[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:mask_file = '/Volumes/behavgenom_archive$/Avelino/screening/David_Miller/MaskedVideos/ATR_110417_fromL4/B3_Ch1_11042017_155332.hdf5'\nfeat_file = mask_file.replace('MaskedVideos', 'Results').replace('.hdf5', '_features.hdf5')\nlight_on, react_frac = get_reaction_data(mask_file, feat_file, th=THRESH, is_reversal=True, time_window_s=time_window_s, fps=fps)\nxx = np.arange(light_on.size)/25\ngraph = plt.figure(figsize=(15, 6))\ngood = ~np.isnan(react_frac)\nd1, = plt.plot(xx[good], react_frac[good], label='fraction of worms going backwards within a window of {}s'.format(time_window_s))\nd2, = plt.plot(xx, light_on, label = 'light on', linewidth=2)\nplt.legend(handles=[d1, d2], loc=2, prop={'size':14})\nplt.ylim((0, 1.2))\nplt.xlabel('Time (s)', fontsize=16)\nwith PdfPages('strainB_time_fraction_backwards.pdf') as pp:\npp.savefig(graph)"}, {'role': 'assistant', 'content': 'data_exploration'}]