{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Visulising the training dataset\n\nThis notebook provides a useful way for visulising raw training eruption data to guide feature exploration. "},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"!pip3 install -U celluloid","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# imports\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import timedelta\nfrom celluloid import Camera\nfrom IPython.display import HTML, clear_output","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# time to eruption dataload\ntrain = pd.read_csv(\"../input/predict-volcanic-eruptions-ingv-oe/train.csv\")\ntrain['datetime_to_eruption'] = train['time_to_eruption'].apply(lambda x: timedelta(seconds = x/100))\ntrain.sort_values('datetime_to_eruption', inplace=True)\n\nprint(len(train), 'training samples')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot raw training samples\nfig, ax = plt.subplots(figsize=(20, 10)) # setup\ncamera = Camera(fig)\n\nfor s_id in train[::-50].segment_id:\n    seq_str = f\"../input/predict-volcanic-eruptions-ingv-oe/train/{s_id}.csv\"\n    sequence = pd.read_csv(seq_str, dtype=\"Int16\") # 10 min period\n    \n    ax = sequence.fillna(0).plot(subplots=True, legend=False, ax=ax)\n    ax[0].text(0, 0, f\"{train[train.segment_id==s_id].datetime_to_eruption.to_list()[0]}\", \n        ha='center', backgroundcolor='white')\n    ax[0].set_title('Spread of training samples')\n    \n    camera.snap() # camera snapshot\n    \nanim = camera.animate() # animating the plots\nclear_output()\nHTML(anim.to_html5_video()) # show HTML vid","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}