{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ddafefc-2a7c-48d0-be79-a02420347b32",
   "metadata": {},
   "source": [
    "# 5 - Conversion to SLR equivalent estimates\n",
    "- creates a fit of how much glacier volume below sea-level is lost vs total volume (by using OGGM data). This fit is used later in `6_csv_tables_creation.ipynb` (this needs some additional raw files from OGGM simulations).\n",
    "- also creates **supplementary figure S14** to show the relationship of the ratio to the total volume (and the applied fit from the OGGM data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec43603a-82be-4dab-b213-2a581833d802",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE = 'Feb12_2024'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5c93e5b-4bc4-4a9a-aa50-8146a32b0b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import oggm\n",
    "import geopandas as gpd\n",
    "from  help_functions import d_reg_num_name_sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9da7174-2e2f-45b0-b8c6-ad2523d2b1e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount of glaciers excluded in RGI region 05 because they are connectivity level 2: 955\n"
     ]
    }
   ],
   "source": [
    "# Define experiments \n",
    "gcms = ['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0', 'ukesm1-0-ll' ]\n",
    "scenarios = ['hist', 'ssp126', 'ssp370', 'ssp585']\n",
    "y0_times = [1851, 1901, 1951, 1995, 2021, 2041, 2061, 2081]\n",
    "\n",
    "# get the RGI area / ITMIX volumes of the glaciers\n",
    "rgi_regs = []\n",
    "for rgi_reg in np.arange(1,20,1):\n",
    "    if rgi_reg < 10:\n",
    "        rgi_reg = '0'+str(rgi_reg)\n",
    "    else:\n",
    "        rgi_reg = str(rgi_reg)\n",
    "    rgi_regs.append(rgi_reg)\n",
    "df_itmix = pd.read_hdf(oggm.utils.get_demo_file('rgi62_itmix_df.h5'))\n",
    "\n",
    "rgidf_dict = {}\n",
    "for rgi_reg in rgi_regs:\n",
    "    path_rgi = oggm.utils.get_rgi_region_file(rgi_reg, version='6')\n",
    "    rgidf = gpd.read_file(path_rgi)\n",
    "    # Greenland periphery : all glaciers with connectivity level 0 and 1 should be included, level 2 glaciers should be excluded (as was the case for GlacierMIP2)\n",
    "    # total RGI area: 89,651km2\n",
    "    if rgi_reg == '05':\n",
    "        rgidf = rgidf.loc[(rgidf['Connect'] == 0) | (rgidf['Connect'] ==1)]\n",
    "    rgidf_dict[rgi_reg] = rgidf\n",
    "    rgidf_dict[rgi_reg] = rgidf_dict[rgi_reg].set_index('RGIId')\n",
    "    df_itmix.loc[rgidf.RGIId.values,'rgi_region'] = rgi_reg\n",
    "# remove 05-connectivity 2 glaciers \n",
    "df_itmix = df_itmix.loc[~df_itmix.rgi_region.isna()]\n",
    "\n",
    "path_rgi = oggm.utils.get_rgi_region_file('05', version='6')\n",
    "rgidf = gpd.read_file(path_rgi)\n",
    "rgidf_c = rgidf.loc[(rgidf['Connect'] == 0) | (rgidf['Connect'] ==1)]\n",
    "n_excluded = len(rgidf) - len(rgidf_c)\n",
    "print(f'amount of glaciers excluded in RGI region 05 because they are connectivity level 2: {n_excluded}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89d1f02b-5ff0-4706-9b7d-0358dcb84176",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path_asl(folder_path='/home/www/lschuster/glacierMIP3_analysis',\n",
    "             rgi_reg='01',\n",
    "             model_author='OGGM_v16',\n",
    "             period='1851-1870', gcm='gfdl-esm4', ssp='hist'):\n",
    "    ''' output the regional simulation file path for the respective\n",
    "    rgi_reg, model_author, period, gcm & ssp '''\n",
    "    if ('OGGM' in model_author):\n",
    "        # was corrected for missing glaciers ... (by Lilian Schuster with method of Fabien)\n",
    "        model_author_f = model_author\n",
    "        if 'OGGM' in model_author and ssp == 'hist':\n",
    "            ssp = 'historical'\n",
    "            path =  f'{folder_path}/{model_author_f}/regional_filled_asl/{rgi_reg}/{model_author}_rgi{rgi_reg}_sum_{period}_{gcm}_{ssp}_filled.nc'\n",
    "        else:\n",
    "            path =  f'{folder_path}/{model_author_f}/regional_filled_asl/{rgi_reg}/{model_author}_rgi{rgi_reg}_sum_{period}_{gcm}_{ssp}_filled.nc'\n",
    "    else:\n",
    "        print('Not implemented...')\n",
    "    return path\n",
    "#path = get_path(rgi_reg=rgi_reg, model_author='OGGM_v153', period=period, gcm='gfdl-esm4', ssp=scenario)\n",
    "#path = get_path(rgi_reg=rgi_reg, model_author='OGGM_v16', period=period, gcm=gcm, ssp=scenario)\n",
    "#xr.open_dataset(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6264d8b5-16c9-43e2-8696-acc1ed51bef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converge the below sea level and total volume estimates into one netcdf file ... \n",
    "model_authors = ['OGGM_v16'] \n",
    "\n",
    "# We need dummy datasets, because some files only go until 2000 and have to be extended then in order to be merged with the other files \n",
    "# dummy dataset ... w. 5001 entries\n",
    "ds_nan_5001 = xr.open_dataset(get_path_asl(rgi_reg='01'))\n",
    "ds_nan_5001.volume_m3.data[...] = np.NaN\n",
    "ds_nan_5001.volume_bsl_m3.data[...] = np.NaN\n",
    "\n",
    "# dummy dataset ... w. 2001 entries\n",
    "ds_nan_2001 = xr.open_dataset(get_path_asl(rgi_reg='02')) \n",
    "ds_nan_2001.volume_m3.data[...] = np.NaN\n",
    "ds_nan_2001.volume_bsl_m3.data[...] = np.NaN\n",
    "\n",
    "\n",
    "# these regions should run until 5000 years:\n",
    "rgi_regs_5000 = ['01', '03', '04', '05', '06','07', '09', '17','19']\n",
    "\n",
    "\n",
    "### this takes some time ... \n",
    "run = False \n",
    "if run:\n",
    "    for apply_scaling in [False]:\n",
    "        l_ds_list = []\n",
    "        for model_author in model_authors: #['OGGM_v153']: #model_authors:\n",
    "            print(model_author)\n",
    "            missing_exp=[]\n",
    "            _ds_reg = []\n",
    "            for rgi_reg in rgi_regs: #'01'\n",
    "                # want to have the right simulation year length\n",
    "                _l_period = []\n",
    "                for y0_time in y0_times[:4]:\n",
    "                    period = f'{y0_time}-{y0_time+19}'\n",
    "                    _l_scenario = []\n",
    "                    for scenario in scenarios[:1]:\n",
    "                        _l_gcm = []\n",
    "                        for gcm in gcms:\n",
    "                            path = get_path_asl(rgi_reg=rgi_reg, model_author=model_author, period=period, gcm=gcm, ssp=scenario)\n",
    "                            try:\n",
    "                                if model_author == 'GloGEMflow' or model_author == 'Huss':\n",
    "                                    _ds = xr.open_dataset(path)\n",
    "                                    # wrong netcdf shape and simulation year 1 year too short ...\n",
    "                                    if len(_ds.volume_m3.squeeze()) == 5000:\n",
    "                                        ds = ds_nan_5001.copy(deep=True)\n",
    "                                    elif len(_ds.volume_m3.squeeze()) == 2000:\n",
    "                                        ds = ds_nan_2001.copy(deep=True)\n",
    "                                    if model_author == 'Huss':\n",
    "                                        ds.volume_m3.data[:-1] = _ds.volume_m3.squeeze() \n",
    "                                    else:\n",
    "                                        ds.volume_m3.data[:-1] = _ds.volume_m3.squeeze()*1e9 # is in km3 instead of m3\n",
    "                                    ds.volume_bsl_m3.data[:-1] = _ds.volume_bsl_m3.squeeze()\n",
    "                                    # we will just fill up the last value with the second to the last value\n",
    "                                    ds.volume_m3[-1] = ds.volume_m3[-2].values\n",
    "                                    ds.volume_bsl_m3[-1] = ds.volume_bsl_m3[-2].values\n",
    "                                    ds.attrs.update(_ds.attrs)\n",
    "                                else:\n",
    "                                    ds = xr.open_dataset(path)\n",
    "                            except:\n",
    "                                # in case of OGGM 11, went for 5000 yrs anyways\n",
    "                                if rgi_reg in rgi_regs_5000:\n",
    "                                    ds = ds_nan_5001.copy(deep=True)\n",
    "                                else:\n",
    "                                    ds = ds_nan_2001.copy(deep=True)\n",
    "                                missing_exp.append(path)\n",
    "                            if model_author == 'CISM2' and rgi_reg == '11':\n",
    "                                ds.volume_m3.data = ds.volume_m3.squeeze()*1e9 # is in km3 instead of m3\n",
    "                                ds.volume_bsl_m3.data = ds.volume_bsl_m3.squeeze()*1e6 # is in km3 instead of m3\n",
    "\n",
    "                            ds = ds.reset_coords()[['volume_m3', 'volume_bsl_m3']]\n",
    "                            #if apply_scaling:\n",
    "                            #    ds = scale_area_vol(ds, rgi_reg = rgi_reg)\n",
    "                            ds = ds.expand_dims({'gcm':[gcm], 'ssp':[scenario], 'period':[period], 'rgi_reg':[rgi_reg]})\n",
    "                            _l_gcm.append(ds)\n",
    "                        _l_scenario.append(xr.concat(_l_gcm, dim='gcm'))\n",
    "\n",
    "                    _l_period.append(xr.concat(_l_scenario, dim='ssp'))\n",
    "                ds_past = xr.concat(_l_period, dim='period')\n",
    "\n",
    "                _l_period = []\n",
    "                for y0_time in y0_times[4:]:\n",
    "                    period = f'{y0_time}-{y0_time+19}'\n",
    "                    _l_scenario = []\n",
    "                    for scenario in scenarios[1:]:\n",
    "                        _l_gcm = []\n",
    "                        for gcm in gcms:\n",
    "                            path = get_path_asl(rgi_reg=rgi_reg, model_author=model_author, period=period, gcm=gcm, ssp=scenario)\n",
    "                            try:\n",
    "                                #if model_author == 'Compagno' and gcm=='ukesm1-0-ll' and rgi_reg == '05':\n",
    "                                #    raise ValueError('duplicate values, fill with np.NaN')\n",
    "                                if model_author == 'GloGEMflow' or model_author == 'Huss':\n",
    "                                    _ds = xr.open_dataset(path)\n",
    "                                    # wrong netcdf shape and simulation year 1 year too short ...\n",
    "                                    if len(_ds.volume_m3.squeeze()) == 5000:\n",
    "                                        ds = ds_nan_5001.copy(deep=True)\n",
    "                                    elif len(_ds.volume_m3.squeeze()) == 2000:\n",
    "                                        ds = ds_nan_2001.copy(deep=True)\n",
    "                                    if model_author == 'Huss':\n",
    "                                        ds.volume_m3.data[:-1] = _ds.volume_m3.squeeze() \n",
    "                                    else:\n",
    "                                        ds.volume_m3.data[:-1] = _ds.volume_m3.squeeze()*1e9 # is in km3 instead of m3\n",
    "                                    ds.volume_bsl_m3.data[:-1] = _ds.volume_bsl_m3.squeeze()\n",
    "                                    ds.volume_m3[-1] = ds.volume_m3[-2].values\n",
    "                                    ds.volume_bsl_m3[-1] = ds.volume_bsl_m3[-2].values\n",
    "                                    ds.attrs.update(_ds.attrs)\n",
    "                                else:\n",
    "                                    ds = xr.open_dataset(path)\n",
    "                            except:\n",
    "                                if rgi_reg in rgi_regs_5000:\n",
    "                                    ds = ds_nan_5001.copy(deep=True)\n",
    "                                else:\n",
    "                                    ds = ds_nan_2001.copy(deep=True)\n",
    "                                missing_exp.append(path)\n",
    "                            if model_author == 'CISM2' and rgi_reg == '11':\n",
    "                                ds.volume_m3.data = ds.volume_m3.squeeze()*1e9 # is in km3 instead of m3\n",
    "                                ds.volume_bsl_m3.data = ds.volume_bsl_m3.squeeze()*1e6 # is in km3 instead of m3\n",
    "                            ds = ds.reset_coords()[['volume_m3', 'volume_bsl_m3']]\n",
    "                            #if apply_scaling:\n",
    "                            #    ds = scale_area_vol(ds, rgi_reg = rgi_reg)\n",
    "                            ds = ds.expand_dims({'gcm':[gcm], 'ssp':[scenario], 'period':[period], 'rgi_reg':[rgi_reg]})\n",
    "                            _l_gcm.append(ds)\n",
    "                        _l_scenario.append(xr.concat(_l_gcm, dim='gcm'))\n",
    "                    _l_period.append(xr.concat(_l_scenario, dim='ssp'))\n",
    "                ds_future = xr.concat(_l_period, dim='period')\n",
    "                _ds_reg_single = xr.concat([ds_past, ds_future], dim = 'ssp')\n",
    "                # make sure that simulation_year is a coordinate\n",
    "                _ds_reg_single.coords['simulation_year'] = _ds_reg_single.simulation_year\n",
    "                _ds_reg.append(_ds_reg_single)\n",
    "            ds = xr.concat(_ds_reg, dim='rgi_reg')\n",
    "\n",
    "            _ds_mod = ds.expand_dims({'model_author':[model_author]})\n",
    "            l_ds_list.append(_ds_mod)\n",
    "\n",
    "        ds_reg_models = xr.concat(l_ds_list, dim='model_author')\n",
    "\n",
    "        # This is the same for all files\n",
    "        encoding = {\n",
    "            'simulation_year': {\"dtype\": \"int16\"},\n",
    "            'volume_m3': {\"dtype\": \"float32\"},\n",
    "            'volume_bsl_m3': {\"dtype\": \"float32\"},\n",
    "        }\n",
    "\n",
    "        ds_reg_models = ds_reg_models.stack(period_scenario = ('period', 'ssp'))\n",
    "        ds_reg_models.coords['period_scenario'] = [a[0]+'_'+a[1] for a in ds_reg_models.period_scenario.values]\n",
    "\n",
    "        l_period_ssp = ['1851-1870_hist', '1901-1920_hist', '1951-1970_hist',\n",
    "           '1995-2014_hist',  '2021-2040_ssp126',\n",
    "           '2021-2040_ssp370', '2021-2040_ssp585', \n",
    "           '2041-2060_ssp126', '2041-2060_ssp370', '2041-2060_ssp585',\n",
    "           '2061-2080_ssp126', '2061-2080_ssp370',\n",
    "           '2061-2080_ssp585', '2081-2100_ssp126',\n",
    "           '2081-2100_ssp370', '2081-2100_ssp585']\n",
    "\n",
    "        ds_reg_models = ds_reg_models.sel(period_scenario = l_period_ssp)\n",
    "\n",
    "        out_path =f'../0_pre_post_processing/_intermediate_data/glacierMIP3_only_oggm_v16_volume_bsl_{DATE}_all_rgi_regions_sum.nc'\n",
    "        # put NaN into the duplicated file \n",
    "        ds_reg_models.attrs = {} # this needs to be filled with some informations, but for know leave it empty \n",
    "        ds_reg_models.to_netcdf(out_path, encoding = encoding)\n",
    "else:\n",
    "    out_path = out_path =f'../0_pre_post_processing/_intermediate_data/glacierMIP3_only_oggm_v16_volume_bsl_{DATE}_all_rgi_regions_sum.nc'\n",
    "    ds_reg_models = xr.open_dataset(out_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1544b5af-5d30-408f-b113-ad3c9c2e382d",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = False\n",
    "if run: \n",
    "    import pymannkendall as mk\n",
    "\n",
    "    # these regions should run until 5000 years:\n",
    "    rgi_regs_5000 = ['01', '03', '04', '05', '06','07', '09', '17','19']        \n",
    "\n",
    "    for fill_option in ['repeat_last_101yrs']: #'repeat_last_21yrs',\n",
    "        n_increasing = 0\n",
    "        n_decreasing = 0\n",
    "        n_no_trend = 0\n",
    "        deltaV_l = []\n",
    "        deltaV_l50 = []\n",
    "        ds_reg_models_extend = ds_reg_models.copy()\n",
    "        for gcm in np.arange(0,len(ds_reg_models_extend.gcm.values),1):\n",
    "            for period_scenario in np.arange(0, len(ds_reg_models_extend.period_scenario),1):\n",
    "                for m in np.arange(0, len(ds_reg_models_extend.model_author),1):\n",
    "\n",
    "                    ds = ds_reg_models_extend.isel(model_author=m).isel(gcm=gcm).isel(period_scenario=period_scenario)\n",
    "                    #print(ds)\n",
    "                    for rgi_reg_id,rgi_reg in enumerate(ds.rgi_reg.values): \n",
    "                        if np.all(np.isnan(ds.sel(rgi_reg =rgi_reg).volume_m3.values)) and np.all(np.isnan(ds.sel(rgi_reg =rgi_reg).volume_bsl_m3.values)):\n",
    "                            # ok we do not have any regional data for that region, model_author, gcm, period_scenarios ... just keep the values np.NaN...\n",
    "                            pass\n",
    "                        #elif rgi_reg in rgi_regs_5000:\n",
    "                        #    assert not np.any(np.isnan(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(2001,5001)).volume_m3.values))\n",
    "                        else:\n",
    "                            # check that all are not nan-values! -> then do not need to extend\n",
    "                            if not np.any(np.isnan(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(2001,5001)).volume_m3.values)):\n",
    "                                pass\n",
    "                            else:\n",
    "                                #try:\n",
    "                                # check that it is really always np.NaN values after simulation year 2000 for that region\n",
    "                                assert np.all(np.isnan(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(2001,5001)).volume_m3.values))\n",
    "                                # Huss has area 0 for some regions \n",
    "                                assert np.all(np.isnan(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(2001,5001)).volume_bsl_m3.values)) or np.all(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(2001,5001)).area_m2.values==0)\n",
    "\n",
    "                                # fill them up with the last simulation year values \n",
    "                                # we fill that up that later \n",
    "                                #for y in np.arange(2001,5001):\n",
    "                                if fill_option == 'last_value':\n",
    "                                    ds['volume_m3'].data[..., rgi_reg_id, 2001:] = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=2000).volume_m3.values\n",
    "                                    ds['volume_bsl_m3'].data[..., rgi_reg_id, 2001:] = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=2000).volume_bsl_m3.values\n",
    "                                elif fill_option == 'repeat_last_21yrs':  # 21-yr period is repeated ~142.9 times to fill up the additional 3000yrs\n",
    "                                    ds['volume_m3'].data[..., rgi_reg_id, 2001:] = np.tile(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1980,2000)).volume_m3.values, 143)[:3000]\n",
    "                                    ds['volume_bsl_m3'].data[..., rgi_reg_id, 2001:] = np.tile(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1980,2000)).volume_bsl_m3.values, 143)[:3000]\n",
    "                                elif fill_option == 'repeat_last_101yrs':  # 101-yr period is repeated ~29.7 times to fill up the additional 3000yrs\n",
    "                                    ds['volume_m3'].data[..., rgi_reg_id, 2001:] = np.tile(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1900,2000)).volume_m3.values, 30)[:3000]\n",
    "                                    ds['volume_bsl_m3'].data[..., rgi_reg_id, 2001:] = np.tile(ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1900,2000)).volume_bsl_m3.values, 30)[:3000]\n",
    "                                #print(gcm, period_scenario, ds_reg_models.isel(model_author=m).model_author.values, rgi_reg)\n",
    "\n",
    "                                #except:\n",
    "                                #    # ok some models did run over all regions for 5000 years\n",
    "                                #    print('runs for 5000 years: ' , ds_reg_models.isel(model_author=m).model_author.values, rgi_reg)\n",
    "                                #    pass\n",
    "\n",
    "                                dend = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1901,2000)).volume_m3\n",
    "                                dendm = dend.mean(dim='simulation_year')\n",
    "                                dendm_e = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1801,1900)).volume_m3.mean(dim='simulation_year')\n",
    "                                deltaV_l.append((dendm.values-dendm_e.values) / (ds.sel(rgi_reg =rgi_reg).volume_m3.sel(simulation_year=0).values))\n",
    "                                mk_output = mk.original_test(dend, alpha=0.01)\n",
    "                                if mk_output.trend =='no trend':\n",
    "                                    n_no_trend +=1\n",
    "                                elif mk_output.trend =='decreasing':\n",
    "                                    n_decreasing +=1\n",
    "                                elif mk_output.trend =='increasing':\n",
    "                                    n_increasing +=1\n",
    "                                #    print(mk_output, gcm, period_scenario, ds_reg_models.isel(model_author=m).model_author.values, rgi_reg)\n",
    "\n",
    "                                dend50 = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1951,2000)).volume_m3\n",
    "                                dendm50 = dend50.mean(dim='simulation_year')\n",
    "                                dendm_e50 = ds.sel(rgi_reg =rgi_reg).sel(simulation_year=slice(1901,1950)).volume_m3.mean(dim='simulation_year')\n",
    "                                deltaV_l50.append((dendm50.values-dendm_e50.values) / (ds.sel(rgi_reg =rgi_reg).volume_m3.sel(simulation_year=0).values))\n",
    "\n",
    "                    assert np.shape(ds_reg_models_extend['volume_m3'][m,gcm,:,:,period_scenario])== (19,5001)\n",
    "                    # add it to the big file \n",
    "                    ds_reg_models_extend['volume_m3'].data[m,gcm,:,:,period_scenario] = ds['volume_m3'].values\n",
    "                    ds_reg_models_extend['volume_bsl_m3'].data[m,gcm,:,:,period_scenario] = ds['volume_bsl_m3'].values\n",
    "\n",
    "        ds_reg_models_extend.coords['extend_option'] = fill_option\n",
    "\n",
    "        # OGGM_v16 gave global estimates -> after extending the timeseries, there should be no NaN values left \n",
    "        assert not np.any(np.isnan(ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').values))\n",
    "\n",
    "        if fill_option == 'repeat_last_21yrs': \n",
    "            # check if values are all equal to the last 20 yr timeseries for a RGI region where there are no \n",
    "            for j in np.arange(0,3000,21):\n",
    "                np.testing.assert_allclose(ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(1980,2000)).isel(gcm=0).isel(period_scenario=0),\n",
    "                                           ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(2001+j,2021+j)).isel(gcm=0).isel(period_scenario=0))\n",
    "\n",
    "        if fill_option == 'repeat_last_101yrs':\n",
    "            print(fill_option)\n",
    "             # check if values are all equal to the last 101 yr timeseries for a RGI region where there are no \n",
    "            for j in np.arange(0,2902,101):\n",
    "                np.testing.assert_allclose(ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(1900,2000)).isel(gcm=0).isel(period_scenario=0),\n",
    "                                           ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(2001+j,2101+j)).isel(gcm=0).isel(period_scenario=0),\n",
    "                                           rtol=1e-3)\n",
    "\n",
    "            np.testing.assert_allclose(ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(1900,2000)).isel(gcm=0).isel(period_scenario=0).mean(dim='simulation_year'),\n",
    "                                           ds_reg_models_extend.volume_m3.sel(model_author='OGGM_v16').sel(rgi_reg='02').sel(simulation_year = slice(4900,5000)).isel(gcm=0).isel(period_scenario=0).mean(dim='simulation_year'),\n",
    "                                         rtol=1e-3 )\n",
    "\n",
    "    assert np.all(np.isnan(ds_reg_models_extend.volume_m3.values) == False)\n",
    "    ds_reg_models_extend_ss = ds_reg_models_extend.sel(simulation_year=slice(4900,5000)).mean(dim='simulation_year')\n",
    "    ds_reg_models_extend_ss['volume_asl_m3'] = ds_reg_models_extend_ss.volume_m3 - ds_reg_models_extend_ss.volume_bsl_m3\n",
    "    ds_reg_models_extend_ss_global = ds_reg_models_extend_ss.sum(dim='rgi_reg')\n",
    "    ds_reg_models_extend_ss_global['ratio_vol_asl_total'] = ds_reg_models_extend_ss_global['volume_asl_m3']/ds_reg_models_extend_ss_global['volume_m3']\n",
    "    pd_oggm_v16_asl_bsl_global_ss = ds_reg_models_extend_ss_global.squeeze().to_dataframe().reset_index()\n",
    "    pd_oggm_v16_asl_bsl_global_ss.to_csv('../0_pre_post_processing/_intermediate_data/oggm_v16_above_vs_total_sea_level_steady_state.csv')\n",
    "    \n",
    "pd_oggm_v16_asl_bsl_global_ss = pd.read_csv('../0_pre_post_processing/_intermediate_data/oggm_v16_above_vs_total_sea_level_steady_state.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a327df5c-4e02-4e94-bbab-d1fca2a5b009",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ed2cad9b-caa9-4d14-9384-2baf6b92b6ca",
   "metadata": {},
   "source": [
    "### at the moment just a small test ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3bf0af0-8dab-48d5-bcf2-83a5f27845d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_vol = pd.read_csv('../data/rgi_vs_2020_volume_hugonnet_estimatesFeb12_2024.csv', index_col=[0])\n",
    "pd_vol = pd_vol.set_index('region')\n",
    "pd_vol_glob_2020_m3 = pd_vol.loc['Globally'][f'regional_volume_m3_2020_via_5yravg']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb0a041-08a0-4724-b922-0ae977d0aa5b",
   "metadata": {},
   "source": [
    "**let's first load the Farinotti et al., 2019, below sea-level estimates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4a4cfe6-fe7e-4c1c-96ff-029e221a25cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "run = False\n",
    "if run:\n",
    "    # use fit over all years as well\n",
    "    volume_l_t = []\n",
    "    volume_bsl_l_t = []\n",
    "    gcm_l = []\n",
    "    per_l = []\n",
    "    ssp_l = []\n",
    "    rgi_reg_l = []\n",
    "\n",
    "    for rgi_reg in rgi_regs_bsl:\n",
    "        gcms = ['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0', 'ukesm1-0-ll' ]\n",
    "        periods_f = ['2021-2040', '2041-2060', '2061-2080', '2081-2100']\n",
    "        for gcm in gcms: \n",
    "            for ssp in ['ssp126','ssp370','ssp585']: \n",
    "                for period in periods_f:\n",
    "                    with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                        dst = ds[['volume','volume_bsl']].sum(dim='rgi_id')\n",
    "                    #plt.plot(dst.volume/1e9, dst.volume_bsl/1e9, 'o', color='k')\n",
    "                    volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                    volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                    repeat_yrs = len(dst.time)\n",
    "                    ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                    per_l.append(np.repeat(period,repeat_yrs))\n",
    "                    gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                    rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "\n",
    "            ssp = 'historical'\n",
    "            periods_h = ['1851-1870', '1901-1920', '1951-1970', '1995-2014']\n",
    "            for period in periods_h:\n",
    "                with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                    dst = ds[['volume','volume_bsl']].sum(dim='rgi_id')\n",
    "                repeat_yrs = len(dst.time)\n",
    "                volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                per_l.append(np.repeat(period,repeat_yrs))\n",
    "                gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "            print(gcm, rgi_reg)\n",
    "    volume_l_t_a = np.concatenate(volume_l_t)\n",
    "    volume_bsl_l_t_a = np.concatenate(volume_bsl_l_t)\n",
    "\n",
    "    pd_slr_oggm_reg  =pd.DataFrame(np.array([volume_l_t_a, volume_bsl_l_t_a])) \n",
    "    pd_slr_oggm_reg = pd_slr_oggm_reg.rename(columns={0:'volume_km3', 1:'volume_km3_bsl'})\n",
    "    pd_slr_oggm_reg['gcm'] = np.concatenate(gcm_l)\n",
    "    pd_slr_oggm_reg['ssp'] = np.concatenate(ssp_l)\n",
    "    pd_slr_oggm_reg['period'] = np.concatenate(per_l)\n",
    "    pd_slr_oggm_reg['rgi_reg'] = np.concatenate(rgi_reg_l)\n",
    "    pd_slr_oggm_reg.to_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates.csv')\n",
    "else:\n",
    "    pd_slr_oggm_reg = pd.read_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates.csv', index_col=[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90a0d371-c1c3-4dc2-8ed6-f6627e32911b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.341297217267051"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "run = False\n",
    "if run:\n",
    "    # use fit over all years as well\n",
    "    volume_l_t = []\n",
    "    volume_bsl_l_t = []\n",
    "    gcm_l = []\n",
    "    per_l = []\n",
    "    ssp_l = []\n",
    "    rgi_reg_l = []\n",
    "    year_l = []\n",
    "\n",
    "    for rgi_reg in rgi_regs:\n",
    "        gcms = ['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0', 'ukesm1-0-ll' ]\n",
    "        periods_f = ['2021-2040', '2041-2060', '2061-2080', '2081-2100']\n",
    "        for gcm in gcms: \n",
    "            for ssp in ['ssp126','ssp370','ssp585']: \n",
    "                for period in periods_f:\n",
    "                    with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                        dst = ds[['volume','volume_bsl']].isel(time=slice(-101,-1)).mean(dim='time').sum(dim='rgi_id')\n",
    "                    #plt.plot(dst.volume/1e9, dst.volume_bsl/1e9, 'o', color='k')\n",
    "                    volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                    volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                    repeat_yrs = 1 #, len(dst.time)\n",
    "                    ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                    per_l.append(np.repeat(period,repeat_yrs))\n",
    "                    gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                    rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "                    year_l.append(5000)\n",
    "\n",
    "            ssp = 'historical'\n",
    "            periods_h = ['1851-1870', '1901-1920', '1951-1970', '1995-2014']\n",
    "            for period in periods_h:\n",
    "                with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                    dst = ds[['volume','volume_bsl']].isel(time=slice(-101,-1)).mean(dim='time').sum(dim='rgi_id')\n",
    "                repeat_yrs = 1 #len(dst.time)\n",
    "                #plt.plot(dst.volume/1e9, dst.volume_bsl/1e9, 'o', color='k')\n",
    "                volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                per_l.append(np.repeat(period,repeat_yrs))\n",
    "                gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "                year_l.append(5000)\n",
    "\n",
    "            print(gcm, rgi_reg)\n",
    "    #volume_bsl_l_t_a = np.array(volume_bsl_l_t).flatten()\n",
    "    #volume_l_t_a = np.array(volume_l_t).flatten()\n",
    "    #year_l_l_a = np.concatenate(year_l)\n",
    "    volume_l_t_a = np.concatenate(volume_l_t)\n",
    "    volume_bsl_l_t_a = np.concatenate(volume_bsl_l_t)\n",
    "\n",
    "    pd_slr_oggm_reg  =pd.DataFrame(np.array([volume_l_t_a, volume_bsl_l_t_a])\n",
    "                                             #, ssp_l, per_l, gcm_l]),\n",
    "                                  ) #columns = ['volume_km3','volume_km3_bsl','ssp','period','gcm'])]\n",
    "    pd_slr_oggm_reg = pd_slr_oggm_reg.T\n",
    "    pd_slr_oggm_reg = pd_slr_oggm_reg.rename(columns={0:'volume_km3', 1:'volume_km3_bsl'})\n",
    "    pd_slr_oggm_reg['gcm'] = np.concatenate(gcm_l)\n",
    "    pd_slr_oggm_reg['ssp'] = np.concatenate(ssp_l)\n",
    "    pd_slr_oggm_reg['period'] = np.concatenate(per_l)\n",
    "    pd_slr_oggm_reg['rgi_reg'] = np.concatenate(rgi_reg_l)\n",
    "    pd_slr_oggm_reg['year'] = '4000-5000 avg' # year_l_l_a\n",
    "    pd_slr_oggm_reg.to_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates_all_regs_steady_state.csv')\n",
    "else:\n",
    "    pd_slr_oggm_all_reg_ss = pd.read_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates_all_regs_steady_state.csv',\n",
    "                                         index_col=[0])\n",
    "pd_slr_oggm_all_reg_ss_global = pd_slr_oggm_all_reg_ss.groupby(['gcm','ssp','period']).sum().reset_index()\n",
    "# all experiments?\n",
    "assert len(pd_slr_oggm_all_reg_ss_global) == 80\n",
    "# all regions\n",
    "assert np.all(pd_slr_oggm_all_reg_ss_global['rgi_reg'] == np.arange(1,20,1).sum())\n",
    "# unfilled vs filled option relatively similar .. \n",
    "np.testing.assert_allclose(pd_slr_oggm_all_reg_ss_global['volume_km3'].max()*1e9, ## unfilled option ... \n",
    "pd_oggm_v16_asl_bsl_global_ss['volume_m3'].max(), rtol=2e-2) ## filled option\n",
    "\n",
    "def convert_rel_ice_2020_mm_slr(perc, frac = 0.85):\n",
    "    #need to compute the volume at steady-state \n",
    "    vol_ss = perc/100 * pd_vol_glob_2020_m3\n",
    "    # then compute volume difference  in m3\n",
    "    vol_diff = pd_vol_glob_2020_m3 - vol_ss\n",
    "    # above sea-level\n",
    "    vol_asl_diff = frac* vol_diff\n",
    "    # convert m3 into sea-level equivalent \n",
    "    #A_ocean = 3.625 * 10^8\n",
    "    A_ocean = 3.625 * 10**8 * 1e6 #km2 -> m2 -> 1e6\n",
    "    m_slr = (vol_asl_diff/A_ocean) *900/1028 # rhoice/rho_ocean\n",
    "    mm_slr = m_slr * 1000\n",
    "    return mm_slr\n",
    "\n",
    "pd_lowess_vol_gmt = pd.read_csv(f'../data/lowess_fit_rel_2020_101yr_avg_steady_state_{DATE}.csv', index_col=[0])\n",
    "pd_lowess_vol_gmt.index = pd_lowess_vol_gmt.temp_ch.round(2)\n",
    "pd_lowess_vol_gmt_glob = pd_lowess_vol_gmt.loc[pd_lowess_vol_gmt.region == 'All']\n",
    "(convert_rel_ice_2020_mm_slr(pd_lowess_vol_gmt_glob.loc[3.0]['0.5']) - convert_rel_ice_2020_mm_slr(pd_lowess_vol_gmt_glob.loc[1.5]['0.5']))/15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7804ec27-e678-423d-9fdc-7fd5d0449df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "run = False\n",
    "if run:\n",
    "    # use fit over all years as well\n",
    "    volume_l_t = []\n",
    "    volume_bsl_l_t = []\n",
    "    gcm_l = []\n",
    "    per_l = []\n",
    "    ssp_l = []\n",
    "    rgi_reg_l = []\n",
    "    year_l = []\n",
    "\n",
    "    for rgi_reg in rgi_regs:\n",
    "        gcms = ['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0', 'ukesm1-0-ll' ]\n",
    "        periods_f = ['2021-2040', '2041-2060', '2061-2080', '2081-2100']\n",
    "        for gcm in gcms: \n",
    "            for ssp in ['ssp126','ssp370','ssp585']: \n",
    "                for period in periods_f:\n",
    "                    with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                        dst = ds[['volume','volume_bsl']].sum(dim='rgi_id')\n",
    "                    #plt.plot(dst.volume/1e9, dst.volume_bsl/1e9, 'o', color='k')\n",
    "                    volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                    volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                    repeat_yrs = len(dst.time)\n",
    "                    ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                    per_l.append(np.repeat(period,repeat_yrs))\n",
    "                    gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                    rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "                    year_l.append(np.arange(0, len(dst.time),1))\n",
    "\n",
    "            ssp = 'historical'\n",
    "            periods_h = ['1851-1870', '1901-1920', '1951-1970', '1995-2014']\n",
    "            for period in periods_h:\n",
    "                with xr.open_dataset(f'/home/www/lschuster/runs_glacierMIP3_oggm_v16/output/RGI{rgi_reg}/{gcm}_{ssp}_{period}.nc') as ds:\n",
    "                    dst = ds[['volume','volume_bsl']].sum(dim='rgi_id')\n",
    "                repeat_yrs = len(dst.time)\n",
    "                #plt.plot(dst.volume/1e9, dst.volume_bsl/1e9, 'o', color='k')\n",
    "                volume_l_t.append((dst.volume/1e9).values.flatten())\n",
    "                volume_bsl_l_t.append((dst.volume_bsl/1e9).values.flatten())\n",
    "                ssp_l.append(np.repeat(ssp,repeat_yrs))\n",
    "                per_l.append(np.repeat(period,repeat_yrs))\n",
    "                gcm_l.append(np.repeat(gcm,repeat_yrs))\n",
    "                rgi_reg_l.append(np.repeat(rgi_reg,repeat_yrs))\n",
    "                year_l.append(np.arange(0, len(dst.time),1))\n",
    "\n",
    "            print(gcm, rgi_reg)\n",
    "    #volume_bsl_l_t_a = np.array(volume_bsl_l_t).flatten()\n",
    "    #volume_l_t_a = np.array(volume_l_t).flatten()\n",
    "    year_l_l_a = np.concatenate(year_l)\n",
    "    volume_l_t_a = np.concatenate(volume_l_t)\n",
    "    volume_bsl_l_t_a = np.concatenate(volume_bsl_l_t)\n",
    "\n",
    "    pd_slr_oggm_reg  =pd.DataFrame(np.array([volume_l_t_a, volume_bsl_l_t_a])\n",
    "                                             #, ssp_l, per_l, gcm_l]),\n",
    "                                  ) #columns = ['volume_km3','volume_km3_bsl','ssp','period','gcm'])]\n",
    "    pd_slr_oggm_reg = pd_slr_oggm_reg.T\n",
    "    pd_slr_oggm_reg = pd_slr_oggm_reg.rename(columns={0:'volume_km3', 1:'volume_km3_bsl'})\n",
    "    pd_slr_oggm_reg['gcm'] = np.concatenate(gcm_l)\n",
    "    pd_slr_oggm_reg['ssp'] = np.concatenate(ssp_l)\n",
    "    pd_slr_oggm_reg['period'] = np.concatenate(per_l)\n",
    "    pd_slr_oggm_reg['rgi_reg'] = np.concatenate(rgi_reg_l)\n",
    "    pd_slr_oggm_reg['year'] = year_l_l_a\n",
    "    pd_slr_oggm_reg.to_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates_all_regs.csv')\n",
    "else:\n",
    "    pd_slr_oggm_all_reg = pd.read_csv('../0_pre_post_processing/_intermediate_data/oggm_sea-level_estimates_all_regs.csv', index_col=[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65229737-2809-4c17-8e9b-9482929e6bdb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e46be7e-7a3c-41c8-9a7d-7a15b2c8d45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_slr_oggm_all_global = pd_slr_oggm_all_reg.loc[pd_slr_oggm_all_reg.year <=2000].groupby(['gcm','period','ssp', 'year']).sum()\n",
    "assert np.all((pd_slr_oggm_all_global.rgi_reg == 190).values)\n",
    "pd_slr_oggm_all_global\n",
    "\n",
    "pd_slr_oggm_all_global = pd_slr_oggm_all_global.reset_index()\n",
    "pd_slr_oggm_all_global_ss = pd_slr_oggm_all_global.loc[pd_slr_oggm_all_global.year>=1900].groupby(['gcm','period','ssp']).mean()\n",
    "\n",
    "# don't take the first 100 years \n",
    "pd_slr_oggm_all_global = pd_slr_oggm_all_global.loc[pd_slr_oggm_all_global.year>=50] #.groupby(['gcm','period','ssp']).mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cfba758-d0a8-4401-b95e-a65092fb1304",
   "metadata": {},
   "source": [
    "**simpler plot for supplements**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f728b06-dbbd-4eba-9080-972e47c930b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 0.15193551735944366\n",
      "LinregressResult(slope=-7.622083219875667e-07, intercept=0.9540695779131954, rvalue=-0.8585714109349116, pvalue=2.4420306947314628e-24, stderr=5.153570154357327e-08, intercept_stderr=0.003907103833654578)\n",
      "0.9383500925992415\n",
      "0.8911916366573802\n",
      "19 0.15193551735944366\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "import seaborn as sns\n",
    "\n",
    "km3_to_gt = 900 * 1e-3# km3 -> Gt\n",
    "m3_to_gt = 900 * 1e-12# m3 -> Gt\n",
    "\n",
    "\n",
    "#### farinotti estimates \n",
    "vol_itmix_m3_reg = df_itmix['vol_itmix_m3'].sum()\n",
    "vol_bsl_itmix_m3 = df_itmix['vol_bsl_itmix_m3'].sum()\n",
    "below_sl_ratio = vol_bsl_itmix_m3/vol_itmix_m3_reg\n",
    "y_farinotti = (vol_itmix_m3_reg- vol_bsl_itmix_m3)*m3_to_gt\n",
    "x_farinotti = vol_itmix_m3_reg*m3_to_gt\n",
    "print(rgi_reg,below_sl_ratio)\n",
    "\n",
    "from moepy import lowess, eda\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.rcParams.update({'font.size': 24})\n",
    "j = 0\n",
    "#fig, axs = plt.subplots(1,1,figsize=(20,10))\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "gs = GridSpec(1, 2, width_ratios=[7.3, 2.7])  # 70% for the plot, 30% for the legend\n",
    "\n",
    "# Create the main plot area\n",
    "ax = fig.add_subplot(gs[0])\n",
    "#ax = axs\n",
    "\n",
    "\n",
    "###################unfilled OGGM \n",
    "pd_slr_oggm_all_reg_ss_global['ratio_vol_asl_total'] = (pd_slr_oggm_all_reg_ss_global['volume_km3'] - pd_slr_oggm_all_reg_ss_global['volume_km3_bsl'])/pd_slr_oggm_all_reg_ss_global['volume_km3']\n",
    "x = pd_slr_oggm_all_reg_ss_global.volume_km3*km3_to_gt#/1e9\n",
    "y = pd_slr_oggm_all_reg_ss_global.ratio_vol_asl_total\n",
    "ax.plot(x, y, 'o', ms=10,\n",
    "         color=sns.color_palette('mako')[-5], label='In steady state (OGGM)', # , this study\n",
    "         alpha = 0.7) # unfilled\n",
    "res = scipy.stats.linregress(x,y)\n",
    "\n",
    "print(res)\n",
    "slope_unfilled, intercept_unfilled, r, p, se = res\n",
    "r2 = np.square(r)\n",
    "_x = np.arange(0,x.max(),1)\n",
    "fit = _x*slope_unfilled + intercept_unfilled\n",
    "fit[fit>1] = 1\n",
    "fit[fit<0.7] = 0.7\n",
    "ax.plot(_x, fit, color=sns.color_palette('mako')[-5],\n",
    "         label=f'Linear fit: F={slope_unfilled:0.2e}'+r'$\\cdot$M + ' + f'{intercept_unfilled.round(3):0.2f}, R²={r2.round(2)}',\n",
    "         lw=3)\n",
    "\n",
    "\n",
    "# get these estimates better from the upper plot ...\n",
    "#df_quantiles['volume_km3'] = _x.round(0).astype(int)\n",
    "#df_quantiles = df_quantiles.set_index('volume_km3')\n",
    "#df_quantiles.rename(columns={'0.5':'ratio_ss_volume_asl_total'})\n",
    "#df_quantiles.to_csv('OGGM_gmip3_unfilled_ratio_asl_total_volume.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### OGGM after 50/100 simulation years\n",
    "color_o = {0:sns.color_palette('mako')[-1],1:sns.color_palette('mako')[-3]}\n",
    "for j, year in enumerate([50,100]):\n",
    "    pd_slr_oggm_reg_sel = pd_slr_oggm_all_global.loc[pd_slr_oggm_all_global.year == year] # pd_slr_oggm_reg.loc[pd_slr_oggm_reg.rgi_reg == rgi_reg]\n",
    "    x = pd_slr_oggm_reg_sel.volume_km3.values*km3_to_gt\n",
    "    y = (pd_slr_oggm_reg_sel.volume_km3.values - pd_slr_oggm_reg_sel.volume_km3_bsl.values)*km3_to_gt\n",
    "\n",
    "    ax.plot(x,y/x, 'o', color=color_o[j], ms=6,\n",
    "            label=f'Around {year} simulation years after year 2000 (OGGM)', #, this study)',\n",
    "            alpha = 0.7, zorder=2) # unfilled -> too much info \n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "ax.axvspan(pd_vol_glob_2020_m3*0.15*m3_to_gt, pd_vol_glob_2020_m3*0.60*m3_to_gt, color='grey', alpha = 0.1,\n",
    "            label='Steady-state median global glacier mass\\nrange between 1.2 and 4.0°C (from LOWESS fit)')\n",
    "\n",
    "print(slope_unfilled*(pd_vol_glob_2020_m3*0.15*m3_to_gt)+ intercept_unfilled)\n",
    "print(slope_unfilled*(pd_vol_glob_2020_m3*0.60*m3_to_gt)+ intercept_unfilled)\n",
    "\n",
    "    \n",
    "##########################################\n",
    "ds_rounce_rcps = xr.open_dataset('/home/www/lschuster/rounce_2023_data/Global_reg_allvns_50sets_2000_2100-rcps.nc')\n",
    "ds_rounce_ssps = xr.open_dataset('/home/www/lschuster/rounce_2023_data/Global_reg_allvns_50sets_2000_2100-ssps.nc')\n",
    "_x_rounce_l = []\n",
    "_ratio_rounce_l = []\n",
    "for j,ds_rounce in enumerate([ds_rounce_rcps, ds_rounce_ssps]): \n",
    "    ds_rounce = ds_rounce.sum(dim='region', min_count=19).sel(year=2100)\n",
    "\n",
    "    x_rounce = ds_rounce.reg_mass_annual *1e-12 ##/900 / 1e9  # kg /m3\n",
    "    y_rounce = (ds_rounce.reg_mass_annual - ds_rounce.reg_mass_bsl_annual)*1e-12\n",
    "    if j ==0: \n",
    "        ax.plot(x_rounce.values.squeeze().flatten(),\n",
    "                 y_rounce.values.squeeze().flatten()/x_rounce.values.squeeze().flatten(), 'v',\n",
    "                 ms=7,\n",
    "                 label='At year 2100', #  (Rounce et al., 2023)\n",
    "                 color='pink', alpha =0.7, zorder = 1)\n",
    "    else:\n",
    "        ax.plot(x_rounce.values.squeeze().flatten(),\n",
    "                 y_rounce.values.squeeze().flatten()/x_rounce.values.squeeze().flatten(), 'v', #label='Rounce et al., 2023\\n(2020-2101)', \n",
    "                         color='pink', alpha =0.7, zorder = 1, ms=7)\n",
    "    _x_rounce_l.append(x_rounce.values.squeeze().flatten())\n",
    "    _ratio_rounce_l.append(y_rounce.values.squeeze().flatten()/x_rounce.values.squeeze().flatten())\n",
    "\n",
    "# Perform linear fit with forced intercept at zero\n",
    "_ratio = np.concatenate(_ratio_rounce_l)\n",
    "_x_s = np.concatenate(_x_rounce_l)\n",
    "slope = np.dot(_x_s[~np.isnan(_ratio)], \n",
    "               _ratio[~np.isnan(_ratio)]*_x_s[~np.isnan(_ratio)]) / np.dot(_x_s[~np.isnan(_ratio)], _x_s[~np.isnan(_ratio)])\n",
    "_x = np.arange(0,_x_s[~np.isnan(_ratio)].max(),1)\n",
    "fit = _x*slope #+ #intercept\n",
    "#fit[_x-fit<0] = 0 # below sea level should be above zero \n",
    "#fit[fit<0] = 0 # if above sea level is below zero -> set it equal tabove sea level should be above zero \n",
    "\n",
    "#plt.plot(_x, fit/_x, color='blue', label=f'Linear Fit (Rounce et al., 2023):\\n'+r'V$_{asl}$'+f'= {slope:0.2f}*V', lw=3)\n",
    "\n",
    "\n",
    "#######################################\n",
    "\n",
    "# Performing linear regression\n",
    "#slope, intercept = np.polyfit(x, y, 1)\n",
    "#_x = np.arange(0,x.max(),0.1)\n",
    "# Perform linear fit with forced intercept at zero\n",
    "slope = np.dot(x, y) / np.dot(x, x)\n",
    "_x = np.arange(0,x.max(),1)\n",
    "fit = _x*slope #+ #intercept\n",
    "#fit[_x-fit<0] = 0 # below sea level should be above zero \n",
    "#fit[fit<0] = 0 # if above sea level is below zero -> set it equal tabove sea level should be above zero \n",
    "\n",
    "#ax.plot(_x, fit/_x, color='black', label=f'Linear Fit (only OGGM):\\n'+r'V$_{asl}$'+f'= {slope:0.2f}*V', lw=3)\n",
    "\n",
    "ax.set_xlabel(f'Global glacier mass $M$ (Gt)')\n",
    "ax.set_ylabel(r'Fraction $F$ of above sea-level and total glacier mass')\n",
    "\n",
    "\n",
    "#farinotti_intercept= (vol_itmix_m3_reg/1e9- vol_bsl_itmix_m3/1e9)-  vol_itmix_m3_reg/1e9*slope\n",
    "#fit_f = _x*slope + farinotti_intercept\n",
    "#fit_f[_x-fit_f<0] = _x[_x-fit_f<0] # if below sea level would get below zero, set it instead to zero \n",
    "#fit_f[fit_f<0] = 0  # above sea level should be above zero \n",
    "\n",
    "#ax.plot(_x, fit_f, color='blue',\n",
    "#         label=f'Linear Fit, rescaled to\\nFarinotti et al., 2019:\\n'+r'V$_{asl}$'+f'={slope:0.2f}*V +{farinotti_intercept:0.1f}\\n'+r'(and V$_{bsl}$≥0 & V$_{asl}$≥0)', lw=3)\n",
    "reg_name = d_reg_num_name_sh[rgi_reg]\n",
    "reg_name = reg_name.replace('\\n',' ')\n",
    "#ax.set_title('Globally')\n",
    "#ax.axhline(1, color='grey', linestyle=':', label='ratio of 1', alpha = 0.5)\n",
    "\n",
    "#ax.set_aspect('equal', adjustable='box')\n",
    "#ax.text(0.98,0.02, \n",
    "#        f'Farinotti et al. (2019)\\nabove sea-level ratio\\nat inventory date: {(1-below_sl_ratio).round(2)}', \n",
    "#        va = 'bottom', ha='right',\n",
    "#        transform=ax.transAxes)\n",
    "# Set xticks and yticks to be equal\n",
    "#max_value = max(max(x), max(y))\n",
    "#max_value = int(np.ceil(max_value))\n",
    "#ax.set_xticks(range(max_value + 1))\n",
    "#ax.set_yticks(range(max_value + 1))\n",
    "\n",
    "\n",
    "\n",
    "#### farinotti estimates \n",
    "vol_itmix_m3_reg = df_itmix['vol_itmix_m3'].sum()\n",
    "vol_bsl_itmix_m3 = df_itmix['vol_bsl_itmix_m3'].sum()\n",
    "below_sl_ratio = vol_bsl_itmix_m3/vol_itmix_m3_reg\n",
    "y_farinotti = (vol_itmix_m3_reg- vol_bsl_itmix_m3)*m3_to_gt\n",
    "x_farinotti = vol_itmix_m3_reg*m3_to_gt\n",
    "print(rgi_reg,below_sl_ratio)\n",
    "\n",
    "ax.plot(x_farinotti, y_farinotti/x_farinotti, 's', ms=12,color='darkred',\n",
    "        label='Around year 2000 (F=0.85)') # FARINOTTI et al., 2019\n",
    "#ax.axvline(x_farinotti, color = 'red', ls= '--',  label='Farinotti et al. (2019)\\nat inventory date' )\n",
    "#ax.axhline(y_farinotti/x_farinotti, color='darkred', lw=4, label=f'constant ratio of {(1-below_sl_ratio).round(2)}\\n(Farinotti et al., 2019)')\n",
    "    \n",
    "#plt.plot(_x, _x*(1-below_sl_ratio)/_x, color='darkred', lw=4, label=f'constant ratio (Farinotti et al., 2019):\\n'+r'V$_{asl}$' + f'={(1-below_sl_ratio).round(2)}' )\n",
    "#ax.axvline(pd_vol_glob_m3/1e9, ls = '--', color='green')\n",
    "# 40 85\n",
    "ax.set_xticks([0,50000,100000,150000])\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#ax.legend(loc='upper left', fontsize=18, bbox_to_anchor=(1.02,1))\n",
    "\n",
    "leg_this_study = ax.legend(handles[:5], labels[:5], title='This study', loc='upper left', \n",
    "                          fontsize=22, bbox_to_anchor=(1.01,1), frameon=False)\n",
    "leg_rounce = ax.legend([handles[5]], [labels[5]], title='Rounce et al. (2023)', frameon=False,\n",
    "                       loc='upper left', fontsize=22, bbox_to_anchor=(1.01,0.63))\n",
    "leg_farinotti = ax.legend([handles[6]], [labels[6]], title='Farinotti et al. (2019)',loc='upper left', \n",
    "                          fontsize=22, bbox_to_anchor=(1.01,0.5), frameon=False)\n",
    "leg_this_study._legend_box.align = \"left\"\n",
    "leg_rounce._legend_box.align = \"left\"\n",
    "\n",
    "leg_farinotti._legend_box.align = \"left\"\n",
    "ax.add_artist(leg_this_study)\n",
    "ax.add_artist(leg_rounce)\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/supplements/suppl_fig_S14.png')\n",
    "plt.savefig('figures/supplements/suppl_fig_S14.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5e493d3d-af1b-4a8d-8cb4-53b6652218f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-7.622083219875667e-07, 0.9540695779131954)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the fit for the notebook where the SL estimates are shown in a table (6_csv_tables_creation.ipynb)\n",
    "import json\n",
    "data = {\n",
    "    \"slope_unfilled\": slope_unfilled, # this is in GT mass \n",
    "    \"intercept_unfilled\": intercept_unfilled # this is the fraction\n",
    "}\n",
    "\n",
    "# Save the variables to a JSON file\n",
    "with open('../0_pre_post_processing/_intermediate_data/linear_fit_above_sea_level_fraction_vs_global_glacier_mass_gt_variables.json', 'w') as json_file:\n",
    "    json.dump(data, json_file)\n",
    "\n",
    "slope_unfilled, intercept_unfilled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "90589e0b-e3f0-4154-aaa4-aeac4b63282e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8335213024434408 0.8480644826405563\n"
     ]
    }
   ],
   "source": [
    "### volume at farinotti et al. data\n",
    "ratio_farinotti = 1- df_itmix.sum()['vol_bsl_itmix_m3']/df_itmix.sum()['vol_itmix_m3'] \n",
    "vol = df_itmix.sum()['vol_itmix_m3'] /1e9\n",
    "\n",
    "ratio = slope_unfilled*vol + intercept_unfilled \n",
    "if ratio <0.7:\n",
    "    ratio = 0.7\n",
    "print(ratio, ratio_farinotti)\n",
    "\n",
    "np.testing.assert_allclose(ratio,ratio_farinotti, rtol=0.02) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06c382a-4caa-49c0-a99b-e7430f7d9a06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a70dfd0-81cb-43ec-90e9-d4675c378205",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "pd_slr_oggm_reg_sel = pd_slr_oggm_all_reg.loc[pd_slr_oggm_all_reg.year <=2000].groupby(['gcm','period','ssp', 'year']).sum().reset_index()\n",
    "\n",
    "pd_slr_oggm_reg_sel['ratio'] = (pd_slr_oggm_reg_sel['volume_km3']- pd_slr_oggm_reg_sel['volume_km3_bsl']) / pd_slr_oggm_reg_sel['volume_km3']\n",
    "fig,axs = plt.subplots(1,5,figsize=(35,10), sharey=True, sharex=True)\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==30], x='volume_km3', y= 'ratio', ax = axs[0]) # palette='coolwarm')\n",
    "axs[0].set_title('after 30 simulation years')\n",
    "\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==50], x='volume_km3', y= 'ratio', ax = axs[1]) # palette='coolwarm')\n",
    "axs[1].set_title('after 50 simulation years')\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==100], x='volume_km3', y= 'ratio', ax = axs[2]) # palette='coolwarm')\n",
    "axs[2].set_title('after 100 simulation years')\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==500], x='volume_km3', y= 'ratio', ax = axs[3]) # palette='coolwarm')\n",
    "axs[3].set_title('after 500 simulation years')\n",
    "\n",
    "axs[4].set_title('after 2000 simulation years')\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==2000], x='volume_km3', y= 'ratio', ax = axs[4]) # palette='coolwarm')\n",
    "#plt.savefig('above_sl_ratio_evolution_gmip3_oggm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ffa0f54-708a-42cd-bc0a-180071daaa54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "pd_slr_oggm_reg_sel = pd_slr_oggm_all_reg.loc[pd_slr_oggm_all_reg.year <=2000].groupby(['gcm','period','ssp', 'year']).sum().reset_index()\n",
    "\n",
    "pd_slr_oggm_reg_sel['ratio'] = (pd_slr_oggm_reg_sel['volume_km3']- pd_slr_oggm_reg_sel['volume_km3_bsl']) / pd_slr_oggm_reg_sel['volume_km3']\n",
    "fig,axs = plt.subplots(1,4,figsize=(35,10), sharey=True, sharex=True)\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==80], x='volume_km3', y= 'ratio', ax = axs[0]) # palette='coolwarm')\n",
    "axs[0].set_title('after 80 simulation years')\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==100], x='volume_km3', y= 'ratio', ax = axs[1]) # palette='coolwarm')\n",
    "axs[1].set_title('after 100 simulation years')\n",
    "\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==500], x='volume_km3', y= 'ratio', ax = axs[2]) # palette='coolwarm')\n",
    "axs[2].set_title('after 500 simulation years')\n",
    "\n",
    "axs[3].set_title('after 2000 simulation years')\n",
    "sns.scatterplot(data=pd_slr_oggm_reg_sel.loc[pd_slr_oggm_reg_sel.year==2000], x='volume_km3', y= 'ratio', ax = axs[3]) # palette='coolwarm')\n",
    "#plt.savefig('above_sl_ratio_evolution_gmip3_oggm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bc9f903-b852-4f57-8db8-c5d42a2ae04c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "487cc365-4e9d-480c-a9b0-3d4b0fb1a536",
   "metadata": {},
   "source": [
    "### Some unsorted analysis..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cb6137-69de-48fa-9bba-29294a801972",
   "metadata": {},
   "source": [
    "*is the SLE mm ratio different ??*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f5eacdb-4dcf-4f0b-a61e-f9c95d779a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_rel_ice_2020_mm_slr(pd_lowess_vol_gmt_glob.loc[3.0]['0.5'], frac= 0.85) / convert_rel_ice_2020_mm_slr(pd_lowess_vol_gmt_glob.loc[3.0]['0.5'], frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a368261d-90ef-4d7a-8b2d-0f7343b7e88a",
   "metadata": {},
   "source": [
    "- need to compute the volume at steady-state \n",
    "    - rel_ice_2020/100 * pd_vol_glob_2020_m3\n",
    "- then compute volume difference \n",
    "    - vol_diff = pd_vol_glob_2020_m3 - rel_ice_2020/100 * pd_vol_glob_2020_\n",
    "    \n",
    "ρice = 900 kg m−3 , an ocean area of Aocean\n",
    "= 3.625 × 108 km2 and a mean ocean density of ρocean = 1,028 kg m−3\n",
    "(ref. 51 ), (2)\n",
    "\n",
    "**around 6mm SL more for every tenth of a degree warming**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ac5283b5-e82f-460d-8255-962d65779532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'difference (lowess vs linear fit)\\nrel. to 2020 volume')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(100*fit_lowess_x*1e9/pd_vol_glob_2020_m3, 100*(fit_lowess.values - fit_lowess_x*0.85)/(pd_vol_glob_2020_m3/1e9))\n",
    "plt.ylabel('difference (lowess vs linear fit)\\nrel. to 2020 volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1627fa8-78cc-4ac5-9e6a-6698d790155d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db6d8685-dd43-4081-b47e-292ca123fa8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "51c10e12-de7a-4859-8cab-aa5af389cd67",
   "metadata": {},
   "source": [
    "-> more analysis on that in older github repository versions (before Oct 2024 clean-up ;-))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e2c3092-7649-4604-97c9-dfe0d7938118",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:oggm_gmip3_working]",
   "language": "python",
   "name": "conda-env-oggm_gmip3_working-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}