{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GlaThiDa to RGI, step 3: check results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assume the HDF file is ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'glathida-v3.1.0/data/TTT_per_rgi_id.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RGI60-01.00570' 'RGI60-01.08989' 'RGI60-01.10006' ... 'RGI60-19.02146'\n",
      " 'RGI60-19.02147' 'RGI60-19.02150']\n"
     ]
    }
   ],
   "source": [
    "with pd.HDFStore(file) as store:\n",
    "    rgi_ids = list(store.keys())\n",
    "rgi_ids = np.array([s[1:] for s in rgi_ids])\n",
    "print(rgi_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index=rgi_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('rgi_ids_with_ttt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_hdf(file, key='RGI60-01.08989')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>GlaThiDa_ID</th>\n",
       "      <th>POLITICAL_UNIT</th>\n",
       "      <th>GLACIER_NAME</th>\n",
       "      <th>SURVEY_DATE</th>\n",
       "      <th>PROFILE_ID</th>\n",
       "      <th>POINT_ID</th>\n",
       "      <th>POINT_LAT</th>\n",
       "      <th>POINT_LON</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>THICKNESS</th>\n",
       "      <th>THICKNESS_UNCERTAINTY</th>\n",
       "      <th>DATA_FLAG</th>\n",
       "      <th>REMARKS</th>\n",
       "      <th>RGI_REG</th>\n",
       "      <th>RGI_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3759818</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>61.196465</td>\n",
       "      <td>-148.972931</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>161</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759819</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>301</td>\n",
       "      <td>61.196686</td>\n",
       "      <td>-148.972885</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>160</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759820</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>302</td>\n",
       "      <td>61.196907</td>\n",
       "      <td>-148.972855</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>159</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759821</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>303</td>\n",
       "      <td>61.197132</td>\n",
       "      <td>-148.972824</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>158</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759822</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>304</td>\n",
       "      <td>61.197353</td>\n",
       "      <td>-148.972778</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>158</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760402</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>2048</td>\n",
       "      <td>61.231388</td>\n",
       "      <td>-149.005554</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>192</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Medium confidence</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760403</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>2049</td>\n",
       "      <td>61.231598</td>\n",
       "      <td>-149.005386</td>\n",
       "      <td>1242.0</td>\n",
       "      <td>190</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Medium confidence</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760404</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>2050</td>\n",
       "      <td>61.231804</td>\n",
       "      <td>-149.005203</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>188</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760405</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>2051</td>\n",
       "      <td>61.232010</td>\n",
       "      <td>-149.005020</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>186</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760406</th>\n",
       "      <td>6577</td>\n",
       "      <td>US</td>\n",
       "      <td>EKLUTNA GLACIER</td>\n",
       "      <td>20120316</td>\n",
       "      <td>16</td>\n",
       "      <td>2052</td>\n",
       "      <td>61.232216</td>\n",
       "      <td>-149.004837</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>184</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RGI60-01.08989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>589 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GlaThiDa_ID POLITICAL_UNIT     GLACIER_NAME SURVEY_DATE PROFILE_ID  \\\n",
       "3759818         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3759819         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3759820         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3759821         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3759822         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "...              ...            ...              ...         ...        ...   \n",
       "3760402         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3760403         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3760404         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3760405         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "3760406         6577             US  EKLUTNA GLACIER    20120316         16   \n",
       "\n",
       "        POINT_ID  POINT_LAT   POINT_LON ELEVATION  THICKNESS  \\\n",
       "3759818      300  61.196465 -148.972931    1471.0        161   \n",
       "3759819      301  61.196686 -148.972885    1470.0        160   \n",
       "3759820      302  61.196907 -148.972855    1470.0        159   \n",
       "3759821      303  61.197132 -148.972824    1470.0        158   \n",
       "3759822      304  61.197353 -148.972778    1470.0        158   \n",
       "...          ...        ...         ...       ...        ...   \n",
       "3760402     2048  61.231388 -149.005554    1244.0        192   \n",
       "3760403     2049  61.231598 -149.005386    1242.0        190   \n",
       "3760404     2050  61.231804 -149.005203    1240.0        188   \n",
       "3760405     2051  61.232010 -149.005020    1239.0        186   \n",
       "3760406     2052  61.232216 -149.004837    1236.0        184   \n",
       "\n",
       "         THICKNESS_UNCERTAINTY  DATA_FLAG            REMARKS RGI_REG  \\\n",
       "3759818                   35.0        NaN                NaN       1   \n",
       "3759819                   35.0        NaN                NaN       1   \n",
       "3759820                   35.0        NaN                NaN       1   \n",
       "3759821                   35.0        NaN                NaN       1   \n",
       "3759822                   35.0        NaN                NaN       1   \n",
       "...                        ...        ...                ...     ...   \n",
       "3760402                   35.0        NaN  Medium confidence       1   \n",
       "3760403                   35.0        NaN  Medium confidence       1   \n",
       "3760404                   35.0        NaN                NaN       1   \n",
       "3760405                   35.0        NaN                NaN       1   \n",
       "3760406                   35.0        NaN                NaN       1   \n",
       "\n",
       "                 RGI_ID  \n",
       "3759818  RGI60-01.08989  \n",
       "3759819  RGI60-01.08989  \n",
       "3759820  RGI60-01.08989  \n",
       "3759821  RGI60-01.08989  \n",
       "3759822  RGI60-01.08989  \n",
       "...                 ...  \n",
       "3760402  RGI60-01.08989  \n",
       "3760403  RGI60-01.08989  \n",
       "3760404  RGI60-01.08989  \n",
       "3760405  RGI60-01.08989  \n",
       "3760406  RGI60-01.08989  \n",
       "\n",
       "[589 rows x 15 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='POINT_LAT', x='POINT_LON', c='THICKNESS', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.10.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}