| import pandas as pd |
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| import os |
| import shutil |
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| def split_csv(input_file, train_file, validate_file, test_file, train_size, validate_size, test_size): |
| |
| data = pd.read_csv(input_file) |
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| |
| train_data = data.iloc[:train_size] |
| validate_data = data.iloc[train_size:train_size + validate_size] |
| test_data = data.iloc[train_size + validate_size:train_size + validate_size + test_size] |
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| |
| train_data.to_csv(train_file, index=False) |
| validate_data.to_csv(validate_file, index=False) |
| test_data.to_csv(test_file, index=False) |
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| def copy_ecg_files(csv_file, destination_folder, src_folder): |
| |
| data = pd.read_csv(csv_file) |
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|
| |
| if not os.path.exists(destination_folder): |
| os.makedirs(destination_folder) |
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|
| |
| for index, row in data.iterrows(): |
| ecg_file = str(row['patid']) + ".asc" |
| src_path = os.path.join(src_folder, ecg_file) |
| dst_path = os.path.join(destination_folder, ecg_file) |
| shutil.copy(src_path, dst_path) |
|
|
| if __name__ == '__main__': |
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| |
| dst_dir = '/work/vajira/data/deepfake_ecg_full_train_validation_test' |
| input_file = '/work/vajira/data/Deepfake-ecg/filtered_all_normals_121977_ground_truth.csv' |
| train_file = f'{dst_dir}/train.csv' |
| validate_file = f'{dst_dir}/validate.csv' |
| test_file = f'{dst_dir}/test.csv' |
| src_folder = '/work/vajira/data/Deepfake-ecg/filtered_all_normals_121977/from_006_chck_2500_150k_filtered_all_normals_121977' |
| |
| |
| train_size = 97581 |
| validate_size = 12198 |
| test_size = 12198 |
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| split_csv(input_file, train_file, validate_file, test_file, train_size, validate_size, test_size) |
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| copy_ecg_files(train_file, f'{dst_dir}/train', src_folder) |
| copy_ecg_files(validate_file, f'{dst_dir}/validation', src_folder) |
| copy_ecg_files(test_file, f'{dst_dir}/test', src_folder) |
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