[{'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:loc_yrs_ghd = np.where((yr>=yr1) & (yr<=yr2))[0];\nloc_yrs_cru = np.where((yr_cru>=yr1) & (yr_cru<=yr2))[0];\nrho_ghd_pdsi  = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_pdsi[:]=np.NAN\nrho_ghd_temp  = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_temp[:]=np.NAN\nrho_ghd_prec  = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_prec[:]=np.NAN\nrho_ghd_pdsi_det = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_pdsi[:]=np.NAN\nrho_ghd_temp_det = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_temp[:]=np.NAN\nrho_ghd_prec_det = np.zeros((np.size(ghd_all_names),np.size(i_lat),np.size(i_lon))); rho_ghd_prec[:]=np.NAN\nsite_names = df_sitelocs.Location\nfor ifile in enumerate(ghd_all_names):\nprint(ifile)\nghd_mean = np.float64(df[ifile[1]]);\nghd_mean = ghd_mean[loc_yrs_ghd];\nrealvals = np.where(~np.isnan(ghd_mean))[0]\nghd_trend = ghd_mean[realvals]\nghd_detrend  = scipy.signal.detrend(ghd_mean[realvals])\nfor n_lat in enumerate(i_lat):\nfor n_lon in enumerate(i_lon):\npdsi_seas_cell = pdsi_seas[loc_yrs_cru,n_lat[0],n_lon[0]]\ntemp_seas_cell = tmp_seas_ave[loc_yrs_cru,n_lat[0],n_lon[0]]\nprec_seas_cell = pre_seas_sum[loc_yrs_cru,n_lat[0],n_lon[0]]\npdsi_cell_trend = pdsi_seas_cell[realvals]\ntmp_cell_trend  = temp_seas_cell[realvals]\npre_cell_trend  = prec_seas_cell[realvals]\npdsi_cell_detrend = scipy.signal.detrend(pdsi_seas_cell[realvals])\ntmp_cell_detrend  = scipy.signal.detrend(temp_seas_cell[realvals])\npre_cell_detrend  = scipy.signal.detrend(prec_seas_cell[realvals])\nif temp_seas_cell[0]>1000:\nrho_ghd_pdsi[ifile[0],n_lat[0],n_lon[0]]     = np.nan\nrho_ghd_pdsi_det[ifile[0],n_lat[0],n_lon[0]] = np.nan\nrho_ghd_temp[ifile[0],n_lat[0],n_lon[0]]     = np.nan\nrho_ghd_temp_det[ifile[0],n_lat[0],n_lon[0]] = np.nan\nrho_ghd_prec[ifile[0],n_lat[0],n_lon[0]]     = np.nan\nrho_ghd_prec_det[ifile[0],n_lat[0],n_lon[0]] = np.nan\nelse:\nrho,pval = stats.spearmanr(pdsi_cell_trend,ghd_trend)\nrho_ghd_pdsi[ifile[0],n_lat[0],n_lon[0]]      = rho\nrho,pval = stats.spearmanr(pdsi_cell_detrend,ghd_detrend)\nrho_ghd_pdsi_det[ifile[0],n_lat[0],n_lon[0]]  = rho\nrho,pval = stats.spearmanr(tmp_cell_trend,ghd_trend)\nrho_ghd_temp[ifile[0],n_lat[0],n_lon[0]]      = rho\nrho,pval = stats.spearmanr(tmp_cell_detrend,ghd_detrend)\nrho_ghd_temp_det[ifile[0],n_lat[0],n_lon[0]]  = rho\nrho,pval = stats.spearmanr(pre_cell_trend,ghd_trend)\nrho_ghd_prec[ifile[0],n_lat[0],n_lon[0]]  = rho\nrho,pval = stats.spearmanr(pre_cell_detrend,ghd_detrend)\nrho_ghd_prec_det[ifile[0],n_lat[0],n_lon[0]]  = rho\nif pdsi_seas_cell[0]>1000:\nrho_ghd_pdsi[ifile[0],n_lat[0],n_lon[0]]  = np.nan\nrho_ghd_pdsi_det[ifile[0],n_lat[0],n_lon[0]]  = np.nan'}, {'role': 'assistant', 'content': 'data_exploration'}]