{"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":"none","dataSources":[{"sourceId":59093,"databundleVersionId":7469972,"sourceType":"competition"},{"sourceId":7537993,"sourceType":"datasetVersion","datasetId":4389508},{"sourceId":7562031,"sourceType":"datasetVersion","datasetId":4403195},{"sourceId":7563702,"sourceType":"datasetVersion","datasetId":4404213},{"sourceId":7572038,"sourceType":"datasetVersion","datasetId":4408129},{"sourceId":7662819,"sourceType":"datasetVersion","datasetId":4412486}],"dockerImageVersionId":30646,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"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)\nimport matplotlib.pyplot as plt\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\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\nfrom PIL import Image\nimport gc\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":"2024-02-13T13:20:03.895130Z","iopub.execute_input":"2024-02-13T13:20:03.895552Z","iopub.status.idle":"2024-02-13T13:20:22.231965Z","shell.execute_reply.started":"2024-02-13T13:20:03.895513Z","shell.execute_reply":"2024-02-13T13:20:22.230819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Path definition","metadata":{}},{"cell_type":"code","source":"EEG_TEST_PATH = '/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/'\nSPEC_TEST_PATH = '/kaggle/input/hms-harmful-brain-activity-classification/test_spectrograms/'\n\nif not os.path.exists('/kaggle/working/test_eegs_img/'):\n os.makedirs('/kaggle/working/test_eegs_img/')\nEEG_IMG_TEST_PATH = '/kaggle/working/test_eegs_img/'\n\nif not os.path.exists('/kaggle/working/test_spec_img/'):\n os.makedirs('/kaggle/working/test_spec_img/')\nSPEC_IMG_TEST_PATH='/kaggle/working/test_spec_img/'\n\nMETA_TEST = '/kaggle/input/hms-harmful-brain-activity-classification/test.csv'","metadata":{"execution":{"iopub.status.busy":"2024-02-13T13:20:22.233833Z","iopub.execute_input":"2024-02-13T13:20:22.234456Z","iopub.status.idle":"2024-02-13T13:20:22.247052Z","shell.execute_reply.started":"2024-02-13T13:20:22.234425Z","shell.execute_reply":"2024-02-13T13:20:22.245840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Functions to generate images of graphs","metadata":{}},{"cell_type":"code","source":"eeg_zone={\n 'Cz-Pz' : ['Cz', 'Pz'],\n 'Fz-Cz' : ['Fz', 'Cz'],\n \n 'P4-O2' : ['P4', 'O2'],\n 'C4-P4' : ['C4', 'P4'],\n 'F4-C4' : ['F4', 'C4'],\n 'Fp2-F4': ['Fp2', 'F4'],\n \n 'P3-O1' : ['P3', 'O1'],\n 'C3-P3' : ['C3', 'P3'],\n 'F3-C3' : ['F3', 'C3'],\n 'Fp1-F3' : ['Fp1', 'F3'],\n \n 'T6-O2' : ['T6','O2'],\n 'T4-T6' : ['T4', 'T6'],\n 'F8-T4' : ['F8', 'T4'],\n 'Fp2-F8' : ['Fp2', 'F8'],\n \n 'T5-O1' : ['T5', 'O1'],\n 'T3-T5' : ['T3', 'T5'],\n 'F7-T3' : ['F7', 'T3'],\n 'Fp1-F7' : ['Fp1', 'F7'],\n}","metadata":{"execution":{"iopub.status.busy":"2024-02-13T13:20:22.255671Z","iopub.execute_input":"2024-02-13T13:20:22.256740Z","iopub.status.idle":"2024-02-13T13:20:22.372789Z","shell.execute_reply.started":"2024-02-13T13:20:22.256688Z","shell.execute_reply":"2024-02-13T13:20:22.371342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def generate_eeg(eegid,input_path=EEG_TEST_PATH,eeg_zone=eeg_zone,linewidth=0.2, eeg_out=EEG_IMG_TEST_PATH):\n #get eeg data\n eeg = pd.read_parquet(f'{input_path}{eegid}.parquet')\n #take subsample of 50sec\n #eeg = eeg.iloc[int(eegoffset*200):int((eegoffset+50)*200)]\n \n # generate the graph\n ysticks=[]\n labels= []\n cicles= 0\n relpos= 0\n fig, ax = plt.subplots(1,1, figsize=(3,3), sharex= True)\n \n for key, values in eeg_zone.items():\n ax.plot(eeg.index/200, eeg[values[0]]-eeg[values[1]]+relpos,color='black',linewidth=linewidth)\n \n ysticks.append(relpos)\n labels.append(key)\n if cicles==1 or cicles==5 or cicles==9 or cicles==13:\n relpos+=200\n else:\n relpos+=40\n \n cicles+=1\n \n #ax.set_yticks(ysticks, labels=labels)\n ax.set_xticks([])\n ax.set_yticks([])\n \n ax.set_xlim(0,50)\n save_path= f'{eeg_out}{eegid}.jpeg' \n fig.savefig(save_path,bbox_inches='tight', dpi=100)\n plt.close()\n \n return save_path","metadata":{"execution":{"iopub.status.busy":"2024-02-13T13:20:22.374242Z","iopub.execute_input":"2024-02-13T13:20:22.374800Z","iopub.status.idle":"2024-02-13T13:20:22.391090Z","shell.execute_reply.started":"2024-02-13T13:20:22.374764Z","shell.execute_reply":"2024-02-13T13:20:22.389854Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Spectrogram graph function","metadata":{}},{"cell_type":"code","source":"spec_zones=['LL','RL','LP','RP']\n\ndef generate_spectrogram(specid,\n input_path=SPEC_TEST_PATH, spec_out=SPEC_IMG_TEST_PATH,\n spec_zones=spec_zones,\n output_filter=0.5):\n \n #get spec data\n spec = pd.read_parquet(f'{input_path}{specid}.parquet')\n spec = spec.fillna(0)\n #take subsample of 600sec (10min)\n #spec= spec.loc[(spec.time>=specoffset) & (spec.time