[{'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:def peak_finder(y):\n'''\nThis is the main function that needs development.\nEssentially it looks through three consequative values of y at the time and checks in the middle value is a max.\nIf it is a max, it saves it to a peak list.\n'''\npeaks = []\ni, j, k = 0, 1, 2\ncounter = 0\nfirst, second, third = y[i], y[j], y[k]\nwhile k < len(y):\nif second >= third:\nif second >= first:\npeaks.append([j, second])\ni += 1\nj += 1\nk += 1\nif k < len(y):\nfirst, second, third = y[i], y[j], y[k]\nelse:\npass\nreturn peaks\ndef plotting_max(x, peak):\n'''\nThis function just automates part of the process\n'''\nlength = len(peak)\ni = 0\nhorizontal = []\nvertical = []\nargument = []\nwhile i < length:\nhorizontal.append(x[peak[i][0]])\nvertical.append(peak[i][1])\ni+=1\nreturn horizontal, vertical\ndef random_spectra_generator(spikes, length):\n'''\nGenerates a more accurate spectra for testing purposes.\nThere is length entries.\nSpikes is how many peaks there should be.\n'''\nhigher = length//3\ni = 0\nspectra = []\nwhile i < higher:\nspectra.append(np.random.randint(low=45, high=55))\ni+=1\nwhile i < length:\nspectra.append(np.random.randint(low=0, high=10))\ni+=1\nj = 0\nwhile j < spikes:\nspectra[np.random.randint(low=0, high=length-1)] = np.random.randint(low=40, high=100)\nj+=1\nreturn spectra"}, {'role': 'assistant', 'content': 'data_preprocessing'}]