[{'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:mon_cru = np.arange(1,13);      mon_all = np.transpose(np.tile(mon_cru,(1,112)))\nyr_cru  = np.arange(1901,2013); yr_all  = np.transpose(np.tile(yr_cru,(12,1))); yr_all = np.reshape(yr_all,(1344,1))\nroot_dir = '/Users/bcook/Documents/GEODATA/cru321/'\nfiles_crupdsi = ['pdsi.3.21.penman.snow.1901-1910.nc', \\\n'pdsi.3.21.penman.snow.1911-1920.nc',\\\n'pdsi.3.21.penman.snow.1911-1920.nc',\\\n'pdsi.3.21.penman.snow.1931-1940.nc',\\\n'pdsi.3.21.penman.snow.1941-1950.nc',\\\n'pdsi.3.21.penman.snow.1951-1960.nc',\\\n'pdsi.3.21.penman.snow.1961-1970.nc',\\\n'pdsi.3.21.penman.snow.1971-1980.nc',\\\n'pdsi.3.21.penman.snow.1981-1990.nc',\\\n'pdsi.3.21.penman.snow.1991-2000.nc',\\\n'pdsi.3.21.penman.snow.2001-2010.nc',\\\n'pdsi.3.21.penman.snow.2011-2012.nc'\\\n]\nfname = root_dir+files_crupdsi[0]\nncfile_pdsi = netCDF4.Dataset(fname)\nlon = ncfile_pdsi.variables['lon'][:]\nlat = ncfile_pdsi.variables['lat'][:]\nncfile_pdsi.close\ni_lat = np.where((lat>=lat1_cru) & (lat<=lat2_cru)); i_lat=i_lat[0]\ni_lon = np.where((lon>=lon1_cru) & (lon<=lon2_cru)); i_lon=i_lon[0]\nlon_map = lon[i_lon]; lat_map = lat[i_lat];\nfor ifile in np.arange(0,np.size(files_crupdsi)):\nfname = root_dir+files_crupdsi[ifile]; print(fname)\nncfile_pdsi = netCDF4.Dataset(fname)\npdsi = np.float64(ncfile_pdsi.variables['pdsi'][:,i_lat,i_lon]);\nif ifile==0:\npdsi_all=pdsi;\nelse:\npdsi_all=np.concatenate((pdsi_all,pdsi),axis=0)\nncfile_pdsi.close\npdsi_month = np.zeros((np.size(mons_ave),np.size(yr_cru),np.size(i_lat),np.size(i_lon)))\nfor i in enumerate(mons_ave):\nprint(i)\nloc_mon = np.where(mon_all==i[1]); loc_mon=loc_mon[0];\npdsi_month[i[0],:,:,:] = pdsi_all[loc_mon,:,:]\npdsi_seas = np.nanmean(pdsi_month,axis=0)\ndel(pdsi_month)\ndel(pdsi_all)\ndel(pdsi)"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]