[{'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:rcParams['figure.figsize'] = 18, 8\nax1 = plt.gca()\nax2 = ax1.twinx()\nax1.plot (counts_dataframe ['position'], moving_average10_secondba, 'c-') #(position, coverage, 'r-')\naxis_lower_limit = 0\naxis_upper_limit = 0.2\nax1.axis ([0, 11000, axis_lower_limit, axis_upper_limit])\ncolor = 0.0\nfcolor = list ('bgrcmybgrcmybgrcmybgrcmy')\ncolor_counter = 0\nax2.plot (counts_dataframe ['position'], moving_average10, 'r-') #(position, coverage, 'r-')\naxis_lower_limit = 0.915\nax2.axis ([0, 11000, axis_lower_limit, 1])\nax2.ylabel ('Proportion of Reads')\nax2.legend (['The most frequent nucleotide'], loc = 'upper right')\nfor gene in annots_list:\ncolor_counter +=1\nplt.axvspan(gene.start, gene.end, color = (fcolor [color_counter]), alpha = 0.1) #, label='NS5B')\nplt.text (gene.start + ((gene.end - gene.start)/10), 10000 + (15000 *(color_counter%3)) , gene.name, rotation=0)\ncolor += 1/len(annots_list)"}, {'role': 'assistant', 'content': "['data_exploration']"}]