[{'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:%matplotlib notebook\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider, Button, RadioButtons\nfig, ax = plt.subplots()\nplt.subplots_adjust(left=0.25, bottom=0.25)\nt = np.arange(0.0, 1.0, 0.001)\na0 = 5\nf0 = 3\ns = a0*np.sin(2*np.pi*f0*t)\nl, = plt.plot(t, s, lw=2, color='red')\nplt.axis([0, 1, -10, 10])\naxcolor = 'lightgoldenrodyellow'\naxfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)\naxamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)\nsfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)\nsamp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)\ndef update(val):\namp = samp.val\nfreq = sfreq.val\nl.set_ydata(amp*np.sin(2*np.pi*freq*t))\nfig.canvas.draw_idle()\nsfreq.on_changed(update)\nsamp.on_changed(update)\nresetax = plt.axes([0.8, 0.025, 0.1, 0.04])\nbutton = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\ndef reset(event):\nsfreq.reset()\nsamp.reset()\nbutton.on_clicked(reset)\nrax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)\nradio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\ndef colorfunc(label):\nl.set_color(label)\nfig.canvas.draw_idle()\nradio.on_clicked(colorfunc)\nplt.show()"}, {'role': 'assistant', 'content': 'result_visualization'}]