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  {
   "cell_type": "markdown",
   "id": "77967766-5722-4bbd-ac99-d14f79ec3441",
   "metadata": {},
   "source": [
    "# MESMER-m analysis  (simple provide-region plots)\n",
    "- always using common running glaciers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e649fe0c-73bc-459d-a4b7-4740d27095bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from func_add_mesmer_m import gcms_mesmer, quantiles, scenarios\n",
    "import json\n",
    "import geopandas as gpd\n",
    "from oggm import utils\n",
    "# get the dataset where coordinates of glaciers are stored\n",
    "frgi = utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/rgi62_stats.h5')\n",
    "#frgi = '/home/users/lschuster/glacierMIP/rgi62_stats.h5'\n",
    "odf = pd.read_hdf(frgi, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e06423-b48c-46c8-ad59-d61feafc14c0",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da28a01f-10ec-40b0-bf19-df1a3d15dff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get the climate files\n",
    "#for g in gcms_mesmer:\n",
    "#    print(f'wget -r --user=climatechange --password=globalwarming 88.198.17.222:/mesmer-m-processed/mesmer-m-processed/{g}/glacier_modellers/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d1d0e34-cc55-4a09-8556-77473a73dcb1",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sbatch --array=1-92 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 1 2100\n",
      "sbatch --array=1-24 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 2 2100\n",
      "sbatch --array=1-39 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 3 2100\n",
      "sbatch --array=1-9 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 4 2100\n",
      "sbatch --array=1-6 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 5 2100\n",
      "sbatch --array=1-5 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 6 2100\n",
      "sbatch --array=1-8 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 7 2100\n",
      "sbatch --array=1-4 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 8 2100\n",
      "sbatch --array=1-197 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 9 2100\n",
      "sbatch --array=1-6 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 10 2100\n",
      "sbatch --array=1-32 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 11 2100\n",
      "sbatch --array=1-8 run_slurm_with_hydro_per_provide_reg_mesmer.slurm 12 2100\n"
     ]
    }
   ],
   "source": [
    "### stuff to run onf cluster ...  www_lschuster/provide/MESMER-M_projections/runs/run_slurm_with_hydro_per_provide_reg_mesmer.slurm\n",
    "n_glac_per_batch = 500\n",
    "#for preg in ['P04', 'P07']:\n",
    "for p in np.arange(1,12.1,1): # No P13 (i.e., antarctic periphery ...)\n",
    "    p = int(p)\n",
    "    preg = f'P{p:02d}'\n",
    "    f = open('/home/www/lschuster/provide/provide_glacier_regions/rgi_ids_per_provide_region.json')\n",
    "    rgis_preg = json.load(f)[preg]\n",
    "    rgis_preg = list(set(rgis_preg)) # the IDs are not unique ... \n",
    "    rgis_preg = np.sort(rgis_preg) # make sure that the RGIs are sorted ...\n",
    "    if preg == 'P03': # omit connectiity level 2 from P03 (i.e., Greenland)\n",
    "        odf_preg = odf.loc[rgis_preg]\n",
    "        odf_preg_sel = odf_preg.loc[odf_preg['Connect'] != 2]\n",
    "        rgis_preg = odf_preg_sel.index\n",
    "\n",
    "    #print(provide_reg_full_name_dict_correct[preg], len(rgis_preg), p, preg)\n",
    "    n_batches = int(np.ceil(len(rgis_preg)/n_glac_per_batch))\n",
    "    print(f'sbatch --array=1-{n_batches} run_slurm_with_hydro_per_provide_reg_mesmer.slurm {p} 2100')\n",
    "    #print('\\n')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "a2fbd082-fa6c-4ef8-9ce1-1eb1d99ba81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gcms_mesmer = [ # 'ACCESS-CM2',  #NO precipitation data, # 'MCM-UA-1-0', no precipitation data\n",
    "               'ACCESS-ESM1-5', 'CESM2-WACCM', 'CESM2',\n",
    "                'CMCC-CM2-SR5', 'CNRM-CM6-1-HR', 'CNRM-CM6-1', 'CNRM-ESM2-1', 'CanESM5', 'E3SM-1-1',\n",
    "                'FGOALS-f3-L', 'FGOALS-g3', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-MM', 'IPSL-CM6A-LR', \n",
    "                'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR' , 'MRI-ESM2-0', 'NorESM2-LM', 'NorESM2-MM', 'UKESM1-0-LL']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fedbbbca-c616-4409-9924-e9908a4b706b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_provide_reg_full_name = gpd.read_file('/home/www/lschuster/provide/provide_glacier_regions/provide_glacier_regions.shp')\n",
    "pd_provide_reg_full_name.index = pd_provide_reg_full_name.provide_id\n",
    "provide_reg_full_name_dict = dict(pd_provide_reg_full_name['full_name'])\n",
    "provide_reg_full_name_dict['P06'] = 'East Asia'\n",
    "provide_reg_full_name_dict_correct = provide_reg_full_name_dict.copy()\n",
    "#provide_reg_full_name_dict_correct['P05'] = 'Svalbard, Jan Mayen\\nand Russian Arctic'\n",
    "#provide_reg_full_name_dict_correct['P13'] = 'Subantarctic and\\nAntarctic Islands'\n",
    "provide_reg_full_name_dict_correct['P09'] = 'High Mountain Asia' # need to rename that probably, but this is better than \"Central Asia\" as that is already the name for only RGI region 13\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea1aa65d-e6ab-4ccf-8746-6cddcd3b4764",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {'0.05':'blue', '0.25':'cyan', '0.5':'black', '0.75':'orange', '0.95':'red'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e100a545-1b43-4ddb-8269-a53b1ba347ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/www/lschuster/provide/MESMER-M_projections/runs/output/oggm_v16/2023.3/2100'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8af4fcce-bbff-47b3-b29e-c29ead3765f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "# test if all files are there \n",
    "missing_f_reg = {}\n",
    "n_exps = len(gcms_mesmer) * len(scenarios)*len(quantiles)\n",
    "print(n_exps)\n",
    "for p in np.arange(1,12.1,1): # No P13 (i.e., antarctic periphery ...)\n",
    "    p = int(p)\n",
    "    preg = f'P{p:02d}'\n",
    "    f = open('/home/www/lschuster/provide/provide_glacier_regions/rgi_ids_per_provide_region.json')\n",
    "    rgis_preg = json.load(f)[preg]\n",
    "    rgis_preg = list(set(rgis_preg)) # the IDs are not unique ... \n",
    "    rgis_preg = np.sort(rgis_preg) # make sure that the RGIs are sorted ...\n",
    "    if preg == 'P03': # omit connectiity level 2 from P03 (i.e., Greenland)\n",
    "        odf_preg = odf.loc[rgis_preg]\n",
    "        odf_preg_sel = odf_preg.loc[odf_preg['Connect'] != 2]\n",
    "        rgis_preg = odf_preg_sel.index\n",
    "    #print(provide_reg_full_name_dict_correct[preg], len(rgis_preg), p, preg)\n",
    "    n_batches = int(np.ceil(len(rgis_preg)/n_glac_per_batch))\n",
    "    try:\n",
    "        assert n_exps == int(len(os.listdir(path+'/'+preg))/n_batches)\n",
    "    except:\n",
    "        print(preg, int(len(os.listdir(path+'/'+preg))/n_batches))\n",
    "    \n",
    "    missing_f_reg_l = []\n",
    "\n",
    "    for j in np.arange(0,int(np.ceil(len(rgis_preg)/500)),1):\n",
    "        ji= j*500\n",
    "        je = (j+1)*500\n",
    "        scenario = scenarios[-1]\n",
    "        model = gcms_mesmer[-1]\n",
    "        q = quantiles[-1]\n",
    "        try:\n",
    "            xr.open_dataset(f'{path}/{preg}/run_hydro_w5e5_gcm_merged_{scenario}_{model}_q{q}_bc_2000_2019_Batch_{ji}_{je}.nc')\n",
    "        except:\n",
    "            missing_f_reg_l.append(j+1) # array that has to be rerun\n",
    "            #print(j+1)\n",
    "    # test that should work at the end:\n",
    "    assert len(missing_f_reg_l) == 0\n",
    "    missing_f_reg[preg] = missing_f_reg_l\n",
    "       "
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "id": "176b1ec4-5ef3-456b-b641-264734c035a2",
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       " 'P02': [],\n",
       " 'P03': [],\n",
       " 'P04': [],\n",
       " 'P05': [],\n",
       " 'P06': [],\n",
       " 'P07': [],\n",
       " 'P08': [],\n",
       " 'P09': [],\n",
       " 'P10': [],\n",
       " 'P11': [],\n",
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       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                       (time: 102, rgi_id: 500, month_2d: 12)\n",
       "Coordinates:\n",
       "  * time                          (time) float64 2e+03 2.001e+03 ... 2.101e+03\n",
       "  * rgi_id                        (rgi_id) object &#x27;RGI60-01.00001&#x27; ... &#x27;RGI60...\n",
       "    hydro_year                    (time) int64 2000 2001 2002 ... 2099 2100 2101\n",
       "    hydro_month                   (time) int64 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4\n",
       "    calendar_year                 (time) int64 2000 2001 2002 ... 2099 2100 2101\n",
       "    calendar_month                (time) int64 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1\n",
       "  * month_2d                      (month_2d) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    calendar_month_2d             (month_2d) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables: (12/22)\n",
       "    volume                        (time, rgi_id) float32 7.034e+06 ... 0.0\n",
       "    volume_bsl                    (time, rgi_id) float32 0.0 0.0 0.0 ... 0.0 0.0\n",
       "    volume_bwl                    (time, rgi_id) float32 0.0 0.0 0.0 ... 0.0 0.0\n",
       "    area                          (time, rgi_id) float32 3.443e+05 ... 0.0\n",
       "    length                        (time, rgi_id) float32 792.0 1.4e+03 ... 0.0\n",
       "    off_area                      (time, rgi_id) float32 3.097e-09 ... nan\n",
       "    ...                            ...\n",
       "    liq_prcp_on_glacier_monthly   (time, month_2d, rgi_id) float32 ...\n",
       "    snowfall_off_glacier_monthly  (time, month_2d, rgi_id) float32 ...\n",
       "    snowfall_on_glacier_monthly   (time, month_2d, rgi_id) float32 ...\n",
       "    water_level                   (rgi_id) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "    glen_a                        (rgi_id) float32 7.607e-24 ... 7.727e-24\n",
       "    fs                            (rgi_id) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2024-01-19 21:30:45</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-03f65243-d2cb-4b12-9671-6ac15e074e9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03f65243-d2cb-4b12-9671-6ac15e074e9b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 102</li><li><span class='xr-has-index'>rgi_id</span>: 500</li><li><span class='xr-has-index'>month_2d</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ee97a154-23cc-4c58-b8c0-aedf5e82edfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee97a154-23cc-4c58-b8c0-aedf5e82edfb' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2e+03 2.001e+03 ... 2.101e+03</div><input id='attrs-ffddb2d9-8cf7-438a-a946-2a88bdf9d1c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffddb2d9-8cf7-438a-a946-2a88bdf9d1c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78fbc9c3-b350-4889-9999-c6bf5c25d30b' class='xr-var-data-in' type='checkbox'><label for='data-78fbc9c3-b350-4889-9999-c6bf5c25d30b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Floating year</dd></dl></div><div class='xr-var-data'><pre>array([2000., 2001., 2002., 2003., 2004., 2005., 2006., 2007., 2008., 2009.,\n",
       "       2010., 2011., 2012., 2013., 2014., 2015., 2016., 2017., 2018., 2019.,\n",
       "       2020., 2021., 2022., 2023., 2024., 2025., 2026., 2027., 2028., 2029.,\n",
       "       2030., 2031., 2032., 2033., 2034., 2035., 2036., 2037., 2038., 2039.,\n",
       "       2040., 2041., 2042., 2043., 2044., 2045., 2046., 2047., 2048., 2049.,\n",
       "       2050., 2051., 2052., 2053., 2054., 2055., 2056., 2057., 2058., 2059.,\n",
       "       2060., 2061., 2062., 2063., 2064., 2065., 2066., 2067., 2068., 2069.,\n",
       "       2070., 2071., 2072., 2073., 2074., 2075., 2076., 2077., 2078., 2079.,\n",
       "       2080., 2081., 2082., 2083., 2084., 2085., 2086., 2087., 2088., 2089.,\n",
       "       2090., 2091., 2092., 2093., 2094., 2095., 2096., 2097., 2098., 2099.,\n",
       "       2100., 2101.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rgi_id</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;RGI60-01.00001&#x27; ... &#x27;RGI60-01.0...</div><input id='attrs-cabee90d-cd8f-42fa-ae25-f114422152a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cabee90d-cd8f-42fa-ae25-f114422152a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2972a26c-89a8-43eb-abe8-7eeef84851bf' class='xr-var-data-in' type='checkbox'><label for='data-2972a26c-89a8-43eb-abe8-7eeef84851bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>RGI glacier identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;RGI60-01.00001&#x27;, &#x27;RGI60-01.00002&#x27;, &#x27;RGI60-01.00003&#x27;, ...,\n",
       "       &#x27;RGI60-01.00498&#x27;, &#x27;RGI60-01.00499&#x27;, &#x27;RGI60-01.00500&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c33d5a10-61ca-49ad-a138-9faaa0d2d8c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c33d5a10-61ca-49ad-a138-9faaa0d2d8c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6edcdca-c89a-48d8-94b4-f32c736a3de3' class='xr-var-data-in' type='checkbox'><label for='data-d6edcdca-c89a-48d8-94b4-f32c736a3de3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological year</dd></dl></div><div class='xr-var-data'><pre>array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "       2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023,\n",
       "       2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035,\n",
       "       2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047,\n",
       "       2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059,\n",
       "       2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071,\n",
       "       2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083,\n",
       "       2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095,\n",
       "       2096, 2097, 2098, 2099, 2100, 2101])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5fadb457-1baf-41bc-a921-a198fb6161d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fadb457-1baf-41bc-a921-a198fb6161d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a3f97c-bf30-4eef-9351-1c8a80a8c898' class='xr-var-data-in' type='checkbox'><label for='data-44a3f97c-bf30-4eef-9351-1c8a80a8c898' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological month</dd></dl></div><div class='xr-var-data'><pre>array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08db92b0-5dda-4baa-a739-72365e59e6c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08db92b0-5dda-4baa-a739-72365e59e6c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb53fc4e-c0f5-4e24-b0d2-5a222f999bd3' class='xr-var-data-in' type='checkbox'><label for='data-fb53fc4e-c0f5-4e24-b0d2-5a222f999bd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar year</dd></dl></div><div class='xr-var-data'><pre>array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "       2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023,\n",
       "       2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035,\n",
       "       2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047,\n",
       "       2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059,\n",
       "       2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071,\n",
       "       2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083,\n",
       "       2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095,\n",
       "       2096, 2097, 2098, 2099, 2100, 2101])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4020dc46-b35e-494f-8b50-8045568f46e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4020dc46-b35e-494f-8b50-8045568f46e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abdc4fba-240b-4ac4-9b12-09ea5dccc5f1' class='xr-var-data-in' type='checkbox'><label for='data-abdc4fba-240b-4ac4-9b12-09ea5dccc5f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar month</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month_2d</span></div><div class='xr-var-dims'>(month_2d)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-7104bf36-5558-4565-b09f-b659cafe2d40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7104bf36-5558-4565-b09f-b659cafe2d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-346127bc-a570-425b-90f8-13c3139bac7e' class='xr-var-data-in' type='checkbox'><label for='data-346127bc-a570-425b-90f8-13c3139bac7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_month_2d</span></div><div class='xr-var-dims'>(month_2d)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3567a336-48c5-4cd5-9c1c-bdbf883b65dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3567a336-48c5-4cd5-9c1c-bdbf883b65dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11002537-6902-4645-9102-8d422cb00a29' class='xr-var-data-in' type='checkbox'><label for='data-11002537-6902-4645-9102-8d422cb00a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0da42b6c-9bb8-4dda-b4ef-2881aa4bf441' class='xr-section-summary-in' type='checkbox'  ><label for='section-0da42b6c-9bb8-4dda-b4ef-2881aa4bf441' class='xr-section-summary' >Data variables: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-551442f8-03bb-4764-a500-9384ed3e4187' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-551442f8-03bb-4764-a500-9384ed3e4187' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae778409-3a51-4172-8faa-21830f78c4d7' class='xr-var-data-in' type='checkbox'><label for='data-ae778409-3a51-4172-8faa-21830f78c4d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Total glacier volume</dd><dt><span>unit :</span></dt><dd>m 3</dd></dl></div><div class='xr-var-data'><pre>array([[ 7033596.  , 22759032.  , 66254324.  , ...,  8934518.  ,  1028363.75,\n",
       "         4162845.2 ],\n",
       "       [ 7413937.5 , 23131022.  , 66352084.  , ...,  9554492.  ,  1059273.  ,\n",
       "         4200816.  ],\n",
       "       [ 7300252.  , 22779244.  , 64430336.  , ...,  9204437.  ,  1001159.75,\n",
       "         3958139.5 ],\n",
       "       ...,\n",
       "       [  643056.2 ,        0.  ,        0.  , ...,        0.  ,        0.  ,\n",
       "               0.  ],\n",
       "       [  651657.25,        0.  ,        0.  , ...,        0.  ,        0.  ,\n",
       "               0.  ],\n",
       "       [  663069.56,        0.  ,        0.  , ...,        0.  ,        0.  ,\n",
       "               0.  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume_bsl</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85172bde-7587-46ea-9766-cf6fec354bf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85172bde-7587-46ea-9766-cf6fec354bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9abe794-8be6-4813-a606-f101b525d1fc' class='xr-var-data-in' type='checkbox'><label for='data-f9abe794-8be6-4813-a606-f101b525d1fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Glacier volume below sea-level</dd><dt><span>unit :</span></dt><dd>m 3</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume_bwl</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70db0f83-117d-476b-a74f-7f75accbdf0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70db0f83-117d-476b-a74f-7f75accbdf0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a88d35e-8347-4a39-93f6-4c0fc1703a0c' class='xr-var-data-in' type='checkbox'><label for='data-3a88d35e-8347-4a39-93f6-4c0fc1703a0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Glacier volume below water-level</dd><dt><span>unit :</span></dt><dd>m 3</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40c73b13-428c-49cf-8bdd-4c764ea28f8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40c73b13-428c-49cf-8bdd-4c764ea28f8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69feda42-1767-48fb-8e36-f4d5c52470b8' class='xr-var-data-in' type='checkbox'><label for='data-69feda42-1767-48fb-8e36-f4d5c52470b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Total glacier area</dd><dt><span>unit :</span></dt><dd>m 2</dd></dl></div><div class='xr-var-data'><pre>array([[ 344269.28 ,  568107.7  , 1720471.5  , ...,  324632.84 ,   46000.   ,\n",
       "         217703.55 ],\n",
       "       [ 346416.22 ,  582961.6  , 1720828.9  , ...,  644156.4  ,   48479.45 ,\n",
       "         218191.78 ],\n",
       "       [ 346532.4  ,  581360.75 , 1716640.4  , ...,  389794.53 ,   45743.062,\n",
       "         216467.52 ],\n",
       "       ...,\n",
       "       [  98600.38 ,       0.   ,       0.   , ...,       0.   ,       0.   ,\n",
       "              0.   ],\n",
       "       [  75856.27 ,       0.   ,       0.   , ...,       0.   ,       0.   ,\n",
       "              0.   ],\n",
       "       [  75918.93 ,       0.   ,       0.   , ...,       0.   ,       0.   ,\n",
       "              0.   ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c4d0fd8-cfb1-4bcc-bd46-84c797aeb1b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c4d0fd8-cfb1-4bcc-bd46-84c797aeb1b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-863bb8d8-dff6-455b-8d90-e6fe3b05fe87' class='xr-var-data-in' type='checkbox'><label for='data-863bb8d8-dff6-455b-8d90-e6fe3b05fe87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Glacier length</dd><dt><span>unit :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[ 792., 1400., 1904., ...,  612.,  312.,  800.],\n",
       "       [ 792., 1440., 1904., ..., 1368.,  338.,  800.],\n",
       "       [ 792., 1440., 1904., ...,  756.,  312.,  800.],\n",
       "       ...,\n",
       "       [ 216.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "       [ 180.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "       [ 180.,    0.,    0., ...,    0.,    0.,    0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>off_area</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0d06df7-f0f5-49c0-9624-b67323d30a45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0d06df7-f0f5-49c0-9624-b67323d30a45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe8bb3b-1cbd-4fcc-973c-62f19b4e6dc5' class='xr-var-data-in' type='checkbox'><label for='data-0fe8bb3b-1cbd-4fcc-973c-62f19b4e6dc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Off-glacier area</dd><dt><span>unit :</span></dt><dd>m 2</dd></dl></div><div class='xr-var-data'><pre>array([[3.096829e-09, 2.328306e-10, 1.109584e-09, ..., 4.129106e-10,\n",
       "        0.000000e+00, 2.155502e-10],\n",
       "       [0.000000e+00, 0.000000e+00, 5.714858e+02, ..., 0.000000e+00,\n",
       "        5.309171e-01, 1.557136e+02],\n",
       "       [1.758780e+01, 1.628823e+02, 3.831282e+03, ..., 0.000000e+00,\n",
       "        2.836725e+02, 1.388618e+03],\n",
       "       ...,\n",
       "       [2.456689e+05, 5.681077e+05, 1.720472e+06, ..., 3.246328e+05,\n",
       "        4.600000e+04, 2.177035e+05],\n",
       "       [2.684130e+05, 5.681077e+05, 1.720472e+06, ..., 3.246328e+05,\n",
       "        4.600000e+04, 2.177035e+05],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>on_area</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3ae1571-d777-4bb3-bf65-c47011cae3dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3ae1571-d777-4bb3-bf65-c47011cae3dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92e43c9-c577-4bc9-b595-3ad90e37c9aa' class='xr-var-data-in' type='checkbox'><label for='data-b92e43c9-c577-4bc9-b595-3ad90e37c9aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>On-glacier area</dd><dt><span>unit :</span></dt><dd>m 2</dd></dl></div><div class='xr-var-data'><pre>array([[ 344269.28 ,  568107.7  , 1720471.5  , ...,  324632.84 ,   46000.   ,\n",
       "         217703.55 ],\n",
       "       [ 346416.22 ,  582961.6  , 1720828.9  , ...,  644156.4  ,   48479.45 ,\n",
       "         218191.78 ],\n",
       "       [ 346532.4  ,  581360.75 , 1716640.4  , ...,  389794.53 ,   45743.062,\n",
       "         216467.52 ],\n",
       "       ...,\n",
       "       [  98600.38 ,       0.   ,       0.   , ...,       0.   ,       0.   ,\n",
       "              0.   ],\n",
       "       [  75856.27 ,       0.   ,       0.   , ...,       0.   ,       0.   ,\n",
       "              0.   ],\n",
       "       [        nan,         nan,         nan, ...,         nan,         nan,\n",
       "                nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melt_off_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-092453a4-c4e5-4435-a031-77e5ee8761ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-092453a4-c4e5-4435-a031-77e5ee8761ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9342af5-897a-471f-bd68-c154bb329117' class='xr-var-data-in' type='checkbox'><label for='data-b9342af5-897a-471f-bd68-c154bb329117' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Off-glacier melt</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[2.371082e-06, 1.832180e-07, 9.476695e-07, ..., 2.863722e-07,\n",
       "        0.000000e+00, 1.722027e-07],\n",
       "       [0.000000e+00, 0.000000e+00, 4.449554e+05, ..., 0.000000e+00,\n",
       "        3.707348e+02, 1.371873e+05],\n",
       "       [1.109134e+04, 1.417195e+05, 2.810095e+06, ..., 0.000000e+00,\n",
       "        2.751798e+05, 1.296721e+06],\n",
       "       ...,\n",
       "       [3.864542e+08, 9.207398e+08, 2.719876e+09, ..., 3.831888e+08,\n",
       "        5.408965e+07, 2.804302e+08],\n",
       "       [4.267046e+08, 1.013254e+09, 2.663543e+09, ..., 4.549835e+08,\n",
       "        6.556823e+07, 2.919449e+08],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melt_on_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29db308b-d568-4b06-97e2-42df5758888e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29db308b-d568-4b06-97e2-42df5758888e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b00d6e65-c8f1-40ea-a5de-8dbc9be39cf4' class='xr-var-data-in' type='checkbox'><label for='data-b00d6e65-c8f1-40ea-a5de-8dbc9be39cf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>On-glacier melt</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[4.000572e+08, 7.510402e+08, 3.115421e+09, ..., 1.834728e+08,\n",
       "        5.978986e+07, 3.867350e+08],\n",
       "       [5.538816e+08, 1.083657e+09, 3.980770e+09, ..., 9.773079e+08,\n",
       "        9.807511e+07, 4.863131e+08],\n",
       "       [4.796700e+08, 9.037018e+08, 3.487400e+09, ..., 4.847660e+08,\n",
       "        6.563464e+07, 4.171431e+08],\n",
       "       ...,\n",
       "       [1.655591e+08, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [1.343086e+08, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>liq_prcp_off_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66787db4-b6f1-4cb5-9e29-c246c4a7a7ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66787db4-b6f1-4cb5-9e29-c246c4a7a7ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f89d4061-b5a0-4c95-85fe-88a3d8939178' class='xr-var-data-in' type='checkbox'><label for='data-f89d4061-b5a0-4c95-85fe-88a3d8939178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Off-glacier liquid precipitation</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[2.693259e-06, 8.484861e-08, 5.660617e-07, ..., 2.066361e-07,\n",
       "        0.000000e+00, 2.944285e-07],\n",
       "       [0.000000e+00, 0.000000e+00, 3.420925e+05, ..., 0.000000e+00,\n",
       "        2.658630e+02, 1.553666e+05],\n",
       "       [1.201203e+04, 7.125209e+04, 3.277661e+06, ..., 0.000000e+00,\n",
       "        5.603775e+04, 2.239953e+06],\n",
       "       ...,\n",
       "       [3.240637e+08, 4.668838e+08, 1.862315e+09, ..., 4.268004e+08,\n",
       "        4.360210e+07, 4.465521e+08],\n",
       "       [3.612306e+08, 5.215172e+08, 1.976097e+09, ..., 4.040905e+08,\n",
       "        4.223230e+07, 4.022981e+08],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>liq_prcp_on_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71610df2-d6d0-408c-aa9e-efb2e1fead00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71610df2-d6d0-408c-aa9e-efb2e1fead00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7479e382-ea62-4a22-abbd-ba9f8a43abf6' class='xr-var-data-in' type='checkbox'><label for='data-7479e382-ea62-4a22-abbd-ba9f8a43abf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>On-glacier liquid precipitation</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[2.061615e+08, 1.941926e+08, 1.034125e+09, ..., 1.609279e+08,\n",
       "        1.652759e+07, 3.183540e+08],\n",
       "       [2.788465e+08, 3.260149e+08, 1.030066e+09, ..., 5.284665e+08,\n",
       "        2.615964e+07, 2.177184e+08],\n",
       "       [3.163321e+08, 3.182031e+08, 1.420718e+09, ..., 3.371350e+08,\n",
       "        9.327609e+06, 3.398683e+08],\n",
       "       ...,\n",
       "       [1.108760e+08, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [8.146014e+07, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snowfall_off_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08ccd3ba-4261-4fb0-87f0-3cf1d8937c40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ccd3ba-4261-4fb0-87f0-3cf1d8937c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b582196c-5d89-44d3-af65-0f05cad13bbb' class='xr-var-data-in' type='checkbox'><label for='data-b582196c-5d89-44d3-af65-0f05cad13bbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Off-glacier solid precipitation</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.839083e-06, 4.397499e-07, 2.166849e-06, ..., 9.411253e-07,\n",
       "        0.000000e+00, 4.375222e-07],\n",
       "       [0.000000e+00, 0.000000e+00, 7.476112e+05, ..., 0.000000e+00,\n",
       "        5.219010e+02, 1.912003e+05],\n",
       "       [3.164276e+04, 2.964793e+05, 6.240080e+06, ..., 0.000000e+00,\n",
       "        5.999187e+05, 2.576549e+06],\n",
       "       ...,\n",
       "       [3.839783e+08, 9.582433e+08, 2.723924e+09, ..., 3.734129e+08,\n",
       "        5.613102e+07, 2.614563e+08],\n",
       "       [4.385981e+08, 9.398941e+08, 2.722429e+09, ..., 4.257183e+08,\n",
       "        5.997344e+07, 3.232634e+08],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snowfall_on_glacier</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3775a808-8a73-4c05-b623-262982407208' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3775a808-8a73-4c05-b623-262982407208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99bc4f0e-e964-457f-9959-3c8e1e966601' class='xr-var-data-in' type='checkbox'><label for='data-99bc4f0e-e964-457f-9959-3c8e1e966601' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>On-glacier solid precipitation</dd><dt><span>unit :</span></dt><dd>kg yr-1</dd></dl></div><div class='xr-var-data'><pre>array([[7.423645e+08, 1.085830e+09, 3.203407e+09, ..., 7.414492e+08,\n",
       "        8.760815e+07, 4.209087e+08],\n",
       "       [4.515647e+08, 7.670578e+08, 2.251195e+09, ..., 6.622587e+08,\n",
       "        4.577318e+07, 2.679044e+08],\n",
       "       [5.437983e+08, 9.943068e+08, 2.843792e+09, ..., 6.850817e+08,\n",
       "        9.644740e+07, 4.109621e+08],\n",
       "       ...,\n",
       "       [1.733000e+08, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [1.445797e+08, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
       "        0.000000e+00, 0.000000e+00],\n",
       "       [         nan,          nan,          nan, ...,          nan,\n",
       "                 nan,          nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melt_off_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04d8ce42-147b-4a9c-a603-149dd31084d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04d8ce42-147b-4a9c-a603-149dd31084d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a051243-208c-45c5-b777-50211623974f' class='xr-var-data-in' type='checkbox'><label for='data-8a051243-208c-45c5-b777-50211623974f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melt_on_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a86b5f9-ee54-461d-aed1-680733679852' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a86b5f9-ee54-461d-aed1-680733679852' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7364a14f-8581-4f1f-9042-1167b1e30f72' class='xr-var-data-in' type='checkbox'><label for='data-7364a14f-8581-4f1f-9042-1167b1e30f72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>liq_prcp_off_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2c38669-7753-41bf-aaef-ba5b744bf05b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2c38669-7753-41bf-aaef-ba5b744bf05b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfce2b28-562b-4f56-9a65-13de9ae1a219' class='xr-var-data-in' type='checkbox'><label for='data-dfce2b28-562b-4f56-9a65-13de9ae1a219' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>liq_prcp_on_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf7278f0-42f3-4f9d-a09d-71f90c35207d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf7278f0-42f3-4f9d-a09d-71f90c35207d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1b05059-0210-4b53-b436-a492ae9b64d2' class='xr-var-data-in' type='checkbox'><label for='data-e1b05059-0210-4b53-b436-a492ae9b64d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snowfall_off_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0285984-e904-4f6e-99ee-21ddbcfcd186' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0285984-e904-4f6e-99ee-21ddbcfcd186' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae2396d3-b43c-4f0c-89c9-d4b458cc3034' class='xr-var-data-in' type='checkbox'><label for='data-ae2396d3-b43c-4f0c-89c9-d4b458cc3034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snowfall_on_glacier_monthly</span></div><div class='xr-var-dims'>(time, month_2d, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d74c401-8b83-450f-8bbf-ffc5cdacc792' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d74c401-8b83-450f-8bbf-ffc5cdacc792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c85eeae0-7192-4b03-95fc-8ff4c3b54c78' class='xr-var-data-in' type='checkbox'><label for='data-c85eeae0-7192-4b03-95fc-8ff4c3b54c78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[612000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_level</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8d6a721-457a-432d-b070-b12a024f69cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8d6a721-457a-432d-b070-b12a024f69cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ac32e08-129c-43fb-b85d-6387ab35299d' class='xr-var-data-in' type='checkbox'><label for='data-7ac32e08-129c-43fb-b85d-6387ab35299d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calving water level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>glen_a</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aaaa0eb6-2df1-4231-85ad-dd7b87ed42eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aaaa0eb6-2df1-4231-85ad-dd7b87ed42eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6b1e075-a8fa-4ad7-9894-10b11a337526' class='xr-var-data-in' type='checkbox'><label for='data-d6b1e075-a8fa-4ad7-9894-10b11a337526' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Simulation Glen A</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([7.606655e-24, 7.606655e-24, 7.464245e-24, ..., 7.606655e-24,\n",
       "       7.606655e-24, 7.726992e-24], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fs</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5866ff1-6d60-44be-b404-dd82ebb87814' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5866ff1-6d60-44be-b404-dd82ebb87814' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b91284aa-f9a2-4850-97e4-2fb3d5e1bbb8' class='xr-var-data-in' type='checkbox'><label for='data-b91284aa-f9a2-4850-97e4-2fb3d5e1bbb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Simulation sliding parameter</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcc0bcbf-6d19-4f75-83b7-2285f151d120' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bcc0bcbf-6d19-4f75-83b7-2285f151d120' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>OGGM model output</dd><dt><span>oggm_version :</span></dt><dd>1.6.1</dd><dt><span>calendar :</span></dt><dd>365-day no leap</dd><dt><span>creation_date :</span></dt><dd>2024-01-19 21:30:45</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                       (time: 102, rgi_id: 500, month_2d: 12)\n",
       "Coordinates:\n",
       "  * time                          (time) float64 2e+03 2.001e+03 ... 2.101e+03\n",
       "  * rgi_id                        (rgi_id) object 'RGI60-01.00001' ... 'RGI60...\n",
       "    hydro_year                    (time) int64 ...\n",
       "    hydro_month                   (time) int64 ...\n",
       "    calendar_year                 (time) int64 ...\n",
       "    calendar_month                (time) int64 ...\n",
       "  * month_2d                      (month_2d) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    calendar_month_2d             (month_2d) int64 ...\n",
       "Data variables: (12/22)\n",
       "    volume                        (time, rgi_id) float32 ...\n",
       "    volume_bsl                    (time, rgi_id) float32 ...\n",
       "    volume_bwl                    (time, rgi_id) float32 ...\n",
       "    area                          (time, rgi_id) float32 ...\n",
       "    length                        (time, rgi_id) float32 ...\n",
       "    off_area                      (time, rgi_id) float32 ...\n",
       "    ...                            ...\n",
       "    liq_prcp_on_glacier_monthly   (time, month_2d, rgi_id) float32 ...\n",
       "    snowfall_off_glacier_monthly  (time, month_2d, rgi_id) float32 ...\n",
       "    snowfall_on_glacier_monthly   (time, month_2d, rgi_id) float32 ...\n",
       "    water_level                   (rgi_id) float32 ...\n",
       "    glen_a                        (rgi_id) float32 ...\n",
       "    fs                            (rgi_id) float32 ...\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2024-01-19 21:30:45"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open example file \n",
    "preg = 'P01'\n",
    "scenario = 'CurPol'\n",
    "f = open('/home/www/lschuster/provide/provide_glacier_regions/rgi_ids_per_provide_region.json')\n",
    "rgis_preg = json.load(f)[preg]\n",
    "model = gcms_mesmer[0]\n",
    "dt_ = []\n",
    "q = '0.5'\n",
    "ji = 0\n",
    "je = 500\n",
    "with xr.open_dataset(f'{path}/{preg}/run_hydro_w5e5_gcm_merged_{scenario}_{model}_q{q}_bc_2000_2019_Batch_{ji}_{je}.nc') as dt:\n",
    "    #dt = dt[['volume']]\n",
    "    dt = dt.sortby('rgi_id')\n",
    "    dt_.append(dt)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c200ae8b-1fa6-474e-b90b-19fcdf4d8c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P05 2840\n",
      "CurPol\n",
      "GS\n",
      "LD\n",
      "ModAct\n",
      "Ref\n",
      "Ren\n",
      "Neg\n",
      "SP\n",
      "ssp119\n",
      "ssp534-over\n",
      "P05 2661 2840 84175.043 85463.881\n",
      "P06 2218\n",
      "CurPol\n",
      "GS\n",
      "LD\n",
      "ModAct\n",
      "Ref\n",
      "Ren\n",
      "Neg\n",
      "SP\n",
      "ssp119\n",
      "ssp534-over\n",
      "P06 2086 2218 1109.17 1194.942\n",
      "P08 1888\n",
      "CurPol\n",
      "GS\n",
      "LD\n",
      "ModAct\n",
      "Ref\n",
      "Ren\n",
      "Neg\n",
      "SP\n",
      "ssp119\n",
      "ssp534-over\n",
      "P08 1411 1888 1135.367 1306.992\n",
      "P10 2898\n",
      "CurPol\n",
      "GS\n",
      "LD\n",
      "ModAct\n",
      "Ref\n",
      "Ren\n",
      "Neg\n",
      "SP\n",
      "ssp119\n",
      "ssp534-over\n",
      "P10 2882 2898 2333.626 2334.4930000000004\n",
      "P12 3537\n",
      "CurPol\n",
      "GS\n",
      "LD\n",
      "ModAct\n",
      "Ref\n",
      "Ren\n",
      "Neg\n",
      "SP\n",
      "ssp119\n"
     ]
    }
   ],
   "source": [
    "DATE = 'Jan29_2024'\n",
    "load = True\n",
    "if load:\n",
    "    # memory issues for regions with too many glaciers\n",
    "    # need to first find the common running glaciers and only select those to reduce that\n",
    "    for p in [4, 5,6,8,10,12]: #np.arange(1,12.1,1):   No P13 (i.e., antarctic periphery ...)\n",
    "        p = int(p)\n",
    "        preg = f'P{p:02d}'\n",
    "        if preg in ['P03']:\n",
    "            continue\n",
    "        f = open('/home/www/lschuster/provide/provide_glacier_regions/rgi_ids_per_provide_region.json')\n",
    "        rgis_preg = json.load(f)[preg]\n",
    "        rgis_preg = list(set(rgis_preg)) # the IDs are not unique ... \n",
    "        rgis_preg = np.sort(rgis_preg) # make sure that the RGIs are sorted ...\n",
    "        print(preg, len(rgis_preg))\n",
    "        if preg == 'P03': # omit connectiity level 2 from P03 (i.e., Greenland)\n",
    "            odf_preg = odf.loc[rgis_preg]\n",
    "            odf_preg_sel = odf_preg.loc[odf_preg['Connect'] != 2]\n",
    "            rgis_preg = odf_preg_sel.index\n",
    "        ds_model_s_ = []\n",
    "        for scenario in scenarios:\n",
    "            ds_model_q_ = []\n",
    "            for q in quantiles:\n",
    "                ds_model_ = []\n",
    "                for model in gcms_mesmer:\n",
    "                    dt_ = []\n",
    "                    for j in np.arange(0,int(np.ceil(len(rgis_preg)/500)),1):\n",
    "                        ji= j*500\n",
    "                        je = (j+1)*500\n",
    "                        with xr.open_dataset(f'{path}/{preg}/run_hydro_w5e5_gcm_merged_{scenario}_{model}_q{q}_bc_2000_2019_Batch_{ji}_{je}.nc') as dt:\n",
    "                            dtx = dt[['volume']].load()\n",
    "                            dt.close()\n",
    "                        dtx = dtx.sortby('rgi_id')\n",
    "                        dt_.append(dtx)\n",
    "                    ds = xr.concat(dt_, dim ='rgi_id')\n",
    "                    #ds = ds.sortby('rgi_id')\n",
    "                    ds['gcm'] = model \n",
    "                    ds_model_.append(ds)\n",
    "                ds_model_c = xr.concat(ds_model_, dim='gcm')\n",
    "                ds_model_c['quantile'] = q\n",
    "                ds_model_q_.append(ds_model_c)\n",
    "            ds_model_sel = xr.concat(ds_model_q_, dim='quantile')\n",
    "            ds_model_sel['scenario'] = scenario\n",
    "            ds_model_s_.append(ds_model_sel)\n",
    "            print(scenario)\n",
    "        ds_model = xr.concat(ds_model_s_, dim='scenario')\n",
    "        n = len(ds_model.rgi_id.values)\n",
    "        area_sum = odf.loc[ds_model.rgi_id.values]['Area'].sum()\n",
    "        assert n == len(rgis_preg)\n",
    "        assert set(rgis_preg) ==  set(ds_model.rgi_id.values)\n",
    "        # only common running glaicers ... \n",
    "        ds_model = ds_model.volume.dropna(dim='rgi_id', how='any')\n",
    "        rgi_ids_preg = ds_model.rgi_id.values\n",
    "        area_sum_working = odf.loc[rgi_ids_preg]['Area'].sum()\n",
    "\n",
    "        ds_model = ds_model.sum(dim='rgi_id')\n",
    "        ds_model.attrs['n_rgi_ids'] = n\n",
    "        ds_model.attrs['n_rgi_ids_working'] = len(rgi_ids_preg)\n",
    "        ds_model.attrs['rgi_ids_working'] = list(rgi_ids_preg)\n",
    "        ds_model.attrs['area_at_rgi_rgi_ids'] = area_sum\n",
    "        ds_model.attrs['area_at_rgi_rgi_ids_working'] = area_sum_working\n",
    "        print(preg, len(rgi_ids_preg), n, area_sum_working, area_sum)\n",
    "\n",
    "        ds_model.to_netcdf(f'volume_{preg}_aggregated_v{DATE}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f743bfd-d11f-459f-8be4-1b11f31c1d1a",
   "metadata": {},
   "source": [
    "**Research questions by doing different comparisons of scenarios** (the actual scenario variable as in the files is in \"scenario\")\n",
    "\n",
    "\n",
    "Spanning the plausible range of GMT outcomes (recommended for adaptation practitioners and put forward on the scenario selector in the 'Explore Future Impacts' page): \n",
    " - Current Policies (\"CurPol\")\n",
    " - Delayed Action (Gradual Strengthening, \"GS\")\n",
    " - Shifting Pathways (\"SP\")\n",
    "\n",
    "\n",
    "Climate pledges versus immediate action: \n",
    "- NDCs pathways (called Moderate Action by the IPCC, \"ModAct\")\n",
    "- Delayed Action (Gradual Strengthening, \"GS\")\n",
    "- Low Demand (\"LD\")\n",
    "\n",
    "\n",
    "Differences between 1.5°C and 2°C-compatible scenarios: \n",
    "- SSP1-19 (\"ssp119\")\n",
    "- Stabilisation at 1.5 (\"Ref\")\n",
    "- Delayed Action (Gradual Strengthening, \"GS\")\n",
    "\n",
    "\n",
    "Differences between 1.5°C-compatible scenarios: \n",
    "- Shifting Pathways (\"SP\")\n",
    "- Stabilisation at 1.5 (\"Ref\")\n",
    "- High Renewables (\"Ren\")\n",
    "\n",
    "Reversibility in the 21st century: \n",
    "- SSP5-34-OS (\"ssp534-over\")\n",
    "- High Negative Emissions (\"Neg\")\n",
    "- Stabilisation at 1.5 (\"Ref\")"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;volume&#x27; (scenario: 10, quantile: 5, gcm: 20, time: 102)&gt;\n",
       "[102000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time            (time) float64 2e+03 2.001e+03 ... 2.1e+03 2.101e+03\n",
       "    hydro_year      (time) int64 2000 2001 2002 2003 ... 2098 2099 2100 2101\n",
       "    hydro_month     (time) int64 4 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4 4 4\n",
       "    calendar_year   (time) int64 2000 2001 2002 2003 ... 2098 2099 2100 2101\n",
       "    calendar_month  (time) int64 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1\n",
       "  * gcm             (gcm) object &#x27;ACCESS-ESM1-5&#x27; &#x27;CESM2-WACCM&#x27; ... &#x27;UKESM1-0-LL&#x27;\n",
       "  * quantile        (quantile) object &#x27;0.05&#x27; &#x27;0.25&#x27; &#x27;0.5&#x27; &#x27;0.75&#x27; &#x27;0.95&#x27;\n",
       "  * scenario        (scenario) object &#x27;CurPol&#x27; &#x27;GS&#x27; ... &#x27;ssp119&#x27; &#x27;ssp534-over&#x27;\n",
       "Attributes:\n",
       "    n_rgi_ids:                    3927\n",
       "    n_rgi_ids_working:            3896\n",
       "    area_at_rgi_rgi_ids:          2092.1459999999997\n",
       "    area_at_rgi_rgi_ids_working:  2091.072</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'volume'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>scenario</span>: 10</li><li><span class='xr-has-index'>quantile</span>: 5</li><li><span class='xr-has-index'>gcm</span>: 20</li><li><span class='xr-has-index'>time</span>: 102</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-487d66c1-401a-4486-a31d-505df757bba2' class='xr-array-in' type='checkbox' checked><label for='section-487d66c1-401a-4486-a31d-505df757bba2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[102000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-c27fa92d-f922-4aeb-8744-a031266d34bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c27fa92d-f922-4aeb-8744-a031266d34bf' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2e+03 2.001e+03 ... 2.101e+03</div><input id='attrs-08fc84ca-1e2d-437f-bd98-ae3e61e2e545' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08fc84ca-1e2d-437f-bd98-ae3e61e2e545' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a63265bb-5026-45eb-bc27-11e33f8f66c8' class='xr-var-data-in' type='checkbox'><label for='data-a63265bb-5026-45eb-bc27-11e33f8f66c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Floating year</dd></dl></div><div class='xr-var-data'><pre>array([2000., 2001., 2002., 2003., 2004., 2005., 2006., 2007., 2008., 2009.,\n",
       "       2010., 2011., 2012., 2013., 2014., 2015., 2016., 2017., 2018., 2019.,\n",
       "       2020., 2021., 2022., 2023., 2024., 2025., 2026., 2027., 2028., 2029.,\n",
       "       2030., 2031., 2032., 2033., 2034., 2035., 2036., 2037., 2038., 2039.,\n",
       "       2040., 2041., 2042., 2043., 2044., 2045., 2046., 2047., 2048., 2049.,\n",
       "       2050., 2051., 2052., 2053., 2054., 2055., 2056., 2057., 2058., 2059.,\n",
       "       2060., 2061., 2062., 2063., 2064., 2065., 2066., 2067., 2068., 2069.,\n",
       "       2070., 2071., 2072., 2073., 2074., 2075., 2076., 2077., 2078., 2079.,\n",
       "       2080., 2081., 2082., 2083., 2084., 2085., 2086., 2087., 2088., 2089.,\n",
       "       2090., 2091., 2092., 2093., 2094., 2095., 2096., 2097., 2098., 2099.,\n",
       "       2100., 2101.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13f2a404-223e-419f-9e17-e1f1aadb2fc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13f2a404-223e-419f-9e17-e1f1aadb2fc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16ede135-2c39-40bb-9643-20446a850860' class='xr-var-data-in' type='checkbox'><label for='data-16ede135-2c39-40bb-9643-20446a850860' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological year</dd></dl></div><div class='xr-var-data'><pre>array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "       2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023,\n",
       "       2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035,\n",
       "       2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047,\n",
       "       2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059,\n",
       "       2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071,\n",
       "       2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083,\n",
       "       2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095,\n",
       "       2096, 2097, 2098, 2099, 2100, 2101])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b94366b4-9fcd-49ea-9841-9b4258c4adad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94366b4-9fcd-49ea-9841-9b4258c4adad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a05d654-2645-4b69-8178-cb014659a406' class='xr-var-data-in' type='checkbox'><label for='data-1a05d654-2645-4b69-8178-cb014659a406' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological month</dd></dl></div><div class='xr-var-data'><pre>array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0dab27d1-65d3-43ef-988a-805adf3657e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dab27d1-65d3-43ef-988a-805adf3657e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cdfb399-e17e-48c8-8169-931e0b9860b9' class='xr-var-data-in' type='checkbox'><label for='data-8cdfb399-e17e-48c8-8169-931e0b9860b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar year</dd></dl></div><div class='xr-var-data'><pre>array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "       2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023,\n",
       "       2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035,\n",
       "       2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047,\n",
       "       2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059,\n",
       "       2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071,\n",
       "       2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083,\n",
       "       2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095,\n",
       "       2096, 2097, 2098, 2099, 2100, 2101])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfeb4640-c70e-41b3-82b9-f4a1c86a72c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfeb4640-c70e-41b3-82b9-f4a1c86a72c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f98c147b-90e9-4cf6-9990-cea9481ee300' class='xr-var-data-in' type='checkbox'><label for='data-f98c147b-90e9-4cf6-9990-cea9481ee300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar month</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gcm</span></div><div class='xr-var-dims'>(gcm)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;ACCESS-ESM1-5&#x27; ... &#x27;UKESM1-0-LL&#x27;</div><input id='attrs-05f2690a-de43-4f96-aa35-8b8eb175784a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05f2690a-de43-4f96-aa35-8b8eb175784a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e4d3199-a973-417a-8a6f-28c568f04369' class='xr-var-data-in' type='checkbox'><label for='data-2e4d3199-a973-417a-8a6f-28c568f04369' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ACCESS-ESM1-5&#x27;, &#x27;CESM2-WACCM&#x27;, &#x27;CESM2&#x27;, &#x27;CMCC-CM2-SR5&#x27;,\n",
       "       &#x27;CNRM-CM6-1-HR&#x27;, &#x27;CNRM-CM6-1&#x27;, &#x27;CNRM-ESM2-1&#x27;, &#x27;CanESM5&#x27;, &#x27;E3SM-1-1&#x27;,\n",
       "       &#x27;FGOALS-f3-L&#x27;, &#x27;FGOALS-g3&#x27;, &#x27;HadGEM3-GC31-LL&#x27;, &#x27;HadGEM3-GC31-MM&#x27;,\n",
       "       &#x27;IPSL-CM6A-LR&#x27;, &#x27;MPI-ESM1-2-HR&#x27;, &#x27;MPI-ESM1-2-LR&#x27;, &#x27;MRI-ESM2-0&#x27;,\n",
       "       &#x27;NorESM2-LM&#x27;, &#x27;NorESM2-MM&#x27;, &#x27;UKESM1-0-LL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>quantile</span></div><div class='xr-var-dims'>(quantile)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;0.05&#x27; &#x27;0.25&#x27; &#x27;0.5&#x27; &#x27;0.75&#x27; &#x27;0.95&#x27;</div><input id='attrs-7b563876-7081-4b8c-835c-19c1e2509a6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b563876-7081-4b8c-835c-19c1e2509a6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d14ae96-9bcf-44b9-9af6-d7fe85197be6' class='xr-var-data-in' type='checkbox'><label for='data-3d14ae96-9bcf-44b9-9af6-d7fe85197be6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0.05&#x27;, &#x27;0.25&#x27;, &#x27;0.5&#x27;, &#x27;0.75&#x27;, &#x27;0.95&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scenario</span></div><div class='xr-var-dims'>(scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;CurPol&#x27; &#x27;GS&#x27; ... &#x27;ssp534-over&#x27;</div><input id='attrs-d5da7a94-face-4a38-9c8b-55d270375dd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5da7a94-face-4a38-9c8b-55d270375dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-088bd220-c051-4218-a081-19625658bd23' class='xr-var-data-in' type='checkbox'><label for='data-088bd220-c051-4218-a081-19625658bd23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;CurPol&#x27;, &#x27;GS&#x27;, &#x27;LD&#x27;, &#x27;ModAct&#x27;, &#x27;Ref&#x27;, &#x27;Ren&#x27;, &#x27;Neg&#x27;, &#x27;SP&#x27;, &#x27;ssp119&#x27;,\n",
       "       &#x27;ssp534-over&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4dcf046e-27d7-474e-a6b2-2df3ef50cde3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4dcf046e-27d7-474e-a6b2-2df3ef50cde3' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>n_rgi_ids :</span></dt><dd>3927</dd><dt><span>n_rgi_ids_working :</span></dt><dd>3896</dd><dt><span>area_at_rgi_rgi_ids :</span></dt><dd>2092.1459999999997</dd><dt><span>area_at_rgi_rgi_ids_working :</span></dt><dd>2091.072</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'volume' (scenario: 10, quantile: 5, gcm: 20, time: 102)>\n",
       "[102000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time            (time) float64 2e+03 2.001e+03 ... 2.1e+03 2.101e+03\n",
       "    hydro_year      (time) int64 ...\n",
       "    hydro_month     (time) int64 ...\n",
       "    calendar_year   (time) int64 ...\n",
       "    calendar_month  (time) int64 ...\n",
       "  * gcm             (gcm) object 'ACCESS-ESM1-5' 'CESM2-WACCM' ... 'UKESM1-0-LL'\n",
       "  * quantile        (quantile) object '0.05' '0.25' '0.5' '0.75' '0.95'\n",
       "  * scenario        (scenario) object 'CurPol' 'GS' ... 'ssp119' 'ssp534-over'\n",
       "Attributes:\n",
       "    n_rgi_ids:                    3927\n",
       "    n_rgi_ids_working:            3896\n",
       "    area_at_rgi_rgi_ids:          2092.1459999999997\n",
       "    area_at_rgi_rgi_ids_working:  2091.072"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preg = 'P07'\n",
    "ds_model  = xr.open_dataset(f'volume_{preg}_aggregated_v{DATE}.nc')\n",
    "\n",
    "ds_model.volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "362b5c77-b3e8-41c9-b521-a132035fe850",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', size=20)\n",
    "\n",
    "RQs_d = {'Spanning plausible GMT outcomes':['CurPol', 'GS', 'SP'],\n",
    "         'Climate pledges versus immediate action':['ModAct','GS','LD'],\n",
    "       'Differences between 1.5°C and 2°C-compatible scenarios':['ssp119','Ref','GS'], \n",
    "       'Differences between 1.5°C-compatible scenarios':['SP','Ref','Ren'],\n",
    "       'Reversibility in the 21st century':['ssp534-over','Neg','Ref']\n",
    "        }\n",
    "scenario_long = {'CurPol': 'Current Policies',\n",
    "                 'GS': 'Gradual Strengthening / Delayed Action (DA)',\n",
    "                 'SP': '1.5 - Shifting Pathways\\n(compatible with Paris Agreement)',\n",
    "                 'ModAct': 'NDCs pathways (Moderate Action)',\n",
    "                 'LD': '1.5 - Low Demand\\n(compatible with Paris Agreement)',\n",
    "                 'Neg': 'High Negative Emissions',\n",
    "                 'Ren': '1.5 - High Renewables\\n(compatible with Paris Agreement)',\n",
    "                 'ssp119' : '(compatible with\\nParis Agreement)',\n",
    "                 'ssp534-over': 'strong 21st century overshoot\\nin radiative forcing',\n",
    "                 'Ref':'Stabilisation at +1.5°C\\n(compatible with Paris Agreement)'\n",
    "                }\n",
    "colors_sel = ['Black','Orange','Blue']\n",
    "\n",
    "\n",
    "for p in [4,5,6,7,8,10,12]: #np.arange(1,12.1,1):   No P13 (i.e., antarctic periphery ...)\n",
    "    p = int(p)\n",
    "    preg = f'P{p:02d}'\n",
    "    ds_model = xr.open_dataset(f'volume_{preg}_aggregated_v{DATE}.nc')\n",
    "    print(ds_model.volume.attrs['n_rgi_ids'], ds_model.volume.attrs['n_rgi_ids_working'])\n",
    "    pd_model = ds_model.to_dataframe().reset_index()\n",
    "    pd_model_rel= (100*ds_model / ds_model.sel(time=2020)).to_dataframe().reset_index()\n",
    "    # do median over GCMs and quantiles (just rough estimate of median) ... \n",
    "    pd_model_rel_med = pd_model_rel.groupby(['time', 'scenario']).median().reset_index()\n",
    "\n",
    "    plt.figure(figsize=(32,20))\n",
    "    jj = 0\n",
    "    for RQ,scenarios_sel in zip(RQs_d.keys(), RQs_d.values()):\n",
    "        plt.subplot(3,2,jj+1)\n",
    "\n",
    "        for j,scenario in enumerate(scenarios_sel): \n",
    "            pd_model_rel_sel = pd_model_rel.loc[pd_model_rel.scenario == scenario]\n",
    "            # this has now 20 GCMs and five quantiles \n",
    "            pd_model_rel_med_sel = pd_model_rel_med.loc[pd_model_rel_med.scenario == scenario]\n",
    "            sns.lineplot(data=pd_model_rel_sel, x='time', y='volume',\n",
    "                         hue='gcm',style='quantile',\n",
    "                         palette=sns.color_palette(np.repeat(colors_sel[j],len(gcms_mesmer))),\n",
    "                         #style='quantile',  #style_order = quantiles,\n",
    "                         legend=False, alpha = 0.4,\n",
    "                         lw = 0.4, \n",
    "                         ls = '-',\n",
    "                         estimator = None, # draw all observations ! \n",
    "                         #dashes = [(2,2),(2,2),(1,1),(2,2),(2,2)], \n",
    "                        #palette=colors.values(),\n",
    "                         )\n",
    "            sns.lineplot(data=pd_model_rel_med_sel,\n",
    "                         x='time', y='volume', lw=4, color = colors_sel[j],\n",
    "                         label=f'{scenario}: {scenario_long[scenario]}')\n",
    "\n",
    "        plt.xlim([2020,2101])\n",
    "        plt.ylim([0, 105])\n",
    "        plt.legend(title='median over 20 GCMs & 5 quantiles')\n",
    "        plt.ylabel('Remaning glacier ice mass\\n(%, relative to 2020)')\n",
    "        plt.xlabel('Year')\n",
    "        plt.title(f'{RQ}: {provide_reg_full_name_dict_correct[preg]}')\n",
    "        jj +=1\n",
    "    working_area_perc = 100*ds_model.volume.attrs['area_at_rgi_rgi_ids_working']/ds_model.volume.attrs['area_at_rgi_rgi_ids']\n",
    "    plt.suptitle(f'{provide_reg_full_name_dict_correct[preg]}, working glacier area (%): {working_area_perc:0.2f}')\n",
    "    plt.tight_layout()\n",
    "    reg_name = provide_reg_full_name_dict_correct[preg].replace(' ', '_')\n",
    "    fig_path='/home/www/lschuster/provide/MESMER-M_projections/mesmer-m_glacier_preg_test_plots'\n",
    "    plt.savefig(f'{fig_path}/glaciers_test_{preg}_{reg_name}.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "549d15ec-8aca-47db-8937-53510ff0893a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae42c803-bb64-41fa-ae80-1d4e028b2729",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44379cd4-e0e2-43f4-8e18-14f57a75c26d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f13b9eb4-b7fc-49d5-9f20-1981c5be3bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Scandinavia_and_Iceland'}, xlabel='time', ylabel='volume'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_name = provide_reg_full_name_dict_correct[preg].replace(' ', '_')\n",
    "plt.figure()\n",
    "plt.title(reg_name)\n",
    "sns.lineplot(data=pd_model_rel_sel, x='time', y='volume', hue='quantile', legend=False,\n",
    "            hue_order = colors.keys(), palette=colors.values())\n",
    "sns.lineplot(data=pd_model_rel_med_sel,\n",
    "             x='time', y='volume', lw=3, legend=False, color='black', label='median over\\ngcm & quantiles')\n",
    "\n",
    "plt.figure()\n",
    "plt.title(reg_name)\n",
    "\n",
    "sns.lineplot(data=pd_model, x='time', y='volume', hue='gcm', legend=False)\n",
    "sns.lineplot(data=pd_model.groupby('time').median().reset_index(),\n",
    "             x='time', y='volume', lw=3, legend=False, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f96c97-a566-401e-8cde-8c223b2eea68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}