[{'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:fbins_w = 0.025 # in MHz\nfbins_MHz = np.arange(fbins_min,fbins_max+fbins_w,fbins_w)\nplt.figure(15) # Zoomed areas\nplt.clf()\nplt.grid(True)\ncounts_in_fbins, fbins_edges, patches_temp = plt.hist(all_freq_32,fbins_MHz,color='blue',histtype='stepfilled',alpha=0.75)\nplt.xlim(1049.5,1054)\nplt.xlabel('Start Frequency (MHz)')\nplt.ylabel('Counts')\nplt.title('31.9 keV lines - Phase I Bathtub')\nif flag_save_fig:\nplt.savefig('/home/les67/Les67P8Analysis/PhaseI/power_slope_identification/multiclass/final_classification_FIGS/spectrum_main_carrier_region.png', dpi=300, bbox_inches='tight')\nplt.show()"}, {'role': 'assistant', 'content': "['data_exploration']"}]