{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "appointed-purchase",
   "metadata": {},
   "source": [
    "# RGI11 (Central Europe)\n",
    "\n",
    "F. Roura-Adseiras & Fabien Maussion\n",
    "\n",
    "Goal:\n",
    "- Alps: updates of the Paul 2003 dataset\n",
    "- Pytrenees: new inventory by Izagirre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "enabling-trainer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from utils import (mkdir, submission_summary, needs_size_filter, size_filter, plot_map, plot_date_hist, \n",
    "                   find_duplicates, open_zip_shapefile, correct_geoms, fix_overaps)\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sustained-riverside",
   "metadata": {},
   "source": [
    "## Files and storage paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "joined-range",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Region of interest\n",
    "reg = 11\n",
    "\n",
    "# go down from rgi7_scripts/workflow\n",
    "data_dir = '../../rgi7_data/'\n",
    "\n",
    "# Level 2 GLIMS files\n",
    "l2_dir = os.path.join(data_dir, 'l2_sel_reg_tars')\n",
    "\n",
    "# Output directories\n",
    "output_dir = mkdir(os.path.join(data_dir, 'l3_rgi7a'))\n",
    "output_dir_tar = mkdir(os.path.join(data_dir, 'l3_rgi7a_tar'))\n",
    "\n",
    "# Izaguirre file for GLIMS check \n",
    "ref_reg_file_p = os.path.join(data_dir, 'l0_support_data', 'pyrenees2000.zip') \n",
    "\n",
    "# Frank file for GLIMS check \n",
    "ref_reg_file_a = os.path.join(data_dir, 'l0_support_data', 'C3S_GI_RGI11_L5_2003.zip') \n",
    "\n",
    "# RGI v6 file for comparison later \n",
    "rgi6_reg_file = os.path.join(data_dir, 'l0_RGIv6', '11_rgi60_CentralEurope.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16f7f1fa-570e-4fd5-a21f-97a5d3dbb196",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support data\n",
    "support_dir = os.path.join(data_dir, 'l0_support_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loving-exclusive",
   "metadata": {},
   "source": [
    "### Load the GLIMS input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sporting-sociology",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read L2 files\n",
    "shp = gpd.read_file('tar://' + l2_dir + f'/RGI{reg:02d}.tar.gz/RGI{reg:02d}/RGI{reg:02d}.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d5a532-0902-416f-b6d8-307363fc7357",
   "metadata": {},
   "source": [
    "### List of submissions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d9c4ab0-c7cf-4e67-80cc-db6073ddb3c5",
   "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>N</th>\n",
       "      <th>A</th>\n",
       "      <th>analysts</th>\n",
       "      <th>submitters</th>\n",
       "      <th>release_date</th>\n",
       "      <th>geog_area</th>\n",
       "      <th>src_date_mode</th>\n",
       "      <th>src_date_min</th>\n",
       "      <th>src_date_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subm_id</th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>41</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Taschner</td>\n",
       "      <td>Ranzi</td>\n",
       "      <td>2006</td>\n",
       "      <td>Italian Alps</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Taschner</td>\n",
       "      <td>Ranzi</td>\n",
       "      <td>2006</td>\n",
       "      <td>Italian Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iacovelli</td>\n",
       "      <td>Ranzi</td>\n",
       "      <td>2006</td>\n",
       "      <td>Italian Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>132</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Rott, Schicker, Schwaizer (nee Bippus)</td>\n",
       "      <td>Rott</td>\n",
       "      <td>2008</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>155</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Schicker, Schwaizer (nee Bippus)</td>\n",
       "      <td>Rott</td>\n",
       "      <td>2008</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>1985</td>\n",
       "      <td>1985</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>896</td>\n",
       "      <td>896.8</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2009</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>10</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Vantadori</td>\n",
       "      <td>Ranzi</td>\n",
       "      <td>2012</td>\n",
       "      <td>Italian Alps</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>660</td>\n",
       "      <td>302.4</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>2013</td>\n",
       "      <td>French Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>537</td>\n",
       "      <td>343.0</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>2013</td>\n",
       "      <td>French Alps</td>\n",
       "      <td>1985</td>\n",
       "      <td>1985</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>1999</td>\n",
       "      <td>944.0</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2013</td>\n",
       "      <td>Swiss Alps</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>7572</td>\n",
       "      <td>2086.8</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2016</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>912</td>\n",
       "      <td>766.0</td>\n",
       "      <td>Wipf</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2016</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1970</td>\n",
       "      <td>1063.1</td>\n",
       "      <td>Maisch</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2016</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2010</td>\n",
       "      <td>944.2</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Swiss Alps</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>399</td>\n",
       "      <td>107.7</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2016</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>1999</td>\n",
       "      <td>1999</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2061</td>\n",
       "      <td>1285.6</td>\n",
       "      <td>Benz, Wipf</td>\n",
       "      <td>Maisch</td>\n",
       "      <td>2016</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>1973</td>\n",
       "      <td>1973</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>871</td>\n",
       "      <td>555.5</td>\n",
       "      <td>Fischer, Kuhn, Lambrecht</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>1331</td>\n",
       "      <td>474.3</td>\n",
       "      <td>Fischer, Kuhn, Lambrecht</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>1403</td>\n",
       "      <td>415.2</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>654</td>\n",
       "      <td>918.3</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>Fischer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>903</td>\n",
       "      <td>368.3</td>\n",
       "      <td>Smiraglia</td>\n",
       "      <td>Smiraglia</td>\n",
       "      <td>2017</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>581</td>\n",
       "      <td>275.3</td>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>2016</td>\n",
       "      <td>French Alps</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>541</td>\n",
       "      <td>368.8</td>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rabatel</td>\n",
       "      <td>2016</td>\n",
       "      <td>French Alps</td>\n",
       "      <td>1967</td>\n",
       "      <td>1939</td>\n",
       "      <td>1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>615</td>\n",
       "      <td>172.0</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>90</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>322</td>\n",
       "      <td>67.7</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>206</td>\n",
       "      <td>66.9</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>196</td>\n",
       "      <td>35.8</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nemec, Schwaizer (nee Bippus)</td>\n",
       "      <td>Schwaizer (nee Bippus)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Austrian Alps</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>4407</td>\n",
       "      <td>1806.2</td>\n",
       "      <td>Azzoni, Fugazza, Le Bris, Nemec, Paul, Rabatel...</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2019</td>\n",
       "      <td>Various (GlobGlacier)</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>47</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Izagirre</td>\n",
       "      <td>Izagirre</td>\n",
       "      <td>2020</td>\n",
       "      <td>Pyrenees</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>4060</td>\n",
       "      <td>2120.3</td>\n",
       "      <td>Frey, Le Bris, Paul, Rastner</td>\n",
       "      <td>Paul</td>\n",
       "      <td>2021</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>3722</td>\n",
       "      <td>2194.1</td>\n",
       "      <td>Braun, Lippl, Malz, Seehaus, Sommer, Zemp</td>\n",
       "      <td>Bajracharya</td>\n",
       "      <td>2022</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>2000</td>\n",
       "      <td>1999</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>4070</td>\n",
       "      <td>1754.5</td>\n",
       "      <td>Braun, Lippl, Malz, Seehaus, Sommer, Zemp</td>\n",
       "      <td>Bajracharya</td>\n",
       "      <td>2022</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>4041</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>Braun, Lippl, Malz, Seehaus, Sommer, Zemp</td>\n",
       "      <td>Bajracharya</td>\n",
       "      <td>2022</td>\n",
       "      <td>European Alps</td>\n",
       "      <td>2014</td>\n",
       "      <td>2013</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N       A                                           analysts  \\\n",
       "subm_id                                                                    \n",
       "1          41     7.4                                           Taschner   \n",
       "301         5     3.3                                           Taschner   \n",
       "501        45     2.3                                          Iacovelli   \n",
       "531       132    37.0             Rott, Schicker, Schwaizer (nee Bippus)   \n",
       "532       155    61.0                   Schicker, Schwaizer (nee Bippus)   \n",
       "533       896   896.8                                               Paul   \n",
       "558        10     8.5                                          Vantadori   \n",
       "568       660   302.4                                            Rabatel   \n",
       "569       537   343.0                                            Rabatel   \n",
       "579      1999   944.0                                            Fischer   \n",
       "593      7572  2086.8                                               Paul   \n",
       "594       912   766.0                                               Wipf   \n",
       "595      1970  1063.1                                             Maisch   \n",
       "596      2010   944.2                                            Fischer   \n",
       "598       399   107.7                                               Paul   \n",
       "599      2061  1285.6                                         Benz, Wipf   \n",
       "601       871   555.5                           Fischer, Kuhn, Lambrecht   \n",
       "602      1331   474.3                           Fischer, Kuhn, Lambrecht   \n",
       "603      1403   415.2                                            Fischer   \n",
       "613       654   918.3                                            Fischer   \n",
       "615       903   368.3                                          Smiraglia   \n",
       "640       581   275.3                                            Antoine   \n",
       "641       541   368.8                                            Antoine   \n",
       "663       615   172.0                      Nemec, Schwaizer (nee Bippus)   \n",
       "664        90    16.5                      Nemec, Schwaizer (nee Bippus)   \n",
       "665       322    67.7                      Nemec, Schwaizer (nee Bippus)   \n",
       "666       206    66.9                      Nemec, Schwaizer (nee Bippus)   \n",
       "667       196    35.8                      Nemec, Schwaizer (nee Bippus)   \n",
       "668         1     6.0                      Nemec, Schwaizer (nee Bippus)   \n",
       "710      4407  1806.2  Azzoni, Fugazza, Le Bris, Nemec, Paul, Rabatel...   \n",
       "715        47     4.2                                           Izagirre   \n",
       "731      4060  2120.3                       Frey, Le Bris, Paul, Rastner   \n",
       "818      3722  2194.1          Braun, Lippl, Malz, Seehaus, Sommer, Zemp   \n",
       "819      4070  1754.5          Braun, Lippl, Malz, Seehaus, Sommer, Zemp   \n",
       "820      4041  1653.0          Braun, Lippl, Malz, Seehaus, Sommer, Zemp   \n",
       "\n",
       "                     submitters  release_date              geog_area  \\\n",
       "subm_id                                                                \n",
       "1                         Ranzi          2006           Italian Alps   \n",
       "301                       Ranzi          2006           Italian Alps   \n",
       "501                       Ranzi          2006           Italian Alps   \n",
       "531                        Rott          2008          Austrian Alps   \n",
       "532                        Rott          2008          Austrian Alps   \n",
       "533                        Paul          2009          European Alps   \n",
       "558                       Ranzi          2012           Italian Alps   \n",
       "568                     Rabatel          2013            French Alps   \n",
       "569                     Rabatel          2013            French Alps   \n",
       "579                     Fischer          2013             Swiss Alps   \n",
       "593                        Paul          2016          European Alps   \n",
       "594                        Paul          2016          European Alps   \n",
       "595                        Paul          2016          European Alps   \n",
       "596                     Fischer          2016             Swiss Alps   \n",
       "598                        Paul          2016          European Alps   \n",
       "599                      Maisch          2016          European Alps   \n",
       "601                     Fischer          2016          Austrian Alps   \n",
       "602                     Fischer          2016          Austrian Alps   \n",
       "603                     Fischer          2016          Austrian Alps   \n",
       "613                     Fischer          2016          Austrian Alps   \n",
       "615                   Smiraglia          2017                  Italy   \n",
       "640                     Rabatel          2016            French Alps   \n",
       "641                     Rabatel          2016            French Alps   \n",
       "663      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "664      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "665      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "666      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "667      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "668      Schwaizer (nee Bippus)          2019          Austrian Alps   \n",
       "710                        Paul          2019  Various (GlobGlacier)   \n",
       "715                    Izagirre          2020               Pyrenees   \n",
       "731                        Paul          2021          European Alps   \n",
       "818                 Bajracharya          2022          European Alps   \n",
       "819                 Bajracharya          2022          European Alps   \n",
       "820                 Bajracharya          2022          European Alps   \n",
       "\n",
       "         src_date_mode  src_date_min  src_date_max  \n",
       "subm_id                                             \n",
       "1                 2001          2001          2001  \n",
       "301               2003          2003          2003  \n",
       "501               2003          2003          2003  \n",
       "531               2003          2003          2003  \n",
       "532               1985          1985          1985  \n",
       "533               1998          1998          1998  \n",
       "558               2007          2007          2007  \n",
       "568               2003          2003          2004  \n",
       "569               1985          1985          1988  \n",
       "579               2009          2009          2009  \n",
       "593               2003          2003          2003  \n",
       "594               1850          1850          1850  \n",
       "595               1850          1850          1850  \n",
       "596               2008          2008          2008  \n",
       "598               1999          1999          1999  \n",
       "599               1973          1973          1973  \n",
       "601               1969          1969          1969  \n",
       "602               1998          1998          1998  \n",
       "603               2012          2012          2012  \n",
       "613               1850          1850          1850  \n",
       "615               2011          2011          2011  \n",
       "640               2004          2004          2004  \n",
       "641               1967          1939          1971  \n",
       "663               2015          2015          2015  \n",
       "664               2016          2016          2016  \n",
       "665               2016          2016          2016  \n",
       "666               2016          2016          2016  \n",
       "667               2016          2016          2016  \n",
       "668               2015          2015          2015  \n",
       "710               2015          2015          2017  \n",
       "715               2000          2000          2000  \n",
       "731               2003          2003          2003  \n",
       "818               2000          1999          2001  \n",
       "819               2011          2011          2011  \n",
       "820               2014          2013          2015  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf, _ = submission_summary(shp)\n",
    "sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "218a037e-fb99-49b5-beaf-c13b814c469d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Optional: write out selection in intermediate shape files for manual GIS review\n",
    "# tmp_output_dir = mkdir(os.path.join(data_dir, 'l0_tmp_data', f'rgi{reg:02d}_inventories'))\n",
    "# tmp_output_dir_tar = mkdir(os.path.join(data_dir, 'l0_tmp_data'))\n",
    "# for subid in shp.subm_id.unique():\n",
    "#     s_loc = shp.loc[shp.subm_id == subid]\n",
    "#     s_loc.to_file(tmp_output_dir + f'/subm_{int(subid):03d}.shp')\n",
    "# print('Taring...')\n",
    "# print(subprocess.run(['tar', '-zcvf', f'{tmp_output_dir_tar}/rgi{reg:02d}_inventories.tar.gz', '-C', \n",
    "#                       os.path.join(data_dir, 'l0_tmp_data'), f'rgi{reg:02d}_inventories']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d199078-7c97-4324-8a7d-68c1e54e27be",
   "metadata": {},
   "source": [
    "## Outline selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "polish-psychology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4060\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4034"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Alps we use 'subm_id' = 731 as our main dataset\n",
    "RGI_a = shp.loc[shp['subm_id'] == 731].copy()\n",
    "\n",
    "# Sel by size\n",
    "print(len(RGI_a))\n",
    "RGI_a = size_filter(RGI_a)\n",
    "len(RGI_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3716f814-f176-4383-a953-033639ea3066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Pyrenees we use 'subm_id' = 715 as our main dataset\n",
    "RGI_p = shp.loc[shp['subm_id'] == 715].copy()\n",
    "\n",
    "# Sel by size\n",
    "print(len(RGI_p))\n",
    "RGI_p = size_filter(RGI_p)\n",
    "len(RGI_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1eb0b519-4b4a-43c7-a898-d186798a5b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine the geodataframes\n",
    "rgi7 = pd.concat([RGI_a, RGI_p])\n",
    "rgi7['is_rgi6'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f5d2ab-23dc-41e5-a555-abbc7ea3f143",
   "metadata": {},
   "source": [
    "### Some sanity checks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6f54928-2996-4571-a791-65ee6b7a1204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 65 invalid geometries out of 4079.\n",
      "After correction, 0 geometries are still invalid.\n",
      "Area changed by 41.0 m2 (0.0000%, or 0 tiny glaciers)\n"
     ]
    }
   ],
   "source": [
    "rgi7 = correct_geoms(rgi7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21428b95-bcc2-4753-8024-b87781e9028e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seems Okay!\n"
     ]
    }
   ],
   "source": [
    "dupes = find_duplicates(rgi7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "143e4c9e-ca4d-410f-b318-49b7b2e4e4ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding intersecting geometries\n",
      "Computing overlap of intersecting pairs\n",
      "Found 14 overlaps out of 4079. Correcting...\n",
      "After correction, Area changed by -35505.1 m2 (-0.0017%, or -3 tiny glaciers)\n",
      "Final check...\n",
      "Finding intersecting geometries\n",
      "Computing overlap of intersecting pairs\n",
      "OK! Check done\n"
     ]
    }
   ],
   "source": [
    "rgi7 = fix_overaps(rgi7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee919aba-a77d-40df-a914-db21ef4d2560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 invalid geometries out of 4079.\n"
     ]
    }
   ],
   "source": [
    "rgi7 = correct_geoms(rgi7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a164c1ac-3c58-46cb-a6c3-23852a4b48c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4079\n",
      "4079\n"
     ]
    }
   ],
   "source": [
    "print(len(rgi7))\n",
    "rgi7 = size_filter(rgi7)\n",
    "print(len(rgi7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98830ee7-84a4-4778-a519-746a1c89f422",
   "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>N</th>\n",
       "      <th>N NA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subm_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>4034</td>\n",
       "      <td>4034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N  N NA\n",
       "subm_id            \n",
       "731      4034  4034\n",
       "715        45    45"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf, df_class = submission_summary(rgi7)\n",
    "df_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9796cc2-97c8-460e-961b-a89f3b9a9a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the orphaned rock outcrops\n",
    "orphan_f = os.path.join(data_dir, 'l1_orphan_interiors', f'RGI{reg:02d}', f'RGI{reg:02d}.shp')\n",
    "if os.path.exists(orphan_f):\n",
    "    orphan_f = gpd.read_file(orphan_f)\n",
    "    check = np.isin(rgi7.subm_id.unique(), orphan_f.subm_id.unique())\n",
    "    if np.any(check):\n",
    "        print(f'Orphan rock outcrops detected in subm_id {rgi7.subm_id.unique()[check]}')\n",
    "        orphan_f['area'] = orphan_f.to_crs({'proj':'cea'}).area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d05c06e-f9da-42de-9944-378973a8744e",
   "metadata": {},
   "source": [
    "### Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c006307-c858-453f-b740-b0af0f72f201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_map(rgi7, reg, loc='upper left', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d12e292-3b6d-47f9-8423-db8a6f7d7dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_map(rgi7, reg, loc='upper left', linewidth=2, is_rgi6=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3ea2264-f825-4c7e-81b5-46b10da63608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_date_hist(rgi7, reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c79653-8b60-4c9d-ac39-2bdf060f31ca",
   "metadata": {},
   "source": [
    "### Text for github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1da5869e-5fb5-497f-a1e8-01ce6989a123",
   "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>subm_id</th>\n",
       "      <th>731</th>\n",
       "      <th>715</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>4034</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2120.1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analysts</th>\n",
       "      <td>Frey, Le Bris, Paul, Rastner</td>\n",
       "      <td>Izagirre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>submitters</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Izagirre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geog_area</th>\n",
       "      <td>European Alps</td>\n",
       "      <td>Pyrenees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>src_date_mode</th>\n",
       "      <td>2003</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>src_date_min</th>\n",
       "      <td>2003</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>src_date_max</th>\n",
       "      <td>2003</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subm_id                                 731       715\n",
       "N                                      4034        45\n",
       "A                                    2120.1       4.2\n",
       "analysts       Frey, Le Bris, Paul, Rastner  Izagirre\n",
       "submitters                             Paul  Izagirre\n",
       "release_date                           2021      2020\n",
       "geog_area                     European Alps  Pyrenees\n",
       "src_date_mode                          2003      2000\n",
       "src_date_min                           2003      2000\n",
       "src_date_max                           2003      2000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fgh = sdf.T\n",
    "fgh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d007533e-1453-48f7-b2c6-6a9e5421911f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| subm_id       | 731                          | 715      |\n",
      "|:--------------|:-----------------------------|:---------|\n",
      "| N             | 4034                         | 45       |\n",
      "| A             | 2120.1                       | 4.2      |\n",
      "| analysts      | Frey, Le Bris, Paul, Rastner | Izagirre |\n",
      "| submitters    | Paul                         | Izagirre |\n",
      "| release_date  | 2021                         | 2020     |\n",
      "| geog_area     | European Alps                | Pyrenees |\n",
      "| src_date_mode | 2003                         | 2000     |\n",
      "| src_date_min  | 2003                         | 2000     |\n",
      "| src_date_max  | 2003                         | 2000     |\n"
     ]
    }
   ],
   "source": [
    "print(fgh.to_markdown(headers=np.append(['subm_id'], fgh.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d30cad1-8680-4de2-aea7-52bb04f8e168",
   "metadata": {},
   "source": [
    "## Write out and tar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc081d3c-839c-4458-8750-e976e1a30b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing...\n",
      "Taring...\n",
      "RGI11/\n",
      "RGI11/RGI11.shx\n",
      "RGI11/RGI11.prj\n",
      "RGI11/RGI11.cpg\n",
      "RGI11/RGI11.dbf\n",
      "RGI11/RGI11.shp\n",
      "CompletedProcess(args=['tar', '-zcvf', '../../rgi7_data/l3_rgi7a_tar/RGI11.tar.gz', '-C', '../../rgi7_data/l3_rgi7a', 'RGI11'], returncode=0)\n"
     ]
    }
   ],
   "source": [
    "dd = mkdir(f'{output_dir}/RGI{reg:02d}/', reset=True)\n",
    "\n",
    "print('Writing...')\n",
    "rgi7.to_file(dd + f'RGI{reg:02d}.shp')\n",
    "\n",
    "print('Taring...')\n",
    "print(subprocess.run(['tar', '-zcvf', f'{output_dir_tar}/RGI{reg:02d}.tar.gz', '-C', output_dir, f'RGI{reg:02d}']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14d6543-64b5-42d5-9224-994d0849fcbd",
   "metadata": {},
   "source": [
    "## New RGI-file created - Check result!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b839e94-efb9-4a4f-9dd4-db6e06940b76",
   "metadata": {},
   "source": [
    "Load reference data (here RGI6 and the original contributions) to enable comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "537252b5-ed4d-49cd-a59c-24b6857caa4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load reference data\n",
    "from utils import open_zip_shapefile\n",
    "ref_rgi6 = open_zip_shapefile(rgi6_reg_file)\n",
    "\n",
    "# Al\n",
    "ref_a = open_zip_shapefile(os.path.join(support_dir, 'C3S_GI_RGI11_L5_2003.zip'))\n",
    "\n",
    "# Py\n",
    "ref_p = open_zip_shapefile(os.path.join(support_dir, 'pyrenees2000.zip'), exclude_pattern='__MACOSX', include_pattern='glaciers_')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba362d6-b9f9-4647-849c-024806e2b0ce",
   "metadata": {},
   "source": [
    "### Compare new RGI7-file to RGI6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d294a41b-e715-4225-8a98-5f1922b788e4",
   "metadata": {},
   "source": [
    "#### Number of elements (differences do not necessarily depict problems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c44989ca-0275-4ce6-b803-a564242be070",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Number of glaciers in new RGI:', len(rgi7))\n",
    "print('Number of glaciers in RGI6:', len(ref_rgi6))\n",
    "print('Difference:', len(rgi7)-len(ref_rgi6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3620ca-ac90-4d72-9a01-1dcc6f1e3bba",
   "metadata": {},
   "source": [
    "### How many nominal glaciers were there in RGI06?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e819451a-665f-49b8-a5c7-7ea811e9e19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ref_rgi6.loc[ref_rgi6.Status == 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9154e53-7ef7-450e-beb1-172552905bb3",
   "metadata": {},
   "source": [
    "### Total area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967bdaa5-95aa-4fb2-ab16-775e11dae494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add an area field to RGI_ss and reference data\n",
    "ref_rgi6['area'] = ref_rgi6.to_crs({'proj':'cea'}).area\n",
    "ref_p['area'] = ref_p.to_crs({'proj':'cea'}).area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db812087-6cf2-4309-978d-5e30987cc401",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print and compare area values\n",
    "Area_RGI = rgi7['area'].sum() * 1e-6\n",
    "print('Area RGI7 [km²]:', Area_RGI)\n",
    "Area_ref = ref_rgi6['area'].sum() * 1e-6\n",
    "print('Area RGI6 [km²]:', Area_ref)\n",
    "d = (Area_RGI - Area_ref)\n",
    "print('Area difference [km²]:', d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a8338f-ac8e-41fb-a531-ac701245b8fb",
   "metadata": {},
   "source": [
    "## Comparison to reference products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a4f2f9-9205-4fa4-93fe-c5c63b1bf685",
   "metadata": {},
   "source": [
    "### Pyrennees (no problem) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef9d371c-891a-4224-9b83-c2ad8c377110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add an area field to RGI_ss and reference data\n",
    "RGI_p['area'] = RGI_p.to_crs({'proj':'cea'}).area\n",
    "ref_p['area'] = ref_p.to_crs({'proj':'cea'}).area\n",
    "\n",
    "print(len(ref_p))\n",
    "ref_p = ref_p.loc[np.round(ref_p['area'] * 1e-6, 3) >= 0.01].copy()\n",
    "len(ref_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3cb4dfe-04dc-496f-aedc-614096e4e00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Number of glaciers in new RGI subset:', len(RGI_p))\n",
    "print('Number of glaciers in reference data (izaguirre):', len(ref_p))\n",
    "print('Difference:', len(RGI_p)-len(ref_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cfe350f-a62d-4ecd-a99f-6c45a4fcfcaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print and compare area values\n",
    "Area_rgi = RGI_p['area'].sum()/1000000\n",
    "print('Area RGI [km²]:', Area_rgi)\n",
    "Area_ref = ref_p['area'].sum()/1000000\n",
    "print('Area ref:', Area_ref)\n",
    "d = (Area_rgi - Area_ref)\n",
    "d_perc = (d/Area_rgi*100)\n",
    "print('Area difference [km²]:',d,'/','percentage:', d_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0497830-6dfa-46d0-be54-21d6db79fd19",
   "metadata": {},
   "source": [
    "### Alps (no problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19a5fd85-45fe-4b13-8149-3d888544da7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add an area field to RGI_ss and reference data\n",
    "RGI_a['area'] = RGI_a.to_crs({'proj':'cea'}).area\n",
    "ref_a['area'] = ref_a.to_crs({'proj':'cea'}).area\n",
    "\n",
    "print(len(ref_a))\n",
    "ref_a = ref_a.loc[np.round(ref_a['area'] * 1e-6, 3) >= 0.01].copy()\n",
    "len(ref_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51f06ade-1db6-4a96-a0b9-a8c4b731d2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Number of glaciers in new RGI subset:', len(RGI_a))\n",
    "print('Number of glaciers in reference data (Franck):', len(ref_a))\n",
    "print('Difference:', len(RGI_a)-len(ref_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f8923f-017e-4581-9ed2-1cf3b9062706",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print and compare area values\n",
    "Area_rgi = RGI_a['area'].sum()/1000000\n",
    "print('Area RGI [km²]:', Area_rgi)\n",
    "Area_ref = ref_a['area'].sum()/1000000\n",
    "print('Area ref:', Area_ref)\n",
    "d = (Area_rgi - Area_ref)\n",
    "d_perc = (d/Area_rgi*100)\n",
    "print('Area difference [km²]:',d,'/','percentage:', d_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8332e5-4377-4eab-8b96-00ec4d748f42",
   "metadata": {},
   "source": [
    "For Alps, no substantial differences between the original Frank and glims inventories, except for a glacier, that we want to find now:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921f801f-18b2-47ff-85ee-67a9240acfe2",
   "metadata": {},
   "source": [
    "### Find the missing glacier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b64802f2-b576-4289-a9ef-3baabba58a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref = ref_a.copy()\n",
    "rgi7 = RGI_a.copy()\n",
    "df_ref = df_ref.to_crs(rgi7.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e6ff19a-b998-4ee4-b474-d668e6e650cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import progressbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4122c984-86f9-4f6c-b941-b887813030a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def xy_coord(geom):\n",
    "    \"\"\"To compute CenLon CenLat ourselves\"\"\"\n",
    "    x, y = geom.xy\n",
    "    return x[0], y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67565232-a5b6-400d-8148-80a586e4ab3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute CenLon CenLat ourselves\n",
    "rp = df_ref.representative_point()\n",
    "\n",
    "coordinates = np.array(list(rp.apply(xy_coord)))\n",
    "df_ref['CenLon'] = coordinates[:, 0]\n",
    "df_ref['CenLat'] = coordinates[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8eadf18-1d70-46d3-af13-3749697657e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref_orig = df_ref.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f7e7124-25a0-4546-a8ee-1d99fb9ace95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop over all RGI7 glaciers and find their equivalent in ref\n",
    "df_ref = df_ref_orig.copy()\n",
    "not_found = {}\n",
    "to_drop = []\n",
    "for i, (ref_area, lon, lat) in progressbar.progressbar(enumerate(zip(rgi7['area'].values, rgi7.CenLon.values, rgi7.CenLat.values)), max_value=len(rgi7)):\n",
    "#     dist = haversine(lon, lat, df_ref.CenLon.values, df_ref.CenLat.values)\n",
    "    dist = (lon - df_ref.CenLon.values)**2 + (lat - df_ref.CenLat.values)**2 \n",
    "    found = False\n",
    "    for j in np.argsort(dist)[:10]:\n",
    "        s6 = df_ref.iloc[j]\n",
    "        if np.allclose(s6['area'], ref_area, rtol=0.01):\n",
    "            found = True\n",
    "            to_drop.append(s6.name)\n",
    "            break\n",
    "    if not found:\n",
    "        not_found[i] = df_ref.iloc[np.argsort(dist)[:10]]\n",
    "    if len(to_drop) > 1000:\n",
    "        df_ref.drop(labels=to_drop, inplace=True)\n",
    "        to_drop = []\n",
    "df_ref.drop(labels=to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff20c876-a302-4f2a-a5a7-6b0fec1ccc5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(not_found), len(df_ref))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ba0099-f07b-495d-a2d0-b2714c5b9884",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref.plot(edgecolor='k', column='area');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c035f6a-600c-41a7-9a17-bbc473425fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_rgi7 = rgi7.iloc[list(not_found.keys())]\n",
    "pb_rgi7.plot(edgecolor='k', column='area');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99b8885-258f-49f7-a2ac-bef43398380d",
   "metadata": {},
   "source": [
    "**Conclusion: there is no problem in GLIMS!!!**"
   ]
  }
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