[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:fig, ax = plt.subplots(1)\nylow, yhigh = ax.set_ylim([0,12])\nnmdb=dat.nmdb_set.resample('1h').median()[GLE1_begin:GLE1_end]\nplt.plot(nmdb.index, ((nmdb[' OULU']/np.mean(dat.nmdb_set[' OULU']))-1.)*100.)\nax.fill_betweenx([ylow,yhigh], GLE1[0],GLE1[1], facecolor='yellow', alpha=0.5, label='GLE 1')\nax.set_xlabel('Time')\nax.set_ylabel('Neutron Counts (Percent Above Mean)')\nax.set_title('{0} {1}'.format('OULU Station Neutron Monitor','for GLE on 2012-05-17'))\nGLE_label = mpatches.Patch(color='yellow', label='GLE 1')\nplt.legend(handles=[GLE_label], loc='upper right')\nfig.autofmt_xdate()\nplt.tight_layout()\nplt.savefig('{0}{1}{2}{3}'.format('/Users/mskirk/Desktop/GooglePlots/GLE1_','OULU','Station','.png'), dpi=200)"}, {'role': 'assistant', 'content': "['data_exploration']"}]