{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acb38dff-3de5-4be7-91cd-dfbeedd909ab",
   "metadata": {},
   "source": [
    "# Analyse the influence of using any GCM vs W5E5 on the specific MB during calibration time period:\n",
    "\n",
    "- idea: use the GCM ISIMIP3b data and check how different the specific MB from 2000-2019 is to the W5E5 calibrated specific MB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e2f0a291-a7ff-488d-ad12-64734ad327b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-10 10:21:19: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
      "2022-10-10 10:21:19: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
      "2022-10-10 10:21:19: oggm.cfg: Multiprocessing: using all available processors (N=32)\n",
      "2022-10-10 10:21:19: oggm.cfg: Multiprocessing switched ON after user settings.\n",
      "2022-10-10 10:21:19: oggm.cfg: PARAMS['border'] changed from `40` to `10`.\n",
      "2022-10-10 10:21:19: oggm.cfg: Multiprocessing: using the requested number of processors (N=28)\n",
      "2022-10-10 10:21:20: oggm.workflow: Execute entity tasks [GlacierDirectory] on 3927 glaciers\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import oggm\n",
    "from oggm import cfg, utils, workflow, tasks, graphics\n",
    "from oggm.core import massbalance, climate\n",
    "\n",
    "cfg.initialize(logging_level='WARNING')\n",
    "cfg.PARAMS['use_multiprocessing'] = True #True\n",
    "\n",
    "cfg.PATHS['working_dir'] = '/home/users/lschuster/oggm_w5e5_vs_gcm_gdir'\n",
    "\n",
    "cfg.PARAMS['border'] = 10\n",
    "cfg.PARAMS['mp_processes'] = 28\n",
    " \n",
    "base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/elev_bands/w5e5/qc0/pcpwin/match_geod_pergla'\n",
    "pd_geod = utils.get_geodetic_mb_dataframe()\n",
    "pd_geod = pd_geod.loc[pd_geod.period == '2000-01-01_2020-01-01']\n",
    "\n",
    "ssps = ['ssp126', 'ssp585']\n",
    "gcms = ['ukesm1-0-ll_r1i1p1f2', 'gfdl-esm4_r1i1p1f1',\n",
    "       'ipsl-cm6a-lr_r1i1p1f1', 'mpi-esm1-2-hr_r1i1p1f1',\n",
    "       'mri-esm2-0_r1i1p1f1']\n",
    "climate_type = 'W5E5'\n",
    "y0 = 2000\n",
    "y1 = 2019\n",
    "reload = False\n",
    "if reload:\n",
    "    gdirs = workflow.init_glacier_directories(pd_geod[pd_geod.reg == 11].index, from_prepro_level=3, prepro_base_url=base_url)\n",
    "    for ensemble in gcms:\n",
    "        for ssp in ssps:\n",
    "            for correction in [True, False]:\n",
    "                if correction is True:\n",
    "                    correct=''\n",
    "                else:\n",
    "                    correct='_no_correction'\n",
    "                workflow.execute_entity_task(oggm.shop.gcm_climate.process_monthly_isimip_data, gdirs, ensemble = ensemble,\n",
    "                                             ssp = ssp, output_filesuffix=f'{ensemble}_{ssp}{correct}',\n",
    "                                             apply_bias_correction = correction)\n",
    "    \n",
    "    # also do the bias correction on the calibration time period only --> does this help to reduce the differences???\n",
    "    for ensemble in gcms:\n",
    "        for ssp in ssps:\n",
    "            workflow.execute_entity_task(oggm.shop.gcm_climate.process_monthly_isimip_data, gdirs, year_range=(str(y0), str(y1)),\n",
    "                                                              ssp = ssp, ensemble = ensemble,\n",
    "                                                              output_filesuffix=f'{ensemble}_{ssp}_bc_{y0}_{y1}',\n",
    "                                                             apply_bias_correction=True)\n",
    "else:\n",
    "    gdirs = workflow.init_glacier_directories(pd_geod[pd_geod.reg == 11].index) #, from_prepro_level=3, prepro_base_url=base_url)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "e9774075-1463-4abd-b724-82b69ad24187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1cf29e1e-6e7c-4353-a906-c51701f70bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t\n"
     ]
    }
   ],
   "source": [
    "                        if correction is True:\n",
    "                            correct=''\n",
    "                        elif correction == f'True: yr {y0}--{y1}':\n",
    "                            print('t')\n",
    "                            correct = f'_bc_{y0}_{y1}'\n",
    "                        else:\n",
    "                            correct='_no_correction'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "55831226-949d-4d2d-be1f-6c493785c3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'True: yr 2000--2019'"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77525f8c-bcb5-4bb3-bfb2-790a08f579df",
   "metadata": {},
   "outputs": [],
   "source": [
    "            hgts, widths = gdir.get_inversion_flowline_hw()\n",
    "            mb_mod = massbalance.PastMassBalance(gdir)\n",
    "            spec_mb_2000_2019_w5e5 = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "            spec_mb_2000_2019_obs = pd_geod.loc[gdir.rgi_id].dmdtda*1000\n",
    "            for ensemble in gcms:\n",
    "                for ssp in ssps:\n",
    "                    for correction in [f'True: yr {y0}--{y1}']: #[True, False]\n",
    "                        if correction is True:\n",
    "                            correct=''\n",
    "                        elif correction == f'True: yr {y0}--{y1}':\n",
    "                            correct = f'_bc_{y0}_{y1}'\n",
    "                        else:\n",
    "                            correct='_no_correction'\n",
    "                        mb_mod = massbalance.PastMassBalance(gdir, filename='gcm_data',\n",
    "                                                             input_filesuffix = f'{ensemble}_{ssp}{correct}')\n",
    "                        spec_mb_2000_2019_gcm = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'rgi_id'] = gdir.rgi_id\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'gcm'] = ensemble\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'ssp'] = ssp\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'apply_bias_correction'] = correction\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_gcm'] = spec_mb_2000_2019_gcm\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_w5e5'] = spec_mb_2000_2019_w5e5\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_obs'] = spec_mb_2000_2019_obs\n",
    "                        j += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "4ea87699-d4fb-43a0-bcde-bbe54caddd16",
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = 2000\n",
    "y1 = 2019\n",
    "load = False\n",
    "if load:\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd.DataFrame(np.NaN, index=np.arange(0, 1000000), #*len(gdirs))),\n",
    "                                columns=['rgi_id', 'gcm', 'ssp',\n",
    "                                        'spec_mb_2000_2019_gcm', 'spec_mb_2000_2019_w5e5',\n",
    "                                         'spec_mb_2000_2019_obs', 'apply_bias_correction'])\n",
    "\n",
    "    j = 0\n",
    "    for gdir in gdirs:\n",
    "        try:\n",
    "            hgts, widths = gdir.get_inversion_flowline_hw()\n",
    "            mb_mod = massbalance.PastMassBalance(gdir)\n",
    "            spec_mb_2000_2019_w5e5 = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "            spec_mb_2000_2019_obs = pd_geod.loc[gdir.rgi_id].dmdtda*1000\n",
    "            for ensemble in gcms:\n",
    "                for ssp in ssps:\n",
    "                    for correction in [f'True: yr {y0}--{y1}']: #[True, False]\n",
    "                        if correction is True:\n",
    "                            correct=''\n",
    "                        elif correction == f'True: yr {y0}--{y1}':\n",
    "                            correct = f'_bc_{y0}_{y1}'\n",
    "                        else:\n",
    "                            correct='_no_correction'\n",
    "                        mb_mod = massbalance.PastMassBalance(gdir, filename='gcm_data',\n",
    "                                                             input_filesuffix = f'{ensemble}_{ssp}{correct}')\n",
    "                        spec_mb_2000_2019_gcm = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'rgi_id'] = gdir.rgi_id\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'gcm'] = ensemble\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'ssp'] = ssp\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'apply_bias_correction'] = correction\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_gcm'] = spec_mb_2000_2019_gcm\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_w5e5'] = spec_mb_2000_2019_w5e5\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_obs'] = spec_mb_2000_2019_obs\n",
    "                        j += 1\n",
    "\n",
    "        except:\n",
    "            pass\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd_spec_mb_gcm_vs_w5e5.dropna()\n",
    "    pd_spec_mb_gcm_vs_w5e5['difference_gcm_vs_w5e5'] = pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_gcm'] - pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "    pd_spec_mb_gcm_vs_w5e5['rel_perc_difference_gcm_vs_w5e5'] = 100 * pd_spec_mb_gcm_vs_w5e5['difference_gcm_vs_w5e5'] / pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "    pd_spec_mb_gcm_vs_w5e5['ratio_gcm_vs_w5e5'] = pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_gcm'] / pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "    pd_spec_mb_gcm_vs_w5e5_bc_2000_2019 = pd_spec_mb_gcm_vs_w5e5.copy()\n",
    "    pd_spec_mb_gcm_vs_w5e5_bc_2000_2019.to_csv(f'alpine_glacier_spec_mb_gcm_vs_w5e5_bc_{y0}_{y1}.csv')\n",
    "else:\n",
    "    pd_spec_mb_gcm_vs_w5e5_bc_2000_2019 = pd.read_csv(f'alpine_glacier_spec_mb_gcm_vs_w5e5_bc_{y0}_{y1}.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c5fdd793-a608-4d62-bb74-ba9d413333dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "load = False\n",
    "if load:\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd.DataFrame(np.NaN, index=np.arange(0, 1000000), #*len(gdirs))),\n",
    "                                columns=['rgi_id', 'gcm', 'ssp',\n",
    "                                        'spec_mb_2000_2019_gcm', 'spec_mb_2000_2019_w5e5',\n",
    "                                         'spec_mb_2000_2019_obs', 'apply_bias_correction'])\n",
    "\n",
    "    j = 0\n",
    "    for gdir in gdirs:\n",
    "        try:\n",
    "            hgts, widths = gdir.get_inversion_flowline_hw()\n",
    "            mb_mod = massbalance.PastMassBalance(gdir)\n",
    "            spec_mb_2000_2019_w5e5 = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "            spec_mb_2000_2019_obs = pd_geod.loc[gdir.rgi_id].dmdtda*1000\n",
    "            for ensemble in gcms:\n",
    "                for ssp in ssps:\n",
    "                    for correction in [True, False]:\n",
    "                        if correction:\n",
    "                            correct=''\n",
    "                        else:\n",
    "                            correct='_no_correction'\n",
    "                        mb_mod = massbalance.PastMassBalance(gdir, filename='gcm_data', input_filesuffix = f'{ensemble}_{ssp}{correct}')\n",
    "                        spec_mb_2000_2019_gcm = mb_mod.get_specific_mb(heights=hgts, widths=widths,\n",
    "                                                                          year=np.arange(2000, # yrs_seasonal_mbs[0]\n",
    "                                                                                         2019 + 1, 1)).mean()\n",
    "\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'rgi_id'] = gdir.rgi_id\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'gcm'] = ensemble\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'ssp'] = ssp\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'apply_bias_correction'] = correction\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_gcm'] = spec_mb_2000_2019_gcm\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_w5e5'] = spec_mb_2000_2019_w5e5\n",
    "                        pd_spec_mb_gcm_vs_w5e5.loc[j, 'spec_mb_2000_2019_obs'] = spec_mb_2000_2019_obs\n",
    "                        j += 1\n",
    "\n",
    "        except:\n",
    "            pass\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd_spec_mb_gcm_vs_w5e5.dropna()\n",
    "    pd_spec_mb_gcm_vs_w5e5['difference_gcm_vs_w5e5'] = pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_gcm'] - pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "    pd_spec_mb_gcm_vs_w5e5['rel_perc_difference_gcm_vs_w5e5'] = 100 * pd_spec_mb_gcm_vs_w5e5['difference_gcm_vs_w5e5'] / pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "    pd_spec_mb_gcm_vs_w5e5['ratio_gcm_vs_w5e5'] = pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_gcm'] / pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs']\n",
    "\n",
    "    pd_spec_mb_gcm_vs_w5e5.to_csv('alpine_glacier_spec_mb_gcm_vs_w5e5.csv')\n",
    "else:\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd.read_csv('alpine_glacier_spec_mb_gcm_vs_w5e5.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "90598449-38fb-4cd1-9115-0f48603f83eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "_pd_spec_mb_gcm_vs_w5e5 = pd.read_csv('alpine_glacier_spec_mb_gcm_vs_w5e5.csv', index_col=0)\n",
    "_pd_spec_mb_gcm_vs_w5e5_bc_2000_2019 = pd.read_csv(f'alpine_glacier_spec_mb_gcm_vs_w5e5_bc_{y0}_{y1}.csv', index_col=0)\n",
    "pd_spec_mb_gcm_vs_w5e5 = pd.concat([_pd_spec_mb_gcm_vs_w5e5, _pd_spec_mb_gcm_vs_w5e5_bc_2000_2019])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "e333a649-3a0c-4765-b316-5d38b442e91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RGI60-11.03778' 'RGI60-11.03818']\n"
     ]
    }
   ],
   "source": [
    "# remove those glaciers with \"0\" observed specific MB out of the statistic\n",
    "rgi_w_0_mb = pd_spec_mb_gcm_vs_w5e5.iloc[np.where(pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs'] == 0)].rgi_id.unique()\n",
    "print(rgi_w_0_mb)\n",
    "for r in rgi_w_0_mb:\n",
    "    pd_spec_mb_gcm_vs_w5e5 = pd_spec_mb_gcm_vs_w5e5.iloc[np.where(pd_spec_mb_gcm_vs_w5e5.index!=r)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d58d98fb-c046-403d-a610-ae858b9d60b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1875130/1366677533.py:4: FutureWarning: Dropping invalid columns in DataFrameGroupBy.quantile is deprecated. In a future version, a TypeError will be raised. Before calling .quantile, select only columns which should be valid for the function.\n",
      "  pd_spec_mb_gcm_vs_w5e5.groupby(by='apply_bias_correction').quantile([0.05, 0.25,0.50,0.75, 0.95])\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>spec_mb_2000_2019_gcm</th>\n",
       "      <th>spec_mb_2000_2019_w5e5</th>\n",
       "      <th>spec_mb_2000_2019_obs</th>\n",
       "      <th>difference_gcm_vs_w5e5</th>\n",
       "      <th>rel_perc_difference_gcm_vs_w5e5</th>\n",
       "      <th>ratio_gcm_vs_w5e5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apply_bias_correction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">False</th>\n",
       "      <th>0.05</th>\n",
       "      <td>-1395.838741</td>\n",
       "      <td>-1.156900e+03</td>\n",
       "      <td>-1156.900000</td>\n",
       "      <td>-501.315651</td>\n",
       "      <td>-87.099273</td>\n",
       "      <td>0.129007</td>\n",
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       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>-893.899883</td>\n",
       "      <td>-7.943000e+02</td>\n",
       "      <td>-794.300000</td>\n",
       "      <td>-164.533389</td>\n",
       "      <td>-21.227245</td>\n",
       "      <td>0.787728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>-630.625525</td>\n",
       "      <td>-5.963719e+02</td>\n",
       "      <td>-596.371893</td>\n",
       "      <td>-15.245944</td>\n",
       "      <td>2.332115</td>\n",
       "      <td>1.023321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>-371.430970</td>\n",
       "      <td>-4.184000e+02</td>\n",
       "      <td>-418.400000</td>\n",
       "      <td>122.811305</td>\n",
       "      <td>29.414121</td>\n",
       "      <td>1.294141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>68.291317</td>\n",
       "      <td>-1.264766e-13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>320.563072</td>\n",
       "      <td>124.210839</td>\n",
       "      <td>2.242108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">True</th>\n",
       "      <th>0.05</th>\n",
       "      <td>-1351.567789</td>\n",
       "      <td>-1.156900e+03</td>\n",
       "      <td>-1156.900000</td>\n",
       "      <td>-494.344949</td>\n",
       "      <td>-94.705103</td>\n",
       "      <td>0.052949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>-852.582931</td>\n",
       "      <td>-7.943000e+02</td>\n",
       "      <td>-794.300000</td>\n",
       "      <td>-129.348306</td>\n",
       "      <td>-26.050633</td>\n",
       "      <td>0.739494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>-597.043709</td>\n",
       "      <td>-5.963719e+02</td>\n",
       "      <td>-596.371893</td>\n",
       "      <td>17.549061</td>\n",
       "      <td>-2.379314</td>\n",
       "      <td>0.976207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>-343.222261</td>\n",
       "      <td>-4.184000e+02</td>\n",
       "      <td>-418.400000</td>\n",
       "      <td>152.655583</td>\n",
       "      <td>22.497070</td>\n",
       "      <td>1.224971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>97.625642</td>\n",
       "      <td>-1.264766e-13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>341.586502</td>\n",
       "      <td>113.835016</td>\n",
       "      <td>2.138350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">True: yr 2000--2019</th>\n",
       "      <th>0.05</th>\n",
       "      <td>-1166.958733</td>\n",
       "      <td>-1.156900e+03</td>\n",
       "      <td>-1156.900000</td>\n",
       "      <td>-73.725384</td>\n",
       "      <td>-12.439902</td>\n",
       "      <td>0.875601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>-811.165777</td>\n",
       "      <td>-7.943000e+02</td>\n",
       "      <td>-794.300000</td>\n",
       "      <td>-37.753931</td>\n",
       "      <td>-1.780337</td>\n",
       "      <td>0.982197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>-617.705575</td>\n",
       "      <td>-5.963719e+02</td>\n",
       "      <td>-596.371893</td>\n",
       "      <td>-15.063724</td>\n",
       "      <td>2.169556</td>\n",
       "      <td>1.021696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>-431.358401</td>\n",
       "      <td>-4.184000e+02</td>\n",
       "      <td>-418.400000</td>\n",
       "      <td>8.896489</td>\n",
       "      <td>6.350496</td>\n",
       "      <td>1.063505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>-8.802774</td>\n",
       "      <td>-1.264766e-13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.701444</td>\n",
       "      <td>20.759904</td>\n",
       "      <td>1.207599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            spec_mb_2000_2019_gcm  spec_mb_2000_2019_w5e5  \\\n",
       "apply_bias_correction                                                       \n",
       "False                 0.05           -1395.838741           -1.156900e+03   \n",
       "                      0.25            -893.899883           -7.943000e+02   \n",
       "                      0.50            -630.625525           -5.963719e+02   \n",
       "                      0.75            -371.430970           -4.184000e+02   \n",
       "                      0.95              68.291317           -1.264766e-13   \n",
       "True                  0.05           -1351.567789           -1.156900e+03   \n",
       "                      0.25            -852.582931           -7.943000e+02   \n",
       "                      0.50            -597.043709           -5.963719e+02   \n",
       "                      0.75            -343.222261           -4.184000e+02   \n",
       "                      0.95              97.625642           -1.264766e-13   \n",
       "True: yr 2000--2019   0.05           -1166.958733           -1.156900e+03   \n",
       "                      0.25            -811.165777           -7.943000e+02   \n",
       "                      0.50            -617.705575           -5.963719e+02   \n",
       "                      0.75            -431.358401           -4.184000e+02   \n",
       "                      0.95              -8.802774           -1.264766e-13   \n",
       "\n",
       "                            spec_mb_2000_2019_obs  difference_gcm_vs_w5e5  \\\n",
       "apply_bias_correction                                                       \n",
       "False                 0.05           -1156.900000             -501.315651   \n",
       "                      0.25            -794.300000             -164.533389   \n",
       "                      0.50            -596.371893              -15.245944   \n",
       "                      0.75            -418.400000              122.811305   \n",
       "                      0.95               0.000000              320.563072   \n",
       "True                  0.05           -1156.900000             -494.344949   \n",
       "                      0.25            -794.300000             -129.348306   \n",
       "                      0.50            -596.371893               17.549061   \n",
       "                      0.75            -418.400000              152.655583   \n",
       "                      0.95               0.000000              341.586502   \n",
       "True: yr 2000--2019   0.05           -1156.900000              -73.725384   \n",
       "                      0.25            -794.300000              -37.753931   \n",
       "                      0.50            -596.371893              -15.063724   \n",
       "                      0.75            -418.400000                8.896489   \n",
       "                      0.95               0.000000               44.701444   \n",
       "\n",
       "                            rel_perc_difference_gcm_vs_w5e5  ratio_gcm_vs_w5e5  \n",
       "apply_bias_correction                                                           \n",
       "False                 0.05                       -87.099273           0.129007  \n",
       "                      0.25                       -21.227245           0.787728  \n",
       "                      0.50                         2.332115           1.023321  \n",
       "                      0.75                        29.414121           1.294141  \n",
       "                      0.95                       124.210839           2.242108  \n",
       "True                  0.05                       -94.705103           0.052949  \n",
       "                      0.25                       -26.050633           0.739494  \n",
       "                      0.50                        -2.379314           0.976207  \n",
       "                      0.75                        22.497070           1.224971  \n",
       "                      0.95                       113.835016           2.138350  \n",
       "True: yr 2000--2019   0.05                       -12.439902           0.875601  \n",
       "                      0.25                        -1.780337           0.982197  \n",
       "                      0.50                         2.169556           1.021696  \n",
       "                      0.75                         6.350496           1.063505  \n",
       "                      0.95                        20.759904           1.207599  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# because of calibration, this should be the same:\n",
    "np.testing.assert_allclose(pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_obs'].values, pd_spec_mb_gcm_vs_w5e5['spec_mb_2000_2019_w5e5'].values, atol=1e-3)\n",
    "# average differences between applying bias correction or not \n",
    "pd_spec_mb_gcm_vs_w5e5.groupby(by='apply_bias_correction').quantile([0.05, 0.25,0.50,0.75, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "c374641f-6e34-46bc-b5b6-926bed06f55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_spec_mb_gcm_vs_w5e5.loc[pd_spec_mb_gcm_vs_w5e5.apply_bias_correction == False, 'apply_bias_correction'] = 'No: only ISIMIP bc\\n(yr 1979-2014)'\n",
    "pd_spec_mb_gcm_vs_w5e5.loc[pd_spec_mb_gcm_vs_w5e5.apply_bias_correction == True, 'apply_bias_correction'] = 'Yes: yr 1979-2014'\n",
    "pd_spec_mb_gcm_vs_w5e5.loc[pd_spec_mb_gcm_vs_w5e5.apply_bias_correction == 'True: yr 2000--2019', 'apply_bias_correction'] = 'Yes: yr 2000-2019'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "828e2d87-8600-43e1-8a6d-003432b0abe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes: yr 1979-2014', 'No: only ISIMIP bc\\n(yr 1979-2014)',\n",
       "       'Yes: yr 2000-2019'], dtype=object)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_spec_mb_gcm_vs_w5e5.apply_bias_correction.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "edc28706-f8f3-4df0-8d99-59c5d6b69ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "n_glac = len(pd_spec_mb_gcm_vs_w5e5.rgi_id.unique())\n",
    "sns.set(font_scale=1.8)\n",
    "sns.set_style('white')\n",
    "marker = 'o'\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, sharex=True, #sharey=True,\n",
    "                            figsize=(12,10)) # 30, 15\n",
    "\n",
    "plt.suptitle(f'for these options:\\n{len(gcms)} gcms, {len(ssps)} ssps, {n_glac} Alpine glaciers')\n",
    "order = ['Yes: yr 1979-2014', 'No: only ISIMIP bc\\n(yr 1979-2014)',\n",
    "       'Yes: yr 2000-2019']\n",
    "\n",
    "\n",
    "param = 'difference_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5, #hue='gcm',\n",
    "              x='apply_bias_correction',\n",
    "              order=order,\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7},\n",
    "              ax = axs[0]\n",
    "             )\n",
    "axs[0].grid()\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel(param)\n",
    "axs[0].axhline(0, ls='--', lw=4, color='grey')\n",
    "\n",
    "g.set_ylim([-550,550])\n",
    "\n",
    "\n",
    "ax = axs[1]\n",
    "param = 'ratio_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5, #hue='gcm',\n",
    "              x='apply_bias_correction',\n",
    "              order=order,\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7},\n",
    "              ax = ax )\n",
    "ax.set_yscale(\"log\")\n",
    "from matplotlib.ticker import ScalarFormatter, NullFormatter\n",
    "ax.yaxis.set_major_formatter(ScalarFormatter())\n",
    "ax.yaxis.set_minor_formatter(NullFormatter())\n",
    "ax.set_ylim([0.05,20])\n",
    "ax.axhline(1, ls='--', lw=4, color='grey')\n",
    "\n",
    "ax.set_yticklabels([])\n",
    "# ERROR\n",
    "#  ax.set_xticklabels([0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0], labels=[0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0])\n",
    "ax.set_yticks(ticks=[0.05,0.1,0.2,0.25,0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0,4,5,10,20],\n",
    "              labels=[0.05, 0.1,0.2,0.25,0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0,4,5,10,20])\n",
    "ax.grid() #axis='y')\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel(param)\n",
    "\n",
    "plt.tight_layout()\n",
    "#g.set_xlabel('surface type distinction')\n",
    "#g.set_xticklabels(['no', 'yes\\n(neg_exp)' , 'yes\\n(linear)'])\n",
    "#g.set_title(calib_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "b56746ac-a6a2-4469-96e1-30aa450eb91a",
   "metadata": {},
   "outputs": [],
   "source": [
    "gcms_sh = ['ukesm1-0-ll',\n",
    " 'gfdl-esm4',\n",
    " 'ipsl-cm6a-lr',\n",
    " 'mpi-esm1-2',\n",
    " 'mri-esm2-0']\n",
    "for j,_ in enumerate(gcms):\n",
    "    pd_spec_mb_gcm_vs_w5e5.loc[pd_spec_mb_gcm_vs_w5e5.gcm==gcms[j], 'gcm_sh'] = gcms_sh[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "bcda535b-fd04-4454-8eda-1d5b0dab137f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_spec_mb_gcm_vs_w5e5_all = pd_spec_mb_gcm_vs_w5e5.copy()\n",
    "pd_spec_mb_gcm_vs_w5e5_all['gcm_sh'] = 'all gcms\\ntogether'\n",
    "pd_spec_mb_gcm_vs_w5e5_all = pd.concat([pd_spec_mb_gcm_vs_w5e5, pd_spec_mb_gcm_vs_w5e5_all])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "c05dcddd-221f-4953-946b-5fd42fa574a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
      "/home/users/lschuster/.local/lib/python3.10/site-packages/numpy/core/_methods.py:180: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n"
     ]
    },
    {
     "data": {
      "image/png": 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OHTpk3B1i2LBhev/99yVJ1atXT7Ss0aNHG9usFN/QTGo4hr1792rp0qU6fvy4sc7atWurX79+VkOUrJCwbf7999+6evWqQkJC5ODgoMqVK6t169bG8ceaq1evaunSpTp06JCuXr2qiIgIOTg4yNnZWaVKlVLt2rXVoUMHubu7J3ptu3btjO/ujh079ODBAy1fvlzr16/X5cuXFRUVpQoVKqhbt2565ZVXLI5Zd+7c0ZIlS7R161b5+/srLi5OTz31lF588UX16dMn1T+KREZG6vPPP5fJZFKXLl3Utm1bnTp1Km0fZA6S0vHh4X1qXFycVq9erbVr18rX11ehoaEqWbKkmjRpogEDBqh27dpW1/fgwQN5eHho27ZtOnv2rIKDg1WgQAEVL15cxYsXV9WqVdW8eXN16tQpS4Pr3bt3a9OmTTp69Kju3LmjyMhIOTk5qUqVKnJzc9Nzzz2nGjVqWLwmqe3/ypUrWrx4sfbu3atbt27JwcFB1atX12uvvZZo+z1y5Ij++OMPHT9+XLdu3ZKTk5Pc3Nz03nvv6emnn062rseOHTP+J926dVPDhg3T9d5zans4YT/auHFjLV68WKGhoVq6dKm2bduma9euKSgoSO7u7lq8eHGm1iM7/s/mvdC3b9+u8uXLa8uWLVq5cqXOnDmjwMBAFS9eXA0aNDCOR8nJqDZgUnx8fLRq1SodPnxYN2/eVEREhJydnVWzZk117NhRPXr0kK2t9XjB09NTq1at0rFjx3Tz5k09ePBARYsWVfHixVW2bFk1bNhQXbp0UeXKlVN8XwEBAfr999+1fft2Xb9+XXZ2dqpataoGDBig5557zmI/f+7cOS1atEj//POPbt68qSJFiqhevXp6/fXX1aRJkwz7jDJKSsfWBKVLl5abm5v27dsnKX6/Xb58+cysWiI5ZZvZtm2b1qxZI29vbwUEBCg6OtqYJyVuU2SU/NhWcnZ2TtVn8/TTT6ty5cq6dOmSIiMjdeXKFavHu9T466+/jOlXX3012XIvvviiZs+erYiICO3YsUP37983fowAkHlyajszNchdyV3JXcldJXLXzEbuaik/nks8jNyV3DUBuWvmIncldyV3/Re5K7lrTm4rkbsmjwtbkW906dIlyd4UMTExunLliq5cuaI///xT7du313//+98c0zPQ09NTQ4cOVXBwsMXzoaGh8vHx0cqVKzV//vxUL++3337TzJkzE125Hx0drcuXL+vy5cvauHGjfHx8NGbMmGSXc+/ePX3wwQfGAT2Br6+vfH19tXHjRi1evFhly5bVpUuX9M477+jy5csWZc+cOaMzZ85o165d+vnnn5PsaXv//n0NHz5cO3fuTDQvICBAAQEBOnfunBE+m/eYxqOJjY3V+PHjtXLlSklSnTp19OOPPybqLfXXX39p4sSJib6bISEhCgkJkbe3t3777TdNmjRJPXv2THJdu3fv1kcffaR79+5ZPB8eHq7w8HBdu3ZNx44d05IlS3TgwAGrPbay6juZXcLDw+Xl5SVJcnBwkJubW7JlCxQooFatWmnp0qWSpD179qh///5ZUs/cKCYmRp999pnWrVtn8fyNGzd048YNbd68Wa+99pqeeeaZDFtnXFycxo8frxUrViS5zq1bt2rQoEHq0KFDhq0zLRYtWqRvvvkm0bEiODhYR48e1dGjR7Vw4ULNmDFDrVq1SnIZK1eu1KRJkyzuqCD9u4+4cuWKPD09tXz5ch09etRqfQICAvT222/L29vb4vmzZ8/q7Nmz2rx5s3799VcVLVpUx44d09ChQ3Xnzh2LssePH9fx48d18OBBff3116kKWWfMmKFr167J2dlZY8eOTbF8XhIeHq733ntPhw4dsnj++vXrWr16tf7880998MEHeuedd5J8/bVr1/TGG28k2s9K/37PT58+rXXr1sne3l6dO3fOjLdh4fr16/r444+T/L4FBQXJy8tLXl5e+uGHH+Tp6SknJ6dkl7V161aNHDlSERERxnORkZG6c+eO/v77bw0fPlzvvvuuTCaT5syZk6jn4t27d7V582bt2LFDs2fPVrt27ZJcT8KxWIofBie9ckN7+PTp0xo6dKiuX7+e5et+WFb9nxPExsbqo48+svhBVZJu376tzZs3a/PmzXr55Zc1fvz4LLtr0YMHDzRx4kR5eHjIZDJZzAsICNDu3bu1e/duLVy4UN9//32iH+Ol+GPexIkTtWzZskTzAgMDFRgYKF9fX+3bt08nTpzQDz/8YLVOXl5eev/993X37t1Ez3t5eengwYOaNGmSbGxstGzZMk2ePFkxMTFGufv37xv1njhxYq5uIzk6OhrTUVFR2ViTeFm9zURLzpWHAABJz0lEQVRHR+uDDz7Q5s2bM+X9WENbKWUZ+f28cOGCcQe1qlWrJrmvMV9vo0aNtHfvXkVEROjw4cNq3bp1utYPIGW5oZ2ZFHJXctesRu5K7prfkLsmxrlEPHJXctfkkLtmHnJXcldy19QjdyV3TUDuarne7MhdubAV+cb9+/dVtGhRNW3aVDVq1FC5cuX02GOPKTw8XGfPntXGjRsVEBCg7du3a8yYMTliGBlfX1+9+eabxgGqatWq6tGjh8qXL6/g4GBt375d+/bt07Bhwyx2Xsn56quv9OuvvxqP3d3d1aZNG5UrV06xsbHy9/c3GiUPN6AeNmbMGO3bt0/16tVTly5dVLp0ad2+fVvLly+Xr6+vrl27ppEjR+q7777TkCFDdPPmTXXq1EktW7aUo6OjLly4oN9//10hISE6fPiw5s+fb9y62tzMmTONcLVEiRLq0qWLnnzySTk7OysqKkp+fn46efJkohNAPJrIyEh99NFHxmfeqlUrzZ49O9FdJJYvX67x48fLZDLJzs5O7du3l7u7u1xcXBQRESEvLy+tX79eUVFR+uyzz2RnZ6euXbtaLOPWrVsaPny48f1u3Lix2rRpo5IlS6pQoUIKCgrS+fPndeDAgSRP0B+WVd/J7OLr66u4uDhJUs2aNVWwYEGr5evUqWMErOfOncv0+uVmEyZMMMJVW1tbde/eXe7u7ipUqJDOnj2rlStX6tdff9WNGzeSfH3CycHBgweNXq0DBw5U06ZNjTIP91aaOnWqEa4WLFhQ3bp1U+PGjVWoUCGjd+aiRYt08+bNDH+/KZk5c6bxw529vb06deqk+vXry9nZWSEhITpw4IC2bNmikJAQvfPOO/rtt98SBf6nT5/W+PHjFRsbq4IFC6ply5Zq3ry5XFxcVKBAAd29e1dnzpzR/v37FRISYrU+0dHRev/99+Xt7a0WLVqoffv2Kl68uPz8/PTHH3/o5s2bOnnypKZOnaphw4bp9ddfV1RUlHr16iU3NzcVKVJEJ0+e1JIlS3T//n2tX79ezZo1U58+fayuN6E3oySNHDlSJUuWTMenmvuMHj1ahw4dUqVKldSjRw898cQTCg0N1a5du7Rz507FxsZq5syZcnBw0MCBAxO9/sMPPzT23VWqVFHnzp1Vrlw5OTk5KTw8XJcuXZKnp6dOnDiRJe/n2rVr6tu3rxHIODs7q0uXLqpVq5YcHR0VEhKiM2fOaPfu3fL397faDvL29tZPP/2kggULasCAAapTp44KFiyow4cPy8PDQzExMfr222/VsGFDnT59Wt99951cXV3Vs2dPValSRREREdq0aZP27dun6OhojR49Wn/99VeSPyL+888/kuKHuqpbt67CwsK0ePFibdmyRVevXpXJZFLp0qXl7u6ufv36pXg3h5zeHg4ODtZ7772nGzduqEWLFmrbtq1cXFx0586dRGFaZsvK/3OCr7/+Wlu2bFGpUqXUu3dvPfXUU7p//74OHDigjRs3Ki4uTv/73/9UuHBhffbZZ5n+GcTExOj111837l5RunRpPf/886pevbqKFCmiW7duacuWLfLy8tL58+f1yiuvaM2aNYne4++//26Eqw4ODurUqZNq166t4sWLKzo6Wrdu3dKpU6e0f//+FOt0/fp1DR06VOHh4erVq5fc3d1VuHBhi/38smXLVL9+fTk6Omr8+PEqXry4evfuraefflqxsbHatWuX0QN5ypQpaty4sapWrZrBn17WOH/+vDFdrly5bKxJ9mwzU6dO1Z49e+Tq6qoXXnhBVapUUVRUlE6cOJGpdwGjrZSyBw8eWJzDpff7aX4+U6dOnRTL16lTR3v37jVey4WtQObL6e3MpJC7krtmNXJXctf8iNzVEucS8chdyV2TQ+6aechdyV1TQu5qidyV3FUid01KduSuXNiKfGPq1Klq3ry57Ozskpz/0UcfacSIEdq2bZs2bdokT09Pqz1zs8KECROM8Klbt26aNm2aRf1ffvllLVu2LFW95bds2WKEq/b29poxY0ayPTHu3LmTYqC1adMmiyFhErz00kvq27evzp07p8OHD2vw4MEKDAzUzz//nOgW6F26dFGvXr0UFRWlP/74Q++++67FgTA2NlarVq2SJFWsWFErV65M9pbi4eHhunr1qtU6w7qgoCC98847OnbsmKT4HopTp05NNLzBmTNnNHnyZJlMJj3xxBOaN29eokZp79699frrr2vw4MG6ffu2xo8fr5YtW1rcQn39+vXG93vcuHFJnqAnOH78eIq9F7PiO5mdLl26ZEynZrgD8zLmr4WlgwcPGj1zHR0d9euvv6pevXrG/K5du2rIkCEaMmSIxW34zSX07g8NDTWeq1mzZrK9/r28vPT7779Lit8f//TTTxbHm27dumnw4MF69dVXtWXLlvS9wTTas2eP0WOzfv36mj17dqJh7fr27SsvLy+9+eabunfvnkaOHKktW7ZY7CtWrlyp2NhYSdLcuXOTPd6YTCZ5enpardPt27cVEBCgKVOmJGro9+rVSz169FBAQID+/PNPnTlzRra2tlq8eLFq1qxplHv++efVpk0bDR48WJK0cOFCqycNUVFRGjNmjOLi4tSsWTP17t3bah3zoi1btujZZ5/VjBkzLO6i0q9fP61fv14jR45UbGysvv76az3zzDMWPfhOnjxp9Fzs3LmzZs6cmWhIwwQphZkZIS4uzqKXcbt27TR9+vRk7wywe/duq3eO2blzp1xdXfXbb79ZvO9u3bqpUaNGGjVqlCTpP//5jy5fvqxnnnlGs2bNsvih5cUXX9SoUaO0Zs0aBQcHy8PDQ2+88YbFesLCwoz2mJOTk65evWqEj+YS7vy0YsUKDRw4UKNHj072R7ic3h4+d+6cChYsqP/+97/q3r17lq03KVn1fza3ZcsWNWjQQD/++KOKFi1qPN+nTx+99NJLevvttxUZGamFCxeqY8eO6R4iLSWzZs0ywtWXXnpJ48aNS7RtDB48WIsWLdKUKVN069YtTZ06VV9//bVFmeXLl0uKH+pp9erVyQ7TGRUVpTNnzlit06FDh+Ts7KylS5da/KDw/PPPq23btnr11VdlMpk0d+5c3bt3T3Xq1NHPP/9s0Qbt0aOHqlSpou+++07R0dH6/fffNWHChFR/LjnFoUOH5OvrKyn+QpTUhE6ZKTu2mT179qhDhw765ptvLL6b6QkGU0JbKXXWr1+vsLAwSVKtWrVUqlSpR16WJF28eNGY5lwIyJlyejszKeSu5K5ZidyV3DU/Ine1xLlEPHJXcldyV3JXcldy15yO3JXcNQG5a2LZcS6UdEsHyIPatGmTbGNSkh577DF99dVXRg/pNWvWZFHNkubt7W30FitfvrymTJmSZP379u2bYgPUZDJZ9PyaOnWq1duLlyxZMsXGdIsWLRIFWVJ8WPDmm29avI9hw4YlCrIk6cknn1S3bt0kxd/2++Heg4GBgcZO+dlnn002XJXigxHzAwTSxs/PT/379zfC1ddff11fffVVonBVim8AREdHq3Dhwvrxxx+T7WlVtWpVffnll5Lih6tKaFwnuHLlijGd0sG7Xr16KQ5PlRXfyexkHt4VL148xfLmJxLmr4WlhQsXGtMjR460CFcTlChRQt98802S28OjWLBggREojRgxIsn9benSpTVz5swU7xCR0b799luZTCaVKFFCP/zwQ6IThgSNGjUyeq36+/snCoITtu8SJUpYPd7Y2NjI3d09xXq9+OKLSe4nSpYsqVdeeUVS/I9yPj4+mjBhQpLHg2bNmqlZs2aS4ntaJncnCCk+VLh06ZKKFCmiyZMnp1i/vKhs2bKaPn16kvverl27Gj+K3b9/3/jBIIH5D569evVKNlyVJFdX11SdKKXHpk2b5OPjIyn+x49Zs2ZZHe6qTZs2KR5zvv766ySH40i4y4IUHxY6OTlpxowZie4eIknDhw83hhtJ6N1oLiAgwJiOi4vTW2+9pRs3bqh8+fL64IMPNHPmTH3xxRfq2LGjsZzFixdr2rRpVt9bTm8Pv/LKK9keribIiv+zOXt7e82aNcsiXE3QpEkTffzxx5Li2/bmdwPLDHfv3tVvv/0mSWrevLm++OKLZLeLQYMGGXeI2rhxo27dumUxP+GY0KxZs2TDVUkqXLhwksfhh40bNy7Ju2Q0adLE2M/7+/srIiJCs2bNsmgTJXjrrbeM73lK/5ecKCoqSpMmTTIev/3221neZkhKVm8zZcqUSfZYlVloK6UsMDDQ4oeWd99995GWYy4hF5DSfi5k/loAmSc3tDPNkbuSu2Ylctd45K75D7mrJc4l4pG7kruSu5K7SuSu5K45F7lrPHLXf5G7WsqO3JULWwEzjo6OqlatmqT4nsrZadu2bcZ0//79rR40Eq7uT86pU6d04cIFSfFX7T/33HPprt+AAQOSndeoUSNjumDBgurfv3+yZc2DhYQ6JnjssceM6dOnTz9KNZEKZ86cUb9+/XTp0iXZ2NhozJgxGjlypNGwMRcaGqrt27dLig+9K1WqZHXZLVq0MHqJ7Nu3z2Ke+TBbCT1M0yMrvpPZKeEuC5JSdTcD8wbrvXv3MqVOuV1UVJTxvSxatKh69uyZbNmqVauqVatW6V7ngwcPtGvXLknxPYFffPHFZMs+/fTTatGiRbrXmVpnz541tsUXX3wxyRNSc127djVC54dPgBK27+DgYPn5+aW7bqndvkuWLKnOnTsnWzY12/eJEyeM4P39999XxYoV01jbvOHll19ONByiuddee80ITjdv3mwxz/z4ferUqcypYBqsXbvWmH7//ffTfUeYWrVqWe2xbT7vhRdeSHbY0rJlyxpDhCT0/jVn/uNYeHi4AgIC1KpVK23YsEFDhw5Vly5d9NJLL2nOnDmaO3euEa4sXrzY+MH0UWR3e3jQoEFZvs6kZNX/2Vz37t2TDWuk+AsrEn4c2LVrl6KioqwuLz02btxoLP/1119PsXzCMTQ2NlYHDhywmJewLzl37pwePHiQrnq5uLioS5cuyc43Pya0bds22UC3SJEiRkjr5+eXqZ9lZhg9erTxfapbt64RoGWn7NhmevfuneLdxTISbaWUPXjwwOJuPR06dNCzzz6b5uU8zPxcKDWBOudCQM6U3e1Mc+Su5K5ZhdzVErlr/kHuaolziXjkrvHIXZNH7pq5yF3JXa0hd41H7hqP3PVf5K6WsuNcKGO6wAG5REhIiNatW6e9e/fq/PnzCgoKUmRkZJJDMdy8eTMbavivkydPGtNNmjSxWrZWrVpycnJK9op4Ly8vY9par4S0qF+/frLzzG93XblyZau980qWLGlMP9y72dHRUfXr19exY8d04MABvfPOOxowYIAaN26cY4Ypyu0OHDigYcOGKTw8XHZ2dpo+fbrVRuuRI0cUFxcnKT7kM/8hIDkODg4KCAhI1EBq0aKFFixYIEkaNmyY3nzzTXXu3NlqjzJrsuI7ibzlzJkzio6OlhTf6E9pv9K0aVPt3LkzS9fZrFkz7dmzJ13rTC3z4RZiY2NTtX3b29srNDQ0ye17y5YtiouL06BBg/TWW2+pQ4cOFttXatnb2xthT1LMl1mrVi2rvdRT2r4fPHigsWPHKjY2VrVq1dKQIUPSXN+8onnz5lbnlylTRlWrVjV6FgYEBBj72oYNG+qxxx5TZGSk5s2bp+DgYPXs2VM1atRI8se7zJbw3S5cuHCSd41Jq5R6Npt/z+rWrWu1bKlSpeTv76+QkJBE8xKOtwmKFi2qr7/+Osmeth06dNDAgQONHwcWLVqU7HExJ7eHS5cunWSv4+yQVf9ncyltd4ULF1ajRo20a9cuRUdHy8fHx2r7Jz0S7mAmxQ+Xm9IxwfxuAQ+HMi1atNCGDRt08eJFDR48WEOGDFHLli0tfoxJrdq1a1vtIZ/W/4sUfyeG0NDQdA/Zk1W++eYbbdiwQVJ87+hvvvnG6h1Bskp2bDNZPYQ1bSXr4uLiNGbMGONzqlixoqZOnZqmZQDI3XJyO/Nh5K7krlmB3DUxctf8g9zVEucS5K7myF2TR+6aechdyV1TQu5K7mqO3PXfdZC7Zj8ubEW+sW3bNo0dO1bBwcGpKh8eHp65FUrB7du3jenU9FosX768MdzDw8wbx08++WT6KydZ7SVhHhik1JvCvGxSPXbGjx+vV199VWFhYdq5c6d27typIkWKqE6dOmrQoIGaNm2qJk2aZNhQNfnJnTt39Oabbyo6OloODg767rvvjFugJ8ff39+Y9vDwkIeHR6rX93ADqVWrVurRo4fWrFmjoKAgTZ8+XdOnT1f58uVVv359ubu7q02bNipbtmyqlp9V38mMlFKjrEOHDsa0eQ/e1PS6u3//vjH9cI8qb29vq7e4r1mzptFjKy8z38+m5oQ+I4btSeu+Pb291j09Pa0e9xo2bKgSJUpIkkWvtZ9//jlN63l4++7du7c2bdqkAwcOyN/fXxMmTNCECRNUpUoVNWjQwNi+E9ZtTbFixayGchm5fX///fc6d+6cChYsqC+++CJHDC+SXRKGLrGmUqVKOn/+vKT473ZCOOHs7KyxY8dq/PjxiomJ0aJFi7Ro0SI5OzurQYMGatiwoVq2bPnIQ1kGBgbqyJEjyc53dnY2TrrDw8ONNl3FihUz5AfatHzPUls2qf36w/vuzp07W11e3759jYD14MGDSZbJiPZwWvYrafX4448/0usyQ1b9n82ldEemh8uYH1NSIy3tDvM236hRo9K0noePCSNGjJCXl5du3rwpLy8veXl5yc7OTjVr1lSDBg3UuHFjtWzZMlW9gTPj/yJlbpvv+vXrVu+CVrZsWdWqVStVy5o3b55++OEHSfE/uvz666855keJ7NhmMmKfQVsp+bJp2S5MJpMmTJigdevWSZLKlSunBQsWJDusdWRkpP7+++9kl1ekSBGLH0XNz4VSUy9r50IAMge5azxyVyQgd025LLlr3kbuyrnEw8hd/0XumjxyV3JXidzVGnJXS+Su8chd80ZbKTnkrlzYinzi6NGj+vDDDxUTEyNJql69upo3b66KFSuqWLFiKlSokLFD+vbbb3X+/PlEvbWyWmRkpDGdml411sqYN46tDW+RFtZ6GzxKueTUqlVLa9eu1dy5c7Vp0yZFRETo/v37+ueff/TPP//oxx9/VMmSJfXmm29q0KBB6V5ffmIymYzveXR0dKpCu/T0pE/oLW3uyy+/VNOmTbVgwQKdPXtWUnzDxc/PT+vXr5eNjY1at26tzz77TFWqVLG6/Kz6TmakoUOHWp2f8JlI8Y3oBEFBQSku27yhaP5aKX64lNWrVyf72mnTpqlXr14priO3M7+1fmr2sxmx/zS/JX9WrHPWrFk6fPhwsvMXLVpk3J0mubvPpMbD27ednZ1++ukn/fHHH/rjjz909epVSdLFixd18eJFrVq1Sra2turcubNGjRql0qVLJ7vstGyz6dm+z5w5o59++klS/FCTqT3ZzavS2vZ4eLiJF198UZUrV9b333+v/fv3Ky4uTsHBwcaPpTNmzFC1atU0YsQItWnTJk11O3/+vNX9Z+PGjbV48eJE9crqNlBayz7s4ZPSlL6TVapUkb29vSIiInT37l3du3fP4qQyo9rDadmvpFVSd0XILln1fzaXmu+ote0uJWlpd2TkMaFcuXJavXq15s+fr7Vr1yo4OFjR0dE6fvy4jh8/roULF8rR0VGDBg3Su+++a/WHkOz4v6TXwYMHNXr06GTn9+zZU19++WWKy/nxxx81a9YsSfHDav7yyy856liVHf+b1ITyKaGtlH4mk0kTJ07U8uXLJcUH37/99pvVixPu3r1rdZ/k6uqqHTt2GI/N7/yW1nMha3eNA5AxyF3JXZEYuWv2I3fNXuSunEuYI3e1RO6aPHJXctf0lLWG3JXc1Rpy18TIXeNl93ZB7hqPC1uRL8yePdtoTI4fP16vvPJKsmW///77rKqWVeaNp8jIyBR7upkHsg9zdHQ0ps0DhdzC1dVV06ZN06RJk3T8+HEdPXpUR44c0aFDhxQREaE7d+5o2rRpOnv2rKZNm5bd1c12qf1xoFSpUvr00081YsQIPXjwQEOHDtXs2bOtDptm3vAfN26cBg4cmK662tjYqGfPnurZs6f8/f3l5eWlY8eO6fDhwzp//rxMJpN2794tT09PLVmyRNWrV0/X+nKzypUrG9PmvZaSY17G/LX4l/n32do+NEFG7D/Nw46sWmdqmX8e33//fbqHULSzs9PgwYM1ePBgXbx4UUeOHNHRo0d16NAhXbt2TTExMVq/fr0OHz6slStXqkyZMul9C+ni4eGh6OhoFShQQHZ2dpo3b16S5cyHifnnn3+McpUrV9Zzzz2XJXXNCpGRkRbth+TKJEiqV56bm5t++eUXhYSEGPt3T09PHT9+XDExMTp37pzeeuutTP1Rx7xeua0NVKZMGSMwlVJ3gujk5GSUDw8Pt3j/ubE9nN+k5jua0naXUcyPCV5eXinuD1JSokQJjRkzRiNHjtTp06eNY8LBgwcVHBys8PBwzZs3T0eOHNGCBQtyTDiaU/z444+aMWOGpPhzu19++SXFYaWQ8fJ7WykpJpNJkyZN0tKlSyXFH7sWLVqU7rtfPcz8YhvOhYCcJze2M8ld/0XumjbkrnkTuWvGI3e1lN/PJchdLZG7Zj9y1/yH3JXcNTnkrjlDfm8rJYXc9V9c2Io8Lzo62ugJUKtWLauNScny9u/ZqXTp0kbvnatXr6pOnTpWy1vbyZjfKvzChQsZU8FsUKhQIbm7u8vd3V1S/K2w16xZoy+++ELR0dHy8PDQK6+8otq1a2dzTTOeecCeVA98c6npSZHgueeek62trT766CNFR0frgw8+0MyZM/Xss88mWd78u2RtSKVH4erqKldXV3Xv3l2S5Ovrq4kTJ+rw4cO6d++eZs6cqfnz52foOrObeQ+9lFStWlUFChRQXFycTp8+rdjYWKvD9Zw8edKYfuqppyzmffnll6nqoZbXmffAunbtWorlU9OYS8s6E3qIWZOaMtYk9J5OjczcvqtUqaIqVaqoT58+kqQTJ05o7NixOnfunG7fvq0ff/xRn3/+eYauM61MJpOk+B+pUruvOXTokA4dOiRJat++fZ4KWK9cuZJij1Tz76e1Ho3FihVTu3btjBPRwMBAfffdd/r9998lSV999ZW6desmOzu7VNWtSZMmqd5/Ojo6ysnJSWFhYbp69aoePHiQIcNiZQUbGxtVr15dR48elZS6Hqvmd4syD2Qzsj2clv0K0ubq1at6+umnUyyTwNp2l5S0tDsef/xxY7jdmzdvZtiwura2tqpbt67q1q2rwYMHKzY2Vlu3btW4ceMUFhamgwcPauvWrerUqVOGrC8n6NWrV7p+RDIPVx0cHPTzzz+rXr16GVW9fI+20qNLCFeXLFkiKX6ftGjRolQN71e+fPk07ZOqVatmTJuf5yTH2rkQgIxF7krumheQu5K7krtmPHJXS/n9XILc1RK5a/Yjd81/yF3JXZNC7pq5aCs9OnJXS1yOjzwvKCjI6CWV0oZ+4sSJNIVTmck8UE04eUuOt7e31Ua3m5ubMW1+W+ncrnDhwurbt6/69+9vPOfp6ZmNNco85kMa3bp1y2rZhBOx1Hr22Wc1Z84cFSpUSNHR0Ro+fLg2bdqUZFk3NzdjuIy9e/emaT1pVbVqVc2ePdvoOZZX/7ep5ejoqIYNG0qKHwLDy8sr2bJxcXHat2+f8bh169aZXr/c6OmnnzYCnaNHj6Y4LNzBgwczZJ0J4c6RI0dSXOeBAwfSvc7USvjxSsr87btu3bqaPn268Ti/b9850d9//211/q1bt4wfbcuVK6dSpUqletklSpTQ559/bgRJwcHBmfoDcEI7KCoqymLfmBuYDxfm7e1ttezFixeNIZIS7jqQILe2h/OblLa7Bw8eGMd/Ozs71ahRI9PqYn5M2LNnT6atp2DBgurcubOGDRtmPMcx4V9JhasNGjTI5lrlX7SV/vVwuFqqVCktWrRITzzxRKas78knn1S5cuUkxV+IY+3CB/Nzpccee0yNGzfOlDoBiJdb25nkrikjd00auWveRO6a8chdLXEuAXPkrjkDuWv+Qu4aj2PCv8hdcxbaSv8id02MC1uR55k3Lq9cuWK17Jw5czK7OqnWvn17Y3rJkiWKiopKtuzChQutLqtWrVrG1fLe3t7666+/MqSOOUX58uWN6djY2GysSeYx7+2wf//+ZMtFRUUZB7m0aNu2rebOnavChQsrJiZGH3/8sdavX5+onIuLixHWnTt3LskyGal48eLGEAx59X+bFl26dDGmf/3112TLbdu2zWh01K9f32Ibwb8KFy6sli1bSpJCQkK0evXqZMv6+vpmSEO6UKFCRmASFhamlStXJlv23LlzKZ5sZ6TatWsbvbJ27dplNcTPCDlt3z127FidPXs2xT/zEGDYsGHG88kNoZVbLVmyxOqwbQsXLjSGYHzUHr7m34GE8C8zvPDCC8b07NmzU/xhIyd5/vnnjbvEbNq0ScHBwcmWXbZsmTH98A9rubU9nN+sW7dOt2/fTnb+ihUrFBoaKim+7Va4cOFMq8vzzz9v/CC4YMECBQYGZtq6pJx3TMgJfvrpJyNctbe3108//WT82I7skd/bSuYmT56cKFzN7KGnzO/QZC0DWb58uTHEYLt27SyGGweQ8XJrO5PcNfVy8vEoo5C75t3/bVqQu2YscldL+f1cgtzVErlrzkDumr+Qu8bLCceEnIDcNefJ720lc+SuiXFhK/I8R0dH4+p1b2/vJHtEx8bGaurUqZnaKyatateubfRM8PPz09ixY5McCmn58uX6888/rS7LxsZGw4cPNx6PGTPG6h0EAgMDM/1gkRqnT5/W3LlzrTY0IyIitHbtWuNxZvagyk5ly5Y1QtajR48mGZI/ePBAo0aN0uXLlx9pHW3atNH333+vIkWKKDY2ViNHjtSaNWsSlfvoo4+M3tbjxo1LMWQNDg7WggULEgXDc+fO1d69e40T9KSsX7/eOJFIaYiI/KBPnz5Gj5mdO3fqjz/+SFTm8uXLmjx5svH4ww8/zLL65UavvvqqMT19+nQdP348UZnAwEB9/PHHGRYADRkyxLgDx9dff53k3T7u3Lmjjz76KEsb0zY2Nvrkk08kxfcGGzp0qNUfdKT43uNz5szRmTNnLJ6fNm2ajhw5YvW1//vf/4xptu+c5/r16xo1alSSYeTGjRv122+/SZKKFCmSaHilP//8UytWrDBObpJy6dIl484YhQsXztSTsk6dOhnDe/n4+OjDDz+0eselvXv3Wv1hOytVrFjRGB4lNDRUn376aZJ127ZtmzGsS8GCBTVkyBCL+bm1PZzf3Lt3T8OHD7cY2izBP//8o6+//lpS/P76tddey9S6PP744xo4cKAk6fbt23rjjTdSHD7yzJkzGj9+vMVzt2/f1pdffml1iMeYmBitWLHCeJxX2/Np8csvvxj/b3t7e/38889q1KhRNtcKtJXiffHFF0bdEsLVKlWqZPp6X3/9dTk4OEiS/vjjD23fvj1RmePHj2vWrFmS4ofgGzp0aKbXC8jvcms7k9yV3NUcuWvOamtkF3LXjEfu+i/OJWCO3JXcFVmP3DVeXm3PpwW5a85EWykeuWvSbLNsTUA2evXVVzVp0iRJ0vDhw9WlSxe5u7urWLFiunLlitatWydfX19Vq1ZNdnZ2KQ45kFUmTZqkPn36KCIiQuvWrZOPj49eeOEFlS9fXsHBwdq+fbv27dunSpUqydHRUd7e3sZJ+8M6dOig1157Tb/++qsiIiL07rvvqnHjxmrTpo3Kli2ruLg43bhxQ0eOHNHff/+tfv36ZftBPCwsTHPmzNF3332nBg0aqEGDBqpcubIcHR0VFhamixcvav369UYAW79+fTVt2jRb65yZ3njjDY0aNUqSNGLECO3Zs0dNmzaVra2tfH19tWbNGvn7+6tbt25at27dI62jRYsW+uGHH/Tuu+8qIiJCo0ePVmxsrHr37m2UqVGjhiZPnqyxY8cqMjJSn3zyiX7++We1bdtWlSpVUpEiRRQWFqarV6/qxIkT8vT0VExMjMUt3aX4od7mzJkjFxcXtWzZUjVq1FDJkiVVoEABBQQEaO/evRYNlnfeeeeR3lNmCA0N1S+//GLx3PXr141pPz8/zZw502J+rVq11LFjx3Stt3DhwpoyZYreeustRUdHa/Lkydq7d6/RI+b06dNasWKFERy89NJLat68ebrWmdc1a9ZMffr00cqVKxUeHq5XXnlF3bp1k7u7uwoVKqSzZ89q5cqVCgwM1HPPPWf8uJHcvjY1GjVqpAEDBmjx4sW6d++eBgwYoO7duxvr9PHx0cqVKxUcHKyOHTtqy5YtGfV2U/TMM8/ogw8+0OzZsxUUFKQhQ4bIzc1NrVq1kqurq2xtbRUWFqZLly7p6NGjOnbsmEwmk5o1a2axnK1bt2rhwoVydXVV8+bNVb16dZUoUUKxsbG6deuWduzYYZxU2NnZ6Y033siy94jU6dSpkzZv3qyzZ8+qZ8+eqlixosLCwrRr1y6LH2lHjBihChUqWLz2ypUrmjt3rqZMmaJmzZqpTp06KleunAoXLqzAwECdPHlSmzdvNgLYgQMHGneJyQwFChTQrFmz9NJLLykwMFA7duxQhw4d1KVLF9WqVUuOjo4KDQ3VuXPntHv3bl29elX//PNPpvbKTouPP/5Ynp6e8vX11Z49e9SlSxf16dNHlSpV0r1797Rnzx5t3bpVJpNJkvTJJ5+oatWqiZaTW9vD+UnCdvfcc8+pT58+evLJJ3X//n0dOHBAGzduNH50Gzx4cJYMi/Txxx/r7Nmz2rdvn7y9vfXcc8+pXbt2atSokUqVKiWTyaSgoCCdO3dOhw4d0uXLl1WwYEGLH3ofPHigBQsWaMGCBapVq5bc3NxUtWpVFS1aVBEREbp27Zo2bNhgBLAVKlSwuFNSdjtw4ECiITFPnz5tTG/ZsiXR3Tj69OmTaL+YFitWrLBoN/fr109BQUHatm2b1ddVrlw5yW0fGSu/t5Vmzpyp33//XVJ8e3jQoEG6ePGiLl68aPV1NWvWNC4UeVQuLi76/PPP9dlnnykuLk7Dhg1Tly5d1KJFCxUoUEBHjhzRmjVrjB8i33//fbYJIIvk1nYmuSu5qzlyV3JXcteMR+5qKb+fS+Bf5K7krjmlPZyfkLuSu0rkrjldfm8rkbsmjwtbkS/0799fp06d0qpVq2QymbRhwwZt2LDBoky1atU0b948jRkzJptqmVjVqlX1008/aejQoQoODtaFCxeM26IncHV11dy5czVhwgRJMq6kT8qoUaPk4uKi2bNnKyoqSocPH9bhw4eTLFugQPbf0DkhwIiLi5OXl5fVuxm4u7tr9uzZOaLemaVHjx46evSoli5dqpiYGHl4eMjDw8OizIABAzR48OBHDlglqWnTpvrpp5/05ptvKiIiQmPHjlVsbKxeeuklo0yvXr1UqlQpjRkzRrdv35aPj498fHySXWahQoVUvHjxJOfdvXtXa9eutbgDhDl7e3uNGzfOGEYoJwgNDdX8+fOTnX/9+vVE83v27JnugFWSmjdvrpkzZ2rMmDEKDQ3Vzp07tXPnzkTlXnrpJU2cODHd68sPJk2apPv372v9+vWKjo5Octt67bXX1KpVKyNgTW8YNGbMGEVGRmrlypXJbs+DBg1Shw4dsjRglaShQ4fK1dVVU6dOVUhIiDw9PeXp6ZlseQcHBzk5OSU5z9/f36I36MOcnZ01ffr0HNUbDvES/v8HDx5M9IORFN9O+OCDD4yexeYSjt+RkZHasWNHsncrsrGx0csvv6yPP/44YyufhAoVKmj58uUaPny4Tp06peDgYIsemUnVLadwdnbWr7/+quHDh+vo0aPy8/PTt99+m6icnZ2dRowYocGDBye5nNzaHs5PPvnkE9nZ2Wn9+vXJDrPXv39/jRw5MkvqY2trq/nz5+ubb77RokWLFB0drc2bN2vz5s3Jvubxxx+3eGy+LXl7e1sN7qtVq6bvvvvO6vlMVvP09LTa5kuqHda8efN0BawPn/P8+uuvVodCTTBs2DC9//77j7xepF5+biuZ3+3AZDIlyieSM23aNPXq1Svd6+/Zs6ciIyP15ZdfKioqSuvXr090J7uCBQvqnXfeyVEX6AB5XW5tZ5K7kruaI3cld5XIXTMDuaul/HwugX+Ru5K7IuuRu5K7SuSuuUF+biuRuyaPC1uRL9jY2Gjq1Kl65plntGzZMp06dUr37t2Ts7OzKleurM6dO6tPnz45poeYOTc3N/31119asGCBduzYIX9/f9na2srV1VUdO3bUgAEDVKxYMQUHB0uSihUrZnV5b7zxhrp166bly5fr77//1uXLlxUWFqZChQqpTJkyqlWrlp555pkMCYLSq3Hjxlq3bp3279+vo0eP6sKFC7p586YiIyNVuHBhlS5dWrVr19bzzz+vdu3aZXd1s8SkSZPUqlUrLV26VKdOnVJ4eLhKlCihevXq6eWXX1azZs3k5+eX7vW4ubnpl19+0Ztvvqnw8HCNHz9e0dHRFsOetGrVStu3b9e6deu0e/dueXt7KzAwUA8ePJCDg4NcXV319NNPq0mTJmrXrp2KFi1qsY758+frwIEDOnz4sE6fPq0rV64oKChIkuTk5KQqVaqoWbNmevHFF1WmTJl0v6e85Nlnn1W9evW0ZMkS7dy5U/7+/oqKilKpUqXUqFEj9enTR40bN87uauYatra2mjFjhp5//nktX75cJ06cUGhoqFxcXFSvXj31799fzZo108aNG43XpLSvTUmBAgU0ZcoUderUSUuWLNHx48eNddapU0d9+/ZVq1atdOjQofS+vUfSo0cPdejQQWvWrNG+fft05swZBQUFKTY2Vo6OjqpQoYJq1qypZs2aqU2bNnrssccsXu/h4aF9+/bJ09NTPj4+unbtmjG8nbOzs5588km1bt1avXv3Tvdniczh6OioBQsWyMPDQ2vXrpWvr69CQ0NVsmRJNW7cWIMGDVLt2rWTfO0777yjJk2a6ODBgzpx4oQuXbqkgIAARUdHy97eXhUqVFDDhg3Vu3dv1axZM8veU4UKFbRy5Upt375df/31l44dO6a7d+8qJiZGRYsWVeXKleXu7q5u3boleyKcXR5//HH973//08aNG7Vhwwb5+Pjozp07KlKkiNHrdMCAAXJ1dU12Gbm5PZxfFCxYUDNmzFDHjh21atUq+fj4KCgoSM7OzmrQoIHR1stKdnZ2GjVqlAYMGKBVq1bp0KFDunLlioKDg1WgQAE5OzvriSeeUL169dSyZUtjSN8Erq6u2rZtm/bu3aujR4/q7NmzunHjhu7duyc7OzuVLFlSNWvWVMeOHfXcc8/J1paIArkDbaXs8/LLL6t58+ZaunSp9u7dqxs3bshkMql06dJq2rSp+vbtm6XtCwC5u51J7kruao7cFRK5a0Yjd02McwmQu5K7IuuRu5K7IvegrZR9cmruamNKuHc6gFwrNDRUTZo0UVxcnNq3b59sTyMAwKP78ssvtWDBAknSmjVrVKNGjWyuEZCxBg4caNxR6OzZs9lcGyB/+Oyzz7R69WpJ0vbt21W+fPlsrhEAAADMkbsCQOYjd0VeR+4KZD1yVwDIG/Lu2DFAPrJkyRLFxcVJkpo0aZLNtQGAvCcsLMwYtq1EiRJ66qmnsrlGAAAAAAAgs5G7AkDmIncFAAAAkBwubAVyOE9PT8XGxiY7f+vWrZozZ44kyd7eXj169MiimgFA3nDt2jX5+/snOz80NFQffvihAgMDJUkvvvgiQ3YAAAAAAJDLkbsCQOYidwUAAACQHpwdADncxIkTFRwcrNatW6tmzZoqUaKE4uLidP36de3evVuenp5G2VGjRqlYsWLZWFsAyH28vb318ccfq2HDhnJ3d1elSpX02GOPKTQ0VKdOndLGjRsVGhoqSapUqZLeeeedbK4xAAAAAABIL3JXAMhc5K4AAAAA0oMLW4FcICAgQKtWrdKqVauSnG9nZ6cRI0aoX79+WVwzAMgbYmNj9c8//+iff/5JtkzNmjU1b9482dvbZ2HNAAAAAABAZiF3BYDMRe4KAAAA4FFxYSuQw02bNk2bN2/WsWPHdOvWLQUFBSkyMlKOjo6qWLGimjZtqn79+snV1TW7qwoAuVLLli01ZcoU7d+/X+fPn1dQUJBCQkJUsGBBlShRQnXq1FGnTp3UuXNnFShQILurCwAAAAAAMgC5KwBkLnJXAAAAAOlhYzKZTNldCQAAAAAAAAAAAAAAAAAAAIDubwAAAAAAAAAAAAAAAAAAAMgRuLAVAAAAAAAAAAAAAAAAAAAAOQIXtgIAAAAAAAAAAAAAAAAAACBH4MJWAFYNHDhQ1atXV/Xq1bO7KgAAAAAAAACQJ5C7AgAAAAAAJM82uysAZLbQ0FD99ttvkqQaNWqoQ4cO2Vyj7LVt2zb5+PhIkl599VUVLVo0m2sEAAAAAAAAILchd7VE7goAAAAAAJBxuLAVeV5oaKjmzp0rSerZsycB67ZtWr16taT4z4OAFQAAAAAAAEBakbtaIncFAAAAAADIOAWyuwIAAAAAAAAAAAAAAAAAAACAxIWtAAAAAAAAAAAAAAAAAAAAyCG4sBUAAAAAAAAAAAAAAAAAAAA5go3JZDJldyWAzODn56f27dunquyiRYvUpEkTi+cuX76sJUuW6MCBA7px44bu37+v4sWLq2bNmurYsaNeeOEFFSxYMMVlBwYGauHChdqxY4f8/f1la2srV1dXdezYUa+88oqKFSumgQMH6vDhw5Kks2fPWl1eQECAli1bpr///ltXr15VSEiIHBwcVLlyZbVu3dpY5sM+++wzrV69OsX6Nm7cWIsXLzYeJ1W3rVu3asWKFfLx8VFQUJCcnZ3VoEEDDRo0SO7u7imuQ5LCwsK0YsUK7dmzRxcuXFBwcLDs7e1Vvnx5tWjRQgMGDFCZMmWSfb35+9m+fbvKly+vbdu2ac2aNfL29lZAQICio6ONeQAAAAAAAADSj9zVErkrAAAAAABAxrPN7goAOdG8efP03XffKSYmxuL5W7du6datW9q5c6d+/fVXzZs3TxUrVkx2OZ6enho6dKiCg4Mtng8NDZWPj49Wrlyp+fPnp7peixYt0jfffKPIyEiL54ODg3X06FEdPXpUCxcu1IwZM9SqVatULze1oqKi9Omnn2rz5s0WzwcEBGjLli3asmWLPv30U73xxhtWl/PXX39p4sSJiT6XkJAQhYSEyNvbW7/99psmTZqknj17pliv6OhoffDBB4nqBQAAAAAAACDnIHdNGrkrAAAAAACAJe7YijwrMjJSf//9t+7evavx48dLkpo0aaJBgwYlKtuwYUOVKFFCkvTtt9/q+++/lyTZ2NioY8eOatmypRwcHHTp0iV5eHjI399fklSyZEl5eHgk2cPd19dXffr0UUREhCSpatWq6tGjh8qXL6/g4GBt375d+/btU6VKleTo6Chvb29Jyd85YObMmUYYa29vr06dOql+/fpydnZWSEiIDhw4oC1btig2Nla2trb67bff5ObmZrze29tbN27c0KJFi3To0CFJ0uTJk+Xi4mKxHmdnZ4vXmd85oGvXrlq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      "text/plain": [
       "<Figure size 2800x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "n_glac = len(pd_spec_mb_gcm_vs_w5e5.rgi_id.unique())\n",
    "sns.set(font_scale=2)\n",
    "sns.set_style('white')\n",
    "marker = 'o'\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, sharex=True, #sharey=True,\n",
    "                            figsize=(28,16)) # 30, 15\n",
    "\n",
    "plt.suptitle(f'avg. specific MB (2000-2019) comparison between GCMs and W5E5 (MB model parameters calibrated on W5E5):\\n for {len(gcms)} ISIMIP3b GCMs, {len(ssps)} SSPs, {n_glac} Alpine glaciers',\n",
    "             fontsize=34)\n",
    "\n",
    "order_bc = ['No: only ISIMIP bc\\n(yr 1979-2014)', 'Yes: yr 1979-2014', 'Yes: yr 2000-2019']\n",
    "\n",
    "param = 'difference_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "axs[0].axvspan(-0.5, 0.5, alpha = 0.3, color='grey')\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5_all, #hue='gcm',\n",
    "              hue='apply_bias_correction',\n",
    "              hue_order = order_bc,\n",
    "              x='gcm_sh',\n",
    "              order = np.concatenate([['all gcms\\ntogether'], gcms_sh]),\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7},\n",
    "              ax = axs[0]\n",
    "             )\n",
    "axs[0].grid()\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel(param)\n",
    "axs[0].axhline(0, ls='--', lw=4, color='grey')\n",
    "g.set_ylim([-550,550])\n",
    "g.legend(loc='upper left', title = 'apply OGGM\\nbias correction')\n",
    "axs[0].set_xlabel('gcm')\n",
    "axs[0].text(-0.4, qs.loc[(False,0.05)]*1.05, '5%', color = 'grey')\n",
    "axs[0].text(-0.4, qs.loc[(False,0.95)], '95%', color = 'grey')\n",
    "\n",
    "\n",
    "ax = axs[1]\n",
    "param = 'ratio_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "ax.axvspan(-0.5, 0.5, alpha = 0.3, color='grey')\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5_all, #hue='gcm',\n",
    "              hue='apply_bias_correction',\n",
    "              hue_order = order_bc,\n",
    "              x='gcm_sh',\n",
    "              order= np.concatenate([['all gcms\\ntogether'], gcms_sh]),\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7},\n",
    "              ax = ax )\n",
    "g.legend(loc='upper left', title = 'apply OGGM\\nbias correction')\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "from matplotlib.ticker import ScalarFormatter, NullFormatter\n",
    "ax.yaxis.set_major_formatter(ScalarFormatter())\n",
    "ax.yaxis.set_minor_formatter(NullFormatter())\n",
    "ax.axhline(1, ls='--', lw=4, color='grey')\n",
    "ax.set_yticklabels([])\n",
    "ax.set_xlabel('gcm')\n",
    "# ERROR\n",
    "#  ax.set_xticklabels([0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0], labels=[0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0])\n",
    "ax.set_yticks(ticks=[0.05,0.1,0.2,0.25,0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0,4,5,10,20],\n",
    "              labels=[0.05, 0.1,0.2,0.25,0.33, 0.5, 0.67, 1,1.5, 2.0, 3.0,4,5,10,20])\n",
    "ax.set_ylim([0.33,3])\n",
    "ax.grid() #axis='y')\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel(param)\n",
    "\n",
    "plt.tight_layout()\n",
    "#g.set_xlabel('surface type distinction')\n",
    "#g.set_xticklabels(['no', 'yes\\n(neg_exp)' , 'yes\\n(linear)'])\n",
    "#g.set_title(calib_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fdb5cdc6-8488-444d-8942-3bcf4daf7819",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1875130/1532525709.py:1: FutureWarning: Dropping invalid columns in DataFrameGroupBy.quantile is deprecated. In a future version, a TypeError will be raised. Before calling .quantile, select only columns which should be valid for the function.\n",
      "  qs = pd_spec_mb_gcm_vs_w5e5.groupby(by='apply_bias_correction').quantile([0.05, 0.25,0.50,0.75, 0.95])['difference_gcm_vs_w5e5']\n"
     ]
    }
   ],
   "source": [
    "qs = pd_spec_mb_gcm_vs_w5e5.groupby(by='apply_bias_correction').quantile([0.05, 0.25,0.50,0.75, 0.95])['difference_gcm_vs_w5e5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "708dfa9a-9eb5-431a-a572-271d190ed570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, -501.31565140926125, '5%')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "ValueError",
     "evalue": "Image size of 601x185694 pixels is too large. It must be less than 2^16 in each direction.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/IPython/core/formatters.py:339\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 339\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    341\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/IPython/core/pylabtools.py:151\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    149\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 151\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    152\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backend_bases.py:2319\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2315\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2316\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m   2317\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m   2318\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2319\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2320\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2321\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2322\u001b[0m \u001b[43m            \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2323\u001b[0m \u001b[43m            \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2324\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2325\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2326\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   2327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backend_bases.py:1648\u001b[0m, in \u001b[0;36m_check_savefig_extra_args.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1640\u001b[0m     _api\u001b[38;5;241m.\u001b[39mwarn_deprecated(\n\u001b[1;32m   1641\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3.3\u001b[39m\u001b[38;5;124m'\u001b[39m, name\u001b[38;5;241m=\u001b[39mname, removal\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3.6\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   1642\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m() got unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1643\u001b[0m                 \u001b[38;5;241m+\u001b[39m arg \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m which is no longer supported as of \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1644\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m and will become an error \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1645\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   1646\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mpop(arg)\n\u001b[0;32m-> 1648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:415\u001b[0m, in \u001b[0;36mdelete_parameter.<locals>.wrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    405\u001b[0m     deprecation_addendum \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    406\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf any parameter follows \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, they should be passed as \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    407\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeyword, not positionally.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    408\u001b[0m     warn_deprecated(\n\u001b[1;32m    409\u001b[0m         since,\n\u001b[1;32m    410\u001b[0m         name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrepr\u001b[39m(name),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    413\u001b[0m                  \u001b[38;5;28;01melse\u001b[39;00m deprecation_addendum,\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 415\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minner_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minner_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:540\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[38;5;129m@_check_savefig_extra_args\u001b[39m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;129m@_api\u001b[39m\u001b[38;5;241m.\u001b[39mdelete_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m3.5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39margs,\n\u001b[1;32m    493\u001b[0m               metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m    496\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 540\u001b[0m     \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    541\u001b[0m     mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[1;32m    542\u001b[0m         filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m\"\u001b[39m, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    543\u001b[0m         dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:431\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;66;03m# docstring inherited\u001b[39;00m\n\u001b[0;32m--> 431\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrenderer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_renderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcleared\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m     \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m    433\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m RendererAgg\u001b[38;5;241m.\u001b[39mlock, \\\n\u001b[1;32m    434\u001b[0m          (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m    435\u001b[0m           \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:447\u001b[0m, in \u001b[0;36mFigureCanvasAgg.get_renderer\u001b[0;34m(self, cleared)\u001b[0m\n\u001b[1;32m    444\u001b[0m reuse_renderer \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrenderer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    445\u001b[0m                   \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_lastKey\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m key)\n\u001b[1;32m    446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m reuse_renderer:\n\u001b[0;32m--> 447\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrenderer \u001b[38;5;241m=\u001b[39m \u001b[43mRendererAgg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    448\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lastKey \u001b[38;5;241m=\u001b[39m key\n\u001b[1;32m    449\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cleared:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:93\u001b[0m, in \u001b[0;36mRendererAgg.__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth \u001b[38;5;241m=\u001b[39m width\n\u001b[1;32m     92\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheight \u001b[38;5;241m=\u001b[39m height\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_renderer \u001b[38;5;241m=\u001b[39m \u001b[43m_RendererAgg\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filter_renderers \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_methods()\n",
      "\u001b[0;31mValueError\u001b[0m: Image size of 601x185694 pixels is too large. It must be less than 2^16 in each direction."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cd35169-1569-4155-973d-228de4bfb4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "n_glac = len(pd_spec_mb_gcm_vs_w5e5.rgi_id.unique())\n",
    "sns.set(font_scale=1.8)\n",
    "sns.set_style('white')\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, sharex=True, #sharey=True,\n",
    "                            figsize=(20,18)) # 30, 15\n",
    "\n",
    "param = 'ratio_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "marker = 'o'\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5, #hue='gcm',\n",
    "              x='apply_bias_correction',\n",
    "              order=[False, True],\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7}\n",
    "             )\n",
    "plt.grid()\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel('avg. specific MB (2000-2019) relative difference:\\n'+r'100 * $\\frac{specMB_{GCM} - specMB_{W5E5}}{specMB_{W5E5}}$')\n",
    "plt.title('for these options: {len(gcms)} gcms, {len(ssps)} ssps, {n_glac} Alpine glaciers')\n",
    "\n",
    "g.set_ylim([0.3,3])\n",
    "g.set_yscale('log')\n",
    "\n",
    "#g.set_xlabel('surface type distinction')\n",
    "#g.set_xticklabels(['no', 'yes\\n(neg_exp)' , 'yes\\n(linear)'])\n",
    "#g.set_title(calib_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49f9f526-11d9-4a50-95b4-5b7de4d4a7e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b496244-25c3-4c27-bc11-a383b084eb04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'for these options: {len(gcms)} gcms, {len(ssps)} ssps, {n_glac} Alpine glaciers')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "n_glac = len(pd_spec_mb_gcm_vs_w5e5.rgi_id.unique())\n",
    "sns.set(font_scale=1.8)\n",
    "sns.set_style('white')\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, sharex=True, #sharey=True,\n",
    "                            figsize=(20,18)) # 30, 15\n",
    "\n",
    "param = 'rel_perc_difference_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "marker = 'o'\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5, #hue='gcm',\n",
    "              x='apply_bias_correction',\n",
    "              order=[False, True],\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7}\n",
    "             )\n",
    "plt.grid()\n",
    "plt.axhline(0, ls='--', lw=4, color='grey')\n",
    "plt.ylabel('avg. specific MB (2000-2019) relative difference:\\n'+r'100 * $\\frac{specMB_{GCM} - specMB_{W5E5}}{specMB_{W5E5}}$')\n",
    "plt.title('for these options: {len(gcms)} gcms, {len(ssps)} ssps, {n_glac} Alpine glaciers')\n",
    "\n",
    "g.set_ylim([-100,100])\n",
    "\n",
    "#g.set_xlabel('surface type distinction')\n",
    "#g.set_xticklabels(['no', 'yes\\n(neg_exp)' , 'yes\\n(linear)'])\n",
    "#g.set_title(calib_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7343e929-5839-462f-86cf-e5ae4f5e9134",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d7bf9287-a840-44d3-94cb-1d6f267a6502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-700.0, 700.0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3vuc2Njb66KOPNH78eBkMBv3666/69ddfVbp0aTVu3FhNmzZV27ZtVb9+/Tznl5P0hc/HHnssz9fHx8fr9u3bku4uX5gTV1dXlSpVSjdv3szxeed1v6Cc+qeftfT666/naey4uDjTcfpnVqNGjTyNU1DSz9bLzfuQvk92M/3Kli2b7TjpC7u5/TMPAAAADw4KTwAAAMADbtOmTZo1a5bpdbNmzdS8eXO5u7vL2dnZ9CVtVFSUZsyYIenuHjvmpF+CzZwSJUqYjtP2nMlN3+z6xMXF6datWzn2zYq9vb369u2rxYsXKygoSMeOHTPtD3P06FEFBQVJursXUKlSpfIVI7dy8/zSL82W0/PLyoQJE0x7FTk7O6t9+/Z6/PHHVaFCBZUoUcI0Y2nLli2m5dmymgnVvn17rV27VnPnztWff/4pg8GguLg47dmzR3v27NGcOXNUu3ZtTZkyRU899VSe8zQnfREsL8vapUn/OcnN5ystzs2bN3P8jJlbFjK//bMr8uYkbYm+e8fJzzMrCHl9H5ydnbO89l4FPeMOAAAARYvCEwAAAPCA++abbyRJtra2+u6779SuXbss++V2H5/ExMQc+6TNNpFy/hL89u3bOS4LljZe+i+m82rQoEH6+eefZTQatWbNGlPhac2aNaY+Bb3MnpS755e+2JTXIsLff/9tKjrVqVNHP//8c4Yl9NJLv8ybOXXq1NH8+fMVHx+vgIAAHTlyRP7+/jpy5IgMBoOCgoI0evRoff7553ruuefylKs56T8P+Sm8pf+cpP8sZictzv18xvIj7f21srLSyZMn811Uud9nVhDy+j6kLzYV9vsAAACABwe/ZgQAAAA8wEJDQ01LeXXs2NFs0UmSLl++nKsxY2Jictw3JW2fFkmqWLFitn0vXbqUbfuNGzdMS4rlNFZ2qlWrZtov6rffftOtW7cUHx+vrVu3Srq7p9ETTzyR7/FzK/2zMSf9M8nrPacVnSRp0qRJZotOUu7fc+luYeOpp57SxIkT5e3trb1792rEiBGS7u6pNHv2bLP7R+VVpUqVTMcXLlzI8/UlS5Y0zbDJzfOOjo7WzZs3Jd3fZyw/0pbiMxqNunbtWr7HSf/Mzp8/f995WUKFChVMx7l5H/Ly5wYAAACKLwpPAAAAwAPs+vXrpuOc9lLau3dvrsc9ePBgtu1+fn6m4wYNGlhsrPvdT2jQoEGS7s4I2bJli7Zs2WKaHVIYs50kKSAgIMMSaVnJy/O7V1RUlOm4SpUqZvslJydniJNXZcuW1bRp00zvSVRUlC5evJjv8dJr2rSp6Xjnzp15vt7Kysr03KKjo3Xq1Kls+6ffC6thw4Z5jnc/mjdvnmUeeeXp6Wma9eTn55fvJfysrKxMx0ajMd/5SBmf5V9//ZVj//RF08J+HwAAAPDgoPAEAAAAPMDS76uS3cyiK1euaN26dbked8mSJWbbkpOTtXLlStPrTp06ZTvWypUrlZycnKtYXbp0yXWOWenUqZNpFsaaNWtMy+zZ29urV69e9zV2bsXExGj9+vVm2/ft22da9rBJkyYZZo3kRvo9pEJDQ832++WXX3Tjxo08jZ0Vd3d307GlZjzVr19fjz32mCTp0KFD+SrIpP+s/PTTT2b7paSkaPHixabXXbt2zXOs+9GjRw/Z2dlJuptnfpfJs7GxUY8ePSTdXbJu4cKF+RrnfvcXSy/95/fPP//UuXPnzPbdtm2bqXDZtGlTubq63ldsAAAAPLwoPAEAAAAPsMcee8z0RbKvr6+OHz+eqc/169f12muvZdhfJScBAQH66quvMs2IMBgMevfdd03L+7Vv395UQDAnNDRU06dP1507dzKcNxqN+uqrr3TkyBFJd2d0tGnTJtc5ZsXOzk79+vWTJAUGBurEiROS7hYpXFxc7mvsvPjss8+yfC8uXbqkd955x/T6pZdeyvPY6WdIzZ8/P8uinq+vr7788stsx9m7d6+WLl1qWoIuKyEhIaZZKk5OTjnOqsstKysrTZgwwfT6jTfeyHZ2VkhISKY9yvr06WMqXvzvf//TsmXLMl2XkpKijz76yDQjqkGDBmrVqpUlbiHXKleurKFDh0qSLl68qDFjxigyMtJs/9TUVO3fv1/fffddprZRo0aZft5//PFHLV261Oyspbi4OB06dCjTeQ8PD9PxyZMn83Qv97K3t9fw4cMlSXfu3NHEiRMVERGRqd/p06c1Y8YM0+vRo0ffV1wAAAA83GyLOgEAAAAA5tnb22vQoEH6+eefZTAYNGTIEPXr108NGjSQra2tTp48qXXr1ikuLk69e/fWhg0bchyzYsWKcnNz0w8//KBDhw6pR48ecnV11eXLl7VhwwZTAaBMmTIZvkw2p1OnTtq4caNOnTql3r17q3LlyoqKitKWLVtMRSd7e3t98sknGZYBy68BAwZo4cKFSk1NNZ1LW4KvMLRr10779+/X4MGD1bt3bzVr1kzW1tYKDAzU2rVrTbNMunbtmq/ZN507d5abm5uuXbum48ePq3v37urfv7+qVKmiuLg4/fnnn9q1a5ecnJzUtWtX/fHHH1mOExkZqU8//VRffPGFWrZsqUaNGqlKlSpydHTUjRs3FBgYqN9//92U7/DhwzPMtrpf3bp1k7+/v5YvX67Y2FgNGzZMXl5eatu2rSpWrCiDwaDQ0FD5+fnJ399fn3zyiWrVqmW6vmTJkpo1a5ZeffVVpaSk6JNPPtH27dv1zDPPqGzZsrpy5Yo2btyoM2fOSJKcnZ312WefWSz/vHjjjTd06tQpHThwQH5+furUqZO6dOmixo0bq1y5cjIYDLp+/bpOnz6t/fv3KzIyUq1atdLYsWMzjFOlShV98sknevPNN5WamqpPP/1Uv/76q7p27apHH31U1tbWunbtmo4ePao9e/bomWeeUYsWLTKM0apVK3l7e0uS3n33XQ0fPlzu7u6ytr77e6dVq1ZV1apVc31vI0eO1K5du3T48GGdO3dOzz77rPr166e6desqJSVFAQEBWr9+valAOnDgQD399NP38TQBAADwsKPwBAAAADzgJk2apJMnT8rPz0/Jycn65Zdf9Msvv2ToM2jQII0aNSpXhSc7Ozt98803Gj16tI4cOWIqDqXn6uqqH374QY888kiO482aNUvR0dEKCAjQ559/nqnd2dlZX331VZ73OjLHw8NDbdu21Z49eyRJ1apVy/Tle0Fq0KCBevTooenTp8vHx0c+Pj6Z+rRr105ffPFFvsZ3dHTUt99+q9GjRys2NlahoaGaM2dOhj6lS5fWf//7Xx0/ftxs4SmtyGcwGLRv3z6zy91ZWVlp6NChGWYoWcp7772ncuXK6fvvv5fBYNDevXvN7kWWVhhJr127dvr22281depU3bx5U4cOHcpyls8jjzyiefPmqUaNGha/h9yws7PTwoUL9dlnn+mXX35RYmKiNm3apE2bNpm9xs3NLcvz3bt3V4kSJTRt2jTduHFDZ86cMRXX7pVVIffpp59W06ZNdfjwYYWEhOijjz7K0D5u3DiNHz8+1/dmY2OjH3/8UW+++aZ27dql2NjYDEsbps/lxRdfzDDjDwAAAP9OFJ4AAACAB5yDg4MWL16sNWvWaNOmTQoKCpLBYFCFChXUsGFD9e/fX23btjUtj5cb7u7u8vHx0fLly/X7778rNDRUycnJcnd3V6dOnTRy5MhcL11XunRpLVu2TGvWrNHmzZsVHByshIQEVapUSe3atdPLL7+sypUr5/Pus9a6dWtT4WnAgAEWHTs3evXqpTp16sjb21sHDx5URESESpQooTp16qh///7q2bPnfY3fuHFjbdy4UT/99JP27NmjK1euyNHRUZUrV1a7du00ePBgPfLII1ku95emd+/eqlGjhvbv36+jR4/qwoULioyMVFJSkpycnOTh4aGmTZuaZq8UlNdee029e/fW6tWrtW/fPoWHh+vmzZtydHSUu7u7GjVqpM6dO8vLyyvL6zt16qTt27dr5cqV+vPPPxUSEqJbt26pdOnSqlWrljp27KiBAwdadLZWftjb2+u9997TsGHDtHbtWvn5+Sk0NFRxcXGys7OTq6uratSooaZNm+rpp5+Wp6en2bHat2+vHTt2aO3atdq9e7fOnj2r2NhY2djYqGLFinr88cf19NNP65lnnsl0rY2NjX7++WctXbpUvr6+unDhguLj4+9r/y5nZ2ctWLBAe/fu1YYNG3TkyBFdv37dlE/Lli01cOBA1a9fP98xAAAAUHxYGc0tGA0AAACgWOnQoYPCw8Pl7u4uX1/f+xpr6NChppkn5mZjFKQXXnhBAQEBsrOz059//mnaCwgAAAAAULQyr2UAAAAAAA+wM2fOKCAgQNLd2TAUnQAAAADgwUHhCQAAAMBDZe7cuabjoUOHFmEmAAAAAIB7sccTAAAAgAdaSEiIQkJCFB8frx07dmj79u2S7u7z1LRp0yLODgAAAACQHoUnAAAAAA+0TZs2ad68eRnOubi46MMPP8zx2suXL+vkyZP5jv3EE0+oXLly+b7+YfTPP//oypUr+b6+U6dOFswGAAAAwMOGwhMAAACAh4K1tbXc3NzUrFkzTZw4UVWqVMnxmoMHD2ratGn5jrls2TK1bNky39c/jLy9vbV+/fp8X3/mzBkLZgMAAADgYUPhCQAAAPiX8PX1tdhY3t7eFhsrJ+PHj9f48eMLLR4AAAAAIP+sjEajsaiTAAAAAAAAAAAAwMPPuqgTAAAAAAAAAAAAQPFA4QkAAAAAAAAAAAAWQeEJAAAAAAAAAAAAFkHhCQAAAAAAAAAAABZB4QkAAAAAAAAAAAAWQeEJAAAAAAAAAAAAFkHhCQAAAAAAAAAAABZB4QkAAAAAAAAAAAAW8f8AebQRnhmfOWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2000x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.8)\n",
    "sns.set_style('white')\n",
    "\n",
    "col_pal = sns.color_palette(['green','lightgreen','blue','lightblue','red', 'orange'])\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, sharex=True, #sharey=True,\n",
    "                            figsize=(20,18)) # 30, 15\n",
    "\n",
    "param = 'difference_gcm_vs_w5e5' #''difference_gcm_vs_w5e5'\n",
    "marker = 'o'\n",
    "g=sns.boxplot(y=param, data=pd_spec_mb_gcm_vs_w5e5, hue='gcm',\n",
    "              x='apply_bias_correction',\n",
    "              order=[False, True],\n",
    "              fliersize=0, whis = [5,95], \n",
    "            linewidth=3,\n",
    "            saturation=0.7,# aspect=1.6, #order = ['var_an_cycle'],\n",
    "            capprops={'color':'grey', 'alpha':0.7},\n",
    "            whiskerprops={'color':'grey', 'alpha':0.7}\n",
    "             )\n",
    "g.set_ylim([-700,700])\n",
    "\n",
    "#g.set_xlabel('surface type distinction')\n",
    "#g.set_xticklabels(['no', 'yes\\n(neg_exp)' , 'yes\\n(linear)'])\n",
    "#g.set_title(calib_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3fd70b5-f564-4f83-ace7-858a1b8973ef",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}