:38.950468Z","shell.execute_reply.started":"2024-04-30T20:38:50.412735Z","shell.execute_reply":"2024-04-30T20:39:38.949446Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"100%|██████████| 1477/1477 [00:48<00:00, 30.44it/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"os.remove('/kaggle/working/Spectrogram/4_epoch001360.png')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:38:47.563405Z","iopub.execute_input":"2024-04-30T20:38:47.563791Z","iopub.status.idle":"2024-04-30T20:38:47.568555Z","shell.execute_reply.started":"2024-04-30T20:38:47.563761Z","shell.execute_reply":"2024-04-30T20:38:47.567708Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"one_hot_test_labels[4]","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:48:39.426563Z","iopub.execute_input":"2024-04-30T18:48:39.427199Z","iopub.status.idle":"2024-04-30T18:48:39.433648Z","shell.execute_reply.started":"2024-04-30T18:48:39.427167Z","shell.execute_reply":"2024-04-30T18:48:39.432649Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.])"},"metadata":{}}]},{"cell_type":"code","source":"labels","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:47:43.417363Z","iopub.execute_input":"2024-04-30T18:47:43.418088Z","iopub.status.idle":"2024-04-30T18:47:43.441659Z","shell.execute_reply.started":"2024-04-30T18:47:43.418052Z","shell.execute_reply":"2024-04-30T18:47:43.440606Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":"[11,\n 3,\n 12,\n 11,\n 13,\n 9,\n 12,\n 4,\n 11,\n 4,\n 5,\n 12,\n 8,\n 12,\n 9,\n 7,\n 3,\n 13,\n 6,\n 7,\n 11,\n 10,\n 10,\n 11,\n 9,\n 7,\n 12,\n 3,\n 8,\n 12,\n 4,\n 11,\n 11,\n 8,\n 12,\n 3,\n 3,\n 11,\n 6,\n 1,\n 10,\n 1,\n 8,\n 1,\n 13,\n 12,\n 12,\n 3,\n 12,\n 5,\n 1,\n 3,\n 9,\n 5,\n 12,\n 6,\n 3,\n 3,\n 3,\n 4,\n 12,\n 10,\n 3,\n 3,\n 1,\n 5,\n 10,\n 11,\n 11,\n 9,\n 3,\n 9,\n 3,\n 4,\n 8,\n 3,\n 5,\n 9,\n 11,\n 13,\n 5,\n 10,\n 4,\n 9,\n 4,\n 3,\n 5,\n 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...]"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder\nencoder = OneHotEncoder()\ntest_labels_reshaped = np.array(labels).reshape(-1, 1)\none_hot_test_labels = encoder.fit_transform(test_labels_reshaped)\nX_train, X_test, y_train, y_test = train_test_split(np.array(images), one_hot_test_labels, test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:40:58.414153Z","iopub.execute_input":"2024-04-30T20:40:58.414661Z","iopub.status.idle":"2024-04-30T20:40:58.767073Z","shell.execute_reply.started":"2024-04-30T20:40:58.414629Z","shell.execute_reply":"2024-04-30T20:40:58.766164Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"X_train.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:40:59.560808Z","iopub.execute_input":"2024-04-30T20:40:59.561086Z","iopub.status.idle":"2024-04-30T20:40:59.565849Z","shell.execute_reply.started":"2024-04-30T20:40:59.561059Z","shell.execute_reply":"2024-04-30T20:40:59.565167Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"(1181, 124, 124, 3)"},"metadata":{}}]},{"cell_type":"code","source":"from tensorflow.keras import layers, models\nimport tensorflow as tf\nnum_classes = 13\nmodel = models.Sequential([\n    layers.Conv2D(128, (3, 3), activation='relu', input_shape=(X_train.shape[1:])),\n    layers.MaxPooling2D((2, 2)),\n    layers.Conv2D(128, (3, 3), activation='relu'),\n    layers.MaxPooling2D((2, 2)),\n    layers.Conv2D(128, (3, 3), activation='relu'),\n    layers.MaxPooling2D((2, 2)),\n    layers.Conv2D(128, (3, 3), activation='relu'),\n    layers.MaxPooling2D((2, 2)),\n    layers.Flatten(),\n    layers.Dense(256, activation='relu'),\n    layers.Dense(128, activation='relu'),\n    layers.Dropout(0.5),\n    layers.Dense(num_classes, activation='softmax')\n])\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nwith tf.device(tf.DeviceSpec(device_type=\"TPU\")):\n    model.fit(X_train, y_train, epochs=20, batch_size=64, validation_split=0.1)\n\n    # Evaluate the model\n    test_loss, test_accuracy = model.evaluate(X_test, y_test)\n    print(\"Test Accuracy:\", test_accuracy)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:41:02.718553Z","iopub.execute_input":"2024-04-30T20:41:02.719541Z","iopub.status.idle":"2024-04-30T20:41:02.850073Z","shell.execute_reply.started":"2024-04-30T20:41:02.719496Z","shell.execute_reply":"2024-04-30T20:41:02.84889Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n  super().__init__(\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[41], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mdevice(tf\u001b[38;5;241m.\u001b[39mDeviceSpec(device_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTPU\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[0;32m---> 25\u001b[0m     \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;66;03m# Evaluate the model\u001b[39;00m\n\u001b[1;32m     28\u001b[0m     test_loss, test_accuracy \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(X_test, y_test)\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:123\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 123\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n","File \u001b[0;32m/usr/local/lib/python3.10/site-packages/keras/src/trainers/data_adapters/data_adapter_utils.py:166\u001b[0m, in \u001b[0;36mtrain_validation_split\u001b[0;34m(arrays, validation_split)\u001b[0m\n\u001b[1;32m    164\u001b[0m unsplitable \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mtype\u001b[39m(t) \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m flat_arrays \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _can_split(t)]\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unsplitable:\n\u001b[0;32m--> 166\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    167\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument `validation_split` is only supported \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    168\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor tensors or NumPy arrays.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    169\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound incompatible type in the input: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00munsplitable\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    170\u001b[0m     )\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(t \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m flat_arrays):\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arrays, arrays\n","\u001b[0;31mValueError\u001b[0m: Argument `validation_split` is only supported for tensors or NumPy arrays.Found incompatible type in the input: [<class 'scipy.sparse._csr.csr_matrix'>]"],"ename":"ValueError","evalue":"Argument `validation_split` is only supported for tensors or NumPy arrays.Found incompatible type in the input: [<class 'scipy.sparse._csr.csr_matrix'>]","output_type":"error"}]},{"cell_type":"code","source":"from tensorflow.keras.applications import Xception\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\nnum_classes = 13\n# Load the pre-trained ResNet50 model\nbase_model = Xception(weights='imagenet', include_top=False, input_shape=(124, 124,3))\n\nx = Flatten()(base_model.output)\nx = Dense(512, activation='relu',name='fc1')(x)\nx = Dense(128, activation='relu',name='fc2')(x)\nx = Dense(64, activation='relu',name='fc3')(x)\n# x = Dense(64, activation='relu',name='fc3')(x)\npredictions = Dense(num_classes, activation='softmax')(x)\n                 \nmodel = Model(inputs=base_model.input, outputs=predictions)\n\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model\n# SGD(lr=0.001, momentum=0.9)\nmodel.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n                 \n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:41:42.891479Z","iopub.execute_input":"2024-04-30T20:41:42.891884Z","iopub.status.idle":"2024-04-30T20:41:44.470475Z","shell.execute_reply.started":"2024-04-30T20:41:42.891853Z","shell.execute_reply":"2024-04-30T20:41:44.469174Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m83683744/83683744\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[42], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Load the pre-trained ResNet50 model\u001b[39;00m\n\u001b[1;32m      7\u001b[0m base_model \u001b[38;5;241m=\u001b[39m Xception(weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimagenet\u001b[39m\u001b[38;5;124m'\u001b[39m, include_top\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, input_shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m124\u001b[39m, \u001b[38;5;241m124\u001b[39m,\u001b[38;5;241m3\u001b[39m))\n\u001b[0;32m----> 9\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mFlatten\u001b[49m()(base_model\u001b[38;5;241m.\u001b[39moutput)\n\u001b[1;32m     10\u001b[0m x \u001b[38;5;241m=\u001b[39m Dense(\u001b[38;5;241m512\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m,name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfc1\u001b[39m\u001b[38;5;124m'\u001b[39m)(x)\n\u001b[1;32m     11\u001b[0m x \u001b[38;5;241m=\u001b[39m Dense(\u001b[38;5;241m128\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m,name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfc2\u001b[39m\u001b[38;5;124m'\u001b[39m)(x)\n","\u001b[0;31mNameError\u001b[0m: name 'Flatten' is not defined"],"ename":"NameError","evalue":"name 'Flatten' is not defined","output_type":"error"}]},{"cell_type":"code","source":"num_epochs = 10\nbatch_size = 64\nimport tensorflow as tf\n# Train the model\n# with tf.device(tf.DeviceSpec(device_type=\"GPU\")):\nmodel.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=num_epochs, batch_size=batch_size)\n\n# Evaluate the model\nloss, accuracy = model.evaluate(test_images, test_labels)\nprint(f'Test Accuracy: {accuracy}')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:42:41.851437Z","iopub.execute_input":"2024-04-30T20:42:41.851829Z","iopub.status.idle":"2024-04-30T20:42:41.882011Z","shell.execute_reply.started":"2024-04-30T20:42:41.851782Z","shell.execute_reply":"2024-04-30T20:42:41.880756Z"},"trusted":true},"execution_count":45,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[45], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# with tf.device(tf.DeviceSpec(device_type=\"GPU\")):\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(\u001b[43mtrain_images\u001b[49m, train_labels, validation_data\u001b[38;5;241m=\u001b[39m(val_images, val_labels), epochs\u001b[38;5;241m=\u001b[39mnum_epochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Evaluate the model\u001b[39;00m\n\u001b[1;32m      9\u001b[0m loss, accuracy \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(test_images, test_labels)\n","\u001b[0;31mNameError\u001b[0m: name 'train_images' is not defined"],"ename":"NameError","evalue":"name 'train_images' is not defined","output_type":"error"}]},{"cell_type":"code","source":"from tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.preprocessing import image\nfrom keras.layers import Dense, Activation, Flatten\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\nnum_classes = 13\n# Load the pre-trained ResNet50 model\nbase_model = ResNet50(weights='imagenet', include_top=False, input_shape=(124, 124,3))\n\nx = Flatten()(base_model.output)\nx = Dense(512, activation='relu',name='fc1')(x)\nx = Dense(128, activation='relu',name='fc2')(x)\nx = Dense(64, activation='relu',name='fc3')(x)\n# x = Dense(64, activation='relu',name='fc3')(x)\npredictions = Dense(num_classes, activation='softmax')(x)\n                 \nmodel = Model(inputs=base_model.input, outputs=predictions)\n\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model\n# SGD(lr=0.001, momentum=0.9)\nmodel.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n                 \n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T20:42:44.411308Z","iopub.execute_input":"2024-04-30T20:42:44.411641Z","iopub.status.idle":"2024-04-30T20:42:45.432485Z","shell.execute_reply.started":"2024-04-30T20:42:44.411613Z","shell.execute_reply":"2024-04-30T20:42:45.431247Z"},"trusted":true},"execution_count":46,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[46], line 17\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# x = Dense(64, activation='relu',name='fc3')(x)\u001b[39;00m\n\u001b[1;32m     15\u001b[0m predictions \u001b[38;5;241m=\u001b[39m Dense(num_classes, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msoftmax\u001b[39m\u001b[38;5;124m'\u001b[39m)(x)\n\u001b[0;32m---> 17\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mModel\u001b[49m(inputs\u001b[38;5;241m=\u001b[39mbase_model\u001b[38;5;241m.\u001b[39minput, outputs\u001b[38;5;241m=\u001b[39mpredictions)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m base_model\u001b[38;5;241m.\u001b[39mlayers:\n\u001b[1;32m     20\u001b[0m     layer\u001b[38;5;241m.\u001b[39mtrainable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mNameError\u001b[0m: name 'Model' is not defined"],"ename":"NameError","evalue":"name 'Model' is not defined","output_type":"error"}]},{"cell_type":"code","source":"num_epochs = 10\nbatch_size = 64\nimport tensorflow as tf\n# Train the model\nwith tf.device(tf.DeviceSpec(device_type=\"GPU\")):\n    model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=num_epochs, batch_size=batch_size)\n\n    # Evaluate the model\n    loss, accuracy = model.evaluate(test_images, test_labels)\n    print(f'Test Accuracy: {accuracy}')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:37:50.711618Z","iopub.execute_input":"2024-04-30T18:37:50.717626Z","iopub.status.idle":"2024-04-30T18:38:06.332824Z","shell.execute_reply.started":"2024-04-30T18:37:50.717565Z","shell.execute_reply":"2024-04-30T18:38:06.33187Z"},"trusted":true},"execution_count":108,"outputs":[{"name":"stdout","text":"Epoch 1/10\n25/25 [==============================] - 2s 66ms/step - loss: 2.4412 - accuracy: 0.1264 - val_loss: 2.4690 - val_accuracy: 0.1026\nEpoch 2/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4395 - accuracy: 0.1354 - val_loss: 2.4947 - val_accuracy: 0.1026\nEpoch 3/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4413 - accuracy: 0.1123 - val_loss: 2.4547 - val_accuracy: 0.1154\nEpoch 4/10\n25/25 [==============================] - 1s 55ms/step - loss: 2.4374 - accuracy: 0.1277 - val_loss: 2.4596 - val_accuracy: 0.1231\nEpoch 5/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4359 - accuracy: 0.1277 - val_loss: 2.4692 - val_accuracy: 0.1077\nEpoch 6/10\n25/25 [==============================] - 1s 55ms/step - loss: 2.4290 - accuracy: 0.1463 - val_loss: 2.4616 - val_accuracy: 0.1256\nEpoch 7/10\n25/25 [==============================] - 1s 55ms/step - loss: 2.4316 - accuracy: 0.1348 - val_loss: 2.4701 - val_accuracy: 0.1256\nEpoch 8/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4403 - accuracy: 0.1335 - val_loss: 2.4626 - val_accuracy: 0.1128\nEpoch 9/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4365 - accuracy: 0.1316 - val_loss: 2.4630 - val_accuracy: 0.1026\nEpoch 10/10\n25/25 [==============================] - 1s 54ms/step - loss: 2.4347 - accuracy: 0.1341 - val_loss: 2.4532 - val_accuracy: 0.1026\n16/16 [==============================] - 0s 25ms/step - loss: 2.4169 - accuracy: 0.1355\nTest Accuracy: 0.13552361726760864\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.layers import Dense, Flatten, Input,Dropout\nfrom tensorflow.keras.optimizers import Adam,SGD\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import Model\nimport librosa\nimport librosa.display\n\nnum_classes = 13\nfine_tune = 2\n# img_path = '/kaggle/working/Spectrogram/S02G1AllChannels.png'\n# img = image.load_img(img_path, target_size=(224, 224))  # Resize to VGG input size\n# img_array = image.img_to_array(img)\n# img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n\n# print(img_array.shape)\n# Preprocess the image for VGG model\n# processed_img = preprocess_input(img_array)\n# Load the VGG model\nvgg_model = VGG16(weights='imagenet', include_top=False, input_shape=(124, 124,3))\n\n        \nif fine_tune > 0:\n    for layer in vgg_model.layers[:-fine_tune]:\n        layer.trainable = False\nelse:\n    for layer in vgg_model.layers:\n        layer.trainable =  False\n\nfor layer in vgg_model.layers:\n    print(layer.name,layer.trainable)\n        \n# Add new fully connected layers for classification\nx = Flatten()(vgg_model.output)\nx = Dense(512, activation='relu',name='fc1')(x)\nx = Dense(128, activation='relu',name='fc2')(x)\nx = Dense(64, activation='relu',name='fc3')(x)\n# x = Dense(64, activation='relu',name='fc3')(x)\n# x = Dropout(0.2)(x)\npredictions = Dense(num_classes, activation='softmax')(x)  # Adjust num_classes according to your problem\n\n# Create the final model\nmodel = Model(inputs=vgg_model.input, outputs=predictions)\n\n# opt = SGD(learning_rate=0.001, momentum=0.09)\n\nmodel.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:03:48.868606Z","iopub.execute_input":"2024-04-30T19:03:48.868979Z","iopub.status.idle":"2024-04-30T19:03:49.381804Z","shell.execute_reply.started":"2024-04-30T19:03:48.86895Z","shell.execute_reply":"2024-04-30T19:03:49.380798Z"},"trusted":true},"execution_count":131,"outputs":[{"name":"stdout","text":"input_14 False\nblock1_conv1 False\nblock1_conv2 False\nblock1_pool False\nblock2_conv1 False\nblock2_conv2 False\nblock2_pool False\nblock3_conv1 False\nblock3_conv2 False\nblock3_conv3 False\nblock3_pool False\nblock4_conv1 False\nblock4_conv2 False\nblock4_conv3 False\nblock4_pool False\nblock5_conv1 False\nblock5_conv2 False\nblock5_conv3 True\nblock5_pool True\n","output_type":"stream"}]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:03:50.066677Z","iopub.execute_input":"2024-04-30T19:03:50.067058Z","iopub.status.idle":"2024-04-30T19:03:50.121548Z","shell.execute_reply.started":"2024-04-30T19:03:50.067027Z","shell.execute_reply":"2024-04-30T19:03:50.120563Z"},"trusted":true},"execution_count":132,"outputs":[{"name":"stdout","text":"Model: \"model_13\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n input_14 (InputLayer)       [(None, 124, 124, 3)]     0         \n                                                                 \n block1_conv1 (Conv2D)       (None, 124, 124, 64)      1792      \n                                                                 \n block1_conv2 (Conv2D)       (None, 124, 124, 64)      36928     \n                                                                 \n block1_pool (MaxPooling2D)  (None, 62, 62, 64)        0         \n                                                                 \n block2_conv1 (Conv2D)       (None, 62, 62, 128)       73856     \n                                                                 \n block2_conv2 (Conv2D)       (None, 62, 62, 128)       147584    \n                                                                 \n block2_pool (MaxPooling2D)  (None, 31, 31, 128)       0         \n                                                                 \n block3_conv1 (Conv2D)       (None, 31, 31, 256)       295168    \n                                                                 \n block3_conv2 (Conv2D)       (None, 31, 31, 256)       590080    \n                                                                 \n block3_conv3 (Conv2D)       (None, 31, 31, 256)       590080    \n                                                                 \n block3_pool (MaxPooling2D)  (None, 15, 15, 256)       0         \n                                                                 \n block4_conv1 (Conv2D)       (None, 15, 15, 512)       1180160   \n                                                                 \n block4_conv2 (Conv2D)       (None, 15, 15, 512)       2359808   \n                                                                 \n block4_conv3 (Conv2D)       (None, 15, 15, 512)       2359808   \n                                                                 \n block4_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n                                                                 \n block5_conv1 (Conv2D)       (None, 7, 7, 512)         2359808   \n                                                                 \n block5_conv2 (Conv2D)       (None, 7, 7, 512)         2359808   \n                                                                 \n block5_conv3 (Conv2D)       (None, 7, 7, 512)         2359808   \n                                                                 \n block5_pool (MaxPooling2D)  (None, 3, 3, 512)         0         \n                                                                 \n flatten_15 (Flatten)        (None, 4608)              0         \n                                                                 \n fc1 (Dense)                 (None, 512)               2359808   \n                                                                 \n fc2 (Dense)                 (None, 128)               65664     \n                                                                 \n fc3 (Dense)                 (None, 64)                8256      \n                                                                 \n dense_19 (Dense)            (None, 13)                845       \n                                                                 \n=================================================================\nTotal params: 17149261 (65.42 MB)\nTrainable params: 4794381 (18.29 MB)\nNon-trainable params: 12354880 (47.13 MB)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"label_list","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:03:51.347085Z","iopub.execute_input":"2024-04-30T19:03:51.347464Z","iopub.status.idle":"2024-04-30T19:03:51.353319Z","shell.execute_reply.started":"2024-04-30T19:03:51.347434Z","shell.execute_reply":"2024-04-30T19:03:51.352394Z"},"trusted":true},"execution_count":133,"outputs":[{"execution_count":133,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\ntest_labels_reshaped = np.array(labels).reshape(-1, 1)\none_hot_test_labels = encoder.fit_transform(test_labels_reshaped)\n\n# Split data into training and testing sets\ntrain_images, test_images, train_labels, test_labels = train_test_split(np.array(images), one_hot_test_labels, test_size=0.2, random_state=42)\n\n# Split training data into training and validation sets\ntrain_images, val_images, train_labels, val_labels = train_test_split(train_images, train_labels, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:03:52.386646Z","iopub.execute_input":"2024-04-30T19:03:52.387498Z","iopub.status.idle":"2024-04-30T19:03:55.133437Z","shell.execute_reply.started":"2024-04-30T19:03:52.387463Z","shell.execute_reply":"2024-04-30T19:03:55.122275Z"},"trusted":true},"execution_count":134,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"one_hot_test_labels[2]","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:46:50.876827Z","iopub.execute_input":"2024-04-30T18:46:50.877462Z","iopub.status.idle":"2024-04-30T18:46:50.883984Z","shell.execute_reply.started":"2024-04-30T18:46:50.877431Z","shell.execute_reply":"2024-04-30T18:46:50.882984Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.])"},"metadata":{}}]},{"cell_type":"code","source":"test_labels_reshaped = np.array(train_labels).reshape(-1, 1)\none_hot_train_labels = encoder.fit_transform(test_labels_reshaped)\n\ntest_labels_reshaped = np.array(test_labels).reshape(-1, 1)\none_hot_test_labels = encoder.fit_transform(test_labels_reshaped)\n\ntest_labels_reshaped = np.array(val_labels).reshape(-1, 1)\none_hot_val_labels = encoder.fit_transform(test_labels_reshaped)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:04:16.633325Z","iopub.execute_input":"2024-04-30T18:04:16.633742Z","iopub.status.idle":"2024-04-30T18:04:16.644428Z","shell.execute_reply.started":"2024-04-30T18:04:16.633711Z","shell.execute_reply":"2024-04-30T18:04:16.643309Z"},"trusted":true},"execution_count":59,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"train_images.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:29:01.911697Z","iopub.execute_input":"2024-04-30T18:29:01.912069Z","iopub.status.idle":"2024-04-30T18:29:01.91834Z","shell.execute_reply.started":"2024-04-30T18:29:01.912041Z","shell.execute_reply":"2024-04-30T18:29:01.91732Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"(1558, 124, 124, 3)"},"metadata":{}}]},{"cell_type":"code","source":"train_labels.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:29:02.236769Z","iopub.execute_input":"2024-04-30T18:29:02.237773Z","iopub.status.idle":"2024-04-30T18:29:02.244144Z","shell.execute_reply.started":"2024-04-30T18:29:02.237732Z","shell.execute_reply":"2024-04-30T18:29:02.243193Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"(1558, 13)"},"metadata":{}}]},{"cell_type":"code","source":"val_images.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:29:02.601749Z","iopub.execute_input":"2024-04-30T18:29:02.602596Z","iopub.status.idle":"2024-04-30T18:29:02.608183Z","shell.execute_reply.started":"2024-04-30T18:29:02.602555Z","shell.execute_reply":"2024-04-30T18:29:02.607184Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"(390, 124, 124, 3)"},"metadata":{}}]},{"cell_type":"code","source":"val_labels.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-30T18:29:03.051411Z","iopub.execute_input":"2024-04-30T18:29:03.052159Z","iopub.status.idle":"2024-04-30T18:29:03.058036Z","shell.execute_reply.started":"2024-04-30T18:29:03.052126Z","shell.execute_reply":"2024-04-30T18:29:03.057098Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"(390, 13)"},"metadata":{}}]},{"cell_type":"code","source":"num_epochs = 10\nbatch_size = 32\nimport tensorflow as tf\n# Train the model\nwith tf.device(tf.DeviceSpec(device_type=\"GPU\")):\n    model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=num_epochs, batch_size=batch_size)\n\n    # Evaluate the model\n    loss, accuracy = model.evaluate(test_images, test_labels)\n    print(f'Test Accuracy: {accuracy}')","metadata":{"execution":{"iopub.status.busy":"2024-04-30T19:04:17.281785Z","iopub.execute_input":"2024-04-30T19:04:17.282624Z","iopub.status.idle":"2024-04-30T19:04:36.33873Z","shell.execute_reply.started":"2024-04-30T19:04:17.28257Z","shell.execute_reply":"2024-04-30T19:04:36.337786Z"},"trusted":true},"execution_count":136,"outputs":[{"name":"stdout","text":"Epoch 1/10\n49/49 [==============================] - 3s 43ms/step - loss: 3.0901 - accuracy: 0.1220 - val_loss: 2.6788 - val_accuracy: 0.1128\nEpoch 2/10\n49/49 [==============================] - 2s 33ms/step - loss: 3.0184 - accuracy: 0.1033 - val_loss: 2.8954 - val_accuracy: 0.1026\nEpoch 3/10\n49/49 [==============================] - 2s 33ms/step - loss: 4.1218 - accuracy: 0.1104 - val_loss: 3.6907 - val_accuracy: 0.1026\nEpoch 4/10\n49/49 [==============================] - 2s 33ms/step - loss: 3.9147 - accuracy: 0.1252 - val_loss: 3.4571 - val_accuracy: 0.1385\nEpoch 5/10\n49/49 [==============================] - 2s 33ms/step - loss: 2.8117 - accuracy: 0.1367 - val_loss: 2.8321 - val_accuracy: 0.1205\nEpoch 6/10\n49/49 [==============================] - 2s 33ms/step - loss: 3.1479 - accuracy: 0.1297 - val_loss: 2.9325 - val_accuracy: 0.1154\nEpoch 7/10\n49/49 [==============================] - 2s 33ms/step - loss: 2.8547 - accuracy: 0.1309 - val_loss: 2.7281 - val_accuracy: 0.1205\nEpoch 8/10\n49/49 [==============================] - 2s 33ms/step - loss: 2.6333 - accuracy: 0.1367 - val_loss: 2.5811 - val_accuracy: 0.1333\nEpoch 9/10\n49/49 [==============================] - 2s 33ms/step - loss: 2.5986 - accuracy: 0.1322 - val_loss: 2.6227 - val_accuracy: 0.1154\nEpoch 10/10\n49/49 [==============================] - 2s 33ms/step - loss: 2.5157 - accuracy: 0.1329 - val_loss: 2.5410 - val_accuracy: 0.1103\n16/16 [==============================] - 0s 23ms/step - loss: 226.3502 - accuracy: 0.1191\nTest Accuracy: 0.11909651011228561\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}