{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":91844,"databundleVersionId":11361821,"sourceType":"competition"}],"dockerImageVersionId":31012,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Initial look into the data and geoplotting the locations","metadata":{}},{"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 list(os.walk('/kaggle/input'))[:4]:\n    print(filenames)\n#    for filename in filenames[:10]:\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":"2025-04-27T08:50:09.785889Z","iopub.execute_input":"2025-04-27T08:50:09.786645Z","iopub.status.idle":"2025-04-27T08:50:10.103434Z","shell.execute_reply.started":"2025-04-27T08:50:09.786614Z","shell.execute_reply":"2025-04-27T08:50:10.102321Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch,librosa, numpy as np, matplotlib.pyplot as plt, torchaudio\nfrom IPython.display import Audio\nfrom pathlib import Path\nimport pandas as pd\nimport seaborn as sns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-27T08:51:06.813114Z","iopub.execute_input":"2025-04-27T08:51:06.813463Z","iopub.status.idle":"2025-04-27T08:51:06.818171Z","shell.execute_reply.started":"2025-04-27T08:51:06.813440Z","shell.execute_reply":"2025-04-27T08:51:06.817090Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"BASE_DIRNAME = Path('/kaggle/input/birdclef-2025/')\n\n# read metadata\nmetadata_file = BASE_DIRNAME/'train.csv'\nmetadata = pd.read_csv(metadata_file)\nmetadata.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-27T08:51:07.209638Z","iopub.execute_input":"2025-04-27T08:51:07.209947Z","iopub.status.idle":"2025-04-27T08:51:07.360914Z","shell.execute_reply.started":"2025-04-27T08:51:07.209923Z","shell.execute_reply":"2025-04-27T08:51:07.360166Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sns.scatterplot(data=metadata, x='longitude', y='latitude')\nplt.title('Locations of audio samples')\nplt.grid(True)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-27T08:51:09.964267Z","iopub.execute_input":"2025-04-27T08:51:09.964569Z","iopub.status.idle":"2025-04-27T08:51:10.250560Z","shell.execute_reply.started":"2025-04-27T08:51:09.964549Z","shell.execute_reply":"2025-04-27T08:51:10.249351Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}