[{'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:hist_bins = numpy.arange(0, 255.1, 10, dtype=numpy.uint8)\nhalf_bins = hist_bins.shape[0] // 2\ndef get_image_stat(fname):\nimg = Image.open(fname)\npixels = numpy.asarray(img)\nreturn numpy.histogram(pixels, bins = hist_bins)[0]\ndef contains_white(fname):\nimg = Image.open(fname)\npixels = numpy.asarray(img)\nreturn (pixels > 0).any()\ndef get_positive_prefixes(dirname, n_jobs=20):\nfilenames = glob.glob(os.path.join(dirname, '*_out.png'))\nwhite_info = Parallel(n_jobs=n_jobs)(delayed(contains_white)(fname)\nfor fname in filenames)\nreturn [fname[:-8] for fname, has_white\nin zip(filenames, white_info)\nif has_white]\ndef copy_only_positive(src_dir, target_dir, n_jobs=20):\nfor prefix in get_positive_prefixes(src_dir, n_jobs=n_jobs):\nshutil.copy2(prefix + '_in.png', target_dir)\nshutil.copy2(prefix + '_out.png', target_dir)\ndef aggregate_stat_for_multiple_images(fnames):\nmarginal_hist = numpy.zeros(hist_bins.shape[0] - 1,\ndtype=numpy.float)\nnumber_of_white_images = 0\nimg_cnt = 0.0\nfor cur_hist in Parallel(n_jobs=20)(delayed(get_image_stat)(fname)\nfor fname in fnames):\nmarginal_hist += cur_hist\nimg_cnt += 1\nif cur_hist[half_bins:].sum() > 0:\nnumber_of_white_images += 1\nreturn marginal_hist / img_cnt, number_of_white_images\ndef folder_stat(dirname):\nfig, (in_ax, out_ax) = plt.subplots(2)\nin_files = list(glob.glob(os.path.join(dirname, '*_in.png')))\nin_files_cnt = len(in_files)\nin_hist, in_white_cnt = aggregate_stat_for_multiple_images(in_files)\nin_ax.bar(hist_bins[:-1], in_hist, label='In')\nin_ax.legend()\nin_percent = in_white_cnt / in_files_cnt * 100.0\nprint(f'In white {in_white_cnt} / {in_files_cnt}, {in_percent}%')\nout_files = glob.glob(os.path.join(dirname, '*_out.png'))\nout_files_cnt = len(out_files)\nout_hist, out_white_cnt = aggregate_stat_for_multiple_images(out_files)\nout_ax.bar(hist_bins[:-1], out_hist, label='Out')\nout_ax.legend()\nout_percent = out_white_cnt / out_files_cnt * 100.0\nprint(f'Out white {out_white_cnt} / {out_files_cnt}, {out_percent}%')"}, {'role': 'assistant', 'content': 'data_preprocessing'}]