[{'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:pdsi_coswtmean = np.zeros((np.size(yr_cru),np.size(ghd_all_names)))\ntmp_coswtmean  = np.zeros((np.size(yr_cru),np.size(ghd_all_names)))\npre_coswtmean  = np.zeros((np.size(yr_cru),np.size(ghd_all_names)))\ndeg_range = 1; range_txt = \'onedeg\';\nfor ifile in enumerate(ghd_all_names):\nprint(ifile)\nif ifile[1] in [\'GHDcore\',\'GHDmean\']:\nprint("lat range = ("+np.str(latmin)+" to "+np.str(latmax)+")")\nprint("lon range = ("+np.str(lonmin)+" to "+np.str(lonmax)+")")\ni_lat_reg = np.where((lat_map>=latmin) & (lat_map<=latmax)); i_lat_reg=i_lat_reg[0]\ni_lon_reg = np.where((lon_map>=lonmin) & (lon_map<=lonmax)); i_lon_reg=i_lon_reg[0]\nlon_reg = lon_map[i_lon_reg]\nlat_reg = lat_map[i_lat_reg]\nlat_wts = scipy.cos(scipy.deg2rad(lat_reg));\nlat_wts_grid,lon_junk = np.meshgrid(lat_wts,lon_reg)\nlat_wts_grid=np.swapaxes(lat_wts_grid,1,0)\nelse:\nlat_site = df_sitelocs.Latitude[ifile[1]]\nlon_site = df_sitelocs.Longitude[ifile[1]]\nlatmin_new = lat_site-deg_range; latmax_new = lat_site+deg_range;\nlonmin_new = lon_site-deg_range; lonmax_new = lon_site+deg_range;\nprint("lat range = ("+np.str(latmin_new)+" to "+np.str(latmax_new)+")")\nprint("lon range = ("+np.str(lonmin_new)+" to "+np.str(lonmax_new)+")")\ni_lat_reg = np.where((lat_map>=latmin_new) & (lat_map<=latmax_new)); i_lat_reg=i_lat_reg[0]\ni_lon_reg = np.where((lon_map>=lonmin_new) & (lon_map<=lonmax_new)); i_lon_reg=i_lon_reg[0]\nlon_reg = lon_map[i_lon_reg]\nlat_reg = lat_map[i_lat_reg]\nlat_wts = scipy.cos(scipy.deg2rad(lat_reg));\nlat_wts_grid,lon_junk = np.meshgrid(lat_wts,lon_reg)\nlat_wts_grid=np.swapaxes(lat_wts_grid,1,0)\nfor i_yr in enumerate(yr_cru):\npdsi_curr = pdsi_seas[i_yr[0],i_lat_reg,:][:,i_lon_reg]\ntemp_curr = tmp_seas_ave[i_yr[0],i_lat_reg,:][:,i_lon_reg]\nprec_curr = pre_seas_sum[i_yr[0],i_lat_reg,:][:,i_lon_reg]\nprec_curr[prec_curr>=100000]=np.nan\ntemp_curr[temp_curr>=100000]=np.nan\npdsi_curr[pdsi_curr>=100000]=np.nan\npdsi_coswtmean[i_yr[0],ifile[0]] = np.ma.average(np.ma.masked_invalid(pdsi_curr),weights=lat_wts_grid)\ntmp_coswtmean[i_yr[0],ifile[0]]  = np.ma.average(np.ma.masked_invalid(temp_curr),weights=lat_wts_grid)\npre_coswtmean[i_yr[0],ifile[0]]  = np.ma.average(np.ma.masked_invalid(prec_curr),weights=lat_wts_grid)'}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing']"}]