notebooks/aggregate_csv_files_1.6.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcf84529-7d87-499e-9111-856df3d3a118",
   "metadata": {},
   "source": [
    "# Aggregate the OGGM raw files to regional or global summed up csv files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f699822-ab84-4b7a-85de-39c05c3a7280",
   "metadata": {},
   "source": [
    "\n",
    "*Note that the paths of this notebook only work if you are running the notebook from the OGGM cluster*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c48538d2-e653-4452-83b4-7615526d4654",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import progressbar\n",
    "import numpy as np\n",
    "from oggm.utils import mkdir\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb7cf223-9aec-4ea3-98f1-3547fe03da77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/notebooks'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0da2049c-6272-4d39-8ce3-0d697434c41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dirpath = '/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3/'\n",
    "outputpath = '/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1'\n",
    "# we only aggregate here `w5e5_gcm_merged`, but you could repeat this here with `gcm_from_2000` as it is also available in the raw per-glacier datafiles\n",
    "historical_future_option = 'w5e5_gcm_merged'\n",
    "\n",
    "# this is the only preprocessed version (i.e., we match the geodetic observations on any individual glacier)\n",
    "exp = 'match_geod_pergla'\n",
    "oggm_version = 'oggm_v1.6.1_2023.3'\n",
    "\n",
    "rgi_meta = pd.read_hdf('/home/www/oggm/rgi/rgi62_stats.h5')\n",
    "rgi_meta = rgi_meta.loc[rgi_meta.Connect != 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf0032e-dc72-423a-a6ba-e61af96f8cc2",
   "metadata": {},
   "source": [
    "## A. Common running glacier aggregations for all projections, until 2100 and until 2300, i.e. `common_running_2100_2300`\n",
    "- the same for only until 2100 is done in Section B further below\n",
    "\n",
    "### A. 1: GCMs - missing glaciers overview  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcf50b5f-1a43-4236-81e4-0f95733136d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "allfiles = []\n",
    "for root, dirs, files in os.walk(dirpath):\n",
    "    for file in files:\n",
    "        if (file.endswith(\".nc\")) and ('run_hydro' in file) and (historical_future_option in file) and ('_0_1000' in file) and ('basin' not in file):\n",
    "             allfiles.append(os.path.join(root, file))\n",
    "\n",
    "df_meta = pd.DataFrame()\n",
    "invalid_per_reg = {}  \n",
    "meta_per_reg = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b01f8351-dd10-4f4b-988d-8fc2edd7d13d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3/CMIP6/2100/RGI12/run_hydro_w5e5_gcm_merged_CAMS-CSM1-0_ssp585_bc_2000_2019_Batch_0_1000.nc\n",
      "/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3/CMIP5/2100/RGI06/run_hydro_w5e5_gcm_merged_MPI-ESM-LR_rcp26_bc_2000_2019_Batch_0_1000.nc\n"
     ]
    }
   ],
   "source": [
    "run = True\n",
    "if run:\n",
    "    # this takes very long !!!\n",
    "    invalid_per_reg = {}  \n",
    "    meta_per_reg = {}\n",
    "    for f in allfiles: #progressbar.progressbar(allfiles):\n",
    "        ename = f.replace(dirpath, '')\n",
    "        ss = ename.split('/')\n",
    "        cmip = ss[0]\n",
    "        endyr = ss[1]\n",
    "        rgi_reg = ss[2]\n",
    "        gcm_original = ss[3].split('_')[5] #ss[2]\n",
    "        gcm = gcm_original.upper()\n",
    "        if 'ISIMIP3b' in cmip:\n",
    "            ssp = ss[3].split('_')[7]\n",
    "            bc = 'ISIMIP3b:1979-2014'\n",
    "        else:\n",
    "            ssp = ss[3].split('_')[6]\n",
    "            bc = '2000-2019'\n",
    "\n",
    "        end0,end1 = ss[3].split('_')[-2:]\n",
    "        end = end0+'_'+end1\n",
    "        ff = f[:-len(end)]+'*'\n",
    "\n",
    "        #if ssp not in ['ssp119', 'ssp126', 'ssp245', 'ssp370', 'ssp585']:\n",
    "        #    continue\n",
    "\n",
    "        # exp is the same everywhere, so we don't need as an id... \n",
    "        run_id = f'{cmip}:{endyr}:{gcm}:{ssp}:{rgi_reg}'\n",
    "        df_meta.loc[run_id, 'cmip'] = cmip\n",
    "        df_meta.loc[run_id, 'gcm_original'] = gcm_original # some have lower and upper cases in the raw output files\n",
    "        df_meta.loc[run_id, 'gcm'] = gcm  # for the aggregated files, all GCMs are in upper cases\n",
    "        df_meta.loc[run_id, 'scenario'] = ssp\n",
    "        df_meta.loc[run_id, 'rgi_reg'] = rgi_reg\n",
    "        df_meta.loc[run_id, 'end_year'] = 0\n",
    "        df_meta.loc[run_id, 'bias_correction'] = bc\n",
    "\n",
    "        df_meta.loc[run_id, 'perc_area_missing'] = 0\n",
    "\n",
    "        df_meta.loc[run_id, 'fpath'] = ff\n",
    "\n",
    "        if rgi_reg not in invalid_per_reg:\n",
    "            invalid_per_reg[rgi_reg] = set()\n",
    "\n",
    "        if rgi_reg not in meta_per_reg:\n",
    "            meta_per_reg[rgi_reg] = rgi_meta.loc[rgi_meta['O1Region'] == rgi_reg[-2:]]\n",
    "\n",
    "        with xr.open_mfdataset(ff) as ds:\n",
    "            ds_t = ds.isel(time=-1).volume.load()\n",
    "        df_meta.loc[run_id, 'end_year'] = str(int(ds_t.time))\n",
    "        missing_ids = ds_t.rgi_id[ds_t.isnull()].data\n",
    "        perc = meta_per_reg[rgi_reg].loc[missing_ids]['Area'].sum() / meta_per_reg[rgi_reg]['Area'].sum() \n",
    "        df_meta.loc[run_id, 'perc_area_missing'] = perc\n",
    "        invalid_per_reg[rgi_reg] = invalid_per_reg[rgi_reg].union(missing_ids)\n",
    "        if f == allfiles[1000] or f==allfiles[2000]:\n",
    "            print(f)\n",
    "    df_meta['exp'] = exp\n",
    "    df_meta['historical_future_option'] = historical_future_option\n",
    "    df_meta['oggm_version'] = oggm_version\n",
    "    df_meta.to_csv(f'{outputpath}/common_running_2100_2300/metadata.csv')\n",
    "else:\n",
    "    df_meta = pd.read_csv(f'{outputpath}/common_running_2100_2300/metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80869bee-aa43-4329-9fba-fe3d86762078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ISIMIP3b_CMIP6', 'CMIP6', 'CMIP5'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.cmip.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "954a20a3-54f4-4723-a129-a334aa9fb484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RGI12', 'RGI06', 'RGI08'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds = df_meta.sort_values('perc_area_missing', ascending=False)\n",
    "dds[dds['perc_area_missing'] > 0.05].rgi_reg.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e986279-e205-4885-b3a5-f6688562fb79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmip</th>\n",
       "      <th>gcm_original</th>\n",
       "      <th>gcm</th>\n",
       "      <th>scenario</th>\n",
       "      <th>rgi_reg</th>\n",
       "      <th>end_year</th>\n",
       "      <th>bias_correction</th>\n",
       "      <th>perc_area_missing</th>\n",
       "      <th>fpath</th>\n",
       "      <th>exp</th>\n",
       "      <th>historical_future_option</th>\n",
       "      <th>oggm_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CMIP5:2300:MPI-ESM-LR:rcp26:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2300</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.130445</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2300:MPI-ESM-LR:rcp85:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2300</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.129181</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2300:MPI-ESM-LR:rcp45:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>rcp45</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2300</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.123297</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CANESM2:rcp26:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CanESM2</td>\n",
       "      <td>CANESM2</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.121243</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:NORESM2-MM:ssp126:RGI12</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NorESM2-MM</td>\n",
       "      <td>NORESM2-MM</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.120892</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:CESM2-WACCM:ssp370:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CESM2-WACCM</td>\n",
       "      <td>CESM2-WACCM</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CAMS-CSM1-0</td>\n",
       "      <td>CAMS-CSM1-0</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:FGOALS-F3-L:ssp585:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>FGOALS-f3-L</td>\n",
       "      <td>FGOALS-F3-L</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>CAMS-CSM1-0</td>\n",
       "      <td>CAMS-CSM1-0</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2300:CCSM4:rcp85:RGI07</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CCSM4</td>\n",
       "      <td>CCSM4</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2300</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2964 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cmip gcm_original          gcm scenario  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12    CMIP5   MPI-ESM-LR   MPI-ESM-LR    rcp26   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12    CMIP5   MPI-ESM-LR   MPI-ESM-LR    rcp85   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12    CMIP5   MPI-ESM-LR   MPI-ESM-LR    rcp45   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12       CMIP5      CanESM2      CANESM2    rcp26   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12   CMIP6   NorESM2-MM   NORESM2-MM   ssp126   \n",
       "...                                    ...          ...          ...      ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07  CMIP6  CESM2-WACCM  CESM2-WACCM   ssp370   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07  CMIP6  CAMS-CSM1-0  CAMS-CSM1-0   ssp370   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07  CMIP6  FGOALS-f3-L  FGOALS-F3-L   ssp585   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07  CMIP6  CAMS-CSM1-0  CAMS-CSM1-0   ssp245   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07         CMIP5        CCSM4        CCSM4    rcp85   \n",
       "\n",
       "                                    rgi_reg  end_year bias_correction  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12     RGI12      2300       2000-2019   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12     RGI12      2300       2000-2019   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12     RGI12      2300       2000-2019   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12        RGI12      2100       2000-2019   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12    RGI12      2100       2000-2019   \n",
       "...                                     ...       ...             ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07   RGI07      2100       2000-2019   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07   RGI07      2100       2000-2019   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07   RGI07      2100       2000-2019   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07   RGI07      2100       2000-2019   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07          RGI07      2300       2000-2019   \n",
       "\n",
       "                                     perc_area_missing  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12             0.130445   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12             0.129181   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12             0.123297   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12                0.121243   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12            0.120892   \n",
       "...                                                ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07           0.000000   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07           0.000000   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07           0.000000   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07           0.000000   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07                  0.000000   \n",
       "\n",
       "                                                                                 fpath  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12    /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12    /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12    /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12       /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12   /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "...                                                                                ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07         /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "\n",
       "                                                   exp  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12    match_geod_pergla   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12    match_geod_pergla   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12    match_geod_pergla   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12       match_geod_pergla   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12   match_geod_pergla   \n",
       "...                                                ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07  match_geod_pergla   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07  match_geod_pergla   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07  match_geod_pergla   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07  match_geod_pergla   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07         match_geod_pergla   \n",
       "\n",
       "                                    historical_future_option  \\\n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12            w5e5_gcm_merged   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12            w5e5_gcm_merged   \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12            w5e5_gcm_merged   \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12               w5e5_gcm_merged   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12           w5e5_gcm_merged   \n",
       "...                                                      ...   \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07          w5e5_gcm_merged   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07          w5e5_gcm_merged   \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07          w5e5_gcm_merged   \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07          w5e5_gcm_merged   \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07                 w5e5_gcm_merged   \n",
       "\n",
       "                                           oggm_version  \n",
       "CMIP5:2300:MPI-ESM-LR:rcp26:RGI12    oggm_v1.6.1_2023.3  \n",
       "CMIP5:2300:MPI-ESM-LR:rcp85:RGI12    oggm_v1.6.1_2023.3  \n",
       "CMIP5:2300:MPI-ESM-LR:rcp45:RGI12    oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12       oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12   oggm_v1.6.1_2023.3  \n",
       "...                                                 ...  \n",
       "CMIP6:2100:CESM2-WACCM:ssp370:RGI07  oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp370:RGI07  oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:FGOALS-F3-L:ssp585:RGI07  oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:CAMS-CSM1-0:ssp245:RGI07  oggm_v1.6.1_2023.3  \n",
       "CMIP5:2300:CCSM4:rcp85:RGI07         oggm_v1.6.1_2023.3  \n",
       "\n",
       "[2964 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa094b4e-fcfc-4f47-9922-d8d9c7d040bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in invalid_per_reg.items():\n",
    "    invalid_per_reg[k] = list(v)\n",
    "with open(f'{outputpath}/common_running_2100_2300/rgi_ids_missing.json', 'w') as f:\n",
    "    json.dump(invalid_per_reg, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f07d89cc-956f-4fae-bedf-a2ecd69ed4f5",
   "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>n_glaciers</th>\n",
       "      <th>n_missing_glaciers</th>\n",
       "      <th>rgi_area_km2</th>\n",
       "      <th>missing_area_km2</th>\n",
       "      <th>missing_area_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RGI12</th>\n",
       "      <td>1888</td>\n",
       "      <td>409</td>\n",
       "      <td>1306.992</td>\n",
       "      <td>160.691</td>\n",
       "      <td>0.122947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI06</th>\n",
       "      <td>568</td>\n",
       "      <td>420</td>\n",
       "      <td>11059.700</td>\n",
       "      <td>963.393</td>\n",
       "      <td>0.087108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI08</th>\n",
       "      <td>3417</td>\n",
       "      <td>1152</td>\n",
       "      <td>2949.103</td>\n",
       "      <td>237.021</td>\n",
       "      <td>0.080371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI10</th>\n",
       "      <td>5151</td>\n",
       "      <td>243</td>\n",
       "      <td>2410.051</td>\n",
       "      <td>94.172</td>\n",
       "      <td>0.039075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI05</th>\n",
       "      <td>19306</td>\n",
       "      <td>1662</td>\n",
       "      <td>89717.066</td>\n",
       "      <td>1467.785</td>\n",
       "      <td>0.016360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI07</th>\n",
       "      <td>1615</td>\n",
       "      <td>48</td>\n",
       "      <td>33958.934</td>\n",
       "      <td>238.343</td>\n",
       "      <td>0.007019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI13</th>\n",
       "      <td>54429</td>\n",
       "      <td>862</td>\n",
       "      <td>49303.415</td>\n",
       "      <td>311.203</td>\n",
       "      <td>0.006312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI14</th>\n",
       "      <td>27988</td>\n",
       "      <td>501</td>\n",
       "      <td>33568.298</td>\n",
       "      <td>102.782</td>\n",
       "      <td>0.003062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI19</th>\n",
       "      <td>2752</td>\n",
       "      <td>633</td>\n",
       "      <td>132867.219</td>\n",
       "      <td>377.671</td>\n",
       "      <td>0.002842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI04</th>\n",
       "      <td>7415</td>\n",
       "      <td>313</td>\n",
       "      <td>40888.228</td>\n",
       "      <td>100.893</td>\n",
       "      <td>0.002468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI17</th>\n",
       "      <td>15908</td>\n",
       "      <td>132</td>\n",
       "      <td>29429.080</td>\n",
       "      <td>58.999</td>\n",
       "      <td>0.002005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI03</th>\n",
       "      <td>4556</td>\n",
       "      <td>126</td>\n",
       "      <td>105110.642</td>\n",
       "      <td>201.883</td>\n",
       "      <td>0.001921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI02</th>\n",
       "      <td>18855</td>\n",
       "      <td>461</td>\n",
       "      <td>14524.224</td>\n",
       "      <td>18.732</td>\n",
       "      <td>0.001290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI15</th>\n",
       "      <td>13119</td>\n",
       "      <td>58</td>\n",
       "      <td>14734.204</td>\n",
       "      <td>14.869</td>\n",
       "      <td>0.001009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI09</th>\n",
       "      <td>1069</td>\n",
       "      <td>9</td>\n",
       "      <td>51591.600</td>\n",
       "      <td>40.895</td>\n",
       "      <td>0.000793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI11</th>\n",
       "      <td>3927</td>\n",
       "      <td>36</td>\n",
       "      <td>2092.146</td>\n",
       "      <td>1.327</td>\n",
       "      <td>0.000634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI16</th>\n",
       "      <td>2939</td>\n",
       "      <td>12</td>\n",
       "      <td>2341.036</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI01</th>\n",
       "      <td>27108</td>\n",
       "      <td>73</td>\n",
       "      <td>86725.053</td>\n",
       "      <td>14.635</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI18</th>\n",
       "      <td>3537</td>\n",
       "      <td>1</td>\n",
       "      <td>1161.801</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       n_glaciers  n_missing_glaciers  rgi_area_km2  missing_area_km2  \\\n",
       "RGI12        1888                 409      1306.992           160.691   \n",
       "RGI06         568                 420     11059.700           963.393   \n",
       "RGI08        3417                1152      2949.103           237.021   \n",
       "RGI10        5151                 243      2410.051            94.172   \n",
       "RGI05       19306                1662     89717.066          1467.785   \n",
       "RGI07        1615                  48     33958.934           238.343   \n",
       "RGI13       54429                 862     49303.415           311.203   \n",
       "RGI14       27988                 501     33568.298           102.782   \n",
       "RGI19        2752                 633    132867.219           377.671   \n",
       "RGI04        7415                 313     40888.228           100.893   \n",
       "RGI17       15908                 132     29429.080            58.999   \n",
       "RGI03        4556                 126    105110.642           201.883   \n",
       "RGI02       18855                 461     14524.224            18.732   \n",
       "RGI15       13119                  58     14734.204            14.869   \n",
       "RGI09        1069                   9     51591.600            40.895   \n",
       "RGI11        3927                  36      2092.146             1.327   \n",
       "RGI16        2939                  12      2341.036             0.710   \n",
       "RGI01       27108                  73     86725.053            14.635   \n",
       "RGI18        3537                   1      1161.801             0.052   \n",
       "\n",
       "       missing_area_perc  \n",
       "RGI12           0.122947  \n",
       "RGI06           0.087108  \n",
       "RGI08           0.080371  \n",
       "RGI10           0.039075  \n",
       "RGI05           0.016360  \n",
       "RGI07           0.007019  \n",
       "RGI13           0.006312  \n",
       "RGI14           0.003062  \n",
       "RGI19           0.002842  \n",
       "RGI04           0.002468  \n",
       "RGI17           0.002005  \n",
       "RGI03           0.001921  \n",
       "RGI02           0.001290  \n",
       "RGI15           0.001009  \n",
       "RGI09           0.000793  \n",
       "RGI11           0.000634  \n",
       "RGI16           0.000303  \n",
       "RGI01           0.000169  \n",
       "RGI18           0.000045  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# these are now the common running glaciers over all scenarios and GCMs and all cmip variants going until 2100 or until 2300\n",
    "odf = pd.DataFrame()\n",
    "for rgi_reg, missing_ids in invalid_per_reg.items():\n",
    "    odf.loc[rgi_reg, 'n_glaciers'] = len(meta_per_reg[rgi_reg])\n",
    "    odf.loc[rgi_reg, 'n_missing_glaciers'] = len(missing_ids)\n",
    "    odf.loc[rgi_reg, 'rgi_area_km2'] = meta_per_reg[rgi_reg]['Area'].sum() \n",
    "    odf.loc[rgi_reg, 'missing_area_km2'] = meta_per_reg[rgi_reg].loc[missing_ids]['Area'].sum()\n",
    "    odf.loc[rgi_reg, 'missing_area_perc'] = odf.loc[rgi_reg, 'missing_area_km2'] / odf.loc[rgi_reg, 'rgi_area_km2']\n",
    "odf[['n_glaciers', 'n_missing_glaciers']] = odf[['n_glaciers', 'n_missing_glaciers']].astype(int)\n",
    "odf = odf.sort_values(by='missing_area_perc', ascending=False)\n",
    "odf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9db72b31-9394-4063-83fa-07abed7be1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "odf.to_csv(f'{outputpath}/common_running_2100_2300/missing_region_overview.csv')\n",
    "odf.to_html(f'{outputpath}/common_running_2100_2300/missing_region_overview.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23c0c44-b3bc-4c98-ab2b-fe0ff6239dcd",
   "metadata": {},
   "source": [
    "### A. 2: GCMs - aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e65e9f51-200e-440d-9f41-fbc75e23c94b",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir =f'{outputpath}/common_running_2100_2300'\n",
    "df_meta = pd.read_csv(f'{base_dir}/metadata.csv', index_col = 0)\n",
    "import json\n",
    "with open(f'{base_dir}/rgi_ids_missing.json', 'r') as f:\n",
    "    invalid_per_reg = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50d030a0-5080-41cd-a27f-f5855770a4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "for cmip in df_meta.cmip.unique():\n",
    "    #print(cmip, flush=True)\n",
    "    if cmip == 'CMIP6' or cmip == 'CMIP5':\n",
    "        end_years = [2100, 2300]\n",
    "    else:\n",
    "        end_years = [2100]\n",
    "    for end_year in end_years:\n",
    "        for rgi_reg in sorted(df_meta.rgi_reg.unique()): #progressbar.progressbar(\n",
    "            for scenario in sorted(df_meta.loc[(df_meta.cmip==cmip) & (df_meta.end_year == end_year)].scenario.unique()):\n",
    "                df_meta_s = df_meta.loc[(df_meta.end_year == end_year) & (df_meta.cmip == cmip) & (df_meta.rgi_reg == rgi_reg) & (df_meta.scenario == scenario)]\n",
    "                odf_v = pd.DataFrame()\n",
    "                odf_a = pd.DataFrame()\n",
    "                ods = []\n",
    "                gcms = []\n",
    "                for i, s in df_meta_s.iterrows():\n",
    "                    with xr.open_mfdataset(s.fpath) as ds:\n",
    "                        ds = ds[['volume', 'area']].load().isel(rgi_id=~ds.rgi_id.isin(invalid_per_reg[s.rgi_reg]))\n",
    "                        odf_v[s.gcm] = ds.volume.sum(dim='rgi_id').to_series()\n",
    "                        odf_a[s.gcm] = ds.area.sum(dim='rgi_id').to_series()\n",
    "                        ods.append(ds)\n",
    "                        gcms.append(s.gcm)\n",
    "                ods = xr.concat(ods, 'gcm')\n",
    "\n",
    "                # we don't like to have these kind of summary statistics as the user does not know\n",
    "                # whether the different projections are gaussian distributed ... \n",
    "                #ods.mean(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_avg_{scenario}.nc')\n",
    "                #ods.std(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_std_{scenario}.nc')\n",
    "                odir =  f'{base_dir}/volume/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                mkdir(odir)\n",
    "                odf_v.to_csv(odir + f'{scenario}.csv')\n",
    "                odir = f'{base_dir}/area/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                mkdir(odir)\n",
    "                odf_a.to_csv(odir + f'{scenario}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05c3f9c8-8441-4817-88c0-57ddebb35d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# globally: sum over all RGI regions\n",
    "for var in ['volume', 'area']:\n",
    "    for cmip in df_meta.cmip.unique():\n",
    "        if cmip == 'CMIP6' or cmip == 'CMIP5':\n",
    "            end_years = [2100, 2300]\n",
    "        else:\n",
    "            end_years = [2100]\n",
    "        for end_year in end_years:\n",
    "            odir = f'{base_dir}/{var}/{cmip}/{end_year}/global/'\n",
    "            mkdir(odir)\n",
    "            for scenario in sorted(df_meta.loc[(df_meta.cmip==cmip) & (df_meta.end_year == end_year)].scenario.unique()):\n",
    "                odf = 0\n",
    "                for rgi_reg in sorted(df_meta.rgi_reg.unique()):\n",
    "                    idir = f'{base_dir}/{var}/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                    df = pd.read_csv(idir + f'/{scenario}.csv', index_col=0)\n",
    "                    odf += df\n",
    "                odf.to_csv(odir + f'/{scenario}.csv')\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bcfbb60-1b41-4d46-88d1-9d37de783ab0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b8793253-337a-4c34-96a8-f7536adea19d",
   "metadata": {},
   "source": [
    "## B. Common running glacier aggregations for all projections until 2100, i.e. `common_running_2100`\n",
    "\n",
    "### B. 1: GCMs - missing glaciers overview  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "753a2509-9762-42e9-85b8-6603ce8b9013",
   "metadata": {},
   "outputs": [],
   "source": [
    "allfiles = []\n",
    "for root, dirs, files in os.walk(dirpath):\n",
    "    if not '/2300/' in root:\n",
    "        for file in files:\n",
    "            if (file.endswith(\".nc\")) and ('run_hydro' in file) and (historical_future_option in file) and ('_0_1000' in file) and ('basin' not in file):\n",
    "                 allfiles.append(os.path.join(root, file))\n",
    "\n",
    "df_meta = pd.DataFrame()\n",
    "invalid_per_reg = {}  \n",
    "meta_per_reg = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "115133a9-5cd6-4619-983c-8291c36f6a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2432\n"
     ]
    }
   ],
   "source": [
    "print(len(allfiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f1bca79-3e5c-4510-872f-02299a91aa89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3/ISIMIP3b_CMIP6/2100/RGI12/run_hydro_w5e5_gcm_merged_gfdl-esm4_r1i1p1f1_ssp370_Batch_0_1000.nc\n"
     ]
    }
   ],
   "source": [
    "# this takes very long !!!\n",
    "invalid_per_reg = {}  \n",
    "meta_per_reg = {}\n",
    "for f in allfiles: #progressbar.progressbar(allfiles):\n",
    "    ename = f.replace(dirpath, '')\n",
    "    ss = ename.split('/')\n",
    "    cmip = ss[0]\n",
    "    endyr = ss[1]\n",
    "    rgi_reg = ss[2]\n",
    "    gcm_original = ss[3].split('_')[5] \n",
    "    gcm = gcm_original.upper()\n",
    "    if 'ISIMIP3b' in cmip:\n",
    "        ssp = ss[3].split('_')[7]\n",
    "        bc = 'ISIMIP3b:1979-2014'\n",
    "    else:\n",
    "        ssp = ss[3].split('_')[6]\n",
    "        bc = '2000-2019'\n",
    "\n",
    "    end0,end1 = ss[3].split('_')[-2:]\n",
    "    end = end0+'_'+end1\n",
    "    ff = f[:-len(end)]+'*'\n",
    "\n",
    "    # exp is the same everywhere, so we don't need it as an id... \n",
    "    run_id = f'{cmip}:{endyr}:{gcm}:{ssp}:{rgi_reg}'\n",
    "    df_meta.loc[run_id, 'cmip'] = cmip\n",
    "    df_meta.loc[run_id, 'gcm_original'] = gcm_original # some have lower and upper cases in the raw output files\n",
    "    df_meta.loc[run_id, 'gcm'] = gcm  # for the aggregated files, all GCMs are in upper cases\n",
    "    df_meta.loc[run_id, 'scenario'] = ssp\n",
    "    df_meta.loc[run_id, 'rgi_reg'] = rgi_reg\n",
    "    df_meta.loc[run_id, 'end_year'] = 0  # will be updated later\n",
    "    df_meta.loc[run_id, 'bias_correction'] = bc\n",
    "\n",
    "    df_meta.loc[run_id, 'perc_area_missing'] = 0\n",
    "    \n",
    "    df_meta.loc[run_id, 'fpath'] = ff\n",
    "    \n",
    "    if rgi_reg not in invalid_per_reg:\n",
    "        invalid_per_reg[rgi_reg] = set()\n",
    "    \n",
    "    if rgi_reg not in meta_per_reg:\n",
    "        meta_per_reg[rgi_reg] = rgi_meta.loc[rgi_meta['O1Region'] == rgi_reg[-2:]]\n",
    "    \n",
    "    with xr.open_mfdataset(ff) as ds:\n",
    "        ds_t = ds.isel(time=-1).volume.load()\n",
    "    df_meta.loc[run_id, 'end_year'] = str(int(ds_t.time))\n",
    "    missing_ids = ds_t.rgi_id[ds_t.isnull()].data\n",
    "    perc = meta_per_reg[rgi_reg].loc[missing_ids]['Area'].sum() / meta_per_reg[rgi_reg]['Area'].sum() \n",
    "    df_meta.loc[run_id, 'perc_area_missing'] = perc\n",
    "    invalid_per_reg[rgi_reg] = invalid_per_reg[rgi_reg].union(missing_ids)\n",
    "    if f == allfiles[100]:\n",
    "        print(f)\n",
    "df_meta['exp'] = exp\n",
    "df_meta['historical_future_option'] = historical_future_option\n",
    "df_meta['oggm_version'] = oggm_version\n",
    "df_meta.to_csv(f'{outputpath}/common_running_2100/metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ee244f24-7699-4b1a-9f8f-224265ffac72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RGI12'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds = df_meta.sort_values('perc_area_missing', ascending=False)\n",
    "dds[dds['perc_area_missing'] > 0.05].rgi_reg.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c1b5ee6-3215-4f33-afbf-f734aa906021",
   "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>cmip</th>\n",
       "      <th>gcm_original</th>\n",
       "      <th>gcm</th>\n",
       "      <th>scenario</th>\n",
       "      <th>rgi_reg</th>\n",
       "      <th>end_year</th>\n",
       "      <th>bias_correction</th>\n",
       "      <th>perc_area_missing</th>\n",
       "      <th>fpath</th>\n",
       "      <th>exp</th>\n",
       "      <th>historical_future_option</th>\n",
       "      <th>oggm_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CANESM2:rcp26:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CanESM2</td>\n",
       "      <td>CANESM2</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.121243</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:NORESM2-MM:ssp126:RGI12</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NorESM2-MM</td>\n",
       "      <td>NORESM2-MM</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.120892</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.119703</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:MPI-ESM-LR:rcp26:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>MPI-ESM-LR</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.119397</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CANESM2:rcp45:RGI12</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CanESM2</td>\n",
       "      <td>CANESM2</td>\n",
       "      <td>rcp45</td>\n",
       "      <td>RGI12</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.119350</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CNRM-CM5:rcp26:RGI07</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:MRI-ESM2-0:ssp585:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>ssp534-over</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP6:2100:GFDL-ESM4:ssp126:RGI07</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CNRM-CM5:rcp85:RGI07</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>RGI07</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2432 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            cmip  gcm_original           gcm  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12             CMIP5       CanESM2       CANESM2   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12         CMIP6    NorESM2-MM    NORESM2-MM   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12        CMIP5  IPSL-CM5A-LR  IPSL-CM5A-LR   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12          CMIP5    MPI-ESM-LR    MPI-ESM-LR   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12             CMIP5       CanESM2       CANESM2   \n",
       "...                                          ...           ...           ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07            CMIP5      CNRM-CM5      CNRM-CM5   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07         CMIP6    MRI-ESM2-0    MRI-ESM2-0   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07  CMIP6  IPSL-CM6A-LR  IPSL-CM6A-LR   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07          CMIP6     GFDL-ESM4     GFDL-ESM4   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07            CMIP5      CNRM-CM5      CNRM-CM5   \n",
       "\n",
       "                                              scenario rgi_reg end_year  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12                   rcp26   RGI12     2100   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12              ssp126   RGI12     2100   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12              rcp26   RGI12     2100   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12                rcp26   RGI12     2100   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12                   rcp45   RGI12     2100   \n",
       "...                                                ...     ...      ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07                  rcp26   RGI07     2100   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07              ssp585   RGI07     2100   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07  ssp534-over   RGI07     2100   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07               ssp126   RGI07     2100   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07                  rcp85   RGI07     2100   \n",
       "\n",
       "                                          bias_correction  perc_area_missing  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12                  2000-2019           0.121243   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12              2000-2019           0.120892   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12             2000-2019           0.119703   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12               2000-2019           0.119397   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12                  2000-2019           0.119350   \n",
       "...                                                   ...                ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07                 2000-2019           0.000000   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07              2000-2019           0.000000   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07       2000-2019           0.000000   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07               2000-2019           0.000000   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07                 2000-2019           0.000000   \n",
       "\n",
       "                                                                                       fpath  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12             /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12         /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12        /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12          /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12             /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "...                                                                                      ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07            /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07         /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07          /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07            /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "\n",
       "                                                         exp  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12             match_geod_pergla   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12         match_geod_pergla   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12        match_geod_pergla   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12          match_geod_pergla   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12             match_geod_pergla   \n",
       "...                                                      ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07            match_geod_pergla   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07         match_geod_pergla   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07  match_geod_pergla   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07          match_geod_pergla   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07            match_geod_pergla   \n",
       "\n",
       "                                          historical_future_option  \\\n",
       "CMIP5:2100:CANESM2:rcp26:RGI12                     w5e5_gcm_merged   \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12                 w5e5_gcm_merged   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12                w5e5_gcm_merged   \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12                  w5e5_gcm_merged   \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12                     w5e5_gcm_merged   \n",
       "...                                                            ...   \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07                    w5e5_gcm_merged   \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07                 w5e5_gcm_merged   \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07          w5e5_gcm_merged   \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07                  w5e5_gcm_merged   \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07                    w5e5_gcm_merged   \n",
       "\n",
       "                                                 oggm_version  \n",
       "CMIP5:2100:CANESM2:rcp26:RGI12             oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:NORESM2-MM:ssp126:RGI12         oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp26:RGI12        oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:MPI-ESM-LR:rcp26:RGI12          oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:CANESM2:rcp45:RGI12             oggm_v1.6.1_2023.3  \n",
       "...                                                       ...  \n",
       "CMIP5:2100:CNRM-CM5:rcp26:RGI07            oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:MRI-ESM2-0:ssp585:RGI07         oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:IPSL-CM6A-LR:ssp534-over:RGI07  oggm_v1.6.1_2023.3  \n",
       "CMIP6:2100:GFDL-ESM4:ssp126:RGI07          oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:CNRM-CM5:rcp85:RGI07            oggm_v1.6.1_2023.3  \n",
       "\n",
       "[2432 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e6e2feca-1259-4322-9d58-d5214ed9e5b1",
   "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>n_glaciers</th>\n",
       "      <th>n_missing_glaciers</th>\n",
       "      <th>rgi_area_km2</th>\n",
       "      <th>missing_area_km2</th>\n",
       "      <th>missing_area_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RGI12</th>\n",
       "      <td>1888</td>\n",
       "      <td>409</td>\n",
       "      <td>1306.992</td>\n",
       "      <td>160.691</td>\n",
       "      <td>0.122947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI10</th>\n",
       "      <td>5151</td>\n",
       "      <td>168</td>\n",
       "      <td>2410.051</td>\n",
       "      <td>89.351</td>\n",
       "      <td>0.037074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI19</th>\n",
       "      <td>2752</td>\n",
       "      <td>598</td>\n",
       "      <td>132867.219</td>\n",
       "      <td>301.246</td>\n",
       "      <td>0.002267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI13</th>\n",
       "      <td>54429</td>\n",
       "      <td>345</td>\n",
       "      <td>49303.415</td>\n",
       "      <td>92.609</td>\n",
       "      <td>0.001878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI17</th>\n",
       "      <td>15908</td>\n",
       "      <td>94</td>\n",
       "      <td>29429.080</td>\n",
       "      <td>49.960</td>\n",
       "      <td>0.001698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI03</th>\n",
       "      <td>4556</td>\n",
       "      <td>77</td>\n",
       "      <td>105110.642</td>\n",
       "      <td>176.943</td>\n",
       "      <td>0.001683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI05</th>\n",
       "      <td>19306</td>\n",
       "      <td>344</td>\n",
       "      <td>89717.066</td>\n",
       "      <td>88.617</td>\n",
       "      <td>0.000988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI15</th>\n",
       "      <td>13119</td>\n",
       "      <td>41</td>\n",
       "      <td>14734.204</td>\n",
       "      <td>11.674</td>\n",
       "      <td>0.000792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI14</th>\n",
       "      <td>27988</td>\n",
       "      <td>136</td>\n",
       "      <td>33568.298</td>\n",
       "      <td>23.149</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI11</th>\n",
       "      <td>3927</td>\n",
       "      <td>31</td>\n",
       "      <td>2092.146</td>\n",
       "      <td>1.074</td>\n",
       "      <td>0.000513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI09</th>\n",
       "      <td>1069</td>\n",
       "      <td>4</td>\n",
       "      <td>51591.600</td>\n",
       "      <td>23.984</td>\n",
       "      <td>0.000465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI08</th>\n",
       "      <td>3417</td>\n",
       "      <td>17</td>\n",
       "      <td>2949.103</td>\n",
       "      <td>1.180</td>\n",
       "      <td>0.000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI16</th>\n",
       "      <td>2939</td>\n",
       "      <td>12</td>\n",
       "      <td>2341.036</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI02</th>\n",
       "      <td>18855</td>\n",
       "      <td>96</td>\n",
       "      <td>14524.224</td>\n",
       "      <td>4.392</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI06</th>\n",
       "      <td>568</td>\n",
       "      <td>11</td>\n",
       "      <td>11059.700</td>\n",
       "      <td>2.301</td>\n",
       "      <td>0.000208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI04</th>\n",
       "      <td>7415</td>\n",
       "      <td>35</td>\n",
       "      <td>40888.228</td>\n",
       "      <td>6.180</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI01</th>\n",
       "      <td>27108</td>\n",
       "      <td>51</td>\n",
       "      <td>86725.053</td>\n",
       "      <td>11.696</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI18</th>\n",
       "      <td>3537</td>\n",
       "      <td>1</td>\n",
       "      <td>1161.801</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI07</th>\n",
       "      <td>1615</td>\n",
       "      <td>0</td>\n",
       "      <td>33958.934</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       n_glaciers  n_missing_glaciers  rgi_area_km2  missing_area_km2  \\\n",
       "RGI12        1888                 409      1306.992           160.691   \n",
       "RGI10        5151                 168      2410.051            89.351   \n",
       "RGI19        2752                 598    132867.219           301.246   \n",
       "RGI13       54429                 345     49303.415            92.609   \n",
       "RGI17       15908                  94     29429.080            49.960   \n",
       "RGI03        4556                  77    105110.642           176.943   \n",
       "RGI05       19306                 344     89717.066            88.617   \n",
       "RGI15       13119                  41     14734.204            11.674   \n",
       "RGI14       27988                 136     33568.298            23.149   \n",
       "RGI11        3927                  31      2092.146             1.074   \n",
       "RGI09        1069                   4     51591.600            23.984   \n",
       "RGI08        3417                  17      2949.103             1.180   \n",
       "RGI16        2939                  12      2341.036             0.710   \n",
       "RGI02       18855                  96     14524.224             4.392   \n",
       "RGI06         568                  11     11059.700             2.301   \n",
       "RGI04        7415                  35     40888.228             6.180   \n",
       "RGI01       27108                  51     86725.053            11.696   \n",
       "RGI18        3537                   1      1161.801             0.052   \n",
       "RGI07        1615                   0     33958.934             0.000   \n",
       "\n",
       "       missing_area_perc  \n",
       "RGI12           0.122947  \n",
       "RGI10           0.037074  \n",
       "RGI19           0.002267  \n",
       "RGI13           0.001878  \n",
       "RGI17           0.001698  \n",
       "RGI03           0.001683  \n",
       "RGI05           0.000988  \n",
       "RGI15           0.000792  \n",
       "RGI14           0.000690  \n",
       "RGI11           0.000513  \n",
       "RGI09           0.000465  \n",
       "RGI08           0.000400  \n",
       "RGI16           0.000303  \n",
       "RGI02           0.000302  \n",
       "RGI06           0.000208  \n",
       "RGI04           0.000151  \n",
       "RGI01           0.000135  \n",
       "RGI18           0.000045  \n",
       "RGI07           0.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for k, v in invalid_per_reg.items():\n",
    "    invalid_per_reg[k] = list(v)\n",
    "with open(f'{outputpath}/common_running_2100/rgi_ids_missing.json', 'w') as f:\n",
    "    json.dump(invalid_per_reg, f)\n",
    "\n",
    "# these are now the common running glaciers over all scenarios and GCMs and all cmip variants going until 2100 or until 2300\n",
    "odf = pd.DataFrame()\n",
    "for rgi_reg, missing_ids in invalid_per_reg.items():\n",
    "    odf.loc[rgi_reg, 'n_glaciers'] = len(meta_per_reg[rgi_reg])\n",
    "    odf.loc[rgi_reg, 'n_missing_glaciers'] = len(missing_ids)\n",
    "    odf.loc[rgi_reg, 'rgi_area_km2'] = meta_per_reg[rgi_reg]['Area'].sum() \n",
    "    odf.loc[rgi_reg, 'missing_area_km2'] = meta_per_reg[rgi_reg].loc[missing_ids]['Area'].sum()\n",
    "    odf.loc[rgi_reg, 'missing_area_perc'] = odf.loc[rgi_reg, 'missing_area_km2'] / odf.loc[rgi_reg, 'rgi_area_km2']\n",
    "odf[['n_glaciers', 'n_missing_glaciers']] = odf[['n_glaciers', 'n_missing_glaciers']].astype(int)\n",
    "odf = odf.sort_values(by='missing_area_perc', ascending=False)\n",
    "odf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a06e21bc-da9d-4ede-8712-62bd99cbe1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "odf.to_csv(f'{outputpath}/common_running_2100/missing_region_overview.csv')\n",
    "odf.to_html(f'{outputpath}/common_running_2100/missing_region_overview.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98e9cab-6110-4764-bf5e-ff678ade6b58",
   "metadata": {},
   "source": [
    "### B. 2: GCMs - aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49ee5c98-7c5f-40cc-a425-d78207335fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir =f'{outputpath}/common_running_2100'\n",
    "df_meta = pd.read_csv(f'{base_dir}/metadata.csv', index_col = 0)\n",
    "with open(f'{base_dir}/rgi_ids_missing.json', 'r') as f:\n",
    "    invalid_per_reg = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff408531-6e19-48dd-bd3b-7268dcd56a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIMIP3b_CMIP6\n",
      "CMIP6\n",
      "CMIP5\n"
     ]
    }
   ],
   "source": [
    "for cmip in df_meta.cmip.unique():\n",
    "    print(cmip, flush=True)\n",
    "    if cmip == 'CMIP6':\n",
    "        end_years = [2100]\n",
    "    else:\n",
    "        end_years = [2100]\n",
    "    for end_year in end_years:\n",
    "        for rgi_reg in sorted(df_meta.rgi_reg.unique()): #progressbar.progressbar(\n",
    "            for scenario in sorted(df_meta.loc[(df_meta.cmip==cmip) & (df_meta.end_year == end_year)].scenario.unique()):\n",
    "                df_meta_s = df_meta.loc[(df_meta.end_year == end_year) & (df_meta.cmip == cmip) & (df_meta.rgi_reg == rgi_reg) & (df_meta.scenario == scenario)]\n",
    "                odf_v = pd.DataFrame()\n",
    "                odf_a = pd.DataFrame()\n",
    "                ods = []\n",
    "                gcms = []\n",
    "                for i, s in df_meta_s.iterrows():\n",
    "                    with xr.open_mfdataset(s.fpath) as ds:\n",
    "                        ds = ds[['volume', 'area']].load().isel(rgi_id=~ds.rgi_id.isin(invalid_per_reg[s.rgi_reg]))\n",
    "                        #ds = ds[['volume', 'area', 'length']].load().isel(rgi_id=~ds.rgi_id.isin(invalid_per_reg[s.rgi_reg]))\n",
    "                        odf_v[s.gcm] = ds.volume.sum(dim='rgi_id').to_series()\n",
    "                        odf_a[s.gcm] = ds.area.sum(dim='rgi_id').to_series()\n",
    "                        ods.append(ds)\n",
    "                        gcms.append(s.gcm)\n",
    "                ods = xr.concat(ods, 'gcm')\n",
    "\n",
    "                # I don't like to have these kind of summary statistics as the user does not know whether the different projections are gaussian distributed .. \n",
    "                #ods.mean(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_avg_{scenario}.nc')\n",
    "                #ods.std(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_std_{scenario}.nc')\n",
    "                odir =  f'{base_dir}/volume/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                mkdir(odir)\n",
    "                odf_v.to_csv(odir + f'{scenario}.csv')\n",
    "                odir = f'{base_dir}/area/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                mkdir(odir)\n",
    "                odf_a.to_csv(odir + f'{scenario}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1f3c641a-cb1d-4455-939a-e12fd2f67407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# globally: sum over all RGI regions\n",
    "for var in ['volume', 'area']:\n",
    "    for cmip in df_meta.cmip.unique():\n",
    "        odir = f'{base_dir}/{var}/{cmip}/{end_year}/global/'\n",
    "        mkdir(odir)\n",
    "        for scenario in sorted(df_meta.loc[(df_meta.cmip==cmip) & (df_meta.end_year == end_year)].scenario.unique()):\n",
    "            odf = 0\n",
    "            for rgi_reg in sorted(df_meta.rgi_reg.unique()):\n",
    "                idir = f'{base_dir}/{var}/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                df = pd.read_csv(idir + f'/{scenario}.csv', index_col=0)\n",
    "                odf += df\n",
    "            odf.to_csv(odir + f'/{scenario}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9da6532-6f99-48b2-8e4d-c40f951a3e84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "23692c9e-a914-4196-affa-116b468aab6e",
   "metadata": {},
   "source": [
    "# Visualisation of missing common running glaciers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b7ab6fa-8ba4-4fc6-a664-cb1bcd37f9db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "odf_2100= pd.read_csv(f'{outputpath}/common_running_2100/missing_region_overview.csv', index_col=0)\n",
    "odf_2100.loc['Global'] = odf_2100.sum(axis=0)\n",
    "# need to recompute the percentage\n",
    "odf_2100['missing_area_perc'] = 100*odf_2100['missing_area_km2'] / odf_2100['rgi_area_km2']\n",
    "odf_2100['missing_glaciers_perc'] = 100*odf_2100['n_missing_glaciers'] / odf_2100['n_glaciers']\n",
    "odf_2100 = odf_2100.sort_index()\n",
    "\n",
    "odf_2300= pd.read_csv(f'{outputpath}/common_running_2100_2300/missing_region_overview.csv', index_col=0)\n",
    "odf_2300.loc['Global'] = odf_2300.sum(axis=0)\n",
    "# need to recompute the percentage\n",
    "odf_2300['missing_area_perc'] = 100*odf_2300['missing_area_km2'] / odf_2300['rgi_area_km2']\n",
    "odf_2300['missing_glaciers_perc'] = 100*odf_2300['n_missing_glaciers'] / odf_2300['n_glaciers']\n",
    "odf_2300 = odf_2300.sort_index()\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "odf_area = odf_2100[['missing_area_perc']]\n",
    "odf_area.index.name = 'Region'\n",
    "odf_area.columns.name = 'Option of common running glaciers'\n",
    "odf_area = odf_area.rename(columns={'missing_area_perc':'common_running_2100'})\n",
    "odf_area['common_running_2100_2300'] = odf_2300['missing_area_perc']\n",
    "\n",
    "odf_glac = odf_2100[['missing_glaciers_perc']]\n",
    "odf_glac.index.name = 'Region'\n",
    "odf_glac.columns.name = 'Option of common running glaciers'\n",
    "odf_glac = odf_glac.rename(columns={'missing_glaciers_perc':'common_running_2100'})\n",
    "odf_glac['common_running_2100_2300'] = odf_2300['missing_glaciers_perc']\n",
    "\n",
    "\n",
    "plt.rc('font', size=16)\n",
    "fig, axs = plt.subplots(1,2,figsize=(30,12))\n",
    "ax = axs[0]\n",
    "sns.heatmap(odf_area.astype(float), \n",
    "            ax=ax,\n",
    "            annot=True, fmt=\".2f\",cmap=\"crest_r\",linewidth=.5,\n",
    "            cbar_kws={'label':'Missing glacier area (in %)'})\n",
    "ax.set_title(f'Relative percentage of missing glacier area\\nwhere at least one GCM or Scenario could not be run over the entire time period', fontsize=22)\n",
    "\n",
    "\n",
    "ax = axs[1]\n",
    "sns.heatmap(odf_glac.astype(float), annot=True, fmt=\".1f\",cmap=\"Reds\",linewidth=.5, ax=ax,\n",
    "            cbar_kws={'label':'Missing glaciers (in %)'})\n",
    "ax.set_title(f'Relative percentage of missing glaciers\\nwhere at least one GCM or Scenario could not be run over the entire time period', fontsize=22);\n",
    "plt.savefig('missing_glacier_area_stats.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4218718-b73b-41b6-b135-4e3ca7f0626c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85c2af24-a77c-4918-9b55-8a589e5d0d02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "95f0d351-10c9-46e2-a043-fa51cfbe559b",
   "metadata": {},
   "source": [
    "## Appendix\n",
    "### C: common running with Rounce et al. (2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1766d407-b096-45db-b893-e6dc099c1d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir =f'/home/www/oggm/oggm-standard-projections/analysis_notebooks/1.6.1/common_running_rounce_et_al_2023'\n",
    "df_meta = pd.read_csv(f'{outputpath}/common_running_2100/metadata.csv', index_col = 0)\n",
    "import json\n",
    "# this file has been created here: /home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/notebooks/analyse_csv_files_1.6.1.ipynb\n",
    "with open(f'/home/www/oggm/oggm-standard-projections/analysis_notebooks/1.6.1/common_running_rounce_et_al_2023/rgi_ids_missing_rounce_et_al_or_oggm.json', 'r') as f:\n",
    "    invalid_per_reg = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09aa0933-3ab9-4697-afb1-abb4126c4d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMIP6\n"
     ]
    }
   ],
   "source": [
    "for cmip in ['CMIP6']:\n",
    "    print(cmip, flush=True)\n",
    "    if cmip == 'CMIP6':\n",
    "        end_years = [2100]\n",
    "    else:\n",
    "        end_years = [2100]\n",
    "    for end_year in end_years:\n",
    "        for rgi_reg in sorted(df_meta.rgi_reg.unique()): #progressbar.progressbar(\n",
    "            for scenario in ['ssp126', 'ssp245','ssp370', 'ssp585']:\n",
    "                df_meta_s = df_meta.loc[(df_meta.end_year == end_year) & (df_meta.cmip == cmip) & (df_meta.rgi_reg == rgi_reg) & (df_meta.scenario == scenario)]\n",
    "                odf_v = pd.DataFrame()\n",
    "                odf_a = pd.DataFrame()\n",
    "                ods = []\n",
    "                gcms = []\n",
    "                for i, s in df_meta_s.iterrows():\n",
    "                    with xr.open_mfdataset(s.fpath) as ds:\n",
    "                        ds = ds[['volume']].load().isel(rgi_id=~ds.rgi_id.isin(invalid_per_reg[s.rgi_reg]))\n",
    "                        #ds = ds[['volume', 'area', 'length']].load().isel(rgi_id=~ds.rgi_id.isin(invalid_per_reg[s.rgi_reg]))\n",
    "                        odf_v[s.gcm] = ds.volume.sum(dim='rgi_id').to_series()\n",
    "                        ods.append(ds)\n",
    "                        gcms.append(s.gcm)\n",
    "                ods = xr.concat(ods, 'gcm')\n",
    "\n",
    "                # I don't like to have these kind of summary statistics as the user does not know whether the different projections are gaussian distributed .. \n",
    "                #ods.mean(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_avg_{scenario}.nc')\n",
    "                #ods.std(dim='gcm').to_netcdf(f'output_1.6.1_2023.3/{exp}/{rgi_reg}/all_gcm_std_{scenario}.nc')\n",
    "                odir =  f'{base_dir}/volume/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                mkdir(odir)\n",
    "                odf_v.to_csv(odir + f'{scenario}.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be0524f2-234c-4686-a854-380381365978",
   "metadata": {},
   "outputs": [],
   "source": [
    "# globally: sum over all RGI regions\n",
    "for var in ['volume']:\n",
    "    for cmip in ['CMIP6']:\n",
    "        if cmip == 'CMIP6':\n",
    "            end_years = [2100]\n",
    "        else:\n",
    "            end_years = [2100]\n",
    "        for end_year in end_years:\n",
    "            odir = f'{base_dir}/{var}/{cmip}/{end_year}/global/'\n",
    "            mkdir(odir)\n",
    "            for scenario in ['ssp126', 'ssp245','ssp370', 'ssp585']:\n",
    "                odf = 0\n",
    "                for rgi_reg in sorted(df_meta.rgi_reg.unique()):\n",
    "                    idir = f'{base_dir}/{var}/{cmip}/{end_year}/{rgi_reg}/'\n",
    "                    df = pd.read_csv(idir + f'{scenario}.csv', index_col=0)\n",
    "                    odf += df\n",
    "                odf.to_csv(odir + f'{scenario}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "099747e4-394e-4770-a30f-e77f21aff39b",
   "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>cmip</th>\n",
       "      <th>gcm_original</th>\n",
       "      <th>gcm</th>\n",
       "      <th>scenario</th>\n",
       "      <th>rgi_reg</th>\n",
       "      <th>end_year</th>\n",
       "      <th>bias_correction</th>\n",
       "      <th>perc_area_missing</th>\n",
       "      <th>fpath</th>\n",
       "      <th>exp</th>\n",
       "      <th>historical_future_option</th>\n",
       "      <th>oggm_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11</th>\n",
       "      <td>ISIMIP3b_CMIP6</td>\n",
       "      <td>mri-esm2-0</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>RGI11</td>\n",
       "      <td>2100</td>\n",
       "      <td>ISIMIP3b:1979-2014</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11</th>\n",
       "      <td>ISIMIP3b_CMIP6</td>\n",
       "      <td>ukesm1-0-ll</td>\n",
       "      <td>UKESM1-0-LL</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>RGI11</td>\n",
       "      <td>2100</td>\n",
       "      <td>ISIMIP3b:1979-2014</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11</th>\n",
       "      <td>ISIMIP3b_CMIP6</td>\n",
       "      <td>mri-esm2-0</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>RGI11</td>\n",
       "      <td>2100</td>\n",
       "      <td>ISIMIP3b:1979-2014</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11</th>\n",
       "      <td>ISIMIP3b_CMIP6</td>\n",
       "      <td>ukesm1-0-ll</td>\n",
       "      <td>UKESM1-0-LL</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>RGI11</td>\n",
       "      <td>2100</td>\n",
       "      <td>ISIMIP3b:1979-2014</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11</th>\n",
       "      <td>ISIMIP3b_CMIP6</td>\n",
       "      <td>mri-esm2-0</td>\n",
       "      <td>MRI-ESM2-0</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>RGI11</td>\n",
       "      <td>2100</td>\n",
       "      <td>ISIMIP3b:1979-2014</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:GFDL-CM3:rcp45:RGI04</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>GFDL-CM3</td>\n",
       "      <td>GFDL-CM3</td>\n",
       "      <td>rcp45</td>\n",
       "      <td>RGI04</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>CSIRO-Mk3-6-0</td>\n",
       "      <td>CSIRO-MK3-6-0</td>\n",
       "      <td>rcp26</td>\n",
       "      <td>RGI04</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>rcp45</td>\n",
       "      <td>RGI04</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:GFDL-CM3:rcp85:RGI04</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>GFDL-CM3</td>\n",
       "      <td>GFDL-CM3</td>\n",
       "      <td>rcp85</td>\n",
       "      <td>RGI04</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04</th>\n",
       "      <td>CMIP5</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>rcp60</td>\n",
       "      <td>RGI04</td>\n",
       "      <td>2100</td>\n",
       "      <td>2000-2019</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>/home/www/oggm/oggm-standard-projections/oggm_...</td>\n",
       "      <td>match_geod_pergla</td>\n",
       "      <td>w5e5_gcm_merged</td>\n",
       "      <td>oggm_v1.6.1_2023.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        cmip   gcm_original  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11   ISIMIP3b_CMIP6     mri-esm2-0   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11  ISIMIP3b_CMIP6    ukesm1-0-ll   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11   ISIMIP3b_CMIP6     mri-esm2-0   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11  ISIMIP3b_CMIP6    ukesm1-0-ll   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11   ISIMIP3b_CMIP6     mri-esm2-0   \n",
       "...                                                      ...            ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04                        CMIP5       GFDL-CM3   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04                   CMIP5  CSIRO-Mk3-6-0   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04                    CMIP5   IPSL-CM5A-LR   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04                        CMIP5       GFDL-CM3   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04                    CMIP5   IPSL-CM5A-LR   \n",
       "\n",
       "                                                        gcm scenario rgi_reg  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11      MRI-ESM2-0   ssp585   RGI11   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11    UKESM1-0-LL   ssp585   RGI11   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11      MRI-ESM2-0   ssp126   RGI11   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11    UKESM1-0-LL   ssp370   RGI11   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11      MRI-ESM2-0   ssp370   RGI11   \n",
       "...                                                     ...      ...     ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04                    GFDL-CM3    rcp45   RGI04   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04          CSIRO-MK3-6-0    rcp26   RGI04   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04            IPSL-CM5A-LR    rcp45   RGI04   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04                    GFDL-CM3    rcp85   RGI04   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04            IPSL-CM5A-LR    rcp60   RGI04   \n",
       "\n",
       "                                              end_year     bias_correction  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11       2100  ISIMIP3b:1979-2014   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11      2100  ISIMIP3b:1979-2014   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11       2100  ISIMIP3b:1979-2014   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11      2100  ISIMIP3b:1979-2014   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11       2100  ISIMIP3b:1979-2014   \n",
       "...                                                ...                 ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04                   2100           2000-2019   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04              2100           2000-2019   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04               2100           2000-2019   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04                   2100           2000-2019   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04               2100           2000-2019   \n",
       "\n",
       "                                              perc_area_missing  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11            0.000495   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11           0.000495   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11            0.000495   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11           0.000495   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11            0.000495   \n",
       "...                                                         ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04                        0.000053   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04                   0.000073   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04                    0.000063   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04                        0.000053   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04                    0.000057   \n",
       "\n",
       "                                                                                          fpath  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11   /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11   /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11  /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11   /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "...                                                                                         ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04               /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04          /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04           /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04               /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04           /home/www/oggm/oggm-standard-projections/oggm_...   \n",
       "\n",
       "                                                            exp  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11   match_geod_pergla   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11  match_geod_pergla   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11   match_geod_pergla   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11  match_geod_pergla   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11   match_geod_pergla   \n",
       "...                                                         ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04               match_geod_pergla   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04          match_geod_pergla   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04           match_geod_pergla   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04               match_geod_pergla   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04           match_geod_pergla   \n",
       "\n",
       "                                             historical_future_option  \\\n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11           w5e5_gcm_merged   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11          w5e5_gcm_merged   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11           w5e5_gcm_merged   \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11          w5e5_gcm_merged   \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11           w5e5_gcm_merged   \n",
       "...                                                               ...   \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04                       w5e5_gcm_merged   \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04                  w5e5_gcm_merged   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04                   w5e5_gcm_merged   \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04                       w5e5_gcm_merged   \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04                   w5e5_gcm_merged   \n",
       "\n",
       "                                                    oggm_version  \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11   oggm_v1.6.1_2023.3  \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11  oggm_v1.6.1_2023.3  \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11   oggm_v1.6.1_2023.3  \n",
       "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11  oggm_v1.6.1_2023.3  \n",
       "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11   oggm_v1.6.1_2023.3  \n",
       "...                                                          ...  \n",
       "CMIP5:2100:GFDL-CM3:rcp45:RGI04               oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04          oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04           oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:GFDL-CM3:rcp85:RGI04               oggm_v1.6.1_2023.3  \n",
       "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04           oggm_v1.6.1_2023.3  \n",
       "\n",
       "[2375 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "354b7e85-1963-4a2e-8624-d2a555ece29f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4ac4761-b650-486c-9f1e-aa01b9715a1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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