[{'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:fig, ax = plt.subplots(1)\nylow, yhigh = ax.set_ylim([0.000002,0.001])\nax.set_yscale("log", nonposy=\'clip\')\nfor id_index in range(33):\nerrors=dat.error_set.where(dat.error_set[\'loc_id\'] == dat.site_info[\'ID\'][id_index]).dropna().resample(\n\'1h\').median()[FD1_begin:FD1_end]\nwx = dat.wx_set.where(dat.wx_set[\'loc_id\'] == dat.site_info[\'ID\'][id_index]).dropna()\nwx = wx.reset_index().drop_duplicates(subset=\'DateUTC\', keep=\'first\').set_index(\'DateUTC\')\nwx = wx[wx.MeanSeaLevelPressureIn > 20] # filtering unphysical pressures\nwx = wx.resample(\'1h\').median()[FD1_begin:FD1_end]\natm_corr=(np.exp(((wx[[\'MeanSeaLevelPressureIn\']]*InHg)-sealev)/lambda0)).rename(index=str, columns={\'MeanSeaLevelPressureIn\':\'MeanCorrection\'})\natm_corr.index=pd.to_datetime(atm_corr.index,utc=True)\natm_corr = atm_corr.resample(\'1h\').interpolate(method=\'linear\')\nerrors.rate=atm_corr.MeanCorrection*errors.rate\nax.plot(errors.index, errors.rate, alpha=0.7)\nfor id_index in [3]:\nerrors=dat.error_set.where(dat.error_set[\'loc_id\'] == dat.site_info[\'ID\'][id_index]).dropna().resample(\n\'1h\').median()[FD1_begin:FD1_end]\nwx = dat.wx_set.where(dat.wx_set[\'loc_id\'] == dat.site_info[\'ID\'][id_index]).dropna()\nwx = wx.reset_index().drop_duplicates(subset=\'DateUTC\', keep=\'first\').set_index(\'DateUTC\')\nwx = wx[wx.MeanSeaLevelPressureIn > 20] # filtering unphysical pressures\nwx = wx.resample(\'1h\').median()[FD1_begin:FD1_end]\natm_corr=(np.exp(((wx[[\'MeanSeaLevelPressureIn\']]*InHg)-sealev)/lambda0)).rename(index=str, columns={\'MeanSeaLevelPressureIn\':\'MeanCorrection\'})\natm_corr.index=pd.to_datetime(atm_corr.index,utc=True)\natm_corr = atm_corr.resample(\'1h\').interpolate(method=\'linear\')\nerrors.rate=atm_corr.MeanCorrection*errors.rate\nax.plot(errors.index, errors.rate, lw=3, color=\'k\')\nax.fill_betweenx([ylow,yhigh], FD1[0],FD1[1], facecolor=\'yellow\', alpha=0.5, label=\'Forbush 1\')\nax.fill_betweenx([ylow,yhigh], FD2[0],FD2[1], facecolor=\'blue\', alpha=0.3, label=\'Forbush 2\')\nax.set_xlabel(\'Date in 2012\')\nax.xaxis.set_major_formatter(mdates.DateFormatter(\'%m-%d %H:%M\'))\nax.set_ylabel(\'Error Rate (errors/Gb)\')\nax.set_title(\'{0} {1}\'.format(\'Error Rate\',\'for Forbush Decrease\'))\nFD1_label = mpatches.Patch(color=\'yellow\', label=\'Forbush 1\')\nFD2_label = mpatches.Patch(color=\'blue\', label=\'Forbush 2\')\nplt.legend(handles=[FD1_label,FD2_label], loc=\'upper left\', fontsize=10)\nfig.autofmt_xdate()\nplt.tight_layout()\nplt.savefig(\'{0}{1}{2}\'.format(\'/Users/mskirk/Desktop/GooglePlots/FD1_60minBIn\',\'Merged\',\'.png\'), dpi=200)'}, {'role': 'assistant', 'content': "['data_exploration']"}]