s2coverage_landcovernet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coverage Calculation\n",
    "> examples are in /src/main.py\n",
    "\n",
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = './coverage/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>num_timesteps</th>\n",
       "      <th>num_timesteps_missing</th>\n",
       "      <th>avg_spatial_coverage</th>\n",
       "      <th>num_timesteps_abovecov</th>\n",
       "      <th>temporal_coverage</th>\n",
       "      <th>assesment_temporal</th>\n",
       "      <th>assesment_spatial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</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>30SWH_00</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>61.908604</td>\n",
       "      <td>45</td>\n",
       "      <td>63.380282</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30SWH_01</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>69.344658</td>\n",
       "      <td>102</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30SWH_02</th>\n",
       "      <td>71</td>\n",
       "      <td>42</td>\n",
       "      <td>64.263013</td>\n",
       "      <td>45</td>\n",
       "      <td>63.380282</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30SWH_03</th>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>62.431808</td>\n",
       "      <td>46</td>\n",
       "      <td>64.788732</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30SWH_04</th>\n",
       "      <td>71</td>\n",
       "      <td>47</td>\n",
       "      <td>59.777488</td>\n",
       "      <td>44</td>\n",
       "      <td>61.971831</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_timesteps  num_timesteps_missing  avg_spatial_coverage  \\\n",
       "filename                                                               \n",
       "30SWH_00             71                     71             61.908604   \n",
       "30SWH_01            144                    144             69.344658   \n",
       "30SWH_02             71                     42             64.263013   \n",
       "30SWH_03             71                     50             62.431808   \n",
       "30SWH_04             71                     47             59.777488   \n",
       "\n",
       "          num_timesteps_abovecov  temporal_coverage assesment_temporal  \\\n",
       "filename                                                                 \n",
       "30SWH_00                      45          63.380282               high   \n",
       "30SWH_01                     102          70.833333               high   \n",
       "30SWH_02                      45          63.380282               high   \n",
       "30SWH_03                      46          64.788732               high   \n",
       "30SWH_04                      44          61.971831               high   \n",
       "\n",
       "         assesment_spatial  \n",
       "filename                    \n",
       "30SWH_00              high  \n",
       "30SWH_01              high  \n",
       "30SWH_02              high  \n",
       "30SWH_03              high  \n",
       "30SWH_04              high  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "import pandas as pd\n",
    "from src.visualization.plots import plot_coverage_order\n",
    "\n",
    "thres = 50\n",
    "df_stats_af = pd.read_csv(f\"{input_dir}/landcovernet_af/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "df_stats_as = pd.read_csv(f\"{input_dir}/landcovernet_as/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "df_stats_au = pd.read_csv(f\"{input_dir}/landcovernet_au/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "df_stats_eu = pd.read_csv(f\"{input_dir}/landcovernet_eu/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "df_stats_na = pd.read_csv(f\"{input_dir}/landcovernet_na/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "df_stats_sa = pd.read_csv(f\"{input_dir}/landcovernet_sa/assesment_spat_{thres}_temp_{thres}_sel_000102040506071011.csv\", index_col=0)\n",
    "\n",
    "#stack all\n",
    "df_stats_global = pd.concat([df_stats_af,df_stats_as,df_stats_au,df_stats_eu,df_stats_na,df_stats_sa])\n",
    "\n",
    "df_stats_eu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coverage_order([df_stats_af,df_stats_as,df_stats_au,df_stats_eu,df_stats_na,df_stats_sa], \n",
    "                    [\"Africa\", \"Asia\", \"Australia\", \"Europe\", \"North America\", \"South America\"], \n",
    "                    \"avg_spatial_coverage\", lower_limit=thres, lower_limit_show=True, ylabel=\"spatial coverage\")\n",
    "plt.savefig(f\"imgs/landcovernet_spatial_coverage_{thres}.pdf\",  bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "plot_coverage_order([df_stats_af,df_stats_as,df_stats_au,df_stats_eu,df_stats_na,df_stats_sa], \n",
    "                    [\"Africa\", \"Asia\", \"Australia\", \"Europe\", \"North America\", \"South America\"],\n",
    "                    \"temporal_coverage\", lower_limit=thres, lower_limit_show=True, ylabel=\"temporal coverage\")\n",
    "plt.savefig(f\"imgs/landcovernet_temporal_coverage_{thres}.pdf\",  bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In Africa we found  {'num_low': 246, 'perc_low': 0.12424242424242424}\n",
      "In Asia we found  {'num_low': 509, 'perc_low': 0.1848892117689793}\n",
      "In Australia we found  {'num_low': 38, 'perc_low': 0.06333333333333334}\n",
      "In Europe we found  {'num_low': 275, 'perc_low': 0.3273809523809524}\n",
      "In North America we found  {'num_low': 61, 'perc_low': 0.039077514413837285}\n",
      "In South America we found  {'num_low': 147, 'perc_low': 0.1225}\n"
     ]
    },
    {
     "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>num_timesteps</th>\n",
       "      <th>num_timesteps_missing</th>\n",
       "      <th>avg_spatial_coverage</th>\n",
       "      <th>num_timesteps_abovecov</th>\n",
       "      <th>temporal_coverage</th>\n",
       "      <th>assesment_temporal</th>\n",
       "      <th>assesment_spatial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</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>17NRB_00</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>29.908726</td>\n",
       "      <td>17</td>\n",
       "      <td>29.824561</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_01</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>30.795476</td>\n",
       "      <td>16</td>\n",
       "      <td>24.615385</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_02</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>21.148414</td>\n",
       "      <td>7</td>\n",
       "      <td>12.280702</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_03</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>15.816692</td>\n",
       "      <td>6</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_04</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>25.072886</td>\n",
       "      <td>12</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>low</td>\n",
       "      <td>low</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_22</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>47.226461</td>\n",
       "      <td>32</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_23</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>43.889618</td>\n",
       "      <td>28</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_24</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>45.378971</td>\n",
       "      <td>29</td>\n",
       "      <td>45.312500</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_26</th>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>47.512869</td>\n",
       "      <td>31</td>\n",
       "      <td>52.542373</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_28</th>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>41.010349</td>\n",
       "      <td>25</td>\n",
       "      <td>42.372881</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_timesteps  num_timesteps_missing  avg_spatial_coverage  \\\n",
       "filename                                                               \n",
       "17NRB_00             57                     57             29.908726   \n",
       "17NRB_01             65                     65             30.795476   \n",
       "17NRB_02             57                     57             21.148414   \n",
       "17NRB_03             59                     59             15.816692   \n",
       "17NRB_04             60                     60             25.072886   \n",
       "...                 ...                    ...                   ...   \n",
       "22NCK_22             60                     60             47.226461   \n",
       "22NCK_23             60                     60             43.889618   \n",
       "22NCK_24             64                     64             45.378971   \n",
       "22NCK_26             59                     57             47.512869   \n",
       "22NCK_28             59                     57             41.010349   \n",
       "\n",
       "          num_timesteps_abovecov  temporal_coverage assesment_temporal  \\\n",
       "filename                                                                 \n",
       "17NRB_00                      17          29.824561                low   \n",
       "17NRB_01                      16          24.615385                low   \n",
       "17NRB_02                       7          12.280702                low   \n",
       "17NRB_03                       6          10.169492                low   \n",
       "17NRB_04                      12          20.000000                low   \n",
       "...                          ...                ...                ...   \n",
       "22NCK_22                      32          53.333333               high   \n",
       "22NCK_23                      28          46.666667                low   \n",
       "22NCK_24                      29          45.312500                low   \n",
       "22NCK_26                      31          52.542373               high   \n",
       "22NCK_28                      25          42.372881                low   \n",
       "\n",
       "         assesment_spatial  \n",
       "filename                    \n",
       "17NRB_00               low  \n",
       "17NRB_01               low  \n",
       "17NRB_02               low  \n",
       "17NRB_03               low  \n",
       "17NRB_04               low  \n",
       "...                    ...  \n",
       "22NCK_22               low  \n",
       "22NCK_23               low  \n",
       "22NCK_24               low  \n",
       "22NCK_26               low  \n",
       "22NCK_28               low  \n",
       "\n",
       "[147 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#low coverage fields based on spatial assessment\n",
    "\n",
    "d_ = df_stats_af[df_stats_af[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In Africa we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_af)})\n",
    "\n",
    "d_ = df_stats_as[df_stats_as[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In Asia we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_as)})\n",
    "\n",
    "d_ = df_stats_au[df_stats_au[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In Australia we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_au)})\n",
    "\n",
    "d_ = df_stats_eu[df_stats_eu[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In Europe we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_eu)})\n",
    "\n",
    "d_ = df_stats_na[df_stats_na[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In North America we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_na)})\n",
    "\n",
    "d_ = df_stats_sa[df_stats_sa[\"assesment_spatial\"] == \"low\"]\n",
    "print(\"In South America we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_sa)})\n",
    "\n",
    "d_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In Africa we found  {'num_low': 230, 'perc_low': 0.11616161616161616}\n",
      "In Asia we found  {'num_low': 435, 'perc_low': 0.15800944424264438}\n",
      "In Australia we found  {'num_low': 38, 'perc_low': 0.06333333333333334}\n",
      "In Europe we found  {'num_low': 255, 'perc_low': 0.30357142857142855}\n",
      "In North America we found  {'num_low': 46, 'perc_low': 0.029468289557975657}\n",
      "In South America we found  {'num_low': 124, 'perc_low': 0.10333333333333333}\n"
     ]
    },
    {
     "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>num_timesteps</th>\n",
       "      <th>num_timesteps_missing</th>\n",
       "      <th>avg_spatial_coverage</th>\n",
       "      <th>num_timesteps_abovecov</th>\n",
       "      <th>temporal_coverage</th>\n",
       "      <th>assesment_temporal</th>\n",
       "      <th>assesment_spatial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</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>17NRB_00</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>29.908726</td>\n",
       "      <td>17</td>\n",
       "      <td>29.824561</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_01</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>30.795476</td>\n",
       "      <td>16</td>\n",
       "      <td>24.615385</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_02</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>21.148414</td>\n",
       "      <td>7</td>\n",
       "      <td>12.280702</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_03</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>15.816692</td>\n",
       "      <td>6</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17NRB_04</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>25.072886</td>\n",
       "      <td>12</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>low</td>\n",
       "      <td>low</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_23</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>43.889618</td>\n",
       "      <td>28</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_24</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>45.378971</td>\n",
       "      <td>29</td>\n",
       "      <td>45.312500</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22NCK_28</th>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>41.010349</td>\n",
       "      <td>25</td>\n",
       "      <td>42.372881</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23MKQ_17</th>\n",
       "      <td>59</td>\n",
       "      <td>56</td>\n",
       "      <td>54.219133</td>\n",
       "      <td>29</td>\n",
       "      <td>49.152542</td>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23MKQ_27</th>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>51.230672</td>\n",
       "      <td>29</td>\n",
       "      <td>48.333333</td>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_timesteps  num_timesteps_missing  avg_spatial_coverage  \\\n",
       "filename                                                               \n",
       "17NRB_00             57                     57             29.908726   \n",
       "17NRB_01             65                     65             30.795476   \n",
       "17NRB_02             57                     57             21.148414   \n",
       "17NRB_03             59                     59             15.816692   \n",
       "17NRB_04             60                     60             25.072886   \n",
       "...                 ...                    ...                   ...   \n",
       "22NCK_23             60                     60             43.889618   \n",
       "22NCK_24             64                     64             45.378971   \n",
       "22NCK_28             59                     57             41.010349   \n",
       "23MKQ_17             59                     56             54.219133   \n",
       "23MKQ_27             60                     58             51.230672   \n",
       "\n",
       "          num_timesteps_abovecov  temporal_coverage assesment_temporal  \\\n",
       "filename                                                                 \n",
       "17NRB_00                      17          29.824561                low   \n",
       "17NRB_01                      16          24.615385                low   \n",
       "17NRB_02                       7          12.280702                low   \n",
       "17NRB_03                       6          10.169492                low   \n",
       "17NRB_04                      12          20.000000                low   \n",
       "...                          ...                ...                ...   \n",
       "22NCK_23                      28          46.666667                low   \n",
       "22NCK_24                      29          45.312500                low   \n",
       "22NCK_28                      25          42.372881                low   \n",
       "23MKQ_17                      29          49.152542                low   \n",
       "23MKQ_27                      29          48.333333                low   \n",
       "\n",
       "         assesment_spatial  \n",
       "filename                    \n",
       "17NRB_00               low  \n",
       "17NRB_01               low  \n",
       "17NRB_02               low  \n",
       "17NRB_03               low  \n",
       "17NRB_04               low  \n",
       "...                    ...  \n",
       "22NCK_23               low  \n",
       "22NCK_24               low  \n",
       "22NCK_28               low  \n",
       "23MKQ_17              high  \n",
       "23MKQ_27              high  \n",
       "\n",
       "[124 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#low coverage fields based on temporal assessment\n",
    "\n",
    "d_ = df_stats_af[df_stats_af[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In Africa we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_af)})\n",
    "\n",
    "d_ = df_stats_as[df_stats_as[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In Asia we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_as)})\n",
    "\n",
    "d_ = df_stats_au[df_stats_au[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In Australia we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_au)})\n",
    "\n",
    "d_ = df_stats_eu[df_stats_eu[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In Europe we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_eu)})\n",
    "\n",
    "d_ = df_stats_na[df_stats_na[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In North America we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_na)})\n",
    "\n",
    "d_ = df_stats_sa[df_stats_sa[\"assesment_temporal\"] == \"low\"]\n",
    "print(\"In South America we found \",{\"num_low\": len(d_), \"perc_low\":len(d_)/len(df_stats_sa)})\n",
    "\n",
    "d_"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "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": 2
}