[{'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:figure(1)\nfig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\nfield = h_A\nCS = plt.plot(time, -field,'-',linewidth=3, label=deltaz_A)\nfield = h_B\nCS = plt.plot(time, -field,'-',linewidth=3, label=deltaz_B)\nfield = h_C\nCS = plt.plot(time, -field,'-',linewidth=3, label=deltaz_C)\nfield = h_D\nCS = plt.plot(time, -field,'-',linewidth=3, label=deltaz_D)\nax.legend(fontsize=24,loc='upper right')\nax.tick_params('both', length=10, width=2, which='major',labelsize=20)\nax.tick_params('both', length=10, width=2, which='minor',labelsize=20)\nplt.ylim((-110,0))\nplt.grid()\nplt.xlabel('Time (days)',fontsize=30)\nplt.ylabel('z (m)',fontsize=30)\nplt.title(r'KPP boundary layer depth from MOM6',fontsize=30)\nplt.tight_layout()\nfname = expt_name+'_MOM6_bldepth.png'\nfig.savefig(fname,dpi=dpi);"}, {'role': 'assistant', 'content': "['data_exploration']"}]