Figures/scripts/NORTH-AMERICA_regional_figure/Q100_Quantile_plot_region.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drawing the multipanel barplot for Q100 North-America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensembles = c('CORDEX', 'CMIP5', 'CMIP6')\n",
    "GWLs = c('1.5','2','4')\n",
    "scenarios = c('modern', '2.6_mid', '8.5_mid', '2.6_late', '8.5_late')\n",
    "\n",
    "data = list()\n",
    "for (ensemble in ensembles){\n",
    "    data[[ensemble]] = list()\n",
    "    for (GWL in GWLs){\n",
    "        data[[ensemble]][[GWL]] = list()\n",
    "    }\n",
    "    for (scenario in scenarios){\n",
    "        data[[ensemble]][[scenario]] = list()\n",
    "    }\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CWD = getwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Get the data from the json files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“package ‘rjson’ was built under R version 3.6.3”\n"
     ]
    }
   ],
   "source": [
    "library(rjson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare R list with all the regions => Get the regions list from the CMIP6 json file, for GWL 1.5K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- List of regions for NORTH-AMERICA\n",
    "continent_regions = c('NWN','NEN','WNA','CNA','ENA','NCA','CAR')\n",
    "\n",
    "regions_by_continent = list(\n",
    "    NAM = c('NWN','NEN','WNA','CNA','ENA'),\n",
    "    CAM = c('NCA','CAR')\n",
    "    )\n",
    "\n",
    "\n",
    "all_regions = list()\n",
    "GWLs = c('1.5','2','4')\n",
    "scenarios = c('modern', '2.6_mid', '8.5_mid', '2.6_late', '8.5_late')\n",
    "\n",
    "for (region_name in continent_regions){\n",
    "    all_regions[[region_name]] = list()\n",
    "    for (ensemble in c('CMIP6','CMIP5','CORDEX')){\n",
    "        all_regions[[region_name]][[ensemble]] = list()\n",
    "        for (GWL in GWLs){\n",
    "            all_regions[[region_name]][[ensemble]][[GWL]] = list()\n",
    "        }#end for GWL\n",
    "        for (scenario in scenarios){\n",
    "            all_regions[[region_name]][[ensemble]][[scenario]] = list()\n",
    "        }#end for scenario\n",
    "    }#end for ensemble\n",
    "}#end for region_name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to read in the data in the txt files provided by ICTP (Fabio Di Sante)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"metrics_reader\"=function(metrics_filename){\n",
    "    \n",
    "    # -- Read the metrics file provided by Fabio\n",
    "    lines=readLines(file(metrics_filename,open=\"r\"))\n",
    "    dat = c()\n",
    "    for (elt in lines){\n",
    "        tmp = gsub(' ','', elt)\n",
    "        dat = c(dat, tmp)\n",
    "    }#end for elt\n",
    "    # -- Each region has 4 lines => the number of regions is the length / 4\n",
    "    nregions = length(dat)/4\n",
    "    # -- Store the results in res\n",
    "    res = list()\n",
    "    for (i in 1:nregions){\n",
    "        # -- Calculate the index of the region name\n",
    "        startind = (i-1)*4 + 1\n",
    "        resname = dat[startind]\n",
    "        print(resname)\n",
    "        p10 = as.numeric(dat[startind+1])\n",
    "        median = as.numeric(dat[startind+2])\n",
    "        p90 = as.numeric(dat[startind+3])\n",
    "        print(c(p10,median,p90))\n",
    "        res[[resname]] = c(p10, median, p90)\n",
    "    }\n",
    "    return(res)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in CMIP6 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"NAM ==> \"\n",
      "[1] \"NWN\" \"NEN\" \"WNA\" \"CNA\" \"ENA\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CMIP6/Q100_ssp585_1995-2014.nc_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] 0.162349 0.250969 0.360860\n",
      "[1] \"NEN\"\n",
      "[1] 0.139614 0.292752 0.422630\n",
      "[1] \"WNA\"\n",
      "[1] 0.0977557 0.1593390 0.3325610\n",
      "[1] \"CNA\"\n",
      "[1] 0.0672992 0.1854000 0.3549640\n",
      "[1] \"ENA\"\n",
      "[1] 0.228910 0.311488 0.514019\n",
      "[1] \"NCA\"\n",
      "[1] 0.0977182 0.1522970 0.2926260\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp126_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03597670 -0.00344245  0.00395781\n",
      "[1] \"NEN\"\n",
      "[1] -0.03415730 -0.00697205  0.01356170\n",
      "[1] \"WNA\"\n",
      "[1] -0.015452600 -0.000401241  0.021196600\n",
      "[1] \"CNA\"\n",
      "[1] -0.05162050  0.00275973  0.05722360\n",
      "[1] \"ENA\"\n",
      "[1] -3.75982e-02  5.05139e-05  6.90402e-02\n",
      "[1] \"NCA\"\n",
      "[1] -0.02455800  0.00914857  0.10336400\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp585_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03518870 -0.00362411  0.01291830\n",
      "[1] \"NEN\"\n",
      "[1] -0.04750080 -0.00800267  0.02330830\n",
      "[1] \"WNA\"\n",
      "[1] -0.02674710  0.00136933  0.03189210\n",
      "[1] \"CNA\"\n",
      "[1] -0.03698420  0.00959044  0.06798130\n",
      "[1] \"ENA\"\n",
      "[1] -0.0363310  0.0103864  0.0607613\n",
      "[1] \"NCA\"\n",
      "[1] -0.0151062  0.0197144  0.0771408\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp126_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03351930 -0.00468660  0.00735521\n",
      "[1] \"NEN\"\n",
      "[1] -0.04082610 -0.00634695  0.01531600\n",
      "[1] \"WNA\"\n",
      "[1] -0.01404040  0.00743663  0.04194850\n",
      "[1] \"CNA\"\n",
      "[1] -0.0256304  0.0106979  0.0883749\n",
      "[1] \"ENA\"\n",
      "[1] -0.03181440  0.00729482  0.08671730\n",
      "[1] \"NCA\"\n",
      "[1] -0.0246971  0.0121687  0.0922151\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp585_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.02992290 -0.00493202  0.01627490\n",
      "[1] \"NEN\"\n",
      "[1] -0.0793081 -0.0247483  0.0233026\n",
      "[1] \"WNA\"\n",
      "[1] -0.030475100  0.000768767  0.049434500\n",
      "[1] \"CNA\"\n",
      "[1] -0.0441213  0.0282942  0.2240270\n",
      "[1] \"ENA\"\n",
      "[1] -0.0457281  0.0512033  0.1915880\n",
      "[1] \"NCA\"\n",
      "[1] -0.0294724  0.0269551  0.1860230\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/1.5_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.019072900 -0.000555001  0.011968000\n",
      "[1] \"NEN\"\n",
      "[1] -0.02048910 -0.00554645  0.01438160\n",
      "[1] \"WNA\"\n",
      "[1] -0.01425540 -0.00260725  0.01158500\n",
      "[1] \"CNA\"\n",
      "[1] -0.02371460  0.00144349  0.04778220\n",
      "[1] \"ENA\"\n",
      "[1] -0.02999350  0.00628648  0.03372770\n",
      "[1] \"NCA\"\n",
      "[1] -0.0204346  0.0108985  0.0385347\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/2.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03288850 -0.00320907  0.01009800\n",
      "[1] \"NEN\"\n",
      "[1] -0.03329560 -0.01042130  0.00939815\n",
      "[1] \"WNA\"\n",
      "[1] -0.023069200  0.000345952  0.021799100\n",
      "[1] \"CNA\"\n",
      "[1] -0.0308551  0.0125595  0.0575758\n",
      "[1] \"ENA\"\n",
      "[1] -0.0353470  0.0103071  0.0443590\n",
      "[1] \"NCA\"\n",
      "[1] -0.0127058  0.0234533  0.0640883\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/4.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03816860 -0.01134560  0.00835873\n",
      "[1] \"NEN\"\n",
      "[1] -0.0434611 -0.0359651 -0.0181864\n",
      "[1] \"WNA\"\n",
      "[1] -0.02404330  0.00622554  0.02537520\n",
      "[1] \"CNA\"\n",
      "[1] -0.0126199  0.0527109  0.1181950\n",
      "[1] \"ENA\"\n",
      "[1] -0.00314551  0.03756110  0.10674600\n",
      "[1] \"NCA\"\n",
      "[1] 0.0332701 0.0643503 0.1335330\n",
      "[1] \"CAM ==> \"\n",
      "[1] \"NCA\" \"CAR\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CMIP6/Q100_ssp585_1995-2014.nc_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] 0.0559783 0.1220220 0.4874810\n",
      "[1] \"SCA\"\n",
      "[1] 0.0436221 0.3220540 0.7134960\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp126_mid_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.01769040  0.00300405  0.14591300\n",
      "[1] \"SCA\"\n",
      "[1] -0.0290995  0.0405825  0.1305050\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp585_mid_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.0392525  0.0016201  0.0914437\n",
      "[1] \"SCA\"\n",
      "[1] -0.13367300  0.00305421  0.09860770\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp126_far_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.04305870  0.00127014  0.15614800\n",
      "[1] \"SCA\"\n",
      "[1] -0.02152730  0.00804751  0.18645600\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/ssp585_far_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.0639779  0.0156697  0.2137390\n",
      "[1] \"SCA\"\n",
      "[1] -0.1526040  0.0246157  0.2245490\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/1.5_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.032209700  0.000421575  0.094435200\n",
      "[1] \"SCA\"\n",
      "[1] -0.0182532  0.0110981  0.1068120\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/2.0_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.0149297  0.0087086  0.1289460\n",
      "[1] \"SCA\"\n",
      "[1] -0.02764050  0.00510702  0.10563400\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP6/4.0_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.0301758  0.0699238  0.1434540\n",
      "[1] \"SCA\"\n",
      "[1] -0.1515070  0.0786617  0.3165050\n"
     ]
    }
   ],
   "source": [
    "ensemble = 'CMIP6'\n",
    "\n",
    "for (continent in names(regions_by_continent)){\n",
    "    print(paste(continent,'==> '))\n",
    "    print(regions_by_continent[[continent]])\n",
    "    \n",
    "    # -- Recent past raw value\n",
    "    clim_period = 'modern_raw'\n",
    "    wclim_period='ssp585_1995-2014'\n",
    "    if (continent=='EUR_MaskDesert'){\n",
    "        wcontinent='EUR'\n",
    "    }else{\n",
    "        wcontinent=continent\n",
    "    }\n",
    "    metrics_filename = paste(CWD,'/../../data/Figure_12.10/Q100_',ensemble,'/Q100_',wclim_period,'.nc_',wcontinent,'.txt',sep='')\n",
    "    print(paste('Raw values :', metrics_filename))\n",
    "    res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "    for (region_name in names(res_metrics_by_AR6region)){\n",
    "        all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "    }\n",
    "     \n",
    "    # -- Differences for future time periods and GWLs\n",
    "    for (clim_period in c('2.6_mid', '8.5_mid', '2.6_late', '8.5_late','1.5','2','4')){\n",
    "        if (clim_period=='2.6_mid'){ wclim_period='ssp126_mid' }\n",
    "        if (clim_period=='2.6_late'){ wclim_period='ssp126_far' }\n",
    "        if (clim_period=='8.5_mid'){ wclim_period='ssp585_mid' }\n",
    "        if (clim_period=='8.5_late'){ wclim_period='ssp585_far' }\n",
    "        if (clim_period=='1.5'){ wclim_period='1.5' }\n",
    "        if (clim_period=='2'){ wclim_period='2.0' }\n",
    "        if (clim_period=='4'){ wclim_period='4.0' }\n",
    "        \n",
    "        metrics_filename = paste(CWD,'/../../data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_',ensemble,'/',wclim_period,'_',continent,'.txt',sep='')\n",
    "        print(metrics_filename)\n",
    "        res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "        for (region_name in names(res_metrics_by_AR6region)){\n",
    "            all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "        }\n",
    "    }#end for clim_period\n",
    "    \n",
    "    \n",
    "}#end for continent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in CMIP5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"NAM ==> \"\n",
      "[1] \"NWN\" \"NEN\" \"WNA\" \"CNA\" \"ENA\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CMIP5/Q100_rcp85_1995-2014.nc_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] 0.213231 0.353598 0.471989\n",
      "[1] \"NEN\"\n",
      "[1] 0.207503 0.399559 0.552118\n",
      "[1] \"WNA\"\n",
      "[1] 0.113718 0.288211 0.392560\n",
      "[1] \"CNA\"\n",
      "[1] 0.0696352 0.2100710 0.3609580\n",
      "[1] \"ENA\"\n",
      "[1] 0.149243 0.407548 0.573814\n",
      "[1] \"NCA\"\n",
      "[1] 0.0857935 0.1835500 0.3775930\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp26_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.01778550  0.00242893  0.01778940\n",
      "[1] \"NEN\"\n",
      "[1] -0.01491290  0.00810098  0.02623830\n",
      "[1] \"WNA\"\n",
      "[1] -0.02309450 -0.00195463  0.01468310\n",
      "[1] \"CNA\"\n",
      "[1] -0.02813100 -0.00281670  0.00928168\n",
      "[1] \"ENA\"\n",
      "[1] -0.01461840 -0.00879388  0.03644700\n",
      "[1] \"NCA\"\n",
      "[1] -0.0286017  0.0249136  0.0449326\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp85_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03185460 -0.00672151  0.01599750\n",
      "[1] \"NEN\"\n",
      "[1] -0.02526020  0.00963346  0.03035040\n",
      "[1] \"WNA\"\n",
      "[1] -0.03549800 -0.00401176  0.04912610\n",
      "[1] \"CNA\"\n",
      "[1] -0.02548200  0.00918944  0.04474260\n",
      "[1] \"ENA\"\n",
      "[1] -0.02372810 -0.00781641  0.07827100\n",
      "[1] \"NCA\"\n",
      "[1] -0.0281641  0.0122479  0.1022600\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp26_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.02185910  0.00103499  0.02680730\n",
      "[1] \"NEN\"\n",
      "[1] -0.02898880  0.00414555  0.02463350\n",
      "[1] \"WNA\"\n",
      "[1] -0.04050370 -0.00543422  0.01941510\n",
      "[1] \"CNA\"\n",
      "[1] -0.02738890 -0.00341213  0.03838050\n",
      "[1] \"ENA\"\n",
      "[1] -0.03141910 -0.00963403  0.02309430\n",
      "[1] \"NCA\"\n",
      "[1] -0.0309511  0.0101085  0.0452268\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp85_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03472810  0.00400636  0.03135670\n",
      "[1] \"NEN\"\n",
      "[1] -0.03281760  0.00859066  0.04836560\n",
      "[1] \"WNA\"\n",
      "[1] -0.0705371  0.0105036  0.0397167\n",
      "[1] \"CNA\"\n",
      "[1] -0.04507170  0.00882879  0.09235610\n",
      "[1] \"ENA\"\n",
      "[1] -0.0683923 -0.0101506  0.1185440\n",
      "[1] \"NCA\"\n",
      "[1] -0.0489462  0.0267022  0.1984320\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/1.5_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.00854292 -0.00342050  0.00852593\n",
      "[1] \"NEN\"\n",
      "[1] -0.02070560  0.00214448  0.02350400\n",
      "[1] \"WNA\"\n",
      "[1] -0.02448210  0.00756312  0.02718010\n",
      "[1] \"CNA\"\n",
      "[1] -0.02577840 -0.00336817  0.03029970\n",
      "[1] \"ENA\"\n",
      "[1] -0.03446360  0.00977826  0.05659070\n",
      "[1] \"NCA\"\n",
      "[1] -0.023016700  0.000237784  0.035873700\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/2.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.01885770 -0.00184819  0.02403620\n",
      "[1] \"NEN\"\n",
      "[1] -0.02537530  0.00986649  0.02792200\n",
      "[1] \"WNA\"\n",
      "[1] -0.02719500  0.00457601  0.04242830\n",
      "[1] \"CNA\"\n",
      "[1] -0.032476700 -0.000811565  0.039485900\n",
      "[1] \"ENA\"\n",
      "[1] -0.02332800 -0.00721704  0.06653530\n",
      "[1] \"NCA\"\n",
      "[1] -0.027468600 -0.000583514  0.090512100\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/4.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.03472410 -0.00245389  0.03267700\n",
      "[1] \"NEN\"\n",
      "[1] -0.04508440  0.00946719  0.07186110\n",
      "[1] \"WNA\"\n",
      "[1] -0.0012278  0.0186583  0.0338196\n",
      "[1] \"CNA\"\n",
      "[1] -0.01214830 -0.00473801  0.10544400\n",
      "[1] \"ENA\"\n",
      "[1] -0.0366711  0.0158685  0.1215730\n",
      "[1] \"NCA\"\n",
      "[1] -0.03674240 -0.00756182  0.23823200\n",
      "[1] \"CAM ==> \"\n",
      "[1] \"NCA\" \"CAR\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CMIP5/Q100_rcp85_1995-2014.nc_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] 0.00043644 0.06902700 0.32410800\n",
      "[1] \"SCA\"\n",
      "[1] 0.0770375 0.2161320 0.5960130\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp26_mid_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.01712240  0.00287075  0.04129930\n",
      "[1] \"SCA\"\n",
      "[1] -0.02658850  0.00930548  0.08412090\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp85_mid_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.00561967  0.02168610  0.06247770\n",
      "[1] \"SCA\"\n",
      "[1] -0.0452072  0.0131816  0.3410640\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp26_far_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.01673970  0.00113834  0.01233060\n",
      "[1] \"SCA\"\n",
      "[1] -0.02898510  0.00494707  0.08572120\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/rcp85_far_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.00249843  0.01635540  0.04703030\n",
      "[1] \"SCA\"\n",
      "[1] -0.0538786  0.0563606  0.5657640\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/1.5_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.02541940  0.00024144  0.04536800\n",
      "[1] \"SCA\"\n",
      "[1] -0.0285977  0.0107664  0.1074590\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/2.0_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.00483653  0.01822550  0.05730020\n",
      "[1] \"SCA\"\n",
      "[1] -0.03387830  0.00400915  0.31004500\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CMIP5/4.0_CAM.txt\"\n",
      "[1] \"CAR\"\n",
      "[1] -0.0685092  0.0234918  0.0476606\n",
      "[1] \"SCA\"\n",
      "[1] -0.08312900  0.00960012  0.23224400\n"
     ]
    }
   ],
   "source": [
    "ensemble = 'CMIP5'\n",
    "\n",
    "for (continent in names(regions_by_continent)){\n",
    "    print(paste(continent,'==> '))\n",
    "    print(regions_by_continent[[continent]])\n",
    "    \n",
    "    # -- Recent past raw value\n",
    "    clim_period = 'modern_raw'\n",
    "    wclim_period='rcp85_1995-2014'\n",
    "    if (continent=='EUR_MaskDesert'){\n",
    "        wcontinent='EUR'\n",
    "    }else{\n",
    "        wcontinent=continent\n",
    "    }\n",
    "    metrics_filename = paste(CWD,'/../../data/Figure_12.10/Q100_',ensemble,'/Q100_',wclim_period,'.nc_',wcontinent,'.txt',sep='')\n",
    "    print(paste('Raw values :', metrics_filename))\n",
    "    res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "    for (region_name in names(res_metrics_by_AR6region)){\n",
    "        all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "    }\n",
    "     \n",
    "    # -- Differences for future time periods and GWLs\n",
    "    for (clim_period in c('2.6_mid', '8.5_mid', '2.6_late', '8.5_late','1.5','2','4')){\n",
    "        if (clim_period=='2.6_mid'){ wclim_period='rcp26_mid' }\n",
    "        if (clim_period=='2.6_late'){ wclim_period='rcp26_far' }\n",
    "        if (clim_period=='8.5_mid'){ wclim_period='rcp85_mid' }\n",
    "        if (clim_period=='8.5_late'){ wclim_period='rcp85_far' }\n",
    "        if (clim_period=='1.5'){ wclim_period='1.5' }\n",
    "        if (clim_period=='2'){ wclim_period='2.0' }\n",
    "        if (clim_period=='4'){ wclim_period='4.0' }\n",
    "        \n",
    "        metrics_filename = paste(CWD,'/../../data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_',ensemble,'/',wclim_period,'_',continent,'.txt',sep='')\n",
    "        print(metrics_filename)\n",
    "        res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "        for (region_name in names(res_metrics_by_AR6region)){\n",
    "            all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "        }\n",
    "    }#end for clim_period\n",
    "    \n",
    "    \n",
    "}#end for continent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in CORDEX data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"NAM ==> \"\n",
      "[1] \"NWN\" \"NEN\" \"WNA\" \"CNA\" \"ENA\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CORDEX-core/Q100_rcp85_1995-2014.nc_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] 0.363984 0.427003 0.469930\n",
      "[1] \"NEN\"\n",
      "[1] 0.459654 0.507664 0.530789\n",
      "[1] \"WNA\"\n",
      "[1] 0.293783 0.402776 0.488065\n",
      "[1] \"CNA\"\n",
      "[1] 0.326992 0.364491 0.632477\n",
      "[1] \"ENA\"\n",
      "[1] 0.516386 0.556454 0.815486\n",
      "[1] \"NCA\"\n",
      "[1] 0.380500 0.515620 0.729145\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp26_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.00916119  0.00493817  0.03311130\n",
      "[1] \"NEN\"\n",
      "[1] -0.0351532  0.0206229  0.0278818\n",
      "[1] \"WNA\"\n",
      "[1] -0.00370867 -0.00144016  0.04307190\n",
      "[1] \"CNA\"\n",
      "[1] 0.0413091 0.0420886 0.0869718\n",
      "[1] \"ENA\"\n",
      "[1] 0.0645571 0.0736831 0.1122940\n",
      "[1] \"NCA\"\n",
      "[1] -0.0914313  0.0518950  0.1058620\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp85_mid_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.02053640  0.00739312  0.04669780\n",
      "[1] \"NEN\"\n",
      "[1] -0.0282537  0.0253201  0.0306902\n",
      "[1] \"WNA\"\n",
      "[1] -0.0153377  0.0125510  0.0371131\n",
      "[1] \"CNA\"\n",
      "[1] 0.0446589 0.0539559 0.0990229\n",
      "[1] \"ENA\"\n",
      "[1] 0.0219710 0.0365221 0.1401960\n",
      "[1] \"NCA\"\n",
      "[1] -0.14016100  0.00470882  0.08651460\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp26_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.00732038  0.01678480  0.02121780\n",
      "[1] \"NEN\"\n",
      "[1] -0.02314960  0.00432082  0.01295360\n",
      "[1] \"WNA\"\n",
      "[1] -0.01287540 -0.00843198 -0.00136625\n",
      "[1] \"CNA\"\n",
      "[1] 0.0215938 0.0571074 0.0596003\n",
      "[1] \"ENA\"\n",
      "[1] 0.0172181 0.0588112 0.0605356\n",
      "[1] \"NCA\"\n",
      "[1] -0.0971975  0.0288657  0.1651060\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp85_far_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.00350203  0.06116160  0.14044600\n",
      "[1] \"NEN\"\n",
      "[1] 0.0179837 0.0376766 0.0946648\n",
      "[1] \"WNA\"\n",
      "[1] -0.0212702  0.0588239  0.0895108\n",
      "[1] \"CNA\"\n",
      "[1] 0.0732284 0.0902175 0.2151120\n",
      "[1] \"ENA\"\n",
      "[1] 0.0430246 0.1122670 0.2971820\n",
      "[1] \"NCA\"\n",
      "[1] -0.2538280  0.0153188  0.1094910\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/1.5_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.01497380  0.00281702  0.03151990\n",
      "[1] \"NEN\"\n",
      "[1] -0.02388640 -0.00614692  0.04242840\n",
      "[1] \"WNA\"\n",
      "[1] -0.00174443  0.00425908  0.03831630\n",
      "[1] \"CNA\"\n",
      "[1] -0.00355312  0.02061390  0.05018980\n",
      "[1] \"ENA\"\n",
      "[1] -0.0137650  0.0141776  0.0776879\n",
      "[1] \"NCA\"\n",
      "[1] -0.0812847  0.0660255  0.1896660\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/2.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.01803810  0.00476158  0.02965830\n",
      "[1] \"NEN\"\n",
      "[1] -0.00866527  0.01061780  0.02943440\n",
      "[1] \"WNA\"\n",
      "[1] -0.01520590  0.00347544  0.04246380\n",
      "[1] \"CNA\"\n",
      "[1] 0.00474652 0.03897650 0.08864790\n",
      "[1] \"ENA\"\n",
      "[1] 0.00808929 0.02469430 0.11326800\n",
      "[1] \"NCA\"\n",
      "[1] -0.12414600  0.00393727  0.05556900\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/4.0_NAM.txt\"\n",
      "[1] \"NWN\"\n",
      "[1] -0.01395970  0.00993871  0.10608900\n",
      "[1] \"NEN\"\n",
      "[1] 0.00403534 0.02538660 0.08785590\n",
      "[1] \"WNA\"\n",
      "[1] -0.00711391  0.05269130  0.08254080\n",
      "[1] \"CNA\"\n",
      "[1] 0.0535032 0.0791704 0.1776090\n",
      "[1] \"ENA\"\n",
      "[1] 0.0619083 0.0896884 0.2515370\n",
      "[1] \"NCA\"\n",
      "[1] -0.0405797  0.0391568  0.1220400\n",
      "[1] \"CAM ==> \"\n",
      "[1] \"NCA\" \"CAR\"\n",
      "[1] \"Raw values : /home/jservon/Chapter12_IPCC/data/Figure_12.10/Q100_CORDEX-core/Q100_rcp85_1995-2014.nc_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] 0.193494 0.380557 1.121330\n",
      "[1] \"SCA\"\n",
      "[1] 0.315145 1.402520 2.129400\n",
      "[1] \"CAR\"\n",
      "[1] 0.175109 0.526075 2.338800\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp26_mid_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.115958 -0.046093  0.103081\n",
      "[1] \"SCA\"\n",
      "[1] -0.1836740 -0.0287612  0.3553720\n",
      "[1] \"CAR\"\n",
      "[1] -0.15123300 -0.00878529  0.13581500\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp85_mid_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.05731030  0.00777033  0.23260200\n",
      "[1] \"SCA\"\n",
      "[1] -0.2386340 -0.0404783  0.1938850\n",
      "[1] \"CAR\"\n",
      "[1] -0.3242450  0.0150004  0.2417100\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp26_far_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.07672830 -0.00603039  0.14782500\n",
      "[1] \"SCA\"\n",
      "[1] -0.3759920 -0.0391509  0.2263780\n",
      "[1] \"CAR\"\n",
      "[1] -0.3399550 -0.0471479  0.4939970\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/rcp85_far_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.2426590 -0.0211087  0.4659710\n",
      "[1] \"SCA\"\n",
      "[1] -0.5200230 -0.0395511  0.7884020\n",
      "[1] \"CAR\"\n",
      "[1] -0.7595430 -0.0411182  0.8566700\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/1.5_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.06195520  0.00539743  0.12389500\n",
      "[1] \"SCA\"\n",
      "[1] -0.272348000 -0.000118571  0.178990000\n",
      "[1] \"CAR\"\n",
      "[1] -0.5810510 -0.0315588  0.0937526\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/2.0_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.05198160  0.00563257  0.06199010\n",
      "[1] \"SCA\"\n",
      "[1] -0.0408129  0.0492063  0.1722910\n",
      "[1] \"CAR\"\n",
      "[1] -0.509306  0.109431  0.263291\n",
      "[1] \"/home/jservon/Chapter12_IPCC/data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_CORDEX-core/4.0_CAM.txt\"\n",
      "[1] \"NCA\"\n",
      "[1] -0.13383400 -0.00138514  0.32219300\n",
      "[1] \"SCA\"\n",
      "[1] -0.461561 -0.109069  0.370773\n",
      "[1] \"CAR\"\n",
      "[1] -0.9191090  0.0259363  0.4576480\n"
     ]
    }
   ],
   "source": [
    "ensemble = 'CORDEX'\n",
    "wensemble = paste(ensemble,'-core',sep='')\n",
    "\n",
    "for (continent in names(regions_by_continent)){\n",
    "    print(paste(continent,'==> '))\n",
    "    print(regions_by_continent[[continent]])\n",
    "    \n",
    "    # -- Recent past raw value\n",
    "    clim_period = 'modern_raw'\n",
    "    wclim_period='rcp85_1995-2014'\n",
    "    if (continent=='EUR_MaskDesert'){\n",
    "        wcontinent='EUR'\n",
    "    }else{\n",
    "        wcontinent=continent\n",
    "    }\n",
    "    metrics_filename = paste(CWD,'/../../data/Figure_12.10/Q100_',wensemble,'/Q100_',wclim_period,'.nc_',wcontinent,'.txt',sep='')\n",
    "    print(paste('Raw values :', metrics_filename))\n",
    "    res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "    for (region_name in names(res_metrics_by_AR6region)){\n",
    "        all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "    }\n",
    "     \n",
    "    # -- Differences for future time periods and GWLs\n",
    "    for (clim_period in c('2.6_mid', '8.5_mid', '2.6_late', '8.5_late','1.5','2','4')){\n",
    "        if (clim_period=='2.6_mid'){ wclim_period='rcp26_mid' }\n",
    "        if (clim_period=='2.6_late'){ wclim_period='rcp26_far' }\n",
    "        if (clim_period=='8.5_mid'){ wclim_period='rcp85_mid' }\n",
    "        if (clim_period=='8.5_late'){ wclim_period='rcp85_far' }\n",
    "        if (clim_period=='1.5'){ wclim_period='1.5' }\n",
    "        if (clim_period=='2'){ wclim_period='2.0' }\n",
    "        if (clim_period=='4'){ wclim_period='4.0' }\n",
    "        \n",
    "        metrics_filename = paste(CWD,'/../../data/PERCENTILES_CHANGE_DIVDRA_ABSOLUTE/percentiles_',wensemble,'/',wclim_period,'_',continent,'.txt',sep='')\n",
    "        print(metrics_filename)\n",
    "        res_metrics_by_AR6region = metrics_reader(metrics_filename)\n",
    "        for (region_name in names(res_metrics_by_AR6region)){\n",
    "            all_regions[[region_name]][[ensemble]][[clim_period]][['vals']] = res_metrics_by_AR6region[[region_name]]\n",
    "        }\n",
    "    }#end for clim_period\n",
    "    \n",
    "    \n",
    "}#end for continent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribute the colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (region_name in continent_regions){\n",
    "    ensemble = 'CMIP6'\n",
    "    all_regions[[region_name]][[ensemble]][['modern_raw']][['color']] = 'black'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_mid']][['color']] = 'dodgerblue4'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_mid']][['color']] = 'red'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_late']][['color']] = 'dodgerblue4'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_late']][['color']] = 'red'\n",
    "    all_regions[[region_name]][[ensemble]][['1.5']][['color']] = 'darkorchid4'\n",
    "    all_regions[[region_name]][[ensemble]][['2']][['color']] = 'darkorange'\n",
    "    all_regions[[region_name]][[ensemble]][['4']][['color']] = 'tan4'\n",
    "\n",
    "    ensemble = 'CMIP5'\n",
    "    all_regions[[region_name]][[ensemble]][['modern_raw']][['color']] = 'grey40'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_mid']][['color']] = 'dodgerblue3'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_mid']][['color']] = 'lightcoral'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_late']][['color']] = 'dodgerblue3'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_late']][['color']] = 'lightcoral'\n",
    "    all_regions[[region_name]][[ensemble]][['1.5']][['color']] = 'darkorchid3'\n",
    "    all_regions[[region_name]][[ensemble]][['2']][['color']] = 'sandybrown'\n",
    "    all_regions[[region_name]][[ensemble]][['4']][['color']] = 'peachpuff4'\n",
    "\n",
    "    ensemble = 'CORDEX'\n",
    "    all_regions[[region_name]][[ensemble]][['modern_raw']][['color']] = 'grey60'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_mid']][['color']] = 'dodgerblue'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_mid']][['color']] = 'lightpink'\n",
    "    all_regions[[region_name]][[ensemble]][['2.6_late']][['color']] = 'dodgerblue'\n",
    "    all_regions[[region_name]][[ensemble]][['8.5_late']][['color']] = 'lightpink'\n",
    "    all_regions[[region_name]][[ensemble]][['1.5']][['color']] = 'mediumpurple1'\n",
    "    all_regions[[region_name]][[ensemble]][['2']][['color']] = 'navajowhite2'\n",
    "    all_regions[[region_name]][[ensemble]][['4']][['color']] = 'peachpuff3'\n",
    "}#end for region_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to draw the barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"vert_scale\"=function(val, ylim){\n",
    "    return( (val - ylim[1]) / (ylim[2] - ylim[1]) )\n",
    "}\n",
    "\n",
    "\n",
    "\"barplot_satellite_chap12\"=function(data, title, ylabel, ylims, do_xlab='TRUE', \n",
    "                                    do_ylab_raw='TRUE', do_ylab_diff='TRUE', mar=c(4,3,2.5,1)){\n",
    "    #\n",
    "    # -- Names\n",
    "    ensembles = c('CORDEX','CMIP5', 'CMIP6')\n",
    "    GWLs = c('1.5','2','4')\n",
    "    scenarios = c('2.6_mid', '8.5_mid', '2.6_late', '8.5_late')\n",
    "    nhorizons = length(GWLs)+length(scenarios)\n",
    "    \n",
    "    ylim_base = c(0,1)\n",
    "    #\n",
    "    ylim_raw = ylims[['ylim_raw']]\n",
    "    ylim_raw_levels = ylims[['ylim_raw_levels']]\n",
    "    ylim_diff = ylims[['ylim_diff']]\n",
    "    ylim_diff_levels = ylims[['ylim_diff_levels']]    \n",
    "        \n",
    "    ylim = ylim_base\n",
    "    \n",
    "    # -- position on the x axis of the ensembles\n",
    "    # RP, GWL 1.5, 2, 4, mid, long\n",
    "    xpos = c(1, 2,3,4, 5,6, 7,8)\n",
    "    xvert = c(1.5, 2.5,3.5,4.5, 6.5)\n",
    "    inner_margin = 0.5\n",
    "    xlim = c(1-inner_margin, max(xpos)+inner_margin)\n",
    "    general_cex=1.3\n",
    "    par(cex=general_cex)\n",
    "    par(mar=mar)\n",
    "    plot(1:nhorizons, rep(NA, nhorizons), col='white', ylim=ylim, xlim=xlim,\n",
    "         xaxt='n', yaxt='n', xlab='', ylab='', xaxs=\"i\", yaxs='i', font=2)\n",
    "    mtext(ylabel, 2, font=2, line=2.2, cex=general_cex)\n",
    "    #     cex.axis=1.2, cex.lab=1.5, font=2)\n",
    "    par(xpd=NA)\n",
    "    par(font=2)\n",
    "    \n",
    "    # -- Add horizontal lines for diff\n",
    "    for (yval in vert_scale(ylim_diff_levels, ylim_diff)){\n",
    "        if (yval==vert_scale(0,ylim_diff)){\n",
    "            lines(c(xvert[1],xlim[2]),rep(yval,2),type='l',lwd=2)\n",
    "        }else{\n",
    "            lines(c(xvert[1],xlim[2]),rep(yval,2),type='l', lty=2)\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    delta = 0.24 # - space between CMIP6/CMIP5/CORDEX\n",
    "    # -- Plot data\n",
    "    # -- Plot baseline\n",
    "    i = 1\n",
    "    for (horizon in c('modern_raw',GWLs, scenarios)){\n",
    "        j = xpos[i] - delta\n",
    "        for (ensemble in ensembles){\n",
    "            print(paste(horizon, ensemble))\n",
    "            if (horizon=='modern_raw'){\n",
    "                dat = vert_scale(data[[ensemble]][[horizon]][['vals']], ylim_raw)\n",
    "            }else{\n",
    "                dumdat = data[[ensemble]][[horizon]][['vals']]\n",
    "                if (dumdat[1]<ylim_diff[1]){dumdat[1]=ylim_diff[1]}\n",
    "                if (dumdat[3]>ylim_diff[2]){dumdat[3]=ylim_diff[2]}\n",
    "                dat = vert_scale(dumdat, ylim_diff)\n",
    "            }\n",
    "            print(dat)\n",
    "            if (is.null(dat)){dat = c(NA,NA,NA)}\n",
    "            col = data[[ensemble]][[horizon]][['color']]\n",
    "            lines(rep(j,2), c(dat[1],dat[3]), type='l', col=col, lwd=4)\n",
    "            points(j, dat[2], cex=1.5, pch=16, col=col)\n",
    "            j = j + delta\n",
    "        }\n",
    "        i = i + 1\n",
    "    }\n",
    "\n",
    "    \n",
    "    # -- Y axis raw\n",
    "    yvals = ylim_raw_levels\n",
    "    dumyvals = vert_scale(yvals, ylim_raw)\n",
    "    ylabels = yvals\n",
    "    par(cex=2.4)\n",
    "    axis(2, at=dumyvals, labels=rep('',length(yvals)))\n",
    "    if (do_ylab_raw=='TRUE'){\n",
    "        axis(2, pos=0.75, at=dumyvals, tick='FALSE', labels=ylabels, las=1)\n",
    "    }\n",
    "    par(cex=general_cex)\n",
    "    \n",
    "    # -- Y axis Differences\n",
    "    yvals = ylim_diff_levels\n",
    "    dumyvals = vert_scale(yvals, ylim_diff)\n",
    "    ylabels = yvals\n",
    "    par(cex=2.2)\n",
    "    axis(4, at=dumyvals, labels=rep('',length(yvals)))\n",
    "    if (do_ylab_diff=='TRUE'){\n",
    "        axis(4, pos=xlim[2]-0.25, at=dumyvals, labels=ylabels, las=1, tick='FALSE')\n",
    "    }\n",
    "    par(cex=general_cex)\n",
    "    \n",
    "\n",
    "    # -- X axis\n",
    "    if (do_xlab=='FALSE'){\n",
    "        axis(1, at=xvert, labels=rep('',length(xvert)))\n",
    "    }\n",
    "    # -- Title\n",
    "    mtext(title,3, font=2, cex=3, line=0.5)\n",
    "\n",
    "    bline = ylim[1] - 0.22*(ylim[2]-ylim[1])\n",
    "    #\n",
    "    # -- X axis\n",
    "    if (do_xlab=='FALSE'){\n",
    "        axis(1, at=xvert, labels=rep('',length(xvert)))\n",
    "    }\n",
    "\n",
    "    # -- Vertical lines\n",
    "    vline = ylim\n",
    "    vline[1] = ylim[1] - (ylim[2]-ylim[1])*0.16\n",
    "    if (do_xlab=='TRUE'){\n",
    "        for (v in xvert){lines(c(v,v),vline,type='l', lwd=1)}\n",
    "    }else{\n",
    "        for (v in xvert){lines(c(v,v),ylim,type='l')}\n",
    "    }\n",
    "    # Make a thicker line to separate r.past from the rest\n",
    "    vline = ylim\n",
    "    v = xvert[1]\n",
    "    lines(c(v,v),vline,type='l', lwd=3)\n",
    "    \n",
    "\n",
    "    if (do_xlab=='TRUE'){\n",
    "        cex_text = 1.8\n",
    "        bline = ylim[1] - 0.2*(ylim[2]-ylim[1])\n",
    "        #\n",
    "        # -- Labels GWLs\n",
    "        GWLs_range = c(1.5,4.35)\n",
    "        btext = ylim[1] - 0.3*(ylim[2]-ylim[1])\n",
    "        lines(GWLs_range, c(bline,bline), type='l', lwd=3)\n",
    "        text(mean(GWLs_range),btext,expression(\"GWL\"), font=1, cex=cex_text*0.9)\n",
    "\n",
    "        btext2 = ylim[1] - 0.10*(ylim[2]-ylim[1])\n",
    "        text(1,btext2-0.12,\"r.past.\", cex=cex_text, font=1, srt=90, adj=c(0.5,0.5))\n",
    "        text(2,btext2,\"1.5\", cex=cex_text, font=1, adj=c(0.5,0.5))#, srt=90\n",
    "        text(3,btext2,\"2\", cex=cex_text, font=1, adj=c(0.5,0.5))#, srt=90\n",
    "        text(4,btext2,\"4\", cex=cex_text, font=1, adj=c(0.5,0.5))#, srt=90\n",
    "\n",
    "        # -- Labels time slices = modern, mid-term, long-term\n",
    "        xmodern = 4.5\n",
    "        xmid = 5.5\n",
    "        xlate = 7.5\n",
    "        text(xmid,btext2, \"mid.\", font=1, cex=cex_text, adj=c(0.5,0.5))#, srt=90\n",
    "        text(xlate,btext2, \"long.\", font=1, cex=cex_text, adj=c(0.5,0.5))#, srt=90\n",
    "\n",
    "        time_slices = c(4.65,8.5)\n",
    "        lines(time_slices, c(bline,bline), type='l', lwd=3)\n",
    "        text(mean(time_slices),btext,\"Time slices\", font=1, cex=cex_text)\n",
    "    }#end if do_ylab\n",
    "    \n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to draw a horizontal legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"draw_legend_box_hor\"=function(data, mar=c(0,0,0,0)){\n",
    "\n",
    "    par(mar=mar)\n",
    "    plot(0:1,0:1,col='white',xaxt='n',yaxt='n', xlab='', ylab='')#, bty='n')\n",
    "    text(-0.02,0.88, \"Legend\", font=2, cex=2, adj=0)\n",
    "    linewidth = 0.07\n",
    "    lwd=13\n",
    "    xcol1 = 0.2\n",
    "    ycol1 = 0.48\n",
    "    space_between_cols = linewidth*1.5\n",
    "    space_between_lines = 0.2\n",
    "    ycol = ycol1\n",
    "    xensemble = xcol1 - 0.05\n",
    "    xensemble = xcol1 - 0.05\n",
    "    cex_text = 1.7\n",
    "    cex_title = 1.9\n",
    "    for (ensemble in c('CMIP6','CMIP5','CORDEX')){\n",
    "        xcol = xcol1\n",
    "        text(xensemble, ycol, ensemble, font=2, cex=cex_text, adj=1)\n",
    "        for (GWL in c('1.5','2','4')){\n",
    "            lines(c(xcol,xcol+linewidth), c(ycol, ycol), type='l', lwd=lwd, col=data[[ensemble]][[GWL]][['color']])\n",
    "            if (ensemble=='CMIP6'){\n",
    "                if (GWL=='1.5'){ tmplab = expression('+1.5'*degree*C) }\n",
    "                if (GWL=='2'){ tmplab = expression('+2'*degree*C) }\n",
    "                if (GWL=='4'){ tmplab = expression('+4'*degree*C) }\n",
    "                text(mean(c(xcol,xcol+linewidth)), ycol1+space_between_lines, tmplab, font=2, cex=cex_text)\n",
    "                if (GWL=='1.5'){\n",
    "                    text(xcol, ycol1+space_between_lines*2.2, 'GWL', font=2, cex=cex_title, adj=0)\n",
    "                }\n",
    "            }\n",
    "            xcol = xcol + space_between_cols\n",
    "        }\n",
    "        ycol = ycol - space_between_lines\n",
    "    }\n",
    "\n",
    "    xcol1 = 0.55\n",
    "    ycol = ycol1\n",
    "    xensemble = xcol1 - 0.05\n",
    "    xensemble = xcol1 - 0.05\n",
    "    for (ensemble in c('CMIP6','CMIP5','CORDEX')){\n",
    "        xcol = xcol1\n",
    "        for (time_slice in c('modern_raw','2.6_late','8.5_late')){\n",
    "            if (time_slice=='modern_raw'){ tmplab = 'r.past' }\n",
    "            if (time_slice=='2.6_late'){ tmplab = '2.6' }\n",
    "            if (time_slice=='8.5_late'){ tmplab = '8.5' }\n",
    "            lines(c(xcol,xcol+linewidth), c(ycol, ycol), type='l', lwd=lwd, col=data[[ensemble]][[time_slice]][['color']])\n",
    "            if (ensemble=='CMIP6'){\n",
    "                text(mean(c(xcol,xcol+linewidth)), ycol1+space_between_lines, tmplab, font=1, cex=cex_text)\n",
    "            }\n",
    "            if (time_slice=='modern_raw'){\n",
    "                text(xcol, ycol1+space_between_lines*2.2, 'RCPs/SSPs', font=2, cex=cex_title, adj=0)\n",
    "            }\n",
    "            xcol = xcol + space_between_cols\n",
    "        }\n",
    "        ycol = ycol - space_between_lines\n",
    "    }\n",
    "        ycol = ycol - space_between_lines\n",
    "    \n",
    "    yline = 0.5\n",
    "    ypos = 0.5\n",
    "    ylen = 0.3\n",
    "    xx = 0.9\n",
    "    lines(c(xx,xx),c(ypos-ylen,ypos+ylen), type='l', lwd=lwd/2, col='black')\n",
    "    points(xx,ypos, pch=16, cex=2.2)\n",
    "    text(xx+0.02,ypos+(ylen*1.1), expression(paste(90^th,\"p\")), font=1, cex=cex_text,adj=0)\n",
    "    text(xx+0.02,ypos-(ylen*1.1), expression(paste(10^th,\"p\")), font=1, cex=cex_text,adj=0)\n",
    "    text(xx+0.02,ypos, \"median\", font=1, cex=cex_text, adj=0)\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2911872 0.3416024 0.3759440\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1705848 0.2828784 0.3775912\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1298792 0.2007752 0.2886880\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3700524 0.4056340 0.4630398\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3829142 0.3931590 0.4170519\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3618542 0.3988900 0.4239360\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3639238 0.4095232 0.4593166\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3622846 0.3963036 0.4480724\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3342230 0.3935819 0.4201960\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.3720806 0.4198774 0.6121780\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3305518 0.3950922 0.4653540\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3236628 0.3773088 0.4167175\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3816776 0.4098763 0.4662226\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3644290 0.4048579 0.4355788\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3280466 0.3931151 0.4079156\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3589272 0.4147862 0.4933956\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3362908 0.3865570 0.4319950\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3296226 0.3927518 0.4258366\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3853592 0.4335696 0.4424356\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3562818 0.4020700 0.4536146\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3329614 0.3906268 0.4147104\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.3929959 0.5223232 0.6808920\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3305438 0.4080127 0.4627134\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3401542 0.3901360 0.4325498\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.3677232 0.4061312 0.4246312\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1660024 0.3196472 0.4416944\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1116912 0.2342016 0.3381040\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3522272 0.3877062 0.4848568\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3585888 0.4042890 0.4470080\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3590218 0.3889071 0.4287632\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3826695 0.4212356 0.4588688\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3492494 0.4197330 0.4558440\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3334088 0.3791574 0.4187963\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.4080707 0.4507732 0.5757118\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3098312 0.4189344 0.5437222\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3130778 0.3280698 0.3636272\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3296936 0.4412458 0.4557636\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3701742 0.4162020 0.4524766\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3316854 0.3860559 0.4271234\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3434926 0.4506402 0.4613804\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3494796 0.4192669 0.4607008\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3049984 0.3839947 0.4466166\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3537008 0.4086416 0.4259072\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3420224 0.4082911 0.4492670\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3183478 0.3873061 0.4306320\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.4359674 0.4753532 0.5893296\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3343648 0.4171813 0.4967312\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.2413838 0.3505034 0.4466052\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2350264 0.3222208 0.3904520\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.0909744 0.2305688 0.3140480\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.07820456 0.12747120 0.26604880\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3965111 0.4085182 0.4766326\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3510358 0.4151262 0.4543602\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3714892 0.3947855 0.4231700\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3695882 0.4069509 0.4849276\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3456100 0.4091520 0.4848566\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3538616 0.4006919 0.4435982\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.3857722 0.5053826 0.5650816\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3975444 0.4373166 0.4676392\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3519134 0.4124511 0.4507504\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3925827 0.3971197 0.4861438\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3538110 0.3960907 0.4293662\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3690948 0.3991975 0.4423932\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3693246 0.4251020 0.4742262\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3290040 0.3919765 0.4982522\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3465058 0.4027387 0.4637842\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3742492 0.3831360 0.3972675\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3189926 0.3891316 0.4388302\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3719192 0.4148733 0.4838970\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.3574596 0.5176478 0.5790216\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.2589258 0.4210072 0.4794334\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3390498 0.4015375 0.4988690\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2615936 0.2915928 0.5059816\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.05570816 0.16805680 0.28876640\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.05383936 0.14832000 0.28397120\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3928938 0.4412278 0.5003796\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3484432 0.3932637 0.4605994\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3525708 0.4028870 0.4955644\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.4094930 0.4779530 0.5772958\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3350466 0.3983769 0.4789718\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3382898 0.4251190 0.5151516\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.5070064 0.5583408 0.7552180\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3757034 0.3905240 0.6108880\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3747602 0.5054218 0.6363900\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.4826182 0.4841772 0.5739436\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3437380 0.3943666 0.4185634\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.2967590 0.4055195 0.5144472\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.4893178 0.5079118 0.5980458\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3490360 0.4183789 0.4894852\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3260316 0.4191809 0.5359626\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.4431876 0.5142148 0.5192006\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3452222 0.3931757 0.4767610\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3487392 0.4213958 0.5767498\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.5464568 0.5804350 0.8302240\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3098566 0.4176576 0.5847122\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3117574 0.4565884 0.8480540\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.4131088 0.4451632 0.6523888\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1193944 0.3260384 0.4590512\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1831280 0.2491904 0.4112152\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3724700 0.4283552 0.5553758\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3310728 0.4195565 0.5131814\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3400130 0.4125730 0.4674554\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.4161786 0.4493886 0.6265360\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3533440 0.3855659 0.5330706\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3293060 0.4206142 0.4887180\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.5238166 0.5793768 0.9030740\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3266578 0.4317370 0.6431460\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3937090 0.4751222 0.6134920\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.5291142 0.5473662 0.6245880\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3707632 0.3824122 0.4728940\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3248036 0.4001010 0.5380804\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.4439420 0.4730442 0.6803920\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3525438 0.3843672 0.5565420\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3273380 0.4207728 0.5215226\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.4344362 0.5176224 0.5210712\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3371618 0.3807319 0.4461886\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3363712 0.4145896 0.5734346\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.4860492 0.6245340 0.9943640\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.2632154 0.3796988 0.6370880\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3085438 0.5024066 0.7831760\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.1547952 0.3044456 0.8970640\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.0686348 0.1468400 0.3020744\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.07817456 0.12183760 0.23410080\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.2760896 0.4107949 0.6477900\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3539666 0.4004756 0.4717474\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3591308 0.4217970 0.4770694\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.2960368 0.4112651 0.5239802\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3450628 0.3988330 0.5810242\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3745884 0.4469066 0.5281766\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.1323320 0.3972297 1.0000000\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3265152 0.3848764 0.8764640\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.4665402 0.5287006 0.6670660\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.168084 0.307814 0.606162\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3427966 0.4498272 0.4898652\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3508840 0.4182971 0.6067280\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.2853794 0.4155407 0.8652040\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3436718 0.4244958 0.6045200\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3697876 0.4394288 0.5542816\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.2465434 0.3879392 0.6956500\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3380978 0.4202170 0.4904536\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3506058 0.4243374 0.5844302\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.0000000 0.3577826 1.0000000\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3021076 0.4534044 0.7968640\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3410552 0.4539102 0.7720460\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.0700436 0.2104300 0.9355200\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.000174576 0.027610800 0.129643200\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.02239132 0.04880880 0.19499240\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.2094745 0.4842206 0.5468763\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.4872903 0.5001207 0.5226840\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.4838951 0.5002108 0.5472176\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.2453470 0.5547155 0.6316455\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.4975817 0.5091128 0.5286501\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.4925351 0.5043543 0.5644730\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.0404455 0.5129681 0.7288240\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.4657454 0.5117459 0.5238303\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.4849121 0.5349619 0.5717270\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.4243835 0.4956074 0.5679075\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.4914388 0.5014354 0.5206496\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.4911548 0.5015020 0.5729565\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3378775 0.5075002 0.6208550\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.4971902 0.5108430 0.5312389\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.4803737 0.5008101 0.5457218\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3300225 0.4764260 0.7469985\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.4916301 0.5005692 0.5061653\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.4784706 0.5006351 0.5780740\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.1202285 0.4794409 0.9283350\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.4987508 0.5081777 0.5235152\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.4680110 0.5078348 0.6068695\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region = 'NORTH-AMERICA'\n",
    "\n",
    "\n",
    "figname = paste(CWD,'/../../figs/Figure_12.10/panel_c_',region,'_Q100_boxplot.png',sep='')\n",
    "png(figname, width=900,height=1100)\n",
    "mat = t(matrix(1:8,2,4))\n",
    "mat[4,2] = 0\n",
    "layout(mat, heights=c(1,1,1,1.35), widths=c(1,1))\n",
    "i = 1\n",
    "for (subregion in continent_regions){\n",
    "    mar = c(1,1,2.5,1)\n",
    "    if (subregion %in% c('NWN','WNA','ENA','CAR')){\n",
    "        do_ylab_raw='TRUE'\n",
    "        mar[2]=4\n",
    "    }else{\n",
    "        do_ylab_raw='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('NEN','CNA','NCA','CAR')){\n",
    "        do_ylab_diff='TRUE'\n",
    "        #mar[4]=4\n",
    "    }else{\n",
    "        do_ylab_diff='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('NEN','CNA','NCA')){mar[4]=4}\n",
    "    if (subregion %in% c('NCA','CAR')){\n",
    "        do_xlab='TRUE'\n",
    "        #mar[1] = 5 # -- bottom margin\n",
    "    }else{\n",
    "        do_xlab='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('CAR')){mar[1] = 5}\n",
    "\n",
    "    plot_title = subregion\n",
    "    ylab = ''\n",
    "    if (subregion=='CAR'){\n",
    "        ylims = list(\n",
    "            ylim_raw = c(0,2.5),\n",
    "            ylim_raw_levels = seq(0,2.5,by=0.5),\n",
    "            ylim_diff = c(-1,1),\n",
    "            ylim_diff_levels = c(-1,-0.5,0,0.5,1)\n",
    "        )           \n",
    "            \n",
    "    }else{\n",
    "        ylims = list(\n",
    "            ylim_raw = c(0,1.25),\n",
    "            ylim_raw_levels = seq(0,1.25,by=0.25),\n",
    "            ylim_diff = c(-0.2,0.3),\n",
    "            ylim_diff_levels = c(-0.2,-0.1,0,0.1,0.2,0.3)\n",
    "        )           \n",
    "    }\n",
    "    barplot_satellite_chap12(all_regions[[subregion]],plot_title, ylab, ylims, mar=mar, do_xlab=do_xlab,\n",
    "                             do_ylab_raw=do_ylab_raw, do_ylab_diff=do_ylab_diff)\n",
    "    \n",
    "    i = i + 1\n",
    "}\n",
    "#draw_legend_box_hor(all_regions[[subregion]], mar=c(0.1,0,2,0))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“package ‘IRdisplay’ was built under R version 3.6.3”\n"
     ]
    },
    {
     "data": {
      "image/png": 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SsrY/abyZKSffv2Xb9+ffv27ZRncadO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Lly5MPpGVlWWAAAEAAABMmqurKxG9ePFC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\nAAAAAGeQjAIAAAAAZ/4Hiwh5dbbc108AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(\"IRdisplay\")\n",
    "display_png(file=figname) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2911872 0.3416024 0.3759440\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1705848 0.2828784 0.3775912\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1298792 0.2007752 0.2886880\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3700524 0.4056340 0.4630398\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3829142 0.3931590 0.4170519\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3618542 0.3988900 0.4239360\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3639238 0.4095232 0.4593166\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3622846 0.3963036 0.4480724\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3342230 0.3935819 0.4201960\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.3720806 0.4198774 0.6121780\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3305518 0.3950922 0.4653540\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3236628 0.3773088 0.4167175\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3816776 0.4098763 0.4662226\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3644290 0.4048579 0.4355788\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3280466 0.3931151 0.4079156\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3589272 0.4147862 0.4933956\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3362908 0.3865570 0.4319950\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3296226 0.3927518 0.4258366\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3853592 0.4335696 0.4424356\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3562818 0.4020700 0.4536146\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3329614 0.3906268 0.4147104\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.3929959 0.5223232 0.6808920\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3305438 0.4080127 0.4627134\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3401542 0.3901360 0.4325498\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.3677232 0.4061312 0.4246312\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1660024 0.3196472 0.4416944\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1116912 0.2342016 0.3381040\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3522272 0.3877062 0.4848568\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3585888 0.4042890 0.4470080\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3590218 0.3889071 0.4287632\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3826695 0.4212356 0.4588688\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3492494 0.4197330 0.4558440\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3334088 0.3791574 0.4187963\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.4080707 0.4507732 0.5757118\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3098312 0.4189344 0.5437222\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3130778 0.3280698 0.3636272\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3296936 0.4412458 0.4557636\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3701742 0.4162020 0.4524766\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3316854 0.3860559 0.4271234\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3434926 0.4506402 0.4613804\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3494796 0.4192669 0.4607008\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3049984 0.3839947 0.4466166\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3537008 0.4086416 0.4259072\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3420224 0.4082911 0.4492670\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3183478 0.3873061 0.4306320\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.4359674 0.4753532 0.5893296\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3343648 0.4171813 0.4967312\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.2413838 0.3505034 0.4466052\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2350264 0.3222208 0.3904520\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.0909744 0.2305688 0.3140480\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.07820456 0.12747120 0.26604880\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3965111 0.4085182 0.4766326\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3510358 0.4151262 0.4543602\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3714892 0.3947855 0.4231700\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.3695882 0.4069509 0.4849276\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3456100 0.4091520 0.4848566\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3538616 0.4006919 0.4435982\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.3857722 0.5053826 0.5650816\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3975444 0.4373166 0.4676392\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3519134 0.4124511 0.4507504\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.3925827 0.3971197 0.4861438\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3538110 0.3960907 0.4293662\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3690948 0.3991975 0.4423932\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3693246 0.4251020 0.4742262\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3290040 0.3919765 0.4982522\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3465058 0.4027387 0.4637842\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3742492 0.3831360 0.3972675\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3189926 0.3891316 0.4388302\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3719192 0.4148733 0.4838970\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.3574596 0.5176478 0.5790216\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.2589258 0.4210072 0.4794334\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3390498 0.4015375 0.4988690\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.2615936 0.2915928 0.5059816\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.05570816 0.16805680 0.28876640\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.05383936 0.14832000 0.28397120\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3928938 0.4412278 0.5003796\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3484432 0.3932637 0.4605994\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3525708 0.4028870 0.4955644\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.4094930 0.4779530 0.5772958\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3350466 0.3983769 0.4789718\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3382898 0.4251190 0.5151516\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.5070064 0.5583408 0.7552180\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3757034 0.3905240 0.6108880\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3747602 0.5054218 0.6363900\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.4826182 0.4841772 0.5739436\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3437380 0.3943666 0.4185634\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.2967590 0.4055195 0.5144472\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.4893178 0.5079118 0.5980458\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3490360 0.4183789 0.4894852\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3260316 0.4191809 0.5359626\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.4431876 0.5142148 0.5192006\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3452222 0.3931757 0.4767610\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3487392 0.4213958 0.5767498\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.5464568 0.5804350 0.8302240\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3098566 0.4176576 0.5847122\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3117574 0.4565884 0.8480540\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.4131088 0.4451632 0.6523888\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.1193944 0.3260384 0.4590512\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.1831280 0.2491904 0.4112152\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.3724700 0.4283552 0.5553758\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3310728 0.4195565 0.5131814\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3400130 0.4125730 0.4674554\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.4161786 0.4493886 0.6265360\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3533440 0.3855659 0.5330706\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3293060 0.4206142 0.4887180\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.5238166 0.5793768 0.9030740\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3266578 0.4317370 0.6431460\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.3937090 0.4751222 0.6134920\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.5291142 0.5473662 0.6245880\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3707632 0.3824122 0.4728940\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3248036 0.4001010 0.5380804\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.4439420 0.4730442 0.6803920\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3525438 0.3843672 0.5565420\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3273380 0.4207728 0.5215226\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.4344362 0.5176224 0.5210712\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3371618 0.3807319 0.4461886\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3363712 0.4145896 0.5734346\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.4860492 0.6245340 0.9943640\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.2632154 0.3796988 0.6370880\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3085438 0.5024066 0.7831760\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.1547952 0.3044456 0.8970640\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.0686348 0.1468400 0.3020744\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.07817456 0.12183760 0.23410080\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.2760896 0.4107949 0.6477900\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.3539666 0.4004756 0.4717474\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.3591308 0.4217970 0.4770694\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.2960368 0.4112651 0.5239802\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.3450628 0.3988330 0.5810242\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.3745884 0.4469066 0.5281766\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.1323320 0.3972297 1.0000000\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.3265152 0.3848764 0.8764640\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.4665402 0.5287006 0.6670660\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.168084 0.307814 0.606162\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.3427966 0.4498272 0.4898652\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.3508840 0.4182971 0.6067280\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.2853794 0.4155407 0.8652040\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.3436718 0.4244958 0.6045200\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.3697876 0.4394288 0.5542816\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.2465434 0.3879392 0.6956500\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.3380978 0.4202170 0.4904536\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.3506058 0.4243374 0.5844302\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.0000000 0.3577826 1.0000000\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.3021076 0.4534044 0.7968640\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.3410552 0.4539102 0.7720460\n",
      "[1] \"modern_raw CORDEX\"\n",
      "[1] 0.0700436 0.2104300 0.9355200\n",
      "[1] \"modern_raw CMIP5\"\n",
      "[1] 0.000174576 0.027610800 0.129643200\n",
      "[1] \"modern_raw CMIP6\"\n",
      "[1] 0.02239132 0.04880880 0.19499240\n",
      "[1] \"1.5 CORDEX\"\n",
      "[1] 0.2094745 0.4842206 0.5468763\n",
      "[1] \"1.5 CMIP5\"\n",
      "[1] 0.4872903 0.5001207 0.5226840\n",
      "[1] \"1.5 CMIP6\"\n",
      "[1] 0.4838951 0.5002108 0.5472176\n",
      "[1] \"2 CORDEX\"\n",
      "[1] 0.2453470 0.5547155 0.6316455\n",
      "[1] \"2 CMIP5\"\n",
      "[1] 0.4975817 0.5091128 0.5286501\n",
      "[1] \"2 CMIP6\"\n",
      "[1] 0.4925351 0.5043543 0.5644730\n",
      "[1] \"4 CORDEX\"\n",
      "[1] 0.0404455 0.5129681 0.7288240\n",
      "[1] \"4 CMIP5\"\n",
      "[1] 0.4657454 0.5117459 0.5238303\n",
      "[1] \"4 CMIP6\"\n",
      "[1] 0.4849121 0.5349619 0.5717270\n",
      "[1] \"2.6_mid CORDEX\"\n",
      "[1] 0.4243835 0.4956074 0.5679075\n",
      "[1] \"2.6_mid CMIP5\"\n",
      "[1] 0.4914388 0.5014354 0.5206496\n",
      "[1] \"2.6_mid CMIP6\"\n",
      "[1] 0.4911548 0.5015020 0.5729565\n",
      "[1] \"8.5_mid CORDEX\"\n",
      "[1] 0.3378775 0.5075002 0.6208550\n",
      "[1] \"8.5_mid CMIP5\"\n",
      "[1] 0.4971902 0.5108430 0.5312389\n",
      "[1] \"8.5_mid CMIP6\"\n",
      "[1] 0.4803737 0.5008101 0.5457218\n",
      "[1] \"2.6_late CORDEX\"\n",
      "[1] 0.3300225 0.4764260 0.7469985\n",
      "[1] \"2.6_late CMIP5\"\n",
      "[1] 0.4916301 0.5005692 0.5061653\n",
      "[1] \"2.6_late CMIP6\"\n",
      "[1] 0.4784706 0.5006351 0.5780740\n",
      "[1] \"8.5_late CORDEX\"\n",
      "[1] 0.1202285 0.4794409 0.9283350\n",
      "[1] \"8.5_late CMIP5\"\n",
      "[1] 0.4987508 0.5081777 0.5235152\n",
      "[1] \"8.5_late CMIP6\"\n",
      "[1] 0.4680110 0.5078348 0.6068695\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figname = paste(CWD,'/../../figs/Figure_12.10/panel_c_',region,'_Q100_boxplot.pdf',sep='')\n",
    "pdf(figname, width=9*1.4,height=11*1.4)\n",
    "mat = t(matrix(1:8,2,4))\n",
    "mat[4,2] = 0\n",
    "layout(mat, heights=c(1,1,1,1.35), widths=c(1,1))\n",
    "i = 1\n",
    "for (subregion in continent_regions){\n",
    "    mar = c(1,1,2.5,1)\n",
    "    if (subregion %in% c('NWN','WNA','ENA','CAR')){\n",
    "        do_ylab_raw='TRUE'\n",
    "        mar[2]=4\n",
    "    }else{\n",
    "        do_ylab_raw='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('NEN','CNA','NCA','CAR')){\n",
    "        do_ylab_diff='TRUE'\n",
    "        #mar[4]=4\n",
    "    }else{\n",
    "        do_ylab_diff='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('NEN','CNA','NCA')){mar[4]=4}\n",
    "    if (subregion %in% c('NCA','CAR')){\n",
    "        do_xlab='TRUE'\n",
    "        #mar[1] = 5 # -- bottom margin\n",
    "    }else{\n",
    "        do_xlab='FALSE'\n",
    "    }#\n",
    "    if (subregion %in% c('CAR')){mar[1] = 5}\n",
    "\n",
    "    plot_title = subregion\n",
    "    ylab = ''\n",
    "    if (subregion=='CAR'){\n",
    "        ylims = list(\n",
    "            ylim_raw = c(0,2.5),\n",
    "            ylim_raw_levels = seq(0,2.5,by=0.5),\n",
    "            ylim_diff = c(-1,1),\n",
    "            ylim_diff_levels = c(-1,-0.5,0,0.5,1)\n",
    "        )           \n",
    "            \n",
    "    }else{\n",
    "        ylims = list(\n",
    "            ylim_raw = c(0,1.25),\n",
    "            ylim_raw_levels = seq(0,1.25,by=0.25),\n",
    "            ylim_diff = c(-0.2,0.3),\n",
    "            ylim_diff_levels = c(-0.2,-0.1,0,0.1,0.2,0.3)\n",
    "        )           \n",
    "    }\n",
    "    barplot_satellite_chap12(all_regions[[subregion]],plot_title, ylab, ylims, mar=mar, do_xlab=do_xlab,\n",
    "                             do_ylab_raw=do_ylab_raw, do_ylab_diff=do_ylab_diff)\n",
    "    \n",
    "    i = i + 1\n",
    "}\n",
    "#draw_legend_box_hor(all_regions[[subregion]], mar=c(0.1,0,2,0))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "mimetype": "text/x-r-source",
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