{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "638df9a2",
   "metadata": {},
   "source": [
    "# sPlotOpen Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15d079d",
   "metadata": {},
   "source": [
    "sPlotOpen (Sabatini et al, 2021) is an open-access and environmentally and spatially balanced subset of the global sPlot vegetation plots data set v2.1 (Bruelheide et al, 2019).\n",
    "\n",
    "This section covers:\n",
    "\n",
    "- Link plot coordinates with community wighted means (cwm)\n",
    "- Visualize plot density"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8581c38",
   "metadata": {},
   "source": [
    "## Download"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f1187e",
   "metadata": {},
   "source": [
    "sPlotOpen Data is available at the *iDiv Data Repository*. For this study we used version 52.\n",
    "\n",
    "https://idata.idiv.de/ddm/Data/ShowData/3474\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e4aba2",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73975506",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import LogNorm, Normalize\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy as cart\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e70409",
   "metadata": {},
   "source": [
    "## Link plot coordinates to cmw data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5557c6b1",
   "metadata": {},
   "source": [
    "The data is stored in various tab-separated files:\n",
    "\n",
    "- **sPlotOpen_header(2).txt** : contains information on each plot, such as coordinates, date, biome, country, etc.\n",
    "\n",
    "\n",
    "- **sPlotOpen_DT(1).txt** : contains information per plot and species with abundance and relative cover\n",
    "\n",
    "\n",
    "- **sPlotOpen_CWM_CWV(1).txt** : contains information on trait community weighted means and variances for each plot and 18 traits (ln-transformed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f78f0c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the community weighted means\n",
    "\n",
    "cwm = pd.read_csv(\"./sPlotOpen/sPlotOpen_CWM_CWV(1).txt\", sep= \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5846917d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/net/home/swolf/.conda/envs/cartopy/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (16) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "plots = pd.read_csv(\"./sPlotOpen/sPlotOpen_header(2).txt\", sep= \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "edaf606d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sPlot = pd.merge(cwm, plots, on='PlotObservationID', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20d2145a",
   "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>PlotObservationID</th>\n",
       "      <th>TraitCoverage_cover</th>\n",
       "      <th>Species_richness</th>\n",
       "      <th>TraitCoverage_pa</th>\n",
       "      <th>LeafArea_CWM</th>\n",
       "      <th>StemDens_CWM</th>\n",
       "      <th>SLA_CWM</th>\n",
       "      <th>LeafC_perdrymass_CWM</th>\n",
       "      <th>LeafN_CWM</th>\n",
       "      <th>LeafP_CWM</th>\n",
       "      <th>...</th>\n",
       "      <th>Height_shrubs_lowest</th>\n",
       "      <th>Height_herbs_average</th>\n",
       "      <th>Height_herbs_lowest</th>\n",
       "      <th>Height_herbs_highest</th>\n",
       "      <th>SoilClim_PC1</th>\n",
       "      <th>SoilClim_PC2</th>\n",
       "      <th>Resample_1</th>\n",
       "      <th>Resample_2</th>\n",
       "      <th>Resample_3</th>\n",
       "      <th>Resample_1_consensus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.686063</td>\n",
       "      <td>-0.907135</td>\n",
       "      <td>2.903715</td>\n",
       "      <td>6.136791</td>\n",
       "      <td>2.929729</td>\n",
       "      <td>0.739181</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>7</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>3.899842</td>\n",
       "      <td>-0.900514</td>\n",
       "      <td>2.917708</td>\n",
       "      <td>6.131968</td>\n",
       "      <td>2.955072</td>\n",
       "      <td>0.733698</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PlotObservationID  TraitCoverage_cover  Species_richness  TraitCoverage_pa  \\\n",
       "0                 16             0.277778                 3          0.333333   \n",
       "1                 17             0.038462                 2          0.500000   \n",
       "2                 18             0.047619                 4          0.250000   \n",
       "3                 20             0.666667                 3          0.333333   \n",
       "4                 22             0.538462                 7          0.571429   \n",
       "\n",
       "   LeafArea_CWM  StemDens_CWM   SLA_CWM  LeafC_perdrymass_CWM  LeafN_CWM  \\\n",
       "0      3.678311     -1.047293  2.890748              6.128157   2.873263   \n",
       "1      3.678311     -1.047293  2.890748              6.128157   2.873263   \n",
       "2      3.678311     -1.047293  2.890748              6.128157   2.873263   \n",
       "3      3.686063     -0.907135  2.903715              6.136791   2.929729   \n",
       "4      3.899842     -0.900514  2.917708              6.131968   2.955072   \n",
       "\n",
       "   LeafP_CWM  ...  Height_shrubs_lowest  Height_herbs_average  \\\n",
       "0   1.114036  ...                   NaN                   NaN   \n",
       "1   1.114036  ...                   NaN                   NaN   \n",
       "2   1.114036  ...                   NaN                   NaN   \n",
       "3   0.739181  ...                   NaN                   NaN   \n",
       "4   0.733698  ...                   NaN                   NaN   \n",
       "\n",
       "   Height_herbs_lowest  Height_herbs_highest  SoilClim_PC1  SoilClim_PC2  \\\n",
       "0                  NaN                   NaN         -3.66         0.546   \n",
       "1                  NaN                   NaN         -3.66         0.546   \n",
       "2                  NaN                   NaN         -3.66         0.546   \n",
       "3                  NaN                   NaN         -3.66         0.546   \n",
       "4                  NaN                   NaN         -3.66         0.546   \n",
       "\n",
       "   Resample_1  Resample_2  Resample_3  Resample_1_consensus  \n",
       "0        True       False       False                  True  \n",
       "1        True       False       False                  True  \n",
       "2       False       False        True                 False  \n",
       "3       False        True       False                 False  \n",
       "4        True       False       False                  True  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sPlot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806650b",
   "metadata": {},
   "source": [
    "### Change columns name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24de0165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PlotObservationID', 'TraitCoverage_cover', 'Species_richness',\n",
       "       'TraitCoverage_pa', 'LeafArea_CWM', 'StemDens_CWM', 'SLA_CWM',\n",
       "       'LeafC_perdrymass_CWM', 'LeafN_CWM', 'LeafP_CWM', 'PlantHeight_CWM',\n",
       "       'SeedMass_CWM', 'Seed_length_CWM', 'LDMC_CWM', 'LeafNperArea_CWM',\n",
       "       'LeafNPratio_CWM', 'Leaf_delta_15N_CWM', 'Seed_num_rep_unit_CWM',\n",
       "       'Leaffreshmass_CWM', 'Stem_cond_dens_CWM', 'Disp_unit_leng_CWM',\n",
       "       'Wood_vessel_length_CWM', 'LeafArea_CWV', 'StemDens_CWV', 'SLA_CWV',\n",
       "       'LeafC_perdrymass_CWV', 'LeafN_CWV', 'LeafP_CWV', 'PlantHeight_CWV',\n",
       "       'SeedMass_CWV', 'Seed_length_CWV', 'LDMC_CWV', 'LeafNperArea_CWV',\n",
       "       'LeafNPratio_CWV', 'Leaf_delta_15N_CWV', 'Seed_num_rep_unit_CWV',\n",
       "       'Leaffreshmass_CWV', 'Stem_cond_dens_CWV', 'Disp_unit_leng_CWV',\n",
       "       'Wood_vessel_length_CWV', 'GIVD_ID', 'Dataset', 'Continent', 'Country',\n",
       "       'Biome', 'Date_of_recording', 'Latitude', 'Longitude',\n",
       "       'Location_uncertainty', 'Releve_area', 'Plant_recorded', 'Elevation',\n",
       "       'Aspect', 'Slope', 'is_forest', 'ESY', 'Naturalness', 'Forest',\n",
       "       'Shrubland', 'Grassland', 'Wetland', 'Sparse_vegetation', 'Cover_total',\n",
       "       'Cover_tree_layer', 'Cover_shrub_layer', 'Cover_herb_layer',\n",
       "       'Cover_moss_layer', 'Cover_lichen_layer', 'Cover_algae_layer',\n",
       "       'Cover_litter_layer', 'Cover_bare_rocks', 'Cover_cryptogams',\n",
       "       'Cover_bare_soil', 'Height_trees_highest', 'Height_trees_lowest',\n",
       "       'Height_shrubs_highest', 'Height_shrubs_lowest', 'Height_herbs_average',\n",
       "       'Height_herbs_lowest', 'Height_herbs_highest', 'SoilClim_PC1',\n",
       "       'SoilClim_PC2', 'Resample_1', 'Resample_2', 'Resample_3',\n",
       "       'Resample_1_consensus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sPlot.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b9d1500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_of_recording     {<class 'str'>, <class 'float'>}\n",
       "is_forest            {<class 'float'>, <class 'bool'>}\n",
       "ESY                   {<class 'str'>, <class 'float'>}\n",
       "Naturalness           {<class 'str'>, <class 'float'>}\n",
       "Forest               {<class 'float'>, <class 'bool'>}\n",
       "Shrubland            {<class 'float'>, <class 'bool'>}\n",
       "Grassland            {<class 'float'>, <class 'bool'>}\n",
       "Wetland              {<class 'float'>, <class 'bool'>}\n",
       "Sparse_vegetation    {<class 'float'>, <class 'bool'>}\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only variables not of interest have mixed data types\n",
    "types = sPlot.applymap(type).apply(set)\n",
    "types[types.apply(len) > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0127c1e",
   "metadata": {},
   "source": [
    "Rename the trait columns to match the TRY summary stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4e6d1df",
   "metadata": {},
   "outputs": [],
   "source": [
    "sPlot.rename(columns = {'StemDens_CWM':'SSD'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafC_perdrymass_CWM':'Leaf C'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafN_CWM':'Leaf N per mass'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafP_CWM':'Leaf P'}, inplace = True)\n",
    "sPlot.rename(columns = {'LDMC_CWM':'LDMC'}, inplace = True)\n",
    "sPlot.rename(columns = {'SeedMass_CWM':'Seed mass'}, inplace = True)\n",
    "sPlot.rename(columns = {'Seed_length_CWM':'Seed length'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafNperArea_CWM':'Leaf N per area'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafNPratio_CWM':'Leaf N P ratio'}, inplace = True)\n",
    "sPlot.rename(columns = {'Leaf_delta_15N_CWM':'Leaf delta15N'}, inplace = True)\n",
    "sPlot.rename(columns = {'Leaffreshmass_CWM':'Leaf fresh mass'}, inplace = True)\n",
    "sPlot.rename(columns = {'Seed_num_rep_unit_CWM':'Seeds per rep. unit'}, inplace = True)\n",
    "sPlot.rename(columns = {'Stem_cond_dens_CWM':'Stem conduit density'}, inplace = True)\n",
    "sPlot.rename(columns = {'Disp_unit_leng_CWM':'Dispersal unit length'}, inplace = True)\n",
    "sPlot.rename(columns = {'Wood_vessel_length_CWM':'Conduit element length'}, inplace = True)\n",
    "sPlot.rename(columns = {'PlantHeight_CWM':'Plant Height'}, inplace = True)\n",
    "sPlot.rename(columns = {'LeafArea_CWM':'Leaf Area'}, inplace = True)\n",
    "sPlot.rename(columns = {'SLA_CWM':'SLA'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaca0c5f",
   "metadata": {},
   "source": [
    "Replace infinite values with ```NaN```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b00c075",
   "metadata": {},
   "outputs": [],
   "source": [
    "sPlot = sPlot.replace(-np.inf, np.nan)\n",
    "sPlot = sPlot.replace(np.inf, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88e89fc4",
   "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>PlotObservationID</th>\n",
       "      <th>TraitCoverage_cover</th>\n",
       "      <th>Species_richness</th>\n",
       "      <th>TraitCoverage_pa</th>\n",
       "      <th>Leaf Area</th>\n",
       "      <th>SSD</th>\n",
       "      <th>SLA</th>\n",
       "      <th>Leaf C</th>\n",
       "      <th>Leaf N per mass</th>\n",
       "      <th>Leaf P</th>\n",
       "      <th>...</th>\n",
       "      <th>Height_shrubs_lowest</th>\n",
       "      <th>Height_herbs_average</th>\n",
       "      <th>Height_herbs_lowest</th>\n",
       "      <th>Height_herbs_highest</th>\n",
       "      <th>SoilClim_PC1</th>\n",
       "      <th>SoilClim_PC2</th>\n",
       "      <th>Resample_1</th>\n",
       "      <th>Resample_2</th>\n",
       "      <th>Resample_3</th>\n",
       "      <th>Resample_1_consensus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.678311</td>\n",
       "      <td>-1.047293</td>\n",
       "      <td>2.890748</td>\n",
       "      <td>6.128157</td>\n",
       "      <td>2.873263</td>\n",
       "      <td>1.114036</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.686063</td>\n",
       "      <td>-0.907135</td>\n",
       "      <td>2.903715</td>\n",
       "      <td>6.136791</td>\n",
       "      <td>2.929729</td>\n",
       "      <td>0.739181</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>7</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>3.899842</td>\n",
       "      <td>-0.900514</td>\n",
       "      <td>2.917708</td>\n",
       "      <td>6.131968</td>\n",
       "      <td>2.955072</td>\n",
       "      <td>0.733698</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>0.546</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PlotObservationID  TraitCoverage_cover  Species_richness  TraitCoverage_pa  \\\n",
       "0                 16             0.277778                 3          0.333333   \n",
       "1                 17             0.038462                 2          0.500000   \n",
       "2                 18             0.047619                 4          0.250000   \n",
       "3                 20             0.666667                 3          0.333333   \n",
       "4                 22             0.538462                 7          0.571429   \n",
       "\n",
       "   Leaf Area       SSD       SLA    Leaf C  Leaf N per mass    Leaf P  ...  \\\n",
       "0   3.678311 -1.047293  2.890748  6.128157         2.873263  1.114036  ...   \n",
       "1   3.678311 -1.047293  2.890748  6.128157         2.873263  1.114036  ...   \n",
       "2   3.678311 -1.047293  2.890748  6.128157         2.873263  1.114036  ...   \n",
       "3   3.686063 -0.907135  2.903715  6.136791         2.929729  0.739181  ...   \n",
       "4   3.899842 -0.900514  2.917708  6.131968         2.955072  0.733698  ...   \n",
       "\n",
       "   Height_shrubs_lowest  Height_herbs_average  Height_herbs_lowest  \\\n",
       "0                   NaN                   NaN                  NaN   \n",
       "1                   NaN                   NaN                  NaN   \n",
       "2                   NaN                   NaN                  NaN   \n",
       "3                   NaN                   NaN                  NaN   \n",
       "4                   NaN                   NaN                  NaN   \n",
       "\n",
       "   Height_herbs_highest  SoilClim_PC1  SoilClim_PC2  Resample_1  Resample_2  \\\n",
       "0                   NaN         -3.66         0.546        True       False   \n",
       "1                   NaN         -3.66         0.546        True       False   \n",
       "2                   NaN         -3.66         0.546       False       False   \n",
       "3                   NaN         -3.66         0.546       False        True   \n",
       "4                   NaN         -3.66         0.546        True       False   \n",
       "\n",
       "   Resample_3  Resample_1_consensus  \n",
       "0       False                  True  \n",
       "1       False                  True  \n",
       "2        True                 False  \n",
       "3       False                 False  \n",
       "4       False                  True  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sPlot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cedce9a9",
   "metadata": {},
   "source": [
    "Save this dataframe as ```csv```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eab8faf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sPlot.to_csv(\"sPlotOpen/cwm_loc.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0360fd",
   "metadata": {},
   "source": [
    "## Plot density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2fa79fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gridmap(long, lat, label, projection, colorbar=True):\n",
    "    \n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "    Z, xedges, yedges = np.histogram2d(np.array(long,dtype=float),\n",
    "                                   np.array(lat),bins = [181, 91])\n",
    "\n",
    "    #https://stackoverflow.com/questions/67801227/color-a-2d-histogram-not-by-density-but-by-the-mean-of-a-third-column\n",
    "    #https://medium.com/analytics-vidhya/custom-strava-heatmap-231267dcd084\n",
    "    \n",
    "    #let function know what projection provided data is in:\n",
    "    data_crs = ccrs.PlateCarree()\n",
    "    \n",
    "    #for colorbar\n",
    "    cmap = plt.get_cmap('cool')\n",
    "    im_ratio = Z.shape[0]/Z.shape[1]\n",
    "\n",
    "    #plot map\n",
    "    #create base plot of a world map\n",
    "    ax = fig.add_subplot(1, 1, 1, projection=projection) # I used the PlateCarree projection from cartopy\n",
    "    \n",
    "    # set figure to map global extent (-180,180,-90,90)\n",
    "    ax.set_global()\n",
    "    \n",
    "    #add coastlines\n",
    "    ax.coastlines(resolution='110m', color='orange', linewidth=1.3)\n",
    "    \n",
    "    #add grid with values\n",
    "    im = ax.pcolormesh(xedges, yedges, Z.T, cmap=\"cool\", norm=LogNorm(), transform=data_crs, vmax = 400000)\n",
    "    \n",
    "    #add color bar\n",
    "    if colorbar==True:\n",
    "        fig.colorbar(im,fraction=0.046*im_ratio, pad=0.04, shrink=0.3, location=\"left\", label=label)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca0044f",
   "metadata": {},
   "source": [
    "Apply the ```gridmap``` function to plot a 2D histogramm of the sPlotOpen plots and save output as ```.pdf```. You can also experiment with other projections. See https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html for inspiration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75d74a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/net/home/swolf/.conda/envs/cartopy/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1797: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n",
      "  result = matplotlib.axes.Axes.pcolormesh(self, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "gridmap(sPlot['Longitude'], sPlot['Latitude'], \"Number of sPlotOpen Plots\", projection = ccrs.Robinson())\n",
    "plt.savefig('../Figures/sPlot_density_Robinson_all.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}