{
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  {
   "cell_type": "markdown",
   "id": "24ece7af-9e94-433d-8fec-6170c7bad246",
   "metadata": {},
   "source": [
    "- script removes unwanted variables that don't need to be stored\n",
    "- So far, I have run it for 'gfdl-esm4' (except for RGI region 03)\n",
    "- need to repeat it for the other gcms\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635f0612-335e-4b5e-9f90-2c9e96c98afc",
   "metadata": {},
   "source": [
    "['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0',\n",
    "       'ukesm1-0-ll']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "588dad5b-98ee-4020-8051-bf71f86185d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddd3ddcf-5f43-403d-a847-75c90dc0c422",
   "metadata": {},
   "outputs": [],
   "source": [
    "rgi_regs = []\n",
    "for rgi_reg in np.arange(1,20,1):\n",
    "    if rgi_reg < 10:\n",
    "        rgi_reg = '0'+str(rgi_reg)\n",
    "    else:\n",
    "        rgi_reg = str(rgi_reg)\n",
    "    rgi_regs.append(rgi_reg)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccfa0306-499f-4869-8859-850358453a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['17', '18', '19']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgi_regs[16:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f567e00-feb1-4600-ad57-e98c0e0724bb",
   "metadata": {},
   "source": [
    "- > just need to load that code once again ... when all RGI regions are finished:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adb260e7-21e7-4b12-9e17-a63a60294152",
   "metadata": {},
   "outputs": [],
   "source": [
    "for rgi in rgi_regs:\n",
    "    fpath = f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi}/'\n",
    "    files = os.listdir(path=fpath)\n",
    "    files_old = files.copy()\n",
    "\n",
    "    for f in files: \n",
    "        if 'x_' not in f and '.nc' in f:\n",
    "            with xr.open_dataset(fpath+f) as ds:\n",
    "                try:\n",
    "                    ds = ds.drop_vars('volume_m3_min_h')\n",
    "                    ds = ds.drop_vars('area_m2_min_h')\n",
    "\n",
    "                    ds.to_netcdf(fpath+'x_'+f)\n",
    "                    # remove the old files\n",
    "                    #for f in files_old:\n",
    "                except:\n",
    "                    # here we did already remove the stuff\n",
    "                    os.rename(fpath+f, fpath+'x_'+f)\n",
    "            try:\n",
    "                os.remove(fpath+f)\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "    # rename the new smaller files the same as the old files\n",
    "    files_new = os.listdir(path=fpath)\n",
    "    for ff in files_new:\n",
    "        if 'x_' in ff:\n",
    "            os.rename(fpath+ff, fpath+ff[2:])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7150d52-3aa3-46d0-a151-d4c62c12779c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'06'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a46ecc-f7d3-44f0-8492-d990a8fbc0ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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