{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":87793,"databundleVersionId":11228175,"sourceType":"competition"}],"dockerImageVersionId":30918,"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)\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","trusted":true,"execution":{"iopub.status.busy":"2025-03-11T16:43:27.566680Z","iopub.execute_input":"2025-03-11T16:43:27.566933Z","iopub.status.idle":"2025-03-11T16:43:28.682657Z","shell.execute_reply.started":"2025-03-11T16:43:27.566908Z","shell.execute_reply":"2025-03-11T16:43:28.681535Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load test sequences\ntest_sequences = pd.read_csv('/kaggle/input/stanford-rna-3d-folding/test_sequences.csv')\ntest_sequences.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-11T16:46:37.367710Z","iopub.execute_input":"2025-03-11T16:46:37.368049Z","iopub.status.idle":"2025-03-11T16:46:37.394039Z","shell.execute_reply.started":"2025-03-11T16:46:37.368021Z","shell.execute_reply":"2025-03-11T16:46:37.393109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Placeholder function for predicting RNA structure\ndef predict_structure(sequence):\n    # Implement actual structure prediction using your chosen method\n    # For demonstration, return placeholder coordinates\n    return np.random.rand(len(sequence), 3)  # Placeholder for actual structure prediction\n\n# Predict structures for test sequences\npredicted_structures = []\nfor sequence in test_sequences['sequence']:\n    structure = predict_structure(sequence)\n    predicted_structures.append(structure)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-11T16:46:00.702216Z","iopub.execute_input":"2025-03-11T16:46:00.702600Z","iopub.status.idle":"2025-03-11T16:46:00.712594Z","shell.execute_reply.started":"2025-03-11T16:46:00.702571Z","shell.execute_reply":"2025-03-11T16:46:00.711398Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Function to convert predicted structures into submission format\ndef prepare_submission(predicted_structures, test_sequences):\n    submission_data = []\n    for i, structure in enumerate(predicted_structures):\n        sequence_id = test_sequences.iloc[i]['target_id']\n        sequence_length = len(structure)\n        for resid in range(sequence_length):\n            row = {\n                'ID': f\"{sequence_id}_{resid+1}\",\n                'resname': test_sequences.iloc[i]['sequence'][resid],\n                'resid': resid+1,\n            }\n            for j in range(5):  # Five predictions per residue\n                row[f\"x_{j+1}\"] = np.random.rand()  # Placeholder for actual x coordinate\n                row[f\"y_{j+1}\"] = np.random.rand()  # Placeholder for actual y coordinate\n                row[f\"z_{j+1}\"] = np.random.rand()  # Placeholder for actual z coordinate\n            submission_data.append(row)\n    \n    # Convert to DataFrame and save as submission.csv\n    submission_df = pd.DataFrame(submission_data)\n    submission_df.to_csv('submission.csv', index=False)\n\nprepare_submission(predicted_structures, test_sequences)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-11T16:47:02.619842Z","iopub.execute_input":"2025-03-11T16:47:02.620230Z","iopub.status.idle":"2025-03-11T16:47:02.813521Z","shell.execute_reply.started":"2025-03-11T16:47:02.620198Z","shell.execute_reply":"2025-03-11T16:47:02.812377Z"}},"outputs":[],"execution_count":null}]}