{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/g/data/zv2/agcd/v2-0-1/precip/total/r005/01month/\"\n",
    "file = \"agcd_v2-0-1_precip_total_r005_monthly_2018.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/g/data/zv2/agcd/v2-0-1/precip/total/r005/01month/agcd_v2-0-1_precip_total_r005_monthly_2018.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/g/data/zv2/agcd/v2-0-1/precip/total/r005/01month/agcd_v2-0-1_precip_total_r005_monthly_2018.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'b0f33c8d-d367-4f9c-91cf-679ecd4b4326']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Open NetCDF file\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Slice the dataset to only include data for the specific lat/lon grid\u001b[39;00m\n\u001b[1;32m      5\u001b[0m ds_sliced \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39msel(lon\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m142\u001b[39m, \u001b[38;5;241m145\u001b[39m), lat\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m25\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m22\u001b[39m))\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/api.py:573\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    562\u001b[0m     decode_cf,\n\u001b[1;32m    563\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    569\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    570\u001b[0m )\n\u001b[1;32m    572\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 573\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    574\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    575\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    576\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    577\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    578\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    579\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    580\u001b[0m     backend_ds,\n\u001b[1;32m    581\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    591\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    592\u001b[0m )\n\u001b[1;32m    593\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:603\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    584\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    600\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    601\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    602\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 603\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    604\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    605\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    606\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    609\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    610\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    611\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    612\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    613\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    615\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    616\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    394\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    395\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    396\u001b[0m )\n\u001b[1;32m    397\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    398\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    399\u001b[0m )\n\u001b[0;32m--> 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:347\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    349\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:409\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 409\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:403\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    402\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 403\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m_nc4_require_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_group\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2464\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2027\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/g/data/zv2/agcd/v2-0-1/precip/total/r005/01month/agcd_v2-0-1_precip_total_r005_monthly_2018.nc'"
     ]
    }
   ],
   "source": [
    "# Open NetCDF file\n",
    "ds = xr.open_dataset(path+file)\n",
    "\n",
    "# Slice the dataset to only include data for the specific lat/lon grid\n",
    "ds_sliced = ds.sel(lon=slice(142, 145), lat=slice(-25, -22))\n",
    "\n",
    "# Convert to DataFrame\n",
    "df = ds_sliced['precip'].to_dataframe().reset_index()\n",
    "\n",
    "# Filter rows where latitude and longitude are integers\n",
    "df = df[df['lat'].apply(lambda x: x.is_integer())]\n",
    "df = df[df['lon'].apply(lambda x: x.is_integer())]\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Close the xarray dataset\n",
    "ds.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        time   lat    lon     precip\n",
       "0 2018-01-16 -25.0  142.0   3.752930\n",
       "1 2018-01-16 -25.0  143.0   0.613281\n",
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       "3 2018-01-16 -25.0  145.0  10.794922\n",
       "4 2018-01-16 -24.0  142.0   7.103516"
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     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44652, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 691, lon: 886, time: 8)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2019-01-16T12:00:00 ... 2019-08-16T12:00:00\n",
       "  * lat      (lat) float32 -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
       "  * lon      (lon) float32 112.0 112.1 112.1 112.2 ... 156.1 156.1 156.2 156.2\n",
       "Data variables:\n",
       "    mask_70  (lat, lon) float32 ...\n",
       "    pr       (time, lat, lon) float32 ...\n",
       "Attributes: (12/45)\n",
       "    date_calibration:         2021-06-25 09:32:04Z\n",
       "    date_created:             2021-06-25 09:32:04Z\n",
       "    keywords:                 multi-week, seasonal, calibrated\n",
       "    summary:                  Data calibrated using quantile-quantile matching\n",
       "    acknowledgement:          Australian Bureau of Meteorology, Commonwealth ...\n",
       "    author:                   Bureau of Meteorology\n",
       "    ...                       ...\n",
       "    url:                      http://www.bom.gov.au/climate/\n",
       "    history:                  Fri Feb 18 12:27:26 2022: ncap2 -O -s mask_70=s...\n",
       "    latest_script_used:       make_time_av_.py\n",
       "    history_raw_model:        Mon Feb  8 06:03:44 2021: ncks -O -v pr pc19811...\n",
       "    obs_history:              Tue May 25 00:01:58 2021 : /S2PrepareData/bin/o...\n",
       "    history_regrid_model:     Thu Jun 24 22:29:35 2021 : /S2PrepareData/bin/m...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9ab559f3-5c40-4f1d-883b-96367f782dbf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ab559f3-5c40-4f1d-883b-96367f782dbf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 691</li><li><span class='xr-has-index'>lon</span>: 886</li><li><span class='xr-has-index'>time</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb9b88d8-73e0-46a4-b89e-f1e83bc5cd58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb9b88d8-73e0-46a4-b89e-f1e83bc5cd58' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-16T12:00:00 ... 2019-08-...</div><input id='attrs-3150268b-354c-4b16-944e-b10aea7119bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3150268b-354c-4b16-944e-b10aea7119bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc4c61fd-fd95-4272-a653-9cf23f096dfb' class='xr-var-data-in' type='checkbox'><label for='data-dc4c61fd-fd95-4272-a653-9cf23f096dfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-16T12:00:00.000000000&#x27;, &#x27;2019-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-16T12:00:00.000000000&#x27;, &#x27;2019-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-16T12:00:00.000000000&#x27;, &#x27;2019-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-16T12:00:00.000000000&#x27;, &#x27;2019-08-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-44.5 -44.45 -44.4 ... -10.05 -10.0</div><input id='attrs-18a88239-bc7d-4f5d-98b1-fec55311e518' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a88239-bc7d-4f5d-98b1-fec55311e518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-150b0c35-4044-4382-be73-8ff1161ee918' class='xr-var-data-in' type='checkbox'><label for='data-150b0c35-4044-4382-be73-8ff1161ee918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-44.5 , -44.45, -44.4 , ..., -10.1 , -10.05, -10.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>112.0 112.1 112.1 ... 156.2 156.2</div><input id='attrs-f4751445-fdd3-4786-9fee-2fc55ba641b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4751445-fdd3-4786-9fee-2fc55ba641b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-816b7959-22ea-459c-be23-3952c20f3d95' class='xr-var-data-in' type='checkbox'><label for='data-816b7959-22ea-459c-be23-3952c20f3d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 156.15, 156.2 , 156.25], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3113a3d-d8d6-4733-b4d6-17c6a8e81003' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3113a3d-d8d6-4733-b4d6-17c6a8e81003' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mask_70</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ec719b5-e096-4d7c-8585-953a8a4598bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ec719b5-e096-4d7c-8585-953a8a4598bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b234280c-f6d6-406d-a56f-7d7db671aef5' class='xr-var-data-in' type='checkbox'><label for='data-b234280c-f6d6-406d-a56f-7d7db671aef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>mask_70_percent_OK_rainfall_data</dd></dl></div><div class='xr-var-data'><pre>[612226 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f2f59da-9f67-423f-8818-b99bff98d383' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f2f59da-9f67-423f-8818-b99bff98d383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c78016b-fd56-4911-92a9-25109acaa4a8' class='xr-var-data-in' type='checkbox'><label for='data-4c78016b-fd56-4911-92a9-25109acaa4a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm day-1</dd><dt><span>long_name :</span></dt><dd>daily precipitation</dd><dt><span>comment :</span></dt><dd>grid based daily rainfall</dd></dl></div><div class='xr-var-data'><pre>[4897808 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0467cffe-7579-4ace-817f-915abb3d8622' class='xr-section-summary-in' type='checkbox'  ><label for='section-0467cffe-7579-4ace-817f-915abb3d8622' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d82fbf57-9909-418f-bc2d-ec43d239f4fd' class='xr-index-data-in' type='checkbox'/><label for='index-d82fbf57-9909-418f-bc2d-ec43d239f4fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-01-16 12:00:00&#x27;, &#x27;2019-02-15 00:00:00&#x27;,\n",
       "               &#x27;2019-03-16 12:00:00&#x27;, &#x27;2019-04-16 00:00:00&#x27;,\n",
       "               &#x27;2019-05-16 12:00:00&#x27;, &#x27;2019-06-16 00:00:00&#x27;,\n",
       "               &#x27;2019-07-16 12:00:00&#x27;, &#x27;2019-08-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9176649c-fe9b-4a24-9868-42fe48a26a48' class='xr-index-data-in' type='checkbox'/><label for='index-9176649c-fe9b-4a24-9868-42fe48a26a48' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -44.5,  -44.45000076293945, -44.400001525878906,\n",
       "       -44.349998474121094,  -44.29999923706055,              -44.25,\n",
       "        -44.20000076293945, -44.150001525878906, -44.099998474121094,\n",
       "        -44.04999923706055,\n",
       "       ...\n",
       "       -10.449999809265137, -10.399999618530273, -10.350000381469727,\n",
       "       -10.300000190734863,              -10.25, -10.199999809265137,\n",
       "       -10.149999618530273, -10.100000381469727, -10.050000190734863,\n",
       "                     -10.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=691))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d913fea-3b7d-464a-a169-7f85fe1c2419' class='xr-index-data-in' type='checkbox'/><label for='index-2d913fea-3b7d-464a-a169-7f85fe1c2419' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             112.0, 112.05000305175781,  112.0999984741211,\n",
       "        112.1500015258789, 112.19999694824219,             112.25,\n",
       "       112.30000305175781,  112.3499984741211,  112.4000015258789,\n",
       "       112.44999694824219,\n",
       "       ...\n",
       "        155.8000030517578, 155.85000610351562, 155.89999389648438,\n",
       "        155.9499969482422,              156.0,  156.0500030517578,\n",
       "       156.10000610351562, 156.14999389648438,  156.1999969482422,\n",
       "                   156.25],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=886))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-876d8b37-3677-4109-8c03-0b20f3b8b741' class='xr-section-summary-in' type='checkbox'  ><label for='section-876d8b37-3677-4109-8c03-0b20f3b8b741' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>date_calibration :</span></dt><dd>2021-06-25 09:32:04Z</dd><dt><span>date_created :</span></dt><dd>2021-06-25 09:32:04Z</dd><dt><span>keywords :</span></dt><dd>multi-week, seasonal, calibrated</dd><dt><span>summary :</span></dt><dd>Data calibrated using quantile-quantile matching</dd><dt><span>acknowledgement :</span></dt><dd>Australian Bureau of Meteorology, Commonwealth of Australia</dd><dt><span>author :</span></dt><dd>Bureau of Meteorology</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>copyright :</span></dt><dd>(C) Copyright Commonwealth of Australia 2017, Bureau of Meteorology (ABN 92637 533532), see http://www.bom.gov.au/other/copyright.shtml for terms and conditions of reuse.</dd><dt><span>creator_name :</span></dt><dd>Bureau of Meteorology</dd><dt><span>creator_url :</span></dt><dd>http://bom.gov.au</dd><dt><span>date_obs_data_processed :</span></dt><dd>2021-05-25T00:01:58.045113</dd><dt><span>date_regrid_model :</span></dt><dd>2021-06-24T22:29:35.898214</dd><dt><span>disclaimer :</span></dt><dd>The data are subject to the Bureau of Meteorology&#x27;s disclaimer. http://www.bom.gov.au/other/disclaimer.shtml</dd><dt><span>distribution_statement :</span></dt><dd>The data are subject to the distribution terms of use outlined in the Bureau of Meteorology&#x27;s Copyright Notice http://www.bom.gov.au/other/copyright.shtml</dd><dt><span>geospatial_lat_max :</span></dt><dd>-9.975</dd><dt><span>geospatial_lat_min :</span></dt><dd>-44.525</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees</dd><dt><span>geospatial_lon_max :</span></dt><dd>156.275</dd><dt><span>geospatial_lon_min :</span></dt><dd>111.975</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees</dd><dt><span>id :</span></dt><dd>access-s2 calibrated</dd><dt><span>input_file_format :</span></dt><dd>netcdf4</dd><dt><span>input_uri :</span></dt><dd>/g/data/ux62/access-s2/hindcast/raw_model/atmos/pr/daily/e01/da_pr_19811231_e01.nc</dd><dt><span>institute_id :</span></dt><dd>ABOM</dd><dt><span>institution :</span></dt><dd>Australian Bureau of Meteorology, GPO Box 1289, Melbourne VIC 3001, Australia</dd><dt><span>licence :</span></dt><dd>Copyright for any data supplied by the Bureau of Meteorology is held in the Commonwealth of Australia and the purchaser shall give acknowledgement of the source in reference to the data. Apart from dealings under the Copyright Act 1968, the purchaser shall not reproduce (electronically or otherwise), modify or supply (by sale or otherwise) these data without written permission from the supplier.</dd><dt><span>license :</span></dt><dd>The Bureau of Meteorology licenses this data under its Express Licence Agreement. Please contact the Bureau if you wish to use the data.</dd><dt><span>model_id :</span></dt><dd>ACCESS-S2</dd><dt><span>naming_authority :</span></dt><dd>au.gov.bom</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.9.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>obs_source :</span></dt><dd>AWAP</dd><dt><span>obs_summary :</span></dt><dd>The calibrated daily rainfall gauge data is scaled so that the sum of gridded daily values matches the gridded monthly accumulation. The daily and monthly rainfall data represents the amount of precipitation of any type (including the liquid equivalent of frozen hydrometeors such as hail and snow) observed by means of rain gauges stationed across Australia. These gauges measure millimetres of liquid water depth over a 24-hour period running from 9am local clock time the previous day to 9am on the observed day. Rainfall data from up to 6500 sites across the country are analysed, with limited quality control, onto 0.05x0.05 degree grids using the technique described in Jones et al. (2009). Monthly rainfall totals are calculated from the accumulation of the contributing daily rainfall totals, and may incorporate multi-day rainfall totals if observations were missing on any one day. Weekly rainfall totals are calculated from the sum of the daily grids. Multi-month rainfall totals are calculated from the sum of monthly grids. Month-to-date and Year-to-date rainfall grids are calculated from the sum of the contributing monthly and daily grids. </dd><dt><span>publisher_institution :</span></dt><dd>Australian Bureau of Meteorology</dd><dt><span>publisher_name :</span></dt><dd>Australian Bureau of Meteorology</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>http://bom.gov.au</dd><dt><span>references :</span></dt><dd>http://www.bom.gov.au/amm/docs/2009/jones_hres.pdf; http://www.csiro.au/awap; http://www.cawcr.gov.au/technical-reports/CTR_013.pdf</dd><dt><span>source :</span></dt><dd>ACCESS-S2</dd><dt><span>title :</span></dt><dd>ACCESS-S2 calibrated</dd><dt><span>url :</span></dt><dd>http://www.bom.gov.au/climate/</dd><dt><span>history :</span></dt><dd>Fri Feb 18 12:27:26 2022: ncap2 -O -s mask_70=short(mask_70) /g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/monthly/e03/maq5_pr_20181231_e03.nc /g/data/dx2/ps7814/access-s2/hc/calibrated/v2/atmos/pr/monthly/e03/maq5_pr_20181231_e03.nc\n",
       "25-06-2021: Compute Time Average\n",
       "Fri Jun 25 09:32:04 2021 : /g/data/dx2/access-s2/calibration/bin/v1/calib402.exe</dd><dt><span>latest_script_used :</span></dt><dd>make_time_av_.py</dd><dt><span>history_raw_model :</span></dt><dd>Mon Feb  8 06:03:44 2021: ncks -O -v pr pc19811231_tot.nc /19811231T0000Z/e01//da_pr_19811231_e01.nc\n",
       "PP/UM file read by cdunif. Converted to netCDF by coupled_trial on 2021-02-08.</dd><dt><span>obs_history :</span></dt><dd>Tue May 25 00:01:58 2021 : /S2PrepareData/bin/obs/regrid_obs.py --var pr --year 1981 --model S2 --no_overwrite False --debug False --proc_no_parallel False</dd><dt><span>history_regrid_model :</span></dt><dd>Thu Jun 24 22:29:35 2021 : /S2PrepareData/bin/model/regrid_model.py --var pr --year 1981 --mmdd 1231 --no_regrid False --no_derived False --model S2 --no_overwrite False --debug False --proc_no_parallel False --ens e01</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 691, lon: 886, time: 8)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2019-01-16T12:00:00 ... 2019-08-16T12:00:00\n",
       "  * lat      (lat) float32 -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
       "  * lon      (lon) float32 112.0 112.1 112.1 112.2 ... 156.1 156.1 156.2 156.2\n",
       "Data variables:\n",
       "    mask_70  (lat, lon) float32 ...\n",
       "    pr       (time, lat, lon) float32 ...\n",
       "Attributes: (12/45)\n",
       "    date_calibration:         2021-06-25 09:32:04Z\n",
       "    date_created:             2021-06-25 09:32:04Z\n",
       "    keywords:                 multi-week, seasonal, calibrated\n",
       "    summary:                  Data calibrated using quantile-quantile matching\n",
       "    acknowledgement:          Australian Bureau of Meteorology, Commonwealth ...\n",
       "    author:                   Bureau of Meteorology\n",
       "    ...                       ...\n",
       "    url:                      http://www.bom.gov.au/climate/\n",
       "    history:                  Fri Feb 18 12:27:26 2022: ncap2 -O -s mask_70=s...\n",
       "    latest_script_used:       make_time_av_.py\n",
       "    history_raw_model:        Mon Feb  8 06:03:44 2021: ncks -O -v pr pc19811...\n",
       "    obs_history:              Tue May 25 00:01:58 2021 : /S2PrepareData/bin/o...\n",
       "    history_regrid_model:     Thu Jun 24 22:29:35 2021 : /S2PrepareData/bin/m..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"/g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/monthly/e03/\"\n",
    "file = \"maq5_pr_20181231_e03.nc\"\n",
    "\n",
    "# Open NetCDF file\n",
    "ds = xr.open_dataset(path+file)\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Producing data for 0 files...\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import os\n",
    "\n",
    "# Initialize an empty DataFrame to hold all the data\n",
    "master_df = pd.DataFrame()\n",
    "\n",
    "# Define the path and file pattern\n",
    "path = \"/g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/monthly/e03/\"\n",
    "pattern = \"*pr_2017*.nc\"\n",
    "\n",
    "# List all files that match the pattern\n",
    "files = glob(os.path.join(path, pattern))\n",
    "print(f\"Producing data for {len(files)} files...\")\n",
    "\n",
    "# Loop through the list of files and load each one\n",
    "for file in files:\n",
    "    # Load the xarray dataset\n",
    "    ds = xr.open_dataset(file)\n",
    "    \n",
    "    # Slice the dataset for specific lat/lon grid\n",
    "    ds_sliced = ds.sel(lon=slice(142, 145), lat=slice(-25, -22))\n",
    "    \n",
    "    # Flatten the sliced data for 'pr' variable\n",
    "    df = ds_sliced['pr'].to_dataframe().reset_index()\n",
    "    \n",
    "    # Filter rows where latitude and longitude are integers\n",
    "    df = df[df['lat'].apply(lambda x: x.is_integer())]\n",
    "    df = df[df['lon'].apply(lambda x: x.is_integer())]\n",
    "    \n",
    "    # Append the DataFrame to the master DataFrame\n",
    "    master_df = pd.concat([master_df, df], ignore_index=True)\n",
    "    \n",
    "    # Close the xarray dataset\n",
    "    ds.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>pr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.020323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.036452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.004516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-16 12:00:00</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time   lat    lon        pr\n",
       "0 2017-08-16 12:00:00 -25.0  142.0  0.000000\n",
       "1 2017-08-16 12:00:00 -25.0  143.0  0.020323\n",
       "2 2017-08-16 12:00:00 -25.0  144.0  0.036452\n",
       "3 2017-08-16 12:00:00 -25.0  145.0  0.004516\n",
       "4 2017-08-16 12:00:00 -24.0  142.0  0.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19840, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-08-16 12:00:00\n",
       "1       2017-08-16 12:00:00\n",
       "2       2017-08-16 12:00:00\n",
       "3       2017-08-16 12:00:00\n",
       "4       2017-08-16 12:00:00\n",
       "                ...        \n",
       "19835   2018-04-16 00:00:00\n",
       "19836   2018-04-16 00:00:00\n",
       "19837   2018-04-16 00:00:00\n",
       "19838   2018-04-16 00:00:00\n",
       "19839   2018-04-16 00:00:00\n",
       "Name: time, Length: 19840, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Producing data for 0 files...\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'time'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 38\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;66;03m# Close the xarray dataset\u001b[39;00m\n\u001b[1;32m     36\u001b[0m     ds\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m---> 38\u001b[0m master_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[43mmaster_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/pandas/core/frame.py:3896\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3895\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3896\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3898\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/envs/iceds/lib/python3.11/site-packages/pandas/core/indexes/range.py:418\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m--> 418\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'time'"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import os\n",
    "\n",
    "# Initialize an empty DataFrame to hold all the data\n",
    "master_df = pd.DataFrame()\n",
    "\n",
    "# Define the path and file pattern\n",
    "path = \"/g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/monthly/e02/\"\n",
    "pattern = \"*pr_2017*.nc\"\n",
    "\n",
    "# List all files that match the pattern\n",
    "files = glob(os.path.join(path, pattern))\n",
    "print(f\"Producing data for {len(files)} files...\")\n",
    "\n",
    "# Loop through the list of files and load each one\n",
    "for file in files:\n",
    "    # Load the xarray dataset\n",
    "    ds = xr.open_dataset(file)\n",
    "    \n",
    "    # Slice the dataset for specific lat/lon grid\n",
    "    ds_sliced = ds.sel(lon=slice(142, 145), lat=slice(-25, -22))\n",
    "    \n",
    "    # Flatten the sliced data for 'pr' variable\n",
    "    df = ds_sliced['pr'].to_dataframe().reset_index()\n",
    "    \n",
    "    # Filter rows where latitude and longitude are integers\n",
    "    df = df[df['lat'].apply(lambda x: x.is_integer())]\n",
    "    df = df[df['lon'].apply(lambda x: x.is_integer())]\n",
    "    \n",
    "    # Append the DataFrame to the master DataFrame\n",
    "    master_df = pd.concat([master_df, df], ignore_index=True)\n",
    "    \n",
    "    # Close the xarray dataset\n",
    "    ds.close()\n",
    "\n",
    "master_df['time'] = pd.to_datetime(master_df['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>pr</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.054194</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.296452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-16 12:00:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.640968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-16 12:00:00</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.224839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time   lat    lon        pr\n",
       "0 2017-10-16 12:00:00 -25.0  142.0  0.054194\n",
       "1 2017-10-16 12:00:00 -25.0  143.0  0.296452\n",
       "2 2017-10-16 12:00:00 -25.0  144.0  0.480000\n",
       "3 2017-10-16 12:00:00 -25.0  145.0  0.640968\n",
       "4 2017-10-16 12:00:00 -24.0  142.0  0.224839"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-10-16 12:00:00\n",
       "1       2017-10-16 12:00:00\n",
       "2       2017-10-16 12:00:00\n",
       "3       2017-10-16 12:00:00\n",
       "4       2017-10-16 12:00:00\n",
       "                ...        \n",
       "19835   2018-06-16 00:00:00\n",
       "19836   2018-06-16 00:00:00\n",
       "19837   2018-06-16 00:00:00\n",
       "19838   2018-06-16 00:00:00\n",
       "19839   2018-06-16 00:00:00\n",
       "Name: time, Length: 19840, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.time"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the ground-truth data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:08<00:00,  8.71s/it]\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Base URL for NetCDF files on Amazon S3\n",
    "BASE_URL = \"https://s3-ap-southeast-2.amazonaws.com/silo-open-data/Official/annual/\"\n",
    "\n",
    "# Variable of interest (e.g., daily_rain, max_temp, etc.)\n",
    "VARIABLE = \"daily_rain\"\n",
    "\n",
    "# Specify the range of years\n",
    "years = range(1989, 1990)\n",
    "\n",
    "# Loop over each year to download the corresponding NetCDF file\n",
    "for year in tqdm(years):\n",
    "    url = f\"{BASE_URL}{VARIABLE}/{year}.{VARIABLE}.nc\"\n",
    "    response = requests.get(url)\n",
    "    \n",
    "    # Check if the request was successful\n",
    "    if response.status_code == 200:\n",
    "        # Save the NetCDF file\n",
    "        with open(f\"data/{year}.{VARIABLE}.nc\", \"wb\") as f:\n",
    "            f.write(response.content)\n",
    "    else:\n",
    "        print(f\"Failed to download data for {year}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (lat: 681, lon: 841, time: 365)\n",
       "Coordinates:\n",
       "  * lat         (lat) float64 -44.0 -43.95 -43.9 -43.85 ... -10.1 -10.05 -10.0\n",
       "  * lon         (lon) float64 112.0 112.0 112.1 112.2 ... 153.9 153.9 154.0\n",
       "  * time        (time) datetime64[ns] 1989-01-01 1989-01-02 ... 1989-12-31\n",
       "Data variables:\n",
       "    daily_rain  (time, lat, lon) float32 ...\n",
       "    crs         |S1 ...\n",
       "Attributes:\n",
       "    department:               Department of Environment and Science\n",
       "    department_short:         DES\n",
       "    copyright:                Copyright - the State of Queensland Department ...\n",
       "    site_url:                 http://www.longpaddock.qld.gov.au\n",
       "    institution:              Queensland Government, Department of Environmen...\n",
       "    raster_source:            Gridded surface was created by interpolating ob...\n",
       "    raster_source_additions:  and other suppliers (see the SILO webpage for d...\n",
       "    metadata_url:             http://qldspatial.information.qld.gov.au/catalo...\n",
       "    reference:                Jeffrey, S.J., Carter, J.O., Moodie, K.B.A. and...\n",
       "    disclaimer:               1. The user accepts all responsibility and risk...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-02a6f35d-0cc5-4f8b-804f-5fd057ae6bbc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02a6f35d-0cc5-4f8b-804f-5fd057ae6bbc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 681</li><li><span class='xr-has-index'>lon</span>: 841</li><li><span class='xr-has-index'>time</span>: 365</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb60489b-7ff3-47ce-9967-dbb1eeb5f03c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb60489b-7ff3-47ce-9967-dbb1eeb5f03c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.0 -43.95 -43.9 ... -10.05 -10.0</div><input id='attrs-a256cd21-6dd6-4f03-ab7e-6d4da68b42fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a256cd21-6dd6-4f03-ab7e-6d4da68b42fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6882e14a-d764-4394-8571-02385fcab6b0' class='xr-var-data-in' type='checkbox'><label for='data-6882e14a-d764-4394-8571-02385fcab6b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-44.  , -43.95, -43.9 , ..., -10.1 , -10.05, -10.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>112.0 112.0 112.1 ... 153.9 154.0</div><input id='attrs-f8e5cc55-d8d5-4b9f-9d28-1d9d79fde43c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8e5cc55-d8d5-4b9f-9d28-1d9d79fde43c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc615b4-e4c4-483b-9219-dc9cf33fa863' class='xr-var-data-in' type='checkbox'><label for='data-9fc615b4-e4c4-483b-9219-dc9cf33fa863' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 153.9 , 153.95, 154.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1989-01-01 ... 1989-12-31</div><input id='attrs-88e18d8d-678a-439c-a9c9-c198b5e896f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88e18d8d-678a-439c-a9c9-c198b5e896f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92fbc68-2fdb-4825-9452-91287fc33a91' class='xr-var-data-in' type='checkbox'><label for='data-b92fbc68-2fdb-4825-9452-91287fc33a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1989-01-01T00:00:00.000000000&#x27;, &#x27;1989-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-01-03T00:00:00.000000000&#x27;, ..., &#x27;1989-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-12-30T00:00:00.000000000&#x27;, &#x27;1989-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7e45f56-4ab3-43ad-849e-f4e894898d6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7e45f56-4ab3-43ad-849e-f4e894898d6a' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>daily_rain</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1078b007-af9d-4f8d-a7be-447743ba8433' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1078b007-af9d-4f8d-a7be-447743ba8433' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49887f2f-d6b6-47ca-9456-e1856b0c501f' class='xr-var-data-in' type='checkbox'><label for='data-49887f2f-d6b6-47ca-9456-e1856b0c501f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Daily rainfall</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>-32765</dd><dt><span>valid_max :</span></dt><dd>32767</dd></dl></div><div class='xr-var-data'><pre>[209043165 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc87a2bf-4fd5-4b35-af12-6303b5613466' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc87a2bf-4fd5-4b35-af12-6303b5613466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a8903a1-a129-495e-bc9f-32ea383b869d' class='xr-var-data-in' type='checkbox'><label for='data-4a8903a1-a129-495e-bc9f-32ea383b869d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coordinate reference system</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S1]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d308c4b-fc36-42de-ba3e-138e7ca5e4fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-6d308c4b-fc36-42de-ba3e-138e7ca5e4fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6cb139cd-2c79-423e-869b-939713d7eb4c' class='xr-index-data-in' type='checkbox'/><label for='index-6cb139cd-2c79-423e-869b-939713d7eb4c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -44.0,              -43.95,               -43.9,\n",
       "                    -43.85,               -43.8,              -43.75,\n",
       "                     -43.7,              -43.65,               -43.6,\n",
       "                    -43.55,\n",
       "       ...\n",
       "       -10.449999999999996, -10.399999999999999, -10.350000000000001,\n",
       "       -10.299999999999997,              -10.25, -10.199999999999996,\n",
       "       -10.149999999999999, -10.100000000000001, -10.049999999999997,\n",
       "                     -10.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=681))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-186c7712-2d52-450f-8a1a-6d30b1449ac2' class='xr-index-data-in' type='checkbox'/><label for='index-186c7712-2d52-450f-8a1a-6d30b1449ac2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 112.0, 112.05,  112.1, 112.15,  112.2, 112.25,  112.3, 112.35,  112.4,\n",
       "       112.45,\n",
       "       ...\n",
       "       153.55,  153.6, 153.65,  153.7, 153.75,  153.8, 153.85,  153.9, 153.95,\n",
       "        154.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=841))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4816cd2-bc7d-4b07-a84c-38be2748d61e' class='xr-index-data-in' type='checkbox'/><label for='index-d4816cd2-bc7d-4b07-a84c-38be2748d61e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1989-01-01&#x27;, &#x27;1989-01-02&#x27;, &#x27;1989-01-03&#x27;, &#x27;1989-01-04&#x27;,\n",
       "               &#x27;1989-01-05&#x27;, &#x27;1989-01-06&#x27;, &#x27;1989-01-07&#x27;, &#x27;1989-01-08&#x27;,\n",
       "               &#x27;1989-01-09&#x27;, &#x27;1989-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;1989-12-22&#x27;, &#x27;1989-12-23&#x27;, &#x27;1989-12-24&#x27;, &#x27;1989-12-25&#x27;,\n",
       "               &#x27;1989-12-26&#x27;, &#x27;1989-12-27&#x27;, &#x27;1989-12-28&#x27;, &#x27;1989-12-29&#x27;,\n",
       "               &#x27;1989-12-30&#x27;, &#x27;1989-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e21ac65-e1c8-4c7b-ba00-4c8c38a26bb6' class='xr-section-summary-in' type='checkbox'  ><label for='section-6e21ac65-e1c8-4c7b-ba00-4c8c38a26bb6' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>department :</span></dt><dd>Department of Environment and Science</dd><dt><span>department_short :</span></dt><dd>DES</dd><dt><span>copyright :</span></dt><dd>Copyright - the State of Queensland Department of Environment and Science 2022</dd><dt><span>site_url :</span></dt><dd>http://www.longpaddock.qld.gov.au</dd><dt><span>institution :</span></dt><dd>Queensland Government, Department of Environment and Science</dd><dt><span>raster_source :</span></dt><dd>Gridded surface was created by interpolating observational data provided by the Australian Bureau of Meteorology</dd><dt><span>raster_source_additions :</span></dt><dd>and other suppliers (see the SILO webpage for details)</dd><dt><span>metadata_url :</span></dt><dd>http://qldspatial.information.qld.gov.au/catalogueadmin/catalog/main/home.page</dd><dt><span>reference :</span></dt><dd>Jeffrey, S.J., Carter, J.O., Moodie, K.B.A. and Beswick, A.R.. \\&quot;Using spatial interpolation to construct a comprehensive archive of Australian climate data\\&quot;. Environmental Modelling and Software, Vol 16/4, pp 309-330, 2001</dd><dt><span>disclaimer :</span></dt><dd>1. The user accepts all responsibility and risks associated with the use of this data. 2. The Queensland Government makes no representations or warranties in relation to this data, and, you agree that to the extent permitted by law, all warranties relating to accuracy, reliability, completeness, currency or suitability for any particular purpose and all liability for any loss, damage or costs (including consequential damage) incurred in any way (including but not limited to that arising from negligence) in connection with any use of or reliance on this data are excluded or limited. 3. You agree to continually indemnify the State of Queensland (and its officers and employees) against any loss, cost, expense, damage and liability of any kind (including liability in negligence) caused by your use of this data or any product made from this data.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (lat: 681, lon: 841, time: 365)\n",
       "Coordinates:\n",
       "  * lat         (lat) float64 -44.0 -43.95 -43.9 -43.85 ... -10.1 -10.05 -10.0\n",
       "  * lon         (lon) float64 112.0 112.0 112.1 112.2 ... 153.9 153.9 154.0\n",
       "  * time        (time) datetime64[ns] 1989-01-01 1989-01-02 ... 1989-12-31\n",
       "Data variables:\n",
       "    daily_rain  (time, lat, lon) float32 ...\n",
       "    crs         |S1 ...\n",
       "Attributes:\n",
       "    department:               Department of Environment and Science\n",
       "    department_short:         DES\n",
       "    copyright:                Copyright - the State of Queensland Department ...\n",
       "    site_url:                 http://www.longpaddock.qld.gov.au\n",
       "    institution:              Queensland Government, Department of Environmen...\n",
       "    raster_source:            Gridded surface was created by interpolating ob...\n",
       "    raster_source_additions:  and other suppliers (see the SILO webpage for d...\n",
       "    metadata_url:             http://qldspatial.information.qld.gov.au/catalo...\n",
       "    reference:                Jeffrey, S.J., Carter, J.O., Moodie, K.B.A. and...\n",
       "    disclaimer:               1. The user accepts all responsibility and risk..."
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(\"/home/156/cn1951/iceds/data/1989.daily_rain.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        time   lat    lon  daily_rain\n",
      "0 1989-01-01 -25.0  142.0    0.000000\n",
      "1 1989-01-01 -25.0  143.0    0.000000\n",
      "2 1989-01-01 -25.0  144.0    0.099854\n",
      "3 1989-01-01 -25.0  145.0    0.000000\n",
      "4 1989-01-01 -24.0  142.0    0.099854\n"
     ]
    }
   ],
   "source": [
    "ds = xr.open_dataset(\"/home/156/cn1951/iceds/data/1989.daily_rain.nc\")\n",
    "\n",
    "# Filter data for specific latitude and longitude\n",
    "# Latitude between -25 and -22, and Longitude between 142 and 145\n",
    "filtered_data = ds.sel(lat=slice(-25, -22), lon=slice(142, 145))\n",
    "\n",
    "# Slice for latitude between -25 and -22, and longitude between 142 and 145\n",
    "filtered_data = ds.sel(lat=slice(-25, -22), lon=slice(142, 145))\n",
    "\n",
    "# Further filter to only keep integer latitudes and longitudes\n",
    "filtered_data = filtered_data.where(\n",
    "    (filtered_data['lat'] % 1 == 0) & (filtered_data['lon'] % 1 == 0), drop=True\n",
    ")\n",
    "\n",
    "# Convert to DataFrame\n",
    "df = filtered_data.to_dataframe().reset_index()\n",
    "\n",
    "df.drop(columns=['crs'], inplace=True)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>daily_rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.099854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.099854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time   lat    lon  daily_rain\n",
       "0 1989-01-01 -25.0  142.0    0.000000\n",
       "1 1989-01-01 -25.0  143.0    0.000000\n",
       "2 1989-01-01 -25.0  144.0    0.099854\n",
       "3 1989-01-01 -25.0  145.0    0.000000\n",
       "4 1989-01-01 -24.0  142.0    0.099854"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"/home/156/cn1951/iceds/data/1989.daily_rain.nc\"\n",
    "\n",
    "def clean_silo(ds):\n",
    "    ds = xr.open_dataset(path)\n",
    "\n",
    "    # Filter data for specific latitude and longitude\n",
    "    # Latitude between -25 and -22, and Longitude between 142 and 145\n",
    "    filtered_data = ds.sel(lat=slice(-25, -22), lon=slice(142, 145))\n",
    "\n",
    "    # Slice for latitude between -25 and -22, and longitude between 142 and 145\n",
    "    filtered_data = ds.sel(lat=slice(-25, -22), lon=slice(142, 145))\n",
    "\n",
    "    # Further filter to only keep integer latitudes and longitudes\n",
    "    filtered_data = filtered_data.where(\n",
    "        (filtered_data['lat'] % 1 == 0) & (filtered_data['lon'] % 1 == 0), drop=True\n",
    "    )\n",
    "\n",
    "    # Convert to DataFrame\n",
    "    df = filtered_data.to_dataframe().reset_index()\n",
    "\n",
    "    df.drop(columns=['crs'], inplace=True)\n",
    "    df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "df = clean_silo(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [07:36<00:00, 15.22s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import requests\n",
    "import os\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "# Define constants\n",
    "BASE_URL = \"https://s3-ap-southeast-2.amazonaws.com/silo-open-data/Official/annual/\"\n",
    "VARIABLE = \"daily_rain\"  # or whatever variable you are interested in\n",
    "years = range(1989, 2019)  # Replace with your actual range\n",
    "\n",
    "def clean_silo(path):\n",
    "    ds = xr.open_dataset(path)\n",
    "    filtered_data = ds.sel(lat=slice(-25, -22), lon=slice(142, 145))\n",
    "    filtered_data = filtered_data.where(\n",
    "        (filtered_data['lat'] % 1 == 0) & (filtered_data['lon'] % 1 == 0), drop=True\n",
    "    )\n",
    "    df = filtered_data.to_dataframe().reset_index()\n",
    "    df.drop(columns=['crs'], inplace=True)\n",
    "    df.reset_index(inplace=True, drop=True)\n",
    "    return df\n",
    "\n",
    "# List to store cleaned DataFrames\n",
    "df_list = []\n",
    "\n",
    "# Loop over each year to download the corresponding NetCDF file\n",
    "for year in tqdm(years):\n",
    "    url = f\"{BASE_URL}{VARIABLE}/{year}.{VARIABLE}.nc\"\n",
    "    response = requests.get(url)\n",
    "    \n",
    "    # Temporary path to save the downloaded NetCDF file\n",
    "    temp_path = f\"{year}.{VARIABLE}.nc\"\n",
    "    \n",
    "    # Check if the request was successful\n",
    "    if response.status_code == 200:\n",
    "        # Save the NetCDF file\n",
    "        with open(temp_path, \"wb\") as f:\n",
    "            f.write(response.content)\n",
    "        \n",
    "        # Clean the data\n",
    "        cleaned_df = clean_silo(temp_path)\n",
    "        df_list.append(cleaned_df)\n",
    "        \n",
    "        # Remove the temporary NetCDF file to save space\n",
    "        os.remove(temp_path)\n",
    "    else:\n",
    "        print(f\"Failed to download data for {year}\")\n",
    "\n",
    "# Concatenate all the cleaned DataFrames\n",
    "final_df = pd.concat(df_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>daily_rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.099854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.099854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175307</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175308</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175309</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175310</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175311</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175312 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             time   lat    lon  daily_rain\n",
       "0      1989-01-01 -25.0  142.0    0.000000\n",
       "1      1989-01-01 -25.0  143.0    0.000000\n",
       "2      1989-01-01 -25.0  144.0    0.099854\n",
       "3      1989-01-01 -25.0  145.0    0.000000\n",
       "4      1989-01-01 -24.0  142.0    0.099854\n",
       "...           ...   ...    ...         ...\n",
       "175307 2018-12-31 -23.0  145.0    0.000000\n",
       "175308 2018-12-31 -22.0  142.0    0.000000\n",
       "175309 2018-12-31 -22.0  143.0    0.000000\n",
       "175310 2018-12-31 -22.0  144.0    0.000000\n",
       "175311 2018-12-31 -22.0  145.0    0.000000\n",
       "\n",
       "[175312 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_parquet('data/silo.parquet')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GloSea5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-06 09:48:29,889 INFO Welcome to the CDS\n",
      "2024-02-06 09:48:29,891 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/seasonal-monthly-single-levels\n",
      "2024-02-06 09:48:30,263 INFO Request is queued\n",
      "2024-02-06 10:39:09,544 INFO Request is running\n",
      "2024-02-06 11:09:29,317 INFO Request is completed\n",
      "2024-02-06 11:09:29,318 INFO Downloading https://download-0004-clone.copernicus-climate.eu/cache-compute-0004/cache/data8/adaptor.mars.external-1707176342.1964428-14567-18-9bd4bd56-8eb1-4627-9c42-422594814c6b.grib to download.grib (5.1M)\n",
      "2024-02-06 11:09:33,462 INFO Download rate 1.2M/s   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=5374080,content_type=application/x-grib,location=https://download-0004-clone.copernicus-climate.eu/cache-compute-0004/cache/data8/adaptor.mars.external-1707176342.1964428-14567-18-9bd4bd56-8eb1-4627-9c42-422594814c6b.grib)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For evaluation\n",
    "import cdsapi\n",
    "\n",
    "c = cdsapi.Client()\n",
    "\n",
    "c.retrieve(\n",
    "    'seasonal-monthly-single-levels',\n",
    "    {\n",
    "        'format': 'grib',\n",
    "        'originating_centre': 'ukmo',\n",
    "        'system': '15',\n",
    "        'variable': [\n",
    "            '10m_wind_speed', '2m_temperature', 'evaporation',\n",
    "            'mean_sea_level_pressure', 'total_cloud_cover', 'total_precipitation',\n",
    "        ],\n",
    "        'product_type': [\n",
    "            'ensemble_mean', 'monthly_mean',\n",
    "        ],\n",
    "        'year': [\n",
    "            '2020', '2021',\n",
    "        ],\n",
    "        'month': [\n",
    "            '01', '02', '03', \n",
    "            '04', '05', '06', \n",
    "            '07', '08', '09', \n",
    "            '10', '11', '12',\n",
    "        ],\n",
    "        'leadtime_month': [\n",
    "            '1', '2', '3',\n",
    "            '4', '5', '6',\n",
    "        ],\n",
    "        'area': [\n",
    "            -17, 143, -20,\n",
    "            146,\n",
    "        ],\n",
    "    },\n",
    "    'download.grib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-30 09:38:54,519 INFO Welcome to the CDS\n",
      "2024-01-30 09:38:54,521 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/seasonal-monthly-single-levels\n",
      "2024-01-30 09:38:54,965 INFO Request is queued\n",
      "2024-01-30 09:41:49,396 INFO Request is running\n",
      "2024-01-30 10:31:46,811 INFO Request is completed\n",
      "2024-01-30 10:31:46,812 INFO Downloading https://download-0011-clone.copernicus-climate.eu/cache-compute-0011/cache/data6/adaptor.mars.external-1706568091.827076-31839-1-6aa81b6c-3e92-43d3-a1a6-77d0056f8ea4.grib to download.grib (55.1M)\n",
      "2024-01-30 10:32:16,473 INFO Download rate 1.9M/s   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=57818880,content_type=application/x-grib,location=https://download-0011-clone.copernicus-climate.eu/cache-compute-0011/cache/data6/adaptor.mars.external-1706568091.827076-31839-1-6aa81b6c-3e92-43d3-a1a6-77d0056f8ea4.grib)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cdsapi\n",
    "\n",
    "c = cdsapi.Client()\n",
    "\n",
    "c.retrieve(\n",
    "    'seasonal-monthly-single-levels',\n",
    "    {\n",
    "        'format': 'grib',\n",
    "        'originating_centre': 'ukmo',\n",
    "        'variable': [\n",
    "            '10m_wind_speed', '2m_temperature', 'evaporation',\n",
    "            'mean_sea_level_pressure', 'total_cloud_cover', 'total_precipitation',\n",
    "        ],\n",
    "        'product_type': 'monthly_mean',\n",
    "        'leadtime_month': [\n",
    "            '1', '2', '3',\n",
    "            '4', '5', '6',\n",
    "        ],\n",
    "        'month': [\n",
    "            '01', '02', '05',\n",
    "            '06', '07', '08',\n",
    "            '09', '10', '11',\n",
    "            '12',\n",
    "        ],\n",
    "        'area': [\n",
    "            -17, 143, -20,\n",
    "            146,\n",
    "        ],\n",
    "        'system': '15',\n",
    "        'year': [\n",
    "            '1993', '1994', '1995',\n",
    "            '1996', '1997', '1998',\n",
    "            '1999', '2000', '2001',\n",
    "            '2002', '2003', '2004',\n",
    "            '2005', '2006', '2007',\n",
    "            '2008', '2009', '2010',\n",
    "            '2011', '2012', '2013',\n",
    "            '2014', '2015', '2016',\n",
    "        ],\n",
    "    },\n",
    "    'download.grib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ignoring index file 'data/raw/glosea_1.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_1.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n",
      "Ignoring index file 'data/raw/glosea_2.grib.923a8.idx' incompatible with GRIB file\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6896\n"
     ]
    }
   ],
   "source": [
    "import cfgrib\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "path = \"data/raw/glosea.grib\"\n",
    "\n",
    "# Open the GRIB file as an xarray dataset\n",
    "ds_1 = cfgrib.open_datasets(\"data/raw/glosea_1.grib\")\n",
    "ds_2 = cfgrib.open_datasets(\"data/raw/glosea_2.grib\")\n",
    "\n",
    "glosea_xarray_1, glosea_xarray_2 = ds_1[0], ds_2[0]\n",
    "\n",
    "# Convert to pandas DataFrame\n",
    "df_1 = glosea_xarray_1.to_dataframe().reset_index()\n",
    "df_2 = glosea_xarray_2.to_dataframe().reset_index()\n",
    "\n",
    "# Concatenate the two DataFrames\n",
    "df = pd.concat([df_1, df_2], ignore_index=True)\n",
    "\n",
    "print(len(df.valid_time.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142., 143., 144., 145., 150., 151., 152., 153.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.longitude.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>pr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993-02-06</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993-02-06</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993-02-06</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-02-06</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-02-06</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220667</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220668</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220669</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220670</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220671</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220672 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             time   lat    lon   pr\n",
       "0      1993-02-06 -29.0  150.0  0.0\n",
       "1      1993-02-06 -29.0  151.0  0.0\n",
       "2      1993-02-06 -29.0  152.0  0.0\n",
       "3      1993-02-06 -29.0  153.0  0.0\n",
       "4      1993-02-06 -28.0  150.0  0.0\n",
       "...           ...   ...    ...  ...\n",
       "220667 2017-06-26 -23.0  145.0  0.0\n",
       "220668 2017-06-26 -22.0  142.0  0.0\n",
       "220669 2017-06-26 -22.0  143.0  0.0\n",
       "220670 2017-06-26 -22.0  144.0  0.0\n",
       "220671 2017-06-26 -22.0  145.0  0.0\n",
       "\n",
       "[220672 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glosea_df = df.copy()\n",
    "\n",
    "# Keep only longitude between 142 and 145\n",
    "# glosea_df = glosea_df[glosea_df['longitude'].between(142, 145)]\n",
    "\n",
    "# # Keep only latitude between -25 and -22\n",
    "# glosea_df = glosea_df[glosea_df['latitude'].between(-25, -22)]\n",
    "\n",
    "# Convert tprate NaN to 0.0\n",
    "glosea_df['tprate'] = glosea_df['tprate'].fillna(0.0)\n",
    "\n",
    "# Keep only step = 28, 60, 120, 150, 182 days\n",
    "#glosea_df = glosea_df[glosea_df['step'].isin(['28 days'])]#, '60 days', '120 days', '150 days', '182 days'])]\n",
    "# Aggregate across steps\n",
    "glosea_df = glosea_df.groupby(['valid_time', 'latitude', 'longitude'])['tprate'].mean().reset_index()\n",
    "\n",
    "# Keep between 1981 and 2019\n",
    "glosea_df = glosea_df[glosea_df['valid_time'].between('1981-01-01', '2018-12-31')]\n",
    "\n",
    "# Rename columns\n",
    "glosea_df.rename(columns={'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon', 'valid_time': 'time'}, inplace=True)\n",
    "\n",
    "glosea_df.reset_index(inplace=True, drop=True)\n",
    "glosea_df = glosea_df.groupby(['time', 'lat', 'lon']).agg({'pr': 'sum'}).reset_index()\n",
    "glosea_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "956"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(glosea_df.time.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatetimeArray>\n",
       "['1993-01-09 00:00:00', '1993-01-17 00:00:00', '1993-01-25 00:00:00',\n",
       " '1993-02-01 00:00:00', '1993-04-09 00:00:00', '1993-04-17 00:00:00',\n",
       " '1993-04-25 00:00:00', '1993-05-01 00:00:00', '1993-05-09 00:00:00',\n",
       " '1993-05-17 00:00:00',\n",
       " ...\n",
       " '2016-09-25 00:00:00', '2016-10-01 00:00:00', '2016-10-09 00:00:00',\n",
       " '2016-10-17 00:00:00', '2016-10-25 00:00:00', '2016-11-01 00:00:00',\n",
       " '2016-11-09 00:00:00', '2016-11-17 00:00:00', '2016-11-25 00:00:00',\n",
       " '2016-12-01 00:00:00']\n",
       "Length: 956, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glosea_df.time.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ECMWF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-06 11:16:52,612 INFO Welcome to the CDS\n",
      "2024-02-06 11:16:52,613 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/seasonal-monthly-single-levels\n",
      "2024-02-06 11:16:53,160 INFO Request is queued\n",
      "2024-02-06 11:41:29,373 INFO Request is running\n",
      "2024-02-06 11:59:41,787 INFO Request is completed\n",
      "2024-02-06 11:59:41,789 INFO Downloading https://download-0012-clone.copernicus-climate.eu/cache-compute-0012/cache/data1/adaptor.mars.external-1707180039.2851062-16433-12-3ec044af-cb3e-47cb-9f97-cdb3e4d29e04.grib to download.grib (10.3M)\n",
      "2024-02-06 12:00:05,301 INFO Download rate 447.9K/s \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=10782720,content_type=application/x-grib,location=https://download-0012-clone.copernicus-climate.eu/cache-compute-0012/cache/data1/adaptor.mars.external-1707180039.2851062-16433-12-3ec044af-cb3e-47cb-9f97-cdb3e4d29e04.grib)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For evaluation\n",
    "import cdsapi\n",
    "\n",
    "c = cdsapi.Client()\n",
    "\n",
    "c.retrieve(\n",
    "    'seasonal-monthly-single-levels',\n",
    "    {\n",
    "        'format': 'grib',\n",
    "        'originating_centre': 'ecmwf',\n",
    "        'system': '51',\n",
    "        'variable': [\n",
    "            '10m_wind_speed', '2m_temperature', 'evaporation',\n",
    "            'mean_sea_level_pressure', 'total_cloud_cover', 'total_precipitation',\n",
    "        ],\n",
    "        'product_type': [\n",
    "            'ensemble_mean', 'monthly_mean',\n",
    "        ],\n",
    "        'year': [\n",
    "            '2020', '2021',\n",
    "        ],\n",
    "        'month': [\n",
    "            '01', '02', '03',\n",
    "            '04', '05', '06',\n",
    "            '07', '08', '09',\n",
    "            '10', '11', '12',\n",
    "        ],\n",
    "        'leadtime_month': [\n",
    "            '1', '2', '3',\n",
    "            '4', '5', '6',\n",
    "        ],\n",
    "        'area': [\n",
    "            -17, 143, -20,\n",
    "            146,\n",
    "        ],\n",
    "    },\n",
    "    'download.grib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-30 10:41:49,922 INFO Welcome to the CDS\n",
      "2024-01-30 10:41:49,922 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/seasonal-monthly-single-levels\n",
      "2024-01-30 10:41:50,258 INFO Request is queued\n",
      "2024-01-30 10:43:08,678 INFO Request is running\n",
      "2024-01-30 11:10:29,302 INFO Request is completed\n",
      "2024-01-30 11:10:29,307 INFO Downloading https://download-0007-clone.copernicus-climate.eu/cache-compute-0007/cache/data0/adaptor.mars.external-1706571771.78567-27117-2-cadfcf36-0be1-4376-960f-e5d7a9a23648.grib to download.grib (23.1M)\n",
      "2024-01-30 11:10:37,956 INFO Download rate 2.7M/s   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=24261120,content_type=application/x-grib,location=https://download-0007-clone.copernicus-climate.eu/cache-compute-0007/cache/data0/adaptor.mars.external-1706571771.78567-27117-2-cadfcf36-0be1-4376-960f-e5d7a9a23648.grib)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cdsapi\n",
    "\n",
    "c = cdsapi.Client()\n",
    "\n",
    "c.retrieve(\n",
    "    'seasonal-monthly-single-levels',\n",
    "    {\n",
    "        'format': 'grib',\n",
    "        'originating_centre': 'ecmwf',\n",
    "        'system': '51',\n",
    "        'variable': [\n",
    "            '2m_temperature', 'total_precipitation',\n",
    "        ],\n",
    "        'product_type': [\n",
    "            'ensemble_mean', 'monthly_mean',\n",
    "        ],\n",
    "        'year': [\n",
    "            '1991', '1992', '1993',\n",
    "            '1994', '1995', '1996',\n",
    "            '1997', '1998', '1999',\n",
    "            '2000', '2001', '2002',\n",
    "            '2003', '2004', '2005',\n",
    "            '2006', '2007', '2008',\n",
    "            '2009', '2010', '2011',\n",
    "            '2012', '2013', '2014',\n",
    "            '2015', '2016', '2017',\n",
    "        ],\n",
    "        'month': [\n",
    "            '01', '02', '03',\n",
    "            '04', '05', '06',\n",
    "            '07', '08', '09',\n",
    "            '10', '11', '12',\n",
    "        ],\n",
    "        'leadtime_month': [\n",
    "            '1', '2', '3',\n",
    "            '4', '5', '6',\n",
    "        ],\n",
    "        'area': [\n",
    "            -17, 143, -20,\n",
    "            146,\n",
    "        ],\n",
    "    },\n",
    "    'download.grib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>step</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>number</th>\n",
       "      <th>surface</th>\n",
       "      <th>valid_time</th>\n",
       "      <th>t2m</th>\n",
       "      <th>tprate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4027</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4029</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4030</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4031</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4032 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           time     step  latitude  longitude  number  surface valid_time  \\\n",
       "0    2017-01-01  28 days     -17.0      143.0       0      0.0 2017-01-29   \n",
       "1    2017-01-01  28 days     -17.0      144.0       0      0.0 2017-01-29   \n",
       "2    2017-01-01  28 days     -17.0      145.0       0      0.0 2017-01-29   \n",
       "3    2017-01-01  28 days     -17.0      146.0       0      0.0 2017-01-29   \n",
       "4    2017-01-01  28 days     -18.0      143.0       0      0.0 2017-01-29   \n",
       "...         ...      ...       ...        ...     ...      ...        ...   \n",
       "4027 2017-12-01 184 days     -19.0      146.0       0      0.0 2018-06-03   \n",
       "4028 2017-12-01 184 days     -20.0      143.0       0      0.0 2018-06-03   \n",
       "4029 2017-12-01 184 days     -20.0      144.0       0      0.0 2018-06-03   \n",
       "4030 2017-12-01 184 days     -20.0      145.0       0      0.0 2018-06-03   \n",
       "4031 2017-12-01 184 days     -20.0      146.0       0      0.0 2018-06-03   \n",
       "\n",
       "      t2m  tprate  \n",
       "0     NaN     0.0  \n",
       "1     NaN     0.0  \n",
       "2     NaN     0.0  \n",
       "3     NaN     0.0  \n",
       "4     NaN     0.0  \n",
       "...   ...     ...  \n",
       "4027  NaN     0.0  \n",
       "4028  NaN     0.0  \n",
       "4029  NaN     0.0  \n",
       "4030  NaN     0.0  \n",
       "4031  NaN     0.0  \n",
       "\n",
       "[4032 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open up the data/raw/ecmwf_3.grib file as a pandas df\n",
    "import cfgrib\n",
    "\n",
    "path = \"data/raw/ecmwf_3.grib\"\n",
    "\n",
    "# Open the GRIB file as an xarray dataset\n",
    "ds_1 = cfgrib.open_datasets(\"data/raw/ecmwf_1.grib\")\n",
    "ds_2 = cfgrib.open_datasets(\"data/raw/ecmwf_2.grib\")\n",
    "ds_3 = cfgrib.open_datasets(\"data/raw/ecmwf_3.grib\")\n",
    "\n",
    "grib_xarray_1 = ds_1[0]\n",
    "grib_xarray_2 = ds_2[0]\n",
    "grib_xarray_3 = ds_3[0]\n",
    "\n",
    "# Convert to pandas DataFrame\n",
    "df_1 = grib_xarray_1.to_dataframe().reset_index()\n",
    "df_1['tprate'] = df_1['tprate'].fillna(0.0)\n",
    "df_2 = grib_xarray_2.to_dataframe().reset_index()\n",
    "df_2['tprate'] = df_2['tprate'].fillna(0.0)\n",
    "df_3 = grib_xarray_3.to_dataframe().reset_index()\n",
    "df_3['tprate'] = df_3['tprate'].fillna(0.0)\n",
    "\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>time</th>\n",
       "      <th>step</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>surface</th>\n",
       "      <th>valid_time</th>\n",
       "      <th>tprate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156684235</th>\n",
       "      <td>50</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156684236</th>\n",
       "      <td>50</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156684237</th>\n",
       "      <td>50</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156684238</th>\n",
       "      <td>50</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156684239</th>\n",
       "      <td>50</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156684240 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           number       time     step  latitude  longitude  surface  \\\n",
       "0               0 1981-01-01  28 days     -15.0      138.0      0.0   \n",
       "1               0 1981-01-01  28 days     -15.0      139.0      0.0   \n",
       "2               0 1981-01-01  28 days     -15.0      140.0      0.0   \n",
       "3               0 1981-01-01  28 days     -15.0      141.0      0.0   \n",
       "4               0 1981-01-01  28 days     -15.0      142.0      0.0   \n",
       "...           ...        ...      ...       ...        ...      ...   \n",
       "156684235      50 2022-10-01 184 days     -29.0      150.0      0.0   \n",
       "156684236      50 2022-10-01 184 days     -29.0      151.0      0.0   \n",
       "156684237      50 2022-10-01 184 days     -29.0      152.0      0.0   \n",
       "156684238      50 2022-10-01 184 days     -29.0      153.0      0.0   \n",
       "156684239      50 2022-10-01 184 days     -29.0      154.0      0.0   \n",
       "\n",
       "          valid_time  tprate  \n",
       "0         1981-01-29     NaN  \n",
       "1         1981-01-29     NaN  \n",
       "2         1981-01-29     NaN  \n",
       "3         1981-01-29     NaN  \n",
       "4         1981-01-29     NaN  \n",
       "...              ...     ...  \n",
       "156684235 2023-04-03     NaN  \n",
       "156684236 2023-04-03     NaN  \n",
       "156684237 2023-04-03     NaN  \n",
       "156684238 2023-04-03     NaN  \n",
       "156684239 2023-04-03     NaN  \n",
       "\n",
       "[156684240 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>28 days</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4027</th>\n",
       "      <td>2017-12-01</td>\n",
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       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4029</th>\n",
       "      <td>2017-12-01</td>\n",
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       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4030</th>\n",
       "      <td>2017-12-01</td>\n",
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       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4031</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>184 days</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>4032 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           time     step  latitude  longitude  number  surface valid_time  \\\n",
       "0    2017-01-01  28 days     -26.0      150.0       0      0.0 2017-01-29   \n",
       "1    2017-01-01  28 days     -26.0      151.0       0      0.0 2017-01-29   \n",
       "2    2017-01-01  28 days     -26.0      152.0       0      0.0 2017-01-29   \n",
       "3    2017-01-01  28 days     -26.0      153.0       0      0.0 2017-01-29   \n",
       "4    2017-01-01  28 days     -27.0      150.0       0      0.0 2017-01-29   \n",
       "...         ...      ...       ...        ...     ...      ...        ...   \n",
       "4027 2017-12-01 184 days     -28.0      153.0       0      0.0 2018-06-03   \n",
       "4028 2017-12-01 184 days     -29.0      150.0       0      0.0 2018-06-03   \n",
       "4029 2017-12-01 184 days     -29.0      151.0       0      0.0 2018-06-03   \n",
       "4030 2017-12-01 184 days     -29.0      152.0       0      0.0 2018-06-03   \n",
       "4031 2017-12-01 184 days     -29.0      153.0       0      0.0 2018-06-03   \n",
       "\n",
       "      t2m  tprate  \n",
       "0     NaN     NaN  \n",
       "1     NaN     NaN  \n",
       "2     NaN     NaN  \n",
       "3     NaN     NaN  \n",
       "4     NaN     NaN  \n",
       "...   ...     ...  \n",
       "4027  NaN     NaN  \n",
       "4028  NaN     NaN  \n",
       "4029  NaN     NaN  \n",
       "4030  NaN     NaN  \n",
       "4031  NaN     NaN  \n",
       "\n",
       "[4032 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>pr</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1981-01-29</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981-01-29</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1981-01-29</td>\n",
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       "      <td>142.0</td>\n",
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       "    <tr>\n",
       "      <th>627295</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018-12-31</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>627297</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627298</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>627299</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>627300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             time   lat    lon   pr\n",
       "0      1981-01-29 -29.0  138.0  0.0\n",
       "1      1981-01-29 -29.0  139.0  0.0\n",
       "2      1981-01-29 -29.0  140.0  0.0\n",
       "3      1981-01-29 -29.0  141.0  0.0\n",
       "4      1981-01-29 -29.0  142.0  0.0\n",
       "...           ...   ...    ...  ...\n",
       "627295 2018-12-31 -15.0  150.0  0.0\n",
       "627296 2018-12-31 -15.0  151.0  0.0\n",
       "627297 2018-12-31 -15.0  152.0  0.0\n",
       "627298 2018-12-31 -15.0  153.0  0.0\n",
       "627299 2018-12-31 -15.0  154.0  0.0\n",
       "\n",
       "[627300 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read in the processed parquet\n",
    "df = pd.read_parquet(\"data/processed/ecmwf.parquet\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the number of unique time values"
   ]
  }
 ],
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