{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-30 15:56:21: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
      "2021-11-30 15:56:21: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
      "2021-11-30 15:56:21: oggm.cfg: Multiprocessing: using all available processors (N=32)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from oggm import utils, cfg\n",
    "cfg.initialize()\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (14, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = '2000-01-01_2020-01-01'\n",
    "\n",
    "dfz = pd.read_csv(utils.get_demo_file('zemp_ref_2006_2016.csv'), index_col=0)\n",
    "dfh = pd.read_csv(utils.get_demo_file('table_hugonnet_regions_10yr_20yr_ar6period.csv'), index_col=0)\n",
    "dfh = dfh.loc[dfh.period == period]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfz['SMB_ZEMP'] = dfz['SMB']\n",
    "dfz['SMB_HUGUONNET'] = dfh['dmdtda'] * 1e-3\n",
    "dfz['SMB_HUGUONNET_err'] = dfh['err_dmdtda'] * 1e-3\n",
    "dfz.index = ['{:02d}'.format(rgi_reg) for rgi_reg in dfz.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "dfz.plot(ax=ax, y='SMB_ZEMP', yerr='SMB_err', marker='o', linestyle='none');\n",
    "dfz.plot(ax=ax, y='SMB_HUGUONNET', yerr='SMB_HUGUONNET_err', marker='o', linestyle='none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfh['dmdt_zemp'] = dfz.SMB.values * 1000\n",
    "dfh['dmdt_zemp_err'] = dfz.SMB_err.values * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfh.index = ['{:02d}'.format(int(rgi_reg)) for rgi_reg in dfh.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/www/oggm/gdirs/oggm_v1.4/'\n",
    "odirs = [f'{path}/L3-L5_files/ERA5/elev_bands/qc3/pcp1.6/no_match/RGI62/b_160/L3/summary/',\n",
    "         f'{path}/L3-L5_files/ERA5/elev_bands/qc3/pcp1.6/match_geod/RGI62/b_160/L3/summary/',\n",
    "         f'{path}/L3-L5_files/ERA5/elev_bands/qc3/pcp1.6/match_geod_pergla/RGI62/b_160/L3/summary/',\n",
    "        ]\n",
    "names = ['old', 'regio', 'pergla'\n",
    "        ]         \n",
    "\n",
    "for odir, name in zip(odirs, names):\n",
    "    for rgi_reg in np.arange(1, 20):\n",
    "        rgi_reg = '{:02d}'.format(rgi_reg)\n",
    "\n",
    "        try:\n",
    "            df = pd.read_csv(odir + f'fixed_geometry_mass_balance_{rgi_reg}.csv'.format(rgi_reg), index_col=0, low_memory=False)\n",
    "            dfs = pd.read_csv(odir + f'glacier_statistics_{rgi_reg}.csv'.format(rgi_reg), index_col=0, low_memory=False)\n",
    "        except FileNotFoundError:\n",
    "            print('Not here:', odir, rgi_reg)\n",
    "            continue\n",
    "        df = df.dropna(axis=0, how='all')\n",
    "        df = df.dropna(axis=1, how='all')\n",
    "\n",
    "#         odf = pd.DataFrame(df.loc[2006:2016].mean(), columns=['SMB'])\n",
    "#         odf['AREA'] = dfs.rgi_area_km2\n",
    "\n",
    "#         dfh.loc[rgi_reg, 'AREA_OGGM'] = odf['AREA'].sum()\n",
    "#         dfh.loc[rgi_reg, 'SMB_OGGM'] = np.average(odf['SMB'], weights=odf['AREA']) / 1000\n",
    "\n",
    "        odf = pd.DataFrame(df.loc[2000:].mean(), columns=['SMB'])\n",
    "        odf['AREA'] = dfs.rgi_area_km2\n",
    "        dfh.loc[rgi_reg, f'dmdtda_OGGM_{name}'] = np.average(odf['SMB'], weights=odf['AREA'])\n",
    "        \n",
    "        \n",
    "        odir_5 = odir.replace('L3/', 'L5/')\n",
    "        \n",
    "        with xr.open_dataset(odir_5 + f'historical_run_output_extended_{rgi_reg}.nc') as ds:\n",
    "            ds = ds[['volume', 'area']].sum(dim='rgi_id')\n",
    "        vol_ts = ds.volume.to_series()\n",
    "        area_ts = ds.area.to_series()\n",
    "        dfh.loc[rgi_reg, f'dmdt_OGGM_{name}'] = (vol_ts.loc[2019] - vol_ts.loc[2000]) * cfg.PARAMS['ice_density'] * 1e-12 / 20\n",
    "        dfh.loc[rgi_reg, f'dmdtda_dyna_OGGM_{name}'] = (vol_ts.loc[2019] - vol_ts.loc[2000]) / area_ts.loc[2000] * cfg.PARAMS['ice_density'] / 20 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "dfh.plot(ax=ax, y='dmdtda_OGGM_old', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdt_zemp', yerr='dmdt_zemp_err', marker='o', linestyle='none');\n",
    "dfh.plot(ax=ax, y='dmdtda', yerr='err_dmdtda', marker='o', linestyle='none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dmdtda            -495.951579\n",
       "dmdt_zemp         -581.578947\n",
       "dmdtda_OGGM_old   -608.398797\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh[['dmdtda', 'dmdt_zemp', 'dmdtda_OGGM_old']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "dfh.plot(ax=ax, y='dmdtda_OGGM_regio', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdtda_OGGM_pergla', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdtda', yerr='err_dmdtda', marker='o', linestyle='none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "dfh.plot(ax=ax, y='dmdtda_dyna_OGGM_regio', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdtda_dyna_OGGM_pergla', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdtda', yerr='err_dmdtda', marker='o', linestyle='none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "dfh.plot(ax=ax, y='dmdt_OGGM_regio', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdt_OGGM_pergla', marker='o', linestyle='none', markersize=8);\n",
    "dfh.plot(ax=ax, y='dmdt', yerr='err_dmdt', marker='o', linestyle='none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['period', 'dmdt', 'err_dmdt', 'dmdtda', 'err_dmdtda', 'dmdt_zemp',\n",
       "       'dmdt_zemp_err', 'dmdtda_OGGM_old', 'dmdt_OGGM_old',\n",
       "       'dmdtda_dyna_OGGM_old', 'dmdtda_OGGM_regio', 'dmdt_OGGM_regio',\n",
       "       'dmdtda_dyna_OGGM_regio', 'dmdtda_OGGM_pergla', 'dmdt_OGGM_pergla',\n",
       "       'dmdtda_dyna_OGGM_pergla'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dmdtda</th>\n",
       "      <th>dmdtda_OGGM_old</th>\n",
       "      <th>dmdtda_OGGM_regio</th>\n",
       "      <th>dmdtda_OGGM_pergla</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dmdtda</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dmdtda_OGGM_old</th>\n",
       "      <td>0.334927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334927</td>\n",
       "      <td>0.423244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dmdtda_OGGM_regio</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dmdtda_OGGM_pergla</th>\n",
       "      <td>0.968575</td>\n",
       "      <td>0.423244</td>\n",
       "      <td>0.968575</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dmdtda  dmdtda_OGGM_old  dmdtda_OGGM_regio  \\\n",
       "dmdtda              1.000000         0.334927           1.000000   \n",
       "dmdtda_OGGM_old     0.334927         1.000000           0.334927   \n",
       "dmdtda_OGGM_regio   1.000000         0.334927           1.000000   \n",
       "dmdtda_OGGM_pergla  0.968575         0.423244           0.968575   \n",
       "\n",
       "                    dmdtda_OGGM_pergla  \n",
       "dmdtda                        0.968575  \n",
       "dmdtda_OGGM_old               0.423244  \n",
       "dmdtda_OGGM_regio             0.968575  \n",
       "dmdtda_OGGM_pergla            1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh[['dmdtda', 'dmdtda_OGGM_old', 'dmdtda_OGGM_regio', 'dmdtda_OGGM_pergla']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dmdt               -266.380000\n",
       "dmdt_OGGM_old      -361.180263\n",
       "dmdt_OGGM_regio    -266.220926\n",
       "dmdt_OGGM_pergla   -268.440202\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh[['dmdt', 'dmdt_OGGM_old', 'dmdt_OGGM_regio', 'dmdt_OGGM_pergla']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick check area volume "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/www/oggm/gdirs/oggm_v1.4'\n",
    "dd = f'{path}/L3-L5_files'\n",
    "\n",
    "fd = f'{dd}/ERA5/elev_bands/qc3/pcp1.6/no_match/RGI62/b_080/L5/summary/glacier_statistics_11.csv'\n",
    "df = pd.read_csv(fd, index_col=0, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd = f'{dd}/ERA5/elev_bands/qc3/pcp1.6/no_match/RGI62/b_080/L5/summary/historical_run_output_extended_11.nc'\n",
    "ds = xr.open_dataset(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.volume.sum(dim='rgi_id').plot();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check = ds.area.isel(time=0).to_dataframe()[['area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check = ds.area.isel(time=0).to_dataframe()[['area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check = check.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check.loc[~ np.isclose(check.area_rgi, check.area)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_allclose(check.area_rgi, check.area, rtol=3e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### check inversion volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd = f'{dd}/CRU/centerlines/qc3/pcp2.5/no_match/RGI62/b_080/L3/summary/'\n",
    "fs = glob.glob(fd+'glacier_statistics*.csv')\n",
    "assert len(fs) == 18 # 17 @Fabi this is now 18!!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = []\n",
    "for f in fs:\n",
    "    df.append(pd.read_csv(f, index_col=0, low_memory=False))\n",
    "df = pd.concat(df).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv = df[['inv_volume_km3']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv['ref'] = df_ref.loc[dfv.index]['vol_itmix_m3'] * 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dfv)"
   ]
  }
 ],
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