diff --git a/country_summaries/IPCC_classes_DPS/Boreal/Merge_ICESat-2_and_GEDI.ipynb b/country_summaries/IPCC_classes_DPS/Boreal/Merge_ICESat-2_and_GEDI.ipynb
index c2f9925de00858e6022b6e5946cafc524945881c..3bef82cbc0a8b26f7b0c9381419e01fd105e6130 100644
--- a/country_summaries/IPCC_classes_DPS/Boreal/Merge_ICESat-2_and_GEDI.ipynb
+++ b/country_summaries/IPCC_classes_DPS/Boreal/Merge_ICESat-2_and_GEDI.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"id": "84f4bc4e-3184-4c91-91b7-c6dea8c664ae",
"metadata": {
"tags": []
@@ -12,117 +12,52 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Loading required package: viridis\n",
- "\n",
- "Loading required package: viridisLite\n",
- "\n",
- "Loading required package: tidyverse\n",
- "\n",
- "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
- "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.3 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.4\n",
- "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.0\n",
- "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.4.4 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n",
- "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.0\n",
- "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n",
- "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
- "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
- "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
- "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
- "Loading required package: raster\n",
- "\n",
- "Loading required package: sp\n",
- "\n",
- "\n",
- "Attaching package: ‘raster’\n",
- "\n",
- "\n",
- "The following object is masked from ‘package:dplyr’:\n",
- "\n",
- " select\n",
- "\n",
- "\n",
- "Loading required package: rgdal\n",
- "\n",
- "Please note that rgdal will be retired by the end of 2023,\n",
- "plan transition to sf/stars/terra functions using GDAL and PROJ\n",
- "at your earliest convenience.\n",
- "\n",
- "rgdal: version: 1.5-32, (SVN revision 1176)\n",
- "Geospatial Data Abstraction Library extensions to R successfully loaded\n",
- "Loaded GDAL runtime: GDAL 3.6.2, released 2023/01/02\n",
- "Path to GDAL shared files: \n",
- " GDAL does not use iconv for recoding strings.\n",
- "GDAL binary built with GEOS: TRUE \n",
- "Loaded PROJ runtime: Rel. 9.1.0, September 1st, 2022, [PJ_VERSION: 910]\n",
- "Path to PROJ shared files: /projects/.local/share/proj:/opt/conda/share/proj\n",
- "PROJ CDN enabled: TRUE\n",
- "Linking to sp version:1.5-1\n",
- "To mute warnings of possible GDAL/OSR exportToProj4() degradation,\n",
- "use options(\"rgdal_show_exportToProj4_warnings\"=\"none\") before loading sp or rgdal.\n",
- "\n",
- "Loading required package: terra\n",
- "\n",
- "terra 1.7.29\n",
- "\n",
- "\n",
- "Attaching package: ‘terra’\n",
- "\n",
- "\n",
- "The following object is masked from ‘package:rgdal’:\n",
- "\n",
- " project\n",
- "\n",
- "\n",
- "The following object is masked from ‘package:tidyr’:\n",
- "\n",
- " extract\n",
- "\n",
- "\n",
- "Loading required package: RColorBrewer\n",
- "\n",
- "Loading required package: gdalUtils\n",
- "\n",
"Loading required package: parallel\n",
"\n",
"Loading required package: sf\n",
"\n",
- "Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.0; sf_use_s2() is TRUE\n",
+ "Linking to GEOS 3.12.0, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE\n",
"\n",
+ "Loading required package: foreign\n",
"\n",
- "Attaching package: ‘sf’\n",
+ "Loading required package: geojsonio\n",
"\n",
+ "Registered S3 method overwritten by 'geojsonsf':\n",
+ " method from \n",
+ " print.geojson geojson\n",
"\n",
- "The following object is masked from ‘package:gdalUtils’:\n",
"\n",
- " gdal_rasterize\n",
+ "Attaching package: ‘geojsonio’\n",
"\n",
"\n",
- "Loading required package: foreign\n",
+ "The following object is masked from ‘package:base’:\n",
"\n",
- "Loading required package: geojsonio\n",
+ " pretty\n",
"\n",
- "Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :\n",
- "“there is no package called ‘geojsonio’â€\n"
+ "\n"
]
},
{
- "ename": "ERROR",
- "evalue": "Error in library(x, character.only = TRUE, quietly = TRUE): there is no package called ‘geojsonio’\n",
- "output_type": "error",
- "traceback": [
- "Error in library(x, character.only = TRUE, quietly = TRUE): there is no package called ‘geojsonio’\nTraceback:\n",
- "1. lapply(packages, FUN = function(x) {\n . if (!require(x, character.only = TRUE)) {\n . library(x, character.only = TRUE, quietly = TRUE)\n . }\n . })",
- "2. FUN(X[[i]], ...)",
- "3. library(x, character.only = TRUE, quietly = TRUE) # at line 6 of file <text>"
- ]
+ "data": {
+ "text/plain": [
+ "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mterra\u001b[39m::\u001b[32mextract()\u001b[39m masks \u001b[34mraster\u001b[39m::extract(), \u001b[34mtidyr\u001b[39m::extract()\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mraster\u001b[39m::\u001b[32mselect()\u001b[39m masks \u001b[34mdplyr\u001b[39m::select()\n",
+ "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "packages <- c(\"viridis\",\"tidyverse\",\"raster\", \"rgdal\", \"terra\",\"RColorBrewer\",\"gdalUtils\",\"parallel\", \"sf\", \"sp\",\"foreign\",\"dplyr\",\"geojsonio\")\n",
+ "packages <- c(\"viridis\",\"tidyverse\", \"terra\",\"RColorBrewer\",\"parallel\", \"sf\", \"sp\",\"foreign\",\"dplyr\",\"geojsonio\")\n",
"# packages <- c(\"raster\", \"rgdal\", \"terra\", \"sf\", \"sp\")\n",
"package.check <- lapply(packages, FUN = function(x) {\n",
" if (!require(x, character.only = TRUE)) {\n",
- " # install.packages(x, dependencies = TRUE)\n",
+ " install.packages(x, dependencies = TRUE)\n",
" library(x, character.only = TRUE, quietly=TRUE)\n",
" }\n",
"})\n",
@@ -131,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 11,
"id": "c486cf3a-a3c5-4873-bc2c-c7f75d97a49d",
"metadata": {
"tags": []
@@ -150,11 +85,17 @@
"[1] \"GEDI and Boreal Map stats exist for KAZ\"\n",
"[1] \"GEDI and Boreal Map stats exist for MNG\"\n",
"[1] \"GEDI and Boreal Map stats exist for NLD\"\n",
- "[1] \"GEDI and Boreal Map stats exist for NOR\"\n",
"[1] \"GEDI and Boreal Map stats exist for POL\"\n",
"[1] \"GEDI and Boreal Map stats exist for RUS\"\n",
"[1] \"GEDI and Boreal Map stats exist for UKR\"\n",
- "[1] \"GEDI and Boreal Map stats exist for USA\"\n"
+ "[1] \"GEDI and Boreal Map stats exist for USA\"\n",
+ "[1] \"Only boreal stats exist for LTU\"\n",
+ "[1] \"Only boreal stats exist for EST\"\n",
+ "[1] \"Only boreal stats exist for LVA\"\n",
+ "[1] \"Only boreal stats exist for NOR\"\n",
+ "[1] \"Only boreal stats exist for SWE\"\n",
+ "[1] \"Only boreal stats exist for FIN\"\n",
+ "[1] \"Only boreal stats exist for DNK\"\n"
]
}
],
@@ -163,9 +104,17 @@
"############# MERGE GEDI AND ICESAT-2 BOREAL RESULTS #############\n",
"############# MERGE GEDI AND ICESAT-2 BOREAL RESULTS #############\n",
"GEDIv21 <- read_sf('/projects/my-public-bucket/Data/ICESat-2-TILES/merge_Boreal_countries_GEDI_worldbank.gpkg')\n",
+ "GEDIv21 <- GEDIv21 %>% st_drop_geometry()\n",
"GEDIv21$class[GEDIv21$class == \"CAN_3\"] = \"CAN\"\n",
+ "GEDIv21 <- GEDIv21[!grepl(\"NOR\", GEDIv21$class),]\n",
"\n",
"GEDIv21_BOREAL <- GEDIv21 \n",
+ "GEDIv21_BOREAL$ICESAT_2_mean <- 0\n",
+ "GEDIv21_BOREAL$ICESAT_2_SE <- 0\n",
+ "GEDIv21_BOREAL$ICESAT_2_Area <- 0\n",
+ "GEDIv21_BOREAL$BOTH_mean <- 0\n",
+ "GEDIv21_BOREAL$BOTH_SE <- 0\n",
+ "\n",
"BOREAL_LOC <- '/projects/my-private-bucket/dps_output/run_Boreal_IPCC_classes_ADE/BOREAL_compiled_LD_PAPER/'\n",
"for (n in GEDIv21$class) {\n",
" if (file.exists(paste0(BOREAL_LOC,n,\"_STATS.csv\"))) {\n",
@@ -181,174 +130,183 @@
" COMBINED_mean <- ((Boreal_mean * Boreal_area) + (GEDI_mean * GEDI_area))/(Boreal_area + GEDI_area)\n",
" ##### COMBINED_SE <- sqrt(((Boreal_SE^2 * Boreal_area) + (GEDI_SE^2 * GEDI_area))/(Boreal_area + GEDI_area))\n",
" COMBINED_SE <- sqrt(((Boreal_SE^2 * Boreal_area^2) + (GEDI_SE^2 * GEDI_area^2))/(Boreal_area + GEDI_area)^2)\n",
- " GEDIv21_BOREAL$region_mean[GEDIv21$class == n] = COMBINED_mean\n",
- " GEDIv21_BOREAL$region_stderr[GEDIv21$class == n] = COMBINED_SE\n",
+ " GEDIv21_BOREAL$ICESAT_2_Area[GEDIv21$class == n] <- Boreal_area\n",
+ " GEDIv21_BOREAL$ICESAT_2_mean[GEDIv21$class == n] <- Boreal_mean\n",
+ " GEDIv21_BOREAL$ICESAT_2_SE[GEDIv21$class == n] <- Boreal_SE\n",
+ " GEDIv21_BOREAL$BOTH_mean[GEDIv21$class == n] = COMBINED_mean\n",
+ " GEDIv21_BOREAL$BOTH_SE[GEDIv21$class == n] = COMBINED_SE\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "GEDIv21_BOREAL['region_area'] <- GEDIv21_BOREAL['region_area']/10000 ###units ha\n",
+ "GEDIv21_BOREAL <- subset(GEDIv21_BOREAL,select=c('region_area','region_mean','region_stderr','class','ICESAT_2_mean','ICESAT_2_SE','ICESAT_2_Area','BOTH_mean','BOTH_SE'))\n",
+ "\n",
+ "# ############# FILL BOREAL WHERE GEDI VALUES ARE MISSING FOR NORTHERN LATITUDES ############\n",
+ "# ############# FILL BOREAL WHERE GEDI VALUES ARE MISSING FOR NORTHERN LATITUDES ############\n",
+ "# ############# FILL BOREAL WHERE GEDI VALUES ARE MISSING FOR NORTHERN LATITUDES ############\n",
+ "\n",
+ "BOREALv01 <- read.csv('/projects/my-public-bucket/Data/ICESat-2-TILES/Boreal_countries_ICESAT2.csv')\n",
+ "GEDIv21 <- subset(GEDIv21,select=c('region_mean','region_stderr','class'))\n",
+ "\n",
+ "for (n in BOREALv01$IPCC_classes_1km) {\n",
+ " if (file.exists(paste0(BOREAL_LOC,n,\"_STATS.csv\"))) {\n",
+ " if (length(GEDIv21$region_mean[GEDIv21$class == n]) == 0) {\n",
+ " Boreal_stats <- read.csv(paste0(BOREAL_LOC,n,\"_STATS.csv\"))\n",
+ " if (Boreal_stats$value[Boreal_stats$X == 'AGB_mean'] > 0) {\n",
+ " print(paste0(\"Only boreal stats exist for \", n))\n",
+ " Boreal_mean <- Boreal_stats$value[Boreal_stats$X == 'AGB_mean']\n",
+ " Boreal_SE <- Boreal_stats$value[Boreal_stats$X == 'AGB_se']\n",
+ " Row = data.frame(0,0,0,n,Boreal_mean,Boreal_SE,0,0,0)\n",
+ " colnames(Row) <- c('region_area','region_mean','region_stderr','class','ICESAT_2_mean','ICESAT_2_SE','ICESAT_2_Area','BOTH_mean','BOTH_SE')\n",
+ " GEDIv21_BOREAL[nrow(GEDIv21_BOREAL) + 1,] = Row\n",
+ " }\n",
" }\n",
" }\n",
"}\n",
- "# write.csv(GEDIv21_BOREAL, \"/projects/my-public-bucket/Data/Harris_et_al_PAPER/L4B_results_25Jan_BOREAL.csv\", row.names=FALSE)"
+ "write.csv(GEDIv21_BOREAL, \"/projects/my-public-bucket/Data/ICESat-2-TILES/BOREAL_GEDI_merge_Countries.csv\", row.names=FALSE)\n",
+ "# write.csv(GEDI, \"/projects/my-public-bucket/Data/Harris_et_al_PAPER/L4B_results_2vvvvv5Jan_BOREAL.csv\", row.names=FALSE)"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "c91f2dc6-7faf-4b71-9dcf-d2215e402d74",
+ "execution_count": 12,
+ "id": "4f837663-1664-4ef6-9bb0-d56a67f9089a",
"metadata": {
"tags": []
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ERROR while rich displaying an object: Error in loadNamespace(x): there is no package called ‘geojsonio’\n",
- "\n",
- "Traceback:\n",
- "1. tryCatch(withCallingHandlers({\n",
- " . if (!mime %in% names(repr::mime2repr)) \n",
- " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n",
- " . rpr <- repr::mime2repr[[mime]](obj)\n",
- " . if (is.null(rpr)) \n",
- " . return(NULL)\n",
- " . prepare_content(is.raw(rpr), rpr)\n",
- " . }, error = error_handler), error = outer_handler)\n",
- "2. tryCatchList(expr, classes, parentenv, handlers)\n",
- "3. tryCatchOne(expr, names, parentenv, handlers[[1L]])\n",
- "4. doTryCatch(return(expr), name, parentenv, handler)\n",
- "5. withCallingHandlers({\n",
- " . if (!mime %in% names(repr::mime2repr)) \n",
- " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n",
- " . rpr <- repr::mime2repr[[mime]](obj)\n",
- " . if (is.null(rpr)) \n",
- " . return(NULL)\n",
- " . prepare_content(is.raw(rpr), rpr)\n",
- " . }, error = error_handler)\n",
- "6. repr::mime2repr[[mime]](obj)\n",
- "7. repr_geojson.sf(obj)\n",
- "8. repr_geojson(geojsonio::geojson_list(obj), ...)\n",
- "9. loadNamespace(x)\n",
- "10. withRestarts(stop(cond), retry_loadNamespace = function() NULL)\n",
- "11. withOneRestart(expr, restarts[[1L]])\n",
- "12. doWithOneRestart(return(expr), restart)\n"
- ]
- },
{
"data": {
"text/html": [
"<table class=\"dataframe\">\n",
- "<caption>A sf: 14 × 17</caption>\n",
+ "<caption>A tibble: 20 × 9</caption>\n",
"<thead>\n",
- "\t<tr><th></th><th scope=col>class</th><th scope=col>cell_area</th><th scope=col>region_area</th><th scope=col>region_cell_count</th><th scope=col>region_mean</th><th scope=col>sample_var</th><th scope=col>model_var</th><th scope=col>sample_cov</th><th scope=col>model_cov</th><th scope=col>region_var</th><th scope=col>region_stderr</th><th scope=col>region_rel_stderr</th><th scope=col>nshots</th><th scope=col>ntracks</th><th scope=col>nonresponse_cells</th><th scope=col>nmodels</th><th scope=col>geom</th></tr>\n",
- "\t<tr><th></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><MULTIPOLYGON [m]></th></tr>\n",
+ "\t<tr><th scope=col>region_area</th><th scope=col>region_mean</th><th scope=col>region_stderr</th><th scope=col>class</th><th scope=col>ICESAT_2_mean</th><th scope=col>ICESAT_2_SE</th><th scope=col>ICESAT_2_Area</th><th scope=col>BOTH_mean</th><th scope=col>BOTH_SE</th></tr>\n",
+ "\t<tr><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n",
"</thead>\n",
"<tbody>\n",
- "\t<tr><th scope=row>1</th><td>BLR</td><td>5.184e+09</td><td>2.629739e+09</td><td> 11</td><td>71.552379</td><td>2.176783e-01</td><td>8.5113520</td><td>4.142518e-02</td><td>NA</td><td>8.7704554</td><td>9.4446848</td><td> 3.057244</td><td> 454160</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((2277299 573...</td></tr>\n",
- "\t<tr><th scope=row>2</th><td>CAN</td><td>5.184e+09</td><td>2.288319e+12</td><td> 627</td><td>32.727900</td><td>6.916580e-04</td><td>0.4370265</td><td>2.473388e-04</td><td>NA</td><td>0.4379655</td><td>3.9674801</td><td> 1.242924</td><td>205016836</td><td>NA</td><td> 9</td><td>3</td><td>MULTIPOLYGON (((-6332280 50...</td></tr>\n",
- "\t<tr><th scope=row>3</th><td>CHN</td><td>5.184e+09</td><td>9.305738e+12</td><td>2016</td><td>41.324090</td><td>3.444781e-04</td><td>0.2889541</td><td>1.976979e-04</td><td>NA</td><td>0.2894963</td><td>0.5372977</td><td> 1.308148</td><td>331396373</td><td>NA</td><td> 3</td><td>5</td><td>MULTIPOLYGON (((10682088 25...</td></tr>\n",
- "\t<tr><th scope=row>4</th><td>DEU</td><td>5.184e+09</td><td>2.109578e+11</td><td> 65</td><td>79.169095</td><td>7.463370e-03</td><td>1.7981588</td><td>1.852809e-03</td><td>NA</td><td>1.8074750</td><td>4.0881576</td><td> 1.507628</td><td> 17299568</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((1330146 550...</td></tr>\n",
- "\t<tr><th scope=row>5</th><td>GBR</td><td>5.184e+09</td><td>3.915120e+10</td><td> 23</td><td>27.792137</td><td>2.147697e-02</td><td>2.0322501</td><td>2.738371e-03</td><td>NA</td><td>2.0564654</td><td>5.7466947</td><td> 3.564960</td><td> 5069694</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((-259910.5 5...</td></tr>\n",
- "\t<tr><th scope=row>6</th><td>IRL</td><td>5.184e+09</td><td>5.616566e+08</td><td> 3</td><td>19.783808</td><td>5.722809e-01</td><td>7.1520627</td><td>3.202226e-03</td><td>NA</td><td>7.7275459</td><td>5.7063115</td><td> 9.820159</td><td> 101090</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((-839409.2 5...</td></tr>\n",
- "\t<tr><th scope=row>7</th><td>KAZ</td><td>5.184e+09</td><td>2.362658e+12</td><td> 553</td><td> 5.062741</td><td>6.183252e-05</td><td>0.1944473</td><td>2.403520e-05</td><td>NA</td><td>0.1945332</td><td>0.5492681</td><td>11.169949</td><td>226954869</td><td>NA</td><td> 0</td><td>4</td><td>MULTIPOLYGON (((4889731 517...</td></tr>\n",
- "\t<tr><th scope=row>8</th><td>MNG</td><td>5.184e+09</td><td>1.560160e+12</td><td> 362</td><td> 7.912450</td><td>6.998089e-05</td><td>0.1674246</td><td>2.348949e-05</td><td>NA</td><td>0.1675181</td><td>0.4084053</td><td> 5.189205</td><td>144092338</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((11238039 55...</td></tr>\n",
- "\t<tr><th scope=row>9</th><td>NLD</td><td>5.184e+09</td><td>6.720407e+09</td><td> 10</td><td>39.763755</td><td>2.469888e-02</td><td>3.1750318</td><td>2.569909e-03</td><td>NA</td><td>3.2023006</td><td>6.0970993</td><td> 4.043432</td><td> 937626</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((362358.5 57...</td></tr>\n",
- "\t<tr><th scope=row>10</th><td>NOR</td><td>5.184e+09</td><td>5.520980e+07</td><td> 1</td><td>29.603829</td><td>0.000000e+00</td><td>0.0000000</td><td>0.000000e+00</td><td>NA</td><td>0.0000000</td><td>7.3907091</td><td> 0.000000</td><td> 0</td><td>NA</td><td> 1</td><td>0</td><td>MULTIPOLYGON (((335559.4 -5...</td></tr>\n",
- "\t<tr><th scope=row>11</th><td>POL</td><td>5.184e+09</td><td>1.220705e+11</td><td> 45</td><td>69.048231</td><td>7.738243e-03</td><td>2.1043726</td><td>2.185907e-03</td><td>NA</td><td>2.1142968</td><td>6.0383301</td><td> 1.987180</td><td> 13172276</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((1817554 557...</td></tr>\n",
- "\t<tr><th scope=row>12</th><td>RUS</td><td>5.184e+09</td><td>1.807355e+12</td><td> 578</td><td>46.795711</td><td>1.158709e-03</td><td>0.5529233</td><td>4.315925e-04</td><td>NA</td><td>0.5545136</td><td>6.3978540</td><td> 1.301883</td><td>137759303</td><td>NA</td><td> 1</td><td>5</td><td>MULTIPOLYGON (((15227973 57...</td></tr>\n",
- "\t<tr><th scope=row>13</th><td>UKR</td><td>5.184e+09</td><td>5.770285e+11</td><td> 165</td><td>39.054859</td><td>1.260510e-03</td><td>0.9436175</td><td>3.496119e-04</td><td>NA</td><td>0.9452276</td><td>1.0001882</td><td> 2.551920</td><td> 37904754</td><td>NA</td><td> 1</td><td>3</td><td>MULTIPOLYGON (((2868630 523...</td></tr>\n",
- "\t<tr><th scope=row>14</th><td>USA</td><td>5.184e+09</td><td>7.960166e+12</td><td>1734</td><td>49.539453</td><td>2.989194e-04</td><td>0.3659962</td><td>8.492571e-05</td><td>NA</td><td>0.3663800</td><td>1.1685255</td><td> 1.112419</td><td>384983129</td><td>NA</td><td>25</td><td>6</td><td>MULTIPOLYGON (((-9176117 55...</td></tr>\n",
+ "\t<tr><td> 262973.94</td><td>96.868149</td><td>2.9614955</td><td>BLR</td><td>71.227</td><td> 9.566</td><td> 20460400</td><td>71.552379</td><td>9.4446848</td></tr>\n",
+ "\t<tr><td>228831857.19</td><td>53.244565</td><td>0.6617896</td><td>CAN</td><td>23.444</td><td> 5.755</td><td> 505699800</td><td>32.727900</td><td>3.9674801</td></tr>\n",
+ "\t<tr><td>930573822.49</td><td>41.130567</td><td>0.5380486</td><td>CHN</td><td>65.893</td><td> 7.783</td><td> 7329900</td><td>41.324090</td><td>0.5372977</td></tr>\n",
+ "\t<tr><td> 21095784.35</td><td>89.174784</td><td>1.3444237</td><td>DEU</td><td>64.367</td><td> 9.939</td><td> 14260000</td><td>79.169095</td><td>4.0881576</td></tr>\n",
+ "\t<tr><td> 3915119.59</td><td>40.225928</td><td>1.4340381</td><td>GBR</td><td>25.323</td><td> 6.882</td><td> 19715300</td><td>27.792137</td><td>5.7466947</td></tr>\n",
+ "\t<tr><td> 56165.66</td><td>28.307549</td><td>2.7798464</td><td>IRL</td><td>19.714</td><td> 5.753</td><td> 6858000</td><td>19.783808</td><td>5.7063115</td></tr>\n",
+ "\t<tr><td>236265785.09</td><td> 3.948623</td><td>0.4410592</td><td>KAZ</td><td>13.330</td><td> 3.268</td><td> 31839800</td><td> 5.062741</td><td>0.5492681</td></tr>\n",
+ "\t<tr><td>156015970.59</td><td> 7.887330</td><td>0.4092897</td><td>MNG</td><td>16.850</td><td> 5.226</td><td> 438500</td><td> 7.912450</td><td>0.4084053</td></tr>\n",
+ "\t<tr><td> 672040.71</td><td>44.256891</td><td>1.7894973</td><td>NLD</td><td>38.705</td><td> 7.522</td><td> 2852000</td><td>39.763755</td><td>6.0970993</td></tr>\n",
+ "\t<tr><td> 12207049.06</td><td>73.172129</td><td>1.4540622</td><td>POL</td><td>66.354</td><td> 9.938</td><td> 18684600</td><td>69.048231</td><td>6.0383301</td></tr>\n",
+ "\t<tr><td>180735529.43</td><td>57.198421</td><td>0.7446567</td><td>RUS</td><td>45.510</td><td> 7.188</td><td>1462334700</td><td>46.795711</td><td>6.3978540</td></tr>\n",
+ "\t<tr><td> 57702847.75</td><td>38.097908</td><td>0.9722282</td><td>UKR</td><td>64.644</td><td> 9.692</td><td> 2157900</td><td>39.054859</td><td>1.0001882</td></tr>\n",
+ "\t<tr><td>796016587.16</td><td>54.412356</td><td>0.6052933</td><td>USA</td><td>22.908</td><td> 6.792</td><td> 145651500</td><td>49.539453</td><td>1.1685255</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>LTU</td><td>61.866</td><td> 9.616</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>EST</td><td>71.540</td><td> 9.215</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>LVA</td><td>74.486</td><td>10.403</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>NOR</td><td>29.609</td><td> 7.392</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>SWE</td><td>54.810</td><td> 8.283</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>FIN</td><td>60.866</td><td> 7.849</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
+ "\t<tr><td> 0.00</td><td> 0.000000</td><td>0.0000000</td><td>DNK</td><td>43.455</td><td> 9.521</td><td> 0</td><td> 0.000000</td><td>0.0000000</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
- "A sf: 14 × 17\n",
- "\\begin{tabular}{r|lllllllllllllllll}\n",
- " & class & cell\\_area & region\\_area & region\\_cell\\_count & region\\_mean & sample\\_var & model\\_var & sample\\_cov & model\\_cov & region\\_var & region\\_stderr & region\\_rel\\_stderr & nshots & ntracks & nonresponse\\_cells & nmodels & geom\\\\\n",
- " & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <MULTIPOLYGON {[}m{]}>\\\\\n",
+ "A tibble: 20 × 9\n",
+ "\\begin{tabular}{lllllllll}\n",
+ " region\\_area & region\\_mean & region\\_stderr & class & ICESAT\\_2\\_mean & ICESAT\\_2\\_SE & ICESAT\\_2\\_Area & BOTH\\_mean & BOTH\\_SE\\\\\n",
+ " <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
"\\hline\n",
- "\t1 & BLR & 5.184e+09 & 2.629739e+09 & 11 & 71.552379 & 2.176783e-01 & 8.5113520 & 4.142518e-02 & NA & 8.7704554 & 9.4446848 & 3.057244 & 454160 & NA & 0 & 3 & MULTIPOLYGON (((2277299 573...\\\\\n",
- "\t2 & CAN & 5.184e+09 & 2.288319e+12 & 627 & 32.727900 & 6.916580e-04 & 0.4370265 & 2.473388e-04 & NA & 0.4379655 & 3.9674801 & 1.242924 & 205016836 & NA & 9 & 3 & MULTIPOLYGON (((-6332280 50...\\\\\n",
- "\t3 & CHN & 5.184e+09 & 9.305738e+12 & 2016 & 41.324090 & 3.444781e-04 & 0.2889541 & 1.976979e-04 & NA & 0.2894963 & 0.5372977 & 1.308148 & 331396373 & NA & 3 & 5 & MULTIPOLYGON (((10682088 25...\\\\\n",
- "\t4 & DEU & 5.184e+09 & 2.109578e+11 & 65 & 79.169095 & 7.463370e-03 & 1.7981588 & 1.852809e-03 & NA & 1.8074750 & 4.0881576 & 1.507628 & 17299568 & NA & 0 & 3 & MULTIPOLYGON (((1330146 550...\\\\\n",
- "\t5 & GBR & 5.184e+09 & 3.915120e+10 & 23 & 27.792137 & 2.147697e-02 & 2.0322501 & 2.738371e-03 & NA & 2.0564654 & 5.7466947 & 3.564960 & 5069694 & NA & 0 & 3 & MULTIPOLYGON (((-259910.5 5...\\\\\n",
- "\t6 & IRL & 5.184e+09 & 5.616566e+08 & 3 & 19.783808 & 5.722809e-01 & 7.1520627 & 3.202226e-03 & NA & 7.7275459 & 5.7063115 & 9.820159 & 101090 & NA & 0 & 3 & MULTIPOLYGON (((-839409.2 5...\\\\\n",
- "\t7 & KAZ & 5.184e+09 & 2.362658e+12 & 553 & 5.062741 & 6.183252e-05 & 0.1944473 & 2.403520e-05 & NA & 0.1945332 & 0.5492681 & 11.169949 & 226954869 & NA & 0 & 4 & MULTIPOLYGON (((4889731 517...\\\\\n",
- "\t8 & MNG & 5.184e+09 & 1.560160e+12 & 362 & 7.912450 & 6.998089e-05 & 0.1674246 & 2.348949e-05 & NA & 0.1675181 & 0.4084053 & 5.189205 & 144092338 & NA & 0 & 3 & MULTIPOLYGON (((11238039 55...\\\\\n",
- "\t9 & NLD & 5.184e+09 & 6.720407e+09 & 10 & 39.763755 & 2.469888e-02 & 3.1750318 & 2.569909e-03 & NA & 3.2023006 & 6.0970993 & 4.043432 & 937626 & NA & 0 & 3 & MULTIPOLYGON (((362358.5 57...\\\\\n",
- "\t10 & NOR & 5.184e+09 & 5.520980e+07 & 1 & 29.603829 & 0.000000e+00 & 0.0000000 & 0.000000e+00 & NA & 0.0000000 & 7.3907091 & 0.000000 & 0 & NA & 1 & 0 & MULTIPOLYGON (((335559.4 -5...\\\\\n",
- "\t11 & POL & 5.184e+09 & 1.220705e+11 & 45 & 69.048231 & 7.738243e-03 & 2.1043726 & 2.185907e-03 & NA & 2.1142968 & 6.0383301 & 1.987180 & 13172276 & NA & 0 & 3 & MULTIPOLYGON (((1817554 557...\\\\\n",
- "\t12 & RUS & 5.184e+09 & 1.807355e+12 & 578 & 46.795711 & 1.158709e-03 & 0.5529233 & 4.315925e-04 & NA & 0.5545136 & 6.3978540 & 1.301883 & 137759303 & NA & 1 & 5 & MULTIPOLYGON (((15227973 57...\\\\\n",
- "\t13 & UKR & 5.184e+09 & 5.770285e+11 & 165 & 39.054859 & 1.260510e-03 & 0.9436175 & 3.496119e-04 & NA & 0.9452276 & 1.0001882 & 2.551920 & 37904754 & NA & 1 & 3 & MULTIPOLYGON (((2868630 523...\\\\\n",
- "\t14 & USA & 5.184e+09 & 7.960166e+12 & 1734 & 49.539453 & 2.989194e-04 & 0.3659962 & 8.492571e-05 & NA & 0.3663800 & 1.1685255 & 1.112419 & 384983129 & NA & 25 & 6 & MULTIPOLYGON (((-9176117 55...\\\\\n",
+ "\t 262973.94 & 96.868149 & 2.9614955 & BLR & 71.227 & 9.566 & 20460400 & 71.552379 & 9.4446848\\\\\n",
+ "\t 228831857.19 & 53.244565 & 0.6617896 & CAN & 23.444 & 5.755 & 505699800 & 32.727900 & 3.9674801\\\\\n",
+ "\t 930573822.49 & 41.130567 & 0.5380486 & CHN & 65.893 & 7.783 & 7329900 & 41.324090 & 0.5372977\\\\\n",
+ "\t 21095784.35 & 89.174784 & 1.3444237 & DEU & 64.367 & 9.939 & 14260000 & 79.169095 & 4.0881576\\\\\n",
+ "\t 3915119.59 & 40.225928 & 1.4340381 & GBR & 25.323 & 6.882 & 19715300 & 27.792137 & 5.7466947\\\\\n",
+ "\t 56165.66 & 28.307549 & 2.7798464 & IRL & 19.714 & 5.753 & 6858000 & 19.783808 & 5.7063115\\\\\n",
+ "\t 236265785.09 & 3.948623 & 0.4410592 & KAZ & 13.330 & 3.268 & 31839800 & 5.062741 & 0.5492681\\\\\n",
+ "\t 156015970.59 & 7.887330 & 0.4092897 & MNG & 16.850 & 5.226 & 438500 & 7.912450 & 0.4084053\\\\\n",
+ "\t 672040.71 & 44.256891 & 1.7894973 & NLD & 38.705 & 7.522 & 2852000 & 39.763755 & 6.0970993\\\\\n",
+ "\t 12207049.06 & 73.172129 & 1.4540622 & POL & 66.354 & 9.938 & 18684600 & 69.048231 & 6.0383301\\\\\n",
+ "\t 180735529.43 & 57.198421 & 0.7446567 & RUS & 45.510 & 7.188 & 1462334700 & 46.795711 & 6.3978540\\\\\n",
+ "\t 57702847.75 & 38.097908 & 0.9722282 & UKR & 64.644 & 9.692 & 2157900 & 39.054859 & 1.0001882\\\\\n",
+ "\t 796016587.16 & 54.412356 & 0.6052933 & USA & 22.908 & 6.792 & 145651500 & 49.539453 & 1.1685255\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & LTU & 61.866 & 9.616 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & EST & 71.540 & 9.215 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & LVA & 74.486 & 10.403 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & NOR & 29.609 & 7.392 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & SWE & 54.810 & 8.283 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & FIN & 60.866 & 7.849 & 0 & 0.000000 & 0.0000000\\\\\n",
+ "\t 0.00 & 0.000000 & 0.0000000 & DNK & 43.455 & 9.521 & 0 & 0.000000 & 0.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
- "A sf: 14 × 17\n",
+ "A tibble: 20 × 9\n",
"\n",
- "| <!--/--> | class <chr> | cell_area <dbl> | region_area <dbl> | region_cell_count <dbl> | region_mean <dbl> | sample_var <dbl> | model_var <dbl> | sample_cov <dbl> | model_cov <chr> | region_var <dbl> | region_stderr <dbl> | region_rel_stderr <dbl> | nshots <dbl> | ntracks <chr> | nonresponse_cells <dbl> | nmodels <dbl> | geom <MULTIPOLYGON [m]> |\n",
- "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
- "| 1 | BLR | 5.184e+09 | 2.629739e+09 | 11 | 71.552379 | 2.176783e-01 | 8.5113520 | 4.142518e-02 | NA | 8.7704554 | 9.4446848 | 3.057244 | 454160 | NA | 0 | 3 | MULTIPOLYGON (((2277299 573... |\n",
- "| 2 | CAN | 5.184e+09 | 2.288319e+12 | 627 | 32.727900 | 6.916580e-04 | 0.4370265 | 2.473388e-04 | NA | 0.4379655 | 3.9674801 | 1.242924 | 205016836 | NA | 9 | 3 | MULTIPOLYGON (((-6332280 50... |\n",
- "| 3 | CHN | 5.184e+09 | 9.305738e+12 | 2016 | 41.324090 | 3.444781e-04 | 0.2889541 | 1.976979e-04 | NA | 0.2894963 | 0.5372977 | 1.308148 | 331396373 | NA | 3 | 5 | MULTIPOLYGON (((10682088 25... |\n",
- "| 4 | DEU | 5.184e+09 | 2.109578e+11 | 65 | 79.169095 | 7.463370e-03 | 1.7981588 | 1.852809e-03 | NA | 1.8074750 | 4.0881576 | 1.507628 | 17299568 | NA | 0 | 3 | MULTIPOLYGON (((1330146 550... |\n",
- "| 5 | GBR | 5.184e+09 | 3.915120e+10 | 23 | 27.792137 | 2.147697e-02 | 2.0322501 | 2.738371e-03 | NA | 2.0564654 | 5.7466947 | 3.564960 | 5069694 | NA | 0 | 3 | MULTIPOLYGON (((-259910.5 5... |\n",
- "| 6 | IRL | 5.184e+09 | 5.616566e+08 | 3 | 19.783808 | 5.722809e-01 | 7.1520627 | 3.202226e-03 | NA | 7.7275459 | 5.7063115 | 9.820159 | 101090 | NA | 0 | 3 | MULTIPOLYGON (((-839409.2 5... |\n",
- "| 7 | KAZ | 5.184e+09 | 2.362658e+12 | 553 | 5.062741 | 6.183252e-05 | 0.1944473 | 2.403520e-05 | NA | 0.1945332 | 0.5492681 | 11.169949 | 226954869 | NA | 0 | 4 | MULTIPOLYGON (((4889731 517... |\n",
- "| 8 | MNG | 5.184e+09 | 1.560160e+12 | 362 | 7.912450 | 6.998089e-05 | 0.1674246 | 2.348949e-05 | NA | 0.1675181 | 0.4084053 | 5.189205 | 144092338 | NA | 0 | 3 | MULTIPOLYGON (((11238039 55... |\n",
- "| 9 | NLD | 5.184e+09 | 6.720407e+09 | 10 | 39.763755 | 2.469888e-02 | 3.1750318 | 2.569909e-03 | NA | 3.2023006 | 6.0970993 | 4.043432 | 937626 | NA | 0 | 3 | MULTIPOLYGON (((362358.5 57... |\n",
- "| 10 | NOR | 5.184e+09 | 5.520980e+07 | 1 | 29.603829 | 0.000000e+00 | 0.0000000 | 0.000000e+00 | NA | 0.0000000 | 7.3907091 | 0.000000 | 0 | NA | 1 | 0 | MULTIPOLYGON (((335559.4 -5... |\n",
- "| 11 | POL | 5.184e+09 | 1.220705e+11 | 45 | 69.048231 | 7.738243e-03 | 2.1043726 | 2.185907e-03 | NA | 2.1142968 | 6.0383301 | 1.987180 | 13172276 | NA | 0 | 3 | MULTIPOLYGON (((1817554 557... |\n",
- "| 12 | RUS | 5.184e+09 | 1.807355e+12 | 578 | 46.795711 | 1.158709e-03 | 0.5529233 | 4.315925e-04 | NA | 0.5545136 | 6.3978540 | 1.301883 | 137759303 | NA | 1 | 5 | MULTIPOLYGON (((15227973 57... |\n",
- "| 13 | UKR | 5.184e+09 | 5.770285e+11 | 165 | 39.054859 | 1.260510e-03 | 0.9436175 | 3.496119e-04 | NA | 0.9452276 | 1.0001882 | 2.551920 | 37904754 | NA | 1 | 3 | MULTIPOLYGON (((2868630 523... |\n",
- "| 14 | USA | 5.184e+09 | 7.960166e+12 | 1734 | 49.539453 | 2.989194e-04 | 0.3659962 | 8.492571e-05 | NA | 0.3663800 | 1.1685255 | 1.112419 | 384983129 | NA | 25 | 6 | MULTIPOLYGON (((-9176117 55... |\n",
+ "| region_area <dbl> | region_mean <dbl> | region_stderr <dbl> | class <chr> | ICESAT_2_mean <dbl> | ICESAT_2_SE <dbl> | ICESAT_2_Area <dbl> | BOTH_mean <dbl> | BOTH_SE <dbl> |\n",
+ "|---|---|---|---|---|---|---|---|---|\n",
+ "| 262973.94 | 96.868149 | 2.9614955 | BLR | 71.227 | 9.566 | 20460400 | 71.552379 | 9.4446848 |\n",
+ "| 228831857.19 | 53.244565 | 0.6617896 | CAN | 23.444 | 5.755 | 505699800 | 32.727900 | 3.9674801 |\n",
+ "| 930573822.49 | 41.130567 | 0.5380486 | CHN | 65.893 | 7.783 | 7329900 | 41.324090 | 0.5372977 |\n",
+ "| 21095784.35 | 89.174784 | 1.3444237 | DEU | 64.367 | 9.939 | 14260000 | 79.169095 | 4.0881576 |\n",
+ "| 3915119.59 | 40.225928 | 1.4340381 | GBR | 25.323 | 6.882 | 19715300 | 27.792137 | 5.7466947 |\n",
+ "| 56165.66 | 28.307549 | 2.7798464 | IRL | 19.714 | 5.753 | 6858000 | 19.783808 | 5.7063115 |\n",
+ "| 236265785.09 | 3.948623 | 0.4410592 | KAZ | 13.330 | 3.268 | 31839800 | 5.062741 | 0.5492681 |\n",
+ "| 156015970.59 | 7.887330 | 0.4092897 | MNG | 16.850 | 5.226 | 438500 | 7.912450 | 0.4084053 |\n",
+ "| 672040.71 | 44.256891 | 1.7894973 | NLD | 38.705 | 7.522 | 2852000 | 39.763755 | 6.0970993 |\n",
+ "| 12207049.06 | 73.172129 | 1.4540622 | POL | 66.354 | 9.938 | 18684600 | 69.048231 | 6.0383301 |\n",
+ "| 180735529.43 | 57.198421 | 0.7446567 | RUS | 45.510 | 7.188 | 1462334700 | 46.795711 | 6.3978540 |\n",
+ "| 57702847.75 | 38.097908 | 0.9722282 | UKR | 64.644 | 9.692 | 2157900 | 39.054859 | 1.0001882 |\n",
+ "| 796016587.16 | 54.412356 | 0.6052933 | USA | 22.908 | 6.792 | 145651500 | 49.539453 | 1.1685255 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | LTU | 61.866 | 9.616 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | EST | 71.540 | 9.215 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | LVA | 74.486 | 10.403 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | NOR | 29.609 | 7.392 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | SWE | 54.810 | 8.283 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | FIN | 60.866 | 7.849 | 0 | 0.000000 | 0.0000000 |\n",
+ "| 0.00 | 0.000000 | 0.0000000 | DNK | 43.455 | 9.521 | 0 | 0.000000 | 0.0000000 |\n",
"\n"
],
"text/plain": [
- " class cell_area region_area region_cell_count region_mean sample_var \n",
- "1 BLR 5.184e+09 2.629739e+09 11 71.552379 2.176783e-01\n",
- "2 CAN 5.184e+09 2.288319e+12 627 32.727900 6.916580e-04\n",
- "3 CHN 5.184e+09 9.305738e+12 2016 41.324090 3.444781e-04\n",
- "4 DEU 5.184e+09 2.109578e+11 65 79.169095 7.463370e-03\n",
- "5 GBR 5.184e+09 3.915120e+10 23 27.792137 2.147697e-02\n",
- "6 IRL 5.184e+09 5.616566e+08 3 19.783808 5.722809e-01\n",
- "7 KAZ 5.184e+09 2.362658e+12 553 5.062741 6.183252e-05\n",
- "8 MNG 5.184e+09 1.560160e+12 362 7.912450 6.998089e-05\n",
- "9 NLD 5.184e+09 6.720407e+09 10 39.763755 2.469888e-02\n",
- "10 NOR 5.184e+09 5.520980e+07 1 29.603829 0.000000e+00\n",
- "11 POL 5.184e+09 1.220705e+11 45 69.048231 7.738243e-03\n",
- "12 RUS 5.184e+09 1.807355e+12 578 46.795711 1.158709e-03\n",
- "13 UKR 5.184e+09 5.770285e+11 165 39.054859 1.260510e-03\n",
- "14 USA 5.184e+09 7.960166e+12 1734 49.539453 2.989194e-04\n",
- " model_var sample_cov model_cov region_var region_stderr region_rel_stderr\n",
- "1 8.5113520 4.142518e-02 NA 8.7704554 9.4446848 3.057244 \n",
- "2 0.4370265 2.473388e-04 NA 0.4379655 3.9674801 1.242924 \n",
- "3 0.2889541 1.976979e-04 NA 0.2894963 0.5372977 1.308148 \n",
- "4 1.7981588 1.852809e-03 NA 1.8074750 4.0881576 1.507628 \n",
- "5 2.0322501 2.738371e-03 NA 2.0564654 5.7466947 3.564960 \n",
- "6 7.1520627 3.202226e-03 NA 7.7275459 5.7063115 9.820159 \n",
- "7 0.1944473 2.403520e-05 NA 0.1945332 0.5492681 11.169949 \n",
- "8 0.1674246 2.348949e-05 NA 0.1675181 0.4084053 5.189205 \n",
- "9 3.1750318 2.569909e-03 NA 3.2023006 6.0970993 4.043432 \n",
- "10 0.0000000 0.000000e+00 NA 0.0000000 7.3907091 0.000000 \n",
- "11 2.1043726 2.185907e-03 NA 2.1142968 6.0383301 1.987180 \n",
- "12 0.5529233 4.315925e-04 NA 0.5545136 6.3978540 1.301883 \n",
- "13 0.9436175 3.496119e-04 NA 0.9452276 1.0001882 2.551920 \n",
- "14 0.3659962 8.492571e-05 NA 0.3663800 1.1685255 1.112419 \n",
- " nshots ntracks nonresponse_cells nmodels geom \n",
- "1 454160 NA 0 3 MULTIPOLYGON (((2277299 573...\n",
- "2 205016836 NA 9 3 MULTIPOLYGON (((-6332280 50...\n",
- "3 331396373 NA 3 5 MULTIPOLYGON (((10682088 25...\n",
- "4 17299568 NA 0 3 MULTIPOLYGON (((1330146 550...\n",
- "5 5069694 NA 0 3 MULTIPOLYGON (((-259910.5 5...\n",
- "6 101090 NA 0 3 MULTIPOLYGON (((-839409.2 5...\n",
- "7 226954869 NA 0 4 MULTIPOLYGON (((4889731 517...\n",
- "8 144092338 NA 0 3 MULTIPOLYGON (((11238039 55...\n",
- "9 937626 NA 0 3 MULTIPOLYGON (((362358.5 57...\n",
- "10 0 NA 1 0 MULTIPOLYGON (((335559.4 -5...\n",
- "11 13172276 NA 0 3 MULTIPOLYGON (((1817554 557...\n",
- "12 137759303 NA 1 5 MULTIPOLYGON (((15227973 57...\n",
- "13 37904754 NA 1 3 MULTIPOLYGON (((2868630 523...\n",
- "14 384983129 NA 25 6 MULTIPOLYGON (((-9176117 55..."
+ " region_area region_mean region_stderr class ICESAT_2_mean ICESAT_2_SE\n",
+ "1 262973.94 96.868149 2.9614955 BLR 71.227 9.566 \n",
+ "2 228831857.19 53.244565 0.6617896 CAN 23.444 5.755 \n",
+ "3 930573822.49 41.130567 0.5380486 CHN 65.893 7.783 \n",
+ "4 21095784.35 89.174784 1.3444237 DEU 64.367 9.939 \n",
+ "5 3915119.59 40.225928 1.4340381 GBR 25.323 6.882 \n",
+ "6 56165.66 28.307549 2.7798464 IRL 19.714 5.753 \n",
+ "7 236265785.09 3.948623 0.4410592 KAZ 13.330 3.268 \n",
+ "8 156015970.59 7.887330 0.4092897 MNG 16.850 5.226 \n",
+ "9 672040.71 44.256891 1.7894973 NLD 38.705 7.522 \n",
+ "10 12207049.06 73.172129 1.4540622 POL 66.354 9.938 \n",
+ "11 180735529.43 57.198421 0.7446567 RUS 45.510 7.188 \n",
+ "12 57702847.75 38.097908 0.9722282 UKR 64.644 9.692 \n",
+ "13 796016587.16 54.412356 0.6052933 USA 22.908 6.792 \n",
+ "14 0.00 0.000000 0.0000000 LTU 61.866 9.616 \n",
+ "15 0.00 0.000000 0.0000000 EST 71.540 9.215 \n",
+ "16 0.00 0.000000 0.0000000 LVA 74.486 10.403 \n",
+ "17 0.00 0.000000 0.0000000 NOR 29.609 7.392 \n",
+ "18 0.00 0.000000 0.0000000 SWE 54.810 8.283 \n",
+ "19 0.00 0.000000 0.0000000 FIN 60.866 7.849 \n",
+ "20 0.00 0.000000 0.0000000 DNK 43.455 9.521 \n",
+ " ICESAT_2_Area BOTH_mean BOTH_SE \n",
+ "1 20460400 71.552379 9.4446848\n",
+ "2 505699800 32.727900 3.9674801\n",
+ "3 7329900 41.324090 0.5372977\n",
+ "4 14260000 79.169095 4.0881576\n",
+ "5 19715300 27.792137 5.7466947\n",
+ "6 6858000 19.783808 5.7063115\n",
+ "7 31839800 5.062741 0.5492681\n",
+ "8 438500 7.912450 0.4084053\n",
+ "9 2852000 39.763755 6.0970993\n",
+ "10 18684600 69.048231 6.0383301\n",
+ "11 1462334700 46.795711 6.3978540\n",
+ "12 2157900 39.054859 1.0001882\n",
+ "13 145651500 49.539453 1.1685255\n",
+ "14 0 0.000000 0.0000000\n",
+ "15 0 0.000000 0.0000000\n",
+ "16 0 0.000000 0.0000000\n",
+ "17 0 0.000000 0.0000000\n",
+ "18 0 0.000000 0.0000000\n",
+ "19 0 0.000000 0.0000000\n",
+ "20 0 0.000000 0.0000000"
]
},
"metadata": {},
@@ -361,164 +319,178 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "80b13155-d052-4af0-8b60-178c60916b59",
+ "execution_count": 39,
+ "id": "98985286-d7ea-4c74-9d63-1195a6dbf28b",
"metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
"tags": []
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ERROR while rich displaying an object: Error in loadNamespace(x): there is no package called ‘geojsonio’\n",
- "\n",
- "Traceback:\n",
- "1. tryCatch(withCallingHandlers({\n",
- " . if (!mime %in% names(repr::mime2repr)) \n",
- " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n",
- " . rpr <- repr::mime2repr[[mime]](obj)\n",
- " . if (is.null(rpr)) \n",
- " . return(NULL)\n",
- " . prepare_content(is.raw(rpr), rpr)\n",
- " . }, error = error_handler), error = outer_handler)\n",
- "2. tryCatchList(expr, classes, parentenv, handlers)\n",
- "3. tryCatchOne(expr, names, parentenv, handlers[[1L]])\n",
- "4. doTryCatch(return(expr), name, parentenv, handler)\n",
- "5. withCallingHandlers({\n",
- " . if (!mime %in% names(repr::mime2repr)) \n",
- " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n",
- " . rpr <- repr::mime2repr[[mime]](obj)\n",
- " . if (is.null(rpr)) \n",
- " . return(NULL)\n",
- " . prepare_content(is.raw(rpr), rpr)\n",
- " . }, error = error_handler)\n",
- "6. repr::mime2repr[[mime]](obj)\n",
- "7. repr_geojson.sf(obj)\n",
- "8. repr_geojson(geojsonio::geojson_list(obj), ...)\n",
- "9. loadNamespace(x)\n",
- "10. withRestarts(stop(cond), retry_loadNamespace = function() NULL)\n",
- "11. withOneRestart(expr, restarts[[1L]])\n",
- "12. doWithOneRestart(return(expr), restart)\n"
- ]
- },
{
"data": {
"text/html": [
"<table class=\"dataframe\">\n",
- "<caption>A sf: 14 × 17</caption>\n",
+ "<caption>A data.frame: 14 × 53</caption>\n",
"<thead>\n",
- "\t<tr><th></th><th scope=col>class</th><th scope=col>cell_area</th><th scope=col>region_area</th><th scope=col>region_cell_count</th><th scope=col>region_mean</th><th scope=col>sample_var</th><th scope=col>model_var</th><th scope=col>sample_cov</th><th scope=col>model_cov</th><th scope=col>region_var</th><th scope=col>region_stderr</th><th scope=col>region_rel_stderr</th><th scope=col>nshots</th><th scope=col>ntracks</th><th scope=col>nonresponse_cells</th><th scope=col>nmodels</th><th scope=col>geom</th></tr>\n",
- "\t<tr><th></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><MULTIPOLYGON [m]></th></tr>\n",
+ "\t<tr><th scope=col>fid</th><th scope=col>OBJECTID</th><th scope=col>featurecla</th><th scope=col>LEVEL</th><th scope=col>TYPE</th><th scope=col>FORMAL_EN</th><th scope=col>FORMAL_FR</th><th scope=col>POP_EST</th><th scope=col>POP_RANK</th><th scope=col>GDP_MD_EST</th><th scope=col>⋯</th><th scope=col>NAME_RU</th><th scope=col>NAME_SV</th><th scope=col>NAME_TR</th><th scope=col>NAME_VI</th><th scope=col>NAME_ZH</th><th scope=col>WB_NAME</th><th scope=col>WB_RULES</th><th scope=col>WB_REGION</th><th scope=col>Shape_Leng</th><th scope=col>Shape_Area</th></tr>\n",
+ "\t<tr><th scope=col><int></th><th scope=col><int></th><th scope=col><chr></th><th scope=col><int></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><lgl></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col>⋯</th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n",
"</thead>\n",
"<tbody>\n",
- "\t<tr><th scope=row>1</th><td>BLR</td><td>5.184e+09</td><td>2.629739e+09</td><td> 11</td><td>96.868149</td><td>2.176783e-01</td><td>8.5113520</td><td>4.142518e-02</td><td>NA</td><td>8.7704554</td><td>2.9614955</td><td> 3.057244</td><td> 454160</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((2277299 573...</td></tr>\n",
- "\t<tr><th scope=row>2</th><td>CAN</td><td>5.184e+09</td><td>2.288319e+12</td><td> 627</td><td>53.244565</td><td>6.916580e-04</td><td>0.4370265</td><td>2.473388e-04</td><td>NA</td><td>0.4379655</td><td>0.6617896</td><td> 1.242924</td><td>205016836</td><td>NA</td><td> 9</td><td>3</td><td>MULTIPOLYGON (((-6332280 50...</td></tr>\n",
- "\t<tr><th scope=row>3</th><td>CHN</td><td>5.184e+09</td><td>9.305738e+12</td><td>2016</td><td>41.130567</td><td>3.444781e-04</td><td>0.2889541</td><td>1.976979e-04</td><td>NA</td><td>0.2894963</td><td>0.5380486</td><td> 1.308148</td><td>331396373</td><td>NA</td><td> 3</td><td>5</td><td>MULTIPOLYGON (((10682088 25...</td></tr>\n",
- "\t<tr><th scope=row>4</th><td>DEU</td><td>5.184e+09</td><td>2.109578e+11</td><td> 65</td><td>89.174784</td><td>7.463370e-03</td><td>1.7981588</td><td>1.852809e-03</td><td>NA</td><td>1.8074750</td><td>1.3444237</td><td> 1.507628</td><td> 17299568</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((1330146 550...</td></tr>\n",
- "\t<tr><th scope=row>5</th><td>GBR</td><td>5.184e+09</td><td>3.915120e+10</td><td> 23</td><td>40.225928</td><td>2.147697e-02</td><td>2.0322501</td><td>2.738371e-03</td><td>NA</td><td>2.0564654</td><td>1.4340381</td><td> 3.564960</td><td> 5069694</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((-259910.5 5...</td></tr>\n",
- "\t<tr><th scope=row>6</th><td>IRL</td><td>5.184e+09</td><td>5.616566e+08</td><td> 3</td><td>28.307549</td><td>5.722809e-01</td><td>7.1520627</td><td>3.202226e-03</td><td>NA</td><td>7.7275459</td><td>2.7798464</td><td> 9.820159</td><td> 101090</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((-839409.2 5...</td></tr>\n",
- "\t<tr><th scope=row>7</th><td>KAZ</td><td>5.184e+09</td><td>2.362658e+12</td><td> 553</td><td> 3.948623</td><td>6.183252e-05</td><td>0.1944473</td><td>2.403520e-05</td><td>NA</td><td>0.1945332</td><td>0.4410592</td><td>11.169949</td><td>226954869</td><td>NA</td><td> 0</td><td>4</td><td>MULTIPOLYGON (((4889731 517...</td></tr>\n",
- "\t<tr><th scope=row>8</th><td>MNG</td><td>5.184e+09</td><td>1.560160e+12</td><td> 362</td><td> 7.887330</td><td>6.998089e-05</td><td>0.1674246</td><td>2.348949e-05</td><td>NA</td><td>0.1675181</td><td>0.4092897</td><td> 5.189205</td><td>144092338</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((11238039 55...</td></tr>\n",
- "\t<tr><th scope=row>9</th><td>NLD</td><td>5.184e+09</td><td>6.720407e+09</td><td> 10</td><td>44.256891</td><td>2.469888e-02</td><td>3.1750318</td><td>2.569909e-03</td><td>NA</td><td>3.2023006</td><td>1.7894973</td><td> 4.043432</td><td> 937626</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((362358.5 57...</td></tr>\n",
- "\t<tr><th scope=row>10</th><td>NOR</td><td>5.184e+09</td><td>5.520980e+07</td><td> 1</td><td> 0.000000</td><td>0.000000e+00</td><td>0.0000000</td><td>0.000000e+00</td><td>NA</td><td>0.0000000</td><td>0.0000000</td><td> 0.000000</td><td> 0</td><td>NA</td><td> 1</td><td>0</td><td>MULTIPOLYGON (((335559.4 -5...</td></tr>\n",
- "\t<tr><th scope=row>11</th><td>POL</td><td>5.184e+09</td><td>1.220705e+11</td><td> 45</td><td>73.172129</td><td>7.738243e-03</td><td>2.1043726</td><td>2.185907e-03</td><td>NA</td><td>2.1142968</td><td>1.4540622</td><td> 1.987180</td><td> 13172276</td><td>NA</td><td> 0</td><td>3</td><td>MULTIPOLYGON (((1817554 557...</td></tr>\n",
- "\t<tr><th scope=row>12</th><td>RUS</td><td>5.184e+09</td><td>1.807355e+12</td><td> 578</td><td>57.198421</td><td>1.158709e-03</td><td>0.5529233</td><td>4.315925e-04</td><td>NA</td><td>0.5545136</td><td>0.7446567</td><td> 1.301883</td><td>137759303</td><td>NA</td><td> 1</td><td>5</td><td>MULTIPOLYGON (((15227973 57...</td></tr>\n",
- "\t<tr><th scope=row>13</th><td>UKR</td><td>5.184e+09</td><td>5.770285e+11</td><td> 165</td><td>38.097908</td><td>1.260510e-03</td><td>0.9436175</td><td>3.496119e-04</td><td>NA</td><td>0.9452276</td><td>0.9722282</td><td> 2.551920</td><td> 37904754</td><td>NA</td><td> 1</td><td>3</td><td>MULTIPOLYGON (((2868630 523...</td></tr>\n",
- "\t<tr><th scope=row>14</th><td>USA</td><td>5.184e+09</td><td>7.960166e+12</td><td>1734</td><td>54.412356</td><td>2.989194e-04</td><td>0.3659962</td><td>8.492571e-05</td><td>NA</td><td>0.3663800</td><td>0.6052933</td><td> 1.112419</td><td>384983129</td><td>NA</td><td>25</td><td>6</td><td>MULTIPOLYGON (((-9176117 55...</td></tr>\n",
+ "\t<tr><td> 1</td><td> 9</td><td>Admin-0 country</td><td>2</td><td>Country </td><td>People's Republic of China </td><td>NA</td><td>1379302771</td><td>18</td><td>21140000</td><td>⋯</td><td>КитайÑÐºÐ°Ñ ÐÐ°Ñ€Ð¾Ð´Ð½Ð°Ñ Ð ÐµÑпублика</td><td>Kina </td><td>Çin Halk Cumhuriyeti </td><td>Cá»™ng hòa Nhân dân Trung Hoa </td><td>ä¸åŽäººæ°‘共和国</td><td>China </td><td>None </td><td>EAP </td><td> 376.66709</td><td> 951.364850</td></tr>\n",
+ "\t<tr><td> 2</td><td> 32</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Ukraine </td><td>NA</td><td> 44033874</td><td>15</td><td> 352600</td><td>⋯</td><td>Украина </td><td>Ukraina </td><td>Ukrayna </td><td>Ukraina </td><td>乌克兰 </td><td>Ukraine </td><td>None </td><td>ECA </td><td> 84.13558</td><td> 73.649890</td></tr>\n",
+ "\t<tr><td> 3</td><td> 33</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Republic of Belarus </td><td>NA</td><td> 9549747</td><td>13</td><td> 165400</td><td>⋯</td><td>БелоруÑÑÐ¸Ñ </td><td>Vitryssland </td><td>Beyaz Rusya </td><td>Belarus </td><td>白罗斯 </td><td>Belarus </td><td>None </td><td>ECA </td><td> 33.71343</td><td> 28.122390</td></tr>\n",
+ "\t<tr><td> 4</td><td> 40</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Republic of Kazakhstan </td><td>NA</td><td> 18556698</td><td>14</td><td> 460700</td><td>⋯</td><td>КазахÑтан </td><td>Kazakstan </td><td>Kazakistan </td><td>Kazakhstan </td><td>哈è¨å…‹æ–¯å¦ </td><td>Kazakhstan </td><td>None </td><td>ECA </td><td> 158.82012</td><td> 329.367573</td></tr>\n",
+ "\t<tr><td> 5</td><td> 45</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Mongolia </td><td>NA</td><td> 3068243</td><td>12</td><td> 37000</td><td>⋯</td><td>ÐœÐ¾Ð½Ð³Ð¾Ð»Ð¸Ñ </td><td>Mongoliet </td><td>MoÄŸolistan </td><td>Mông Cổ </td><td>è’™å¤å›½ </td><td>Mongolia </td><td>None </td><td>EAP </td><td> 85.72407</td><td> 184.602987</td></tr>\n",
+ "\t<tr><td> 6</td><td> 46</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Russian Federation </td><td>NA</td><td> 142257519</td><td>17</td><td> 3745000</td><td>⋯</td><td>РоÑÑÐ¸Ñ </td><td>Ryssland </td><td>Rusya </td><td>Nga </td><td>ä¿„ç½—æ–¯ </td><td>Russian Federation </td><td>Do not use \"Russia\"</td><td>ECA </td><td>1645.53615</td><td>2925.332609</td></tr>\n",
+ "\t<tr><td> 7</td><td> 48</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Federal Republic of Germany </td><td>NA</td><td> 80594017</td><td>16</td><td> 3979000</td><td>⋯</td><td>Ð“ÐµÑ€Ð¼Ð°Ð½Ð¸Ñ </td><td>Tyskland </td><td>Almanya </td><td>Äức </td><td>德国 </td><td>Germany </td><td>None </td><td>Other</td><td> 68.23519</td><td> 45.930027</td></tr>\n",
+ "\t<tr><td> 8</td><td> 51</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Kingdom of Norway </td><td>NA</td><td> 5320045</td><td>13</td><td> 364700</td><td>⋯</td><td>ÐÐ¾Ñ€Ð²ÐµÐ³Ð¸Ñ </td><td>Norge </td><td>Norveç </td><td>Na Uy </td><td>æŒªå¨ </td><td>Norway </td><td>None </td><td>Other</td><td> 552.73323</td><td> 85.597443</td></tr>\n",
+ "\t<tr><td> 9</td><td> 75</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Republic of Poland </td><td>NA</td><td> 38476269</td><td>15</td><td> 1052000</td><td>⋯</td><td>Польша </td><td>Polen </td><td>Polonya </td><td>Ba Lan </td><td>波兰 </td><td>Poland </td><td>None </td><td>ECA </td><td> 37.81876</td><td> 41.141773</td></tr>\n",
+ "\t<tr><td>10</td><td> 76</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Ireland </td><td>NA</td><td> 5011102</td><td>13</td><td> 322000</td><td>⋯</td><td>Ð˜Ñ€Ð»Ð°Ð½Ð´Ð¸Ñ </td><td>Irland </td><td>Ä°rlanda </td><td>Cá»™ng hòa Ireland </td><td>爱尔兰共和国 </td><td>Ireland </td><td>None </td><td>Other</td><td> 48.24202</td><td> 9.334048</td></tr>\n",
+ "\t<tr><td>11</td><td> 77</td><td>Admin-0 country</td><td>2</td><td>Country </td><td>United Kingdom of Great Britain and Northern Ireland</td><td>NA</td><td> 64769452</td><td>16</td><td> 2788000</td><td>⋯</td><td>Ð’ÐµÐ»Ð¸ÐºÐ¾Ð±Ñ€Ð¸Ñ‚Ð°Ð½Ð¸Ñ </td><td>Storbritannien</td><td>BirleÅŸik Krallık </td><td>VÆ°Æ¡ng quốc Liên hiệp Anh và Bắc Ireland</td><td>英国 </td><td>United Kingdom </td><td>None </td><td>Other</td><td> 155.61409</td><td> 33.545736</td></tr>\n",
+ "\t<tr><td>12</td><td>150</td><td>Admin-0 country</td><td>2</td><td>Country </td><td>United States of America </td><td>NA</td><td> 326625791</td><td>17</td><td>18560000</td><td>⋯</td><td>Соединённые Штаты Ðмерики </td><td>USA </td><td>Amerika BirleÅŸik Devletleri</td><td>Hoa Kỳ </td><td>美国 </td><td>United States of America</td><td>None </td><td>Other</td><td> 948.72432</td><td>1116.361761</td></tr>\n",
+ "\t<tr><td>13</td><td>151</td><td>Admin-0 country</td><td>2</td><td>Sovereign country</td><td>Canada </td><td>NA</td><td> 35623680</td><td>15</td><td> 1674000</td><td>⋯</td><td>Канада </td><td>Kanada </td><td>Kanada </td><td>Canada </td><td>åŠ æ‹¿å¤§ </td><td>Canada </td><td>None </td><td>Other</td><td>2573.71248</td><td>1691.869076</td></tr>\n",
+ "\t<tr><td>14</td><td>238</td><td>Admin-0 country</td><td>2</td><td>Country </td><td>Kingdom of the Netherlands </td><td>NA</td><td> 17084719</td><td>14</td><td> 870800</td><td>⋯</td><td>Ðидерланды </td><td>Nederländerna </td><td>Hollanda </td><td>Hà Lan </td><td>è·è˜ </td><td>Netherlands </td><td>None </td><td>Other</td><td> 22.81639</td><td> 4.884540</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
- "A sf: 14 × 17\n",
- "\\begin{tabular}{r|lllllllllllllllll}\n",
- " & class & cell\\_area & region\\_area & region\\_cell\\_count & region\\_mean & sample\\_var & model\\_var & sample\\_cov & model\\_cov & region\\_var & region\\_stderr & region\\_rel\\_stderr & nshots & ntracks & nonresponse\\_cells & nmodels & geom\\\\\n",
- " & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <MULTIPOLYGON {[}m{]}>\\\\\n",
+ "A data.frame: 14 × 53\n",
+ "\\begin{tabular}{lllllllllllllllllllll}\n",
+ " fid & OBJECTID & featurecla & LEVEL & TYPE & FORMAL\\_EN & FORMAL\\_FR & POP\\_EST & POP\\_RANK & GDP\\_MD\\_EST & ⋯ & NAME\\_RU & NAME\\_SV & NAME\\_TR & NAME\\_VI & NAME\\_ZH & WB\\_NAME & WB\\_RULES & WB\\_REGION & Shape\\_Leng & Shape\\_Area\\\\\n",
+ " <int> & <int> & <chr> & <int> & <chr> & <chr> & <lgl> & <int> & <int> & <int> & ⋯ & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
"\\hline\n",
- "\t1 & BLR & 5.184e+09 & 2.629739e+09 & 11 & 96.868149 & 2.176783e-01 & 8.5113520 & 4.142518e-02 & NA & 8.7704554 & 2.9614955 & 3.057244 & 454160 & NA & 0 & 3 & MULTIPOLYGON (((2277299 573...\\\\\n",
- "\t2 & CAN & 5.184e+09 & 2.288319e+12 & 627 & 53.244565 & 6.916580e-04 & 0.4370265 & 2.473388e-04 & NA & 0.4379655 & 0.6617896 & 1.242924 & 205016836 & NA & 9 & 3 & MULTIPOLYGON (((-6332280 50...\\\\\n",
- "\t3 & CHN & 5.184e+09 & 9.305738e+12 & 2016 & 41.130567 & 3.444781e-04 & 0.2889541 & 1.976979e-04 & NA & 0.2894963 & 0.5380486 & 1.308148 & 331396373 & NA & 3 & 5 & MULTIPOLYGON (((10682088 25...\\\\\n",
- "\t4 & DEU & 5.184e+09 & 2.109578e+11 & 65 & 89.174784 & 7.463370e-03 & 1.7981588 & 1.852809e-03 & NA & 1.8074750 & 1.3444237 & 1.507628 & 17299568 & NA & 0 & 3 & MULTIPOLYGON (((1330146 550...\\\\\n",
- "\t5 & GBR & 5.184e+09 & 3.915120e+10 & 23 & 40.225928 & 2.147697e-02 & 2.0322501 & 2.738371e-03 & NA & 2.0564654 & 1.4340381 & 3.564960 & 5069694 & NA & 0 & 3 & MULTIPOLYGON (((-259910.5 5...\\\\\n",
- "\t6 & IRL & 5.184e+09 & 5.616566e+08 & 3 & 28.307549 & 5.722809e-01 & 7.1520627 & 3.202226e-03 & NA & 7.7275459 & 2.7798464 & 9.820159 & 101090 & NA & 0 & 3 & MULTIPOLYGON (((-839409.2 5...\\\\\n",
- "\t7 & KAZ & 5.184e+09 & 2.362658e+12 & 553 & 3.948623 & 6.183252e-05 & 0.1944473 & 2.403520e-05 & NA & 0.1945332 & 0.4410592 & 11.169949 & 226954869 & NA & 0 & 4 & MULTIPOLYGON (((4889731 517...\\\\\n",
- "\t8 & MNG & 5.184e+09 & 1.560160e+12 & 362 & 7.887330 & 6.998089e-05 & 0.1674246 & 2.348949e-05 & NA & 0.1675181 & 0.4092897 & 5.189205 & 144092338 & NA & 0 & 3 & MULTIPOLYGON (((11238039 55...\\\\\n",
- "\t9 & NLD & 5.184e+09 & 6.720407e+09 & 10 & 44.256891 & 2.469888e-02 & 3.1750318 & 2.569909e-03 & NA & 3.2023006 & 1.7894973 & 4.043432 & 937626 & NA & 0 & 3 & MULTIPOLYGON (((362358.5 57...\\\\\n",
- "\t10 & NOR & 5.184e+09 & 5.520980e+07 & 1 & 0.000000 & 0.000000e+00 & 0.0000000 & 0.000000e+00 & NA & 0.0000000 & 0.0000000 & 0.000000 & 0 & NA & 1 & 0 & MULTIPOLYGON (((335559.4 -5...\\\\\n",
- "\t11 & POL & 5.184e+09 & 1.220705e+11 & 45 & 73.172129 & 7.738243e-03 & 2.1043726 & 2.185907e-03 & NA & 2.1142968 & 1.4540622 & 1.987180 & 13172276 & NA & 0 & 3 & MULTIPOLYGON (((1817554 557...\\\\\n",
- "\t12 & RUS & 5.184e+09 & 1.807355e+12 & 578 & 57.198421 & 1.158709e-03 & 0.5529233 & 4.315925e-04 & NA & 0.5545136 & 0.7446567 & 1.301883 & 137759303 & NA & 1 & 5 & MULTIPOLYGON (((15227973 57...\\\\\n",
- "\t13 & UKR & 5.184e+09 & 5.770285e+11 & 165 & 38.097908 & 1.260510e-03 & 0.9436175 & 3.496119e-04 & NA & 0.9452276 & 0.9722282 & 2.551920 & 37904754 & NA & 1 & 3 & MULTIPOLYGON (((2868630 523...\\\\\n",
- "\t14 & USA & 5.184e+09 & 7.960166e+12 & 1734 & 54.412356 & 2.989194e-04 & 0.3659962 & 8.492571e-05 & NA & 0.3663800 & 0.6052933 & 1.112419 & 384983129 & NA & 25 & 6 & MULTIPOLYGON (((-9176117 55...\\\\\n",
+ "\t 1 & 9 & Admin-0 country & 2 & Country & People's Republic of China & NA & 1379302771 & 18 & 21140000 & ⋯ & КитайÑÐºÐ°Ñ ÐÐ°Ñ€Ð¾Ð´Ð½Ð°Ñ Ð ÐµÑпублика & Kina & Çin Halk Cumhuriyeti & Cá»™ng hòa Nhân dân Trung Hoa & ä¸åŽäººæ°‘共和国 & China & None & EAP & 376.66709 & 951.364850\\\\\n",
+ "\t 2 & 32 & Admin-0 country & 2 & Sovereign country & Ukraine & NA & 44033874 & 15 & 352600 & ⋯ & Украина & Ukraina & Ukrayna & Ukraina & 乌克兰 & Ukraine & None & ECA & 84.13558 & 73.649890\\\\\n",
+ "\t 3 & 33 & Admin-0 country & 2 & Sovereign country & Republic of Belarus & NA & 9549747 & 13 & 165400 & ⋯ & БелоруÑÑÐ¸Ñ & Vitryssland & Beyaz Rusya & Belarus & 白罗斯 & Belarus & None & ECA & 33.71343 & 28.122390\\\\\n",
+ "\t 4 & 40 & Admin-0 country & 2 & Sovereign country & Republic of Kazakhstan & NA & 18556698 & 14 & 460700 & ⋯ & КазахÑтан & Kazakstan & Kazakistan & Kazakhstan & 哈è¨å…‹æ–¯å¦ & Kazakhstan & None & ECA & 158.82012 & 329.367573\\\\\n",
+ "\t 5 & 45 & Admin-0 country & 2 & Sovereign country & Mongolia & NA & 3068243 & 12 & 37000 & ⋯ & ÐœÐ¾Ð½Ð³Ð¾Ð»Ð¸Ñ & Mongoliet & MoÄŸolistan & Mông Cổ & è’™å¤å›½ & Mongolia & None & EAP & 85.72407 & 184.602987\\\\\n",
+ "\t 6 & 46 & Admin-0 country & 2 & Sovereign country & Russian Federation & NA & 142257519 & 17 & 3745000 & ⋯ & РоÑÑÐ¸Ñ & Ryssland & Rusya & Nga & ä¿„ç½—æ–¯ & Russian Federation & Do not use \"Russia\" & ECA & 1645.53615 & 2925.332609\\\\\n",
+ "\t 7 & 48 & Admin-0 country & 2 & Sovereign country & Federal Republic of Germany & NA & 80594017 & 16 & 3979000 & ⋯ & Ð“ÐµÑ€Ð¼Ð°Ð½Ð¸Ñ & Tyskland & Almanya & Äức & 德国 & Germany & None & Other & 68.23519 & 45.930027\\\\\n",
+ "\t 8 & 51 & Admin-0 country & 2 & Sovereign country & Kingdom of Norway & NA & 5320045 & 13 & 364700 & ⋯ & ÐÐ¾Ñ€Ð²ÐµÐ³Ð¸Ñ & Norge & Norveç & Na Uy & æŒªå¨ & Norway & None & Other & 552.73323 & 85.597443\\\\\n",
+ "\t 9 & 75 & Admin-0 country & 2 & Sovereign country & Republic of Poland & NA & 38476269 & 15 & 1052000 & ⋯ & Польша & Polen & Polonya & Ba Lan & 波兰 & Poland & None & ECA & 37.81876 & 41.141773\\\\\n",
+ "\t 10 & 76 & Admin-0 country & 2 & Sovereign country & Ireland & NA & 5011102 & 13 & 322000 & ⋯ & Ð˜Ñ€Ð»Ð°Ð½Ð´Ð¸Ñ & Irland & Ä°rlanda & Cá»™ng hòa Ireland & 爱尔兰共和国 & Ireland & None & Other & 48.24202 & 9.334048\\\\\n",
+ "\t 11 & 77 & Admin-0 country & 2 & Country & United Kingdom of Great Britain and Northern Ireland & NA & 64769452 & 16 & 2788000 & ⋯ & Ð’ÐµÐ»Ð¸ÐºÐ¾Ð±Ñ€Ð¸Ñ‚Ð°Ð½Ð¸Ñ & Storbritannien & BirleÅŸik Krallık & VÆ°Æ¡ng quốc Liên hiệp Anh và Bắc Ireland & 英国 & United Kingdom & None & Other & 155.61409 & 33.545736\\\\\n",
+ "\t 12 & 150 & Admin-0 country & 2 & Country & United States of America & NA & 326625791 & 17 & 18560000 & ⋯ & Соединённые Штаты Ðмерики & USA & Amerika BirleÅŸik Devletleri & Hoa Kỳ & 美国 & United States of America & None & Other & 948.72432 & 1116.361761\\\\\n",
+ "\t 13 & 151 & Admin-0 country & 2 & Sovereign country & Canada & NA & 35623680 & 15 & 1674000 & ⋯ & Канада & Kanada & Kanada & Canada & åŠ æ‹¿å¤§ & Canada & None & Other & 2573.71248 & 1691.869076\\\\\n",
+ "\t 14 & 238 & Admin-0 country & 2 & Country & Kingdom of the Netherlands & NA & 17084719 & 14 & 870800 & ⋯ & Ðидерланды & Nederländerna & Hollanda & Hà Lan & è·è˜ & Netherlands & None & Other & 22.81639 & 4.884540\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
- "A sf: 14 × 17\n",
+ "A data.frame: 14 × 53\n",
"\n",
- "| <!--/--> | class <chr> | cell_area <dbl> | region_area <dbl> | region_cell_count <dbl> | region_mean <dbl> | sample_var <dbl> | model_var <dbl> | sample_cov <dbl> | model_cov <chr> | region_var <dbl> | region_stderr <dbl> | region_rel_stderr <dbl> | nshots <dbl> | ntracks <chr> | nonresponse_cells <dbl> | nmodels <dbl> | geom <MULTIPOLYGON [m]> |\n",
- "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
- "| 1 | BLR | 5.184e+09 | 2.629739e+09 | 11 | 96.868149 | 2.176783e-01 | 8.5113520 | 4.142518e-02 | NA | 8.7704554 | 2.9614955 | 3.057244 | 454160 | NA | 0 | 3 | MULTIPOLYGON (((2277299 573... |\n",
- "| 2 | CAN | 5.184e+09 | 2.288319e+12 | 627 | 53.244565 | 6.916580e-04 | 0.4370265 | 2.473388e-04 | NA | 0.4379655 | 0.6617896 | 1.242924 | 205016836 | NA | 9 | 3 | MULTIPOLYGON (((-6332280 50... |\n",
- "| 3 | CHN | 5.184e+09 | 9.305738e+12 | 2016 | 41.130567 | 3.444781e-04 | 0.2889541 | 1.976979e-04 | NA | 0.2894963 | 0.5380486 | 1.308148 | 331396373 | NA | 3 | 5 | MULTIPOLYGON (((10682088 25... |\n",
- "| 4 | DEU | 5.184e+09 | 2.109578e+11 | 65 | 89.174784 | 7.463370e-03 | 1.7981588 | 1.852809e-03 | NA | 1.8074750 | 1.3444237 | 1.507628 | 17299568 | NA | 0 | 3 | MULTIPOLYGON (((1330146 550... |\n",
- "| 5 | GBR | 5.184e+09 | 3.915120e+10 | 23 | 40.225928 | 2.147697e-02 | 2.0322501 | 2.738371e-03 | NA | 2.0564654 | 1.4340381 | 3.564960 | 5069694 | NA | 0 | 3 | MULTIPOLYGON (((-259910.5 5... |\n",
- "| 6 | IRL | 5.184e+09 | 5.616566e+08 | 3 | 28.307549 | 5.722809e-01 | 7.1520627 | 3.202226e-03 | NA | 7.7275459 | 2.7798464 | 9.820159 | 101090 | NA | 0 | 3 | MULTIPOLYGON (((-839409.2 5... |\n",
- "| 7 | KAZ | 5.184e+09 | 2.362658e+12 | 553 | 3.948623 | 6.183252e-05 | 0.1944473 | 2.403520e-05 | NA | 0.1945332 | 0.4410592 | 11.169949 | 226954869 | NA | 0 | 4 | MULTIPOLYGON (((4889731 517... |\n",
- "| 8 | MNG | 5.184e+09 | 1.560160e+12 | 362 | 7.887330 | 6.998089e-05 | 0.1674246 | 2.348949e-05 | NA | 0.1675181 | 0.4092897 | 5.189205 | 144092338 | NA | 0 | 3 | MULTIPOLYGON (((11238039 55... |\n",
- "| 9 | NLD | 5.184e+09 | 6.720407e+09 | 10 | 44.256891 | 2.469888e-02 | 3.1750318 | 2.569909e-03 | NA | 3.2023006 | 1.7894973 | 4.043432 | 937626 | NA | 0 | 3 | MULTIPOLYGON (((362358.5 57... |\n",
- "| 10 | NOR | 5.184e+09 | 5.520980e+07 | 1 | 0.000000 | 0.000000e+00 | 0.0000000 | 0.000000e+00 | NA | 0.0000000 | 0.0000000 | 0.000000 | 0 | NA | 1 | 0 | MULTIPOLYGON (((335559.4 -5... |\n",
- "| 11 | POL | 5.184e+09 | 1.220705e+11 | 45 | 73.172129 | 7.738243e-03 | 2.1043726 | 2.185907e-03 | NA | 2.1142968 | 1.4540622 | 1.987180 | 13172276 | NA | 0 | 3 | MULTIPOLYGON (((1817554 557... |\n",
- "| 12 | RUS | 5.184e+09 | 1.807355e+12 | 578 | 57.198421 | 1.158709e-03 | 0.5529233 | 4.315925e-04 | NA | 0.5545136 | 0.7446567 | 1.301883 | 137759303 | NA | 1 | 5 | MULTIPOLYGON (((15227973 57... |\n",
- "| 13 | UKR | 5.184e+09 | 5.770285e+11 | 165 | 38.097908 | 1.260510e-03 | 0.9436175 | 3.496119e-04 | NA | 0.9452276 | 0.9722282 | 2.551920 | 37904754 | NA | 1 | 3 | MULTIPOLYGON (((2868630 523... |\n",
- "| 14 | USA | 5.184e+09 | 7.960166e+12 | 1734 | 54.412356 | 2.989194e-04 | 0.3659962 | 8.492571e-05 | NA | 0.3663800 | 0.6052933 | 1.112419 | 384983129 | NA | 25 | 6 | MULTIPOLYGON (((-9176117 55... |\n",
+ "| fid <int> | OBJECTID <int> | featurecla <chr> | LEVEL <int> | TYPE <chr> | FORMAL_EN <chr> | FORMAL_FR <lgl> | POP_EST <int> | POP_RANK <int> | GDP_MD_EST <int> | ⋯ ⋯ | NAME_RU <chr> | NAME_SV <chr> | NAME_TR <chr> | NAME_VI <chr> | NAME_ZH <chr> | WB_NAME <chr> | WB_RULES <chr> | WB_REGION <chr> | Shape_Leng <dbl> | Shape_Area <dbl> |\n",
+ "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| 1 | 9 | Admin-0 country | 2 | Country | People's Republic of China | NA | 1379302771 | 18 | 21140000 | ⋯ | КитайÑÐºÐ°Ñ ÐÐ°Ñ€Ð¾Ð´Ð½Ð°Ñ Ð ÐµÑпублика | Kina | Çin Halk Cumhuriyeti | Cá»™ng hòa Nhân dân Trung Hoa | ä¸åŽäººæ°‘共和国 | China | None | EAP | 376.66709 | 951.364850 |\n",
+ "| 2 | 32 | Admin-0 country | 2 | Sovereign country | Ukraine | NA | 44033874 | 15 | 352600 | ⋯ | Украина | Ukraina | Ukrayna | Ukraina | 乌克兰 | Ukraine | None | ECA | 84.13558 | 73.649890 |\n",
+ "| 3 | 33 | Admin-0 country | 2 | Sovereign country | Republic of Belarus | NA | 9549747 | 13 | 165400 | ⋯ | БелоруÑÑÐ¸Ñ | Vitryssland | Beyaz Rusya | Belarus | 白罗斯 | Belarus | None | ECA | 33.71343 | 28.122390 |\n",
+ "| 4 | 40 | Admin-0 country | 2 | Sovereign country | Republic of Kazakhstan | NA | 18556698 | 14 | 460700 | ⋯ | КазахÑтан | Kazakstan | Kazakistan | Kazakhstan | 哈è¨å…‹æ–¯å¦ | Kazakhstan | None | ECA | 158.82012 | 329.367573 |\n",
+ "| 5 | 45 | Admin-0 country | 2 | Sovereign country | Mongolia | NA | 3068243 | 12 | 37000 | ⋯ | ÐœÐ¾Ð½Ð³Ð¾Ð»Ð¸Ñ | Mongoliet | MoÄŸolistan | Mông Cổ | è’™å¤å›½ | Mongolia | None | EAP | 85.72407 | 184.602987 |\n",
+ "| 6 | 46 | Admin-0 country | 2 | Sovereign country | Russian Federation | NA | 142257519 | 17 | 3745000 | ⋯ | РоÑÑÐ¸Ñ | Ryssland | Rusya | Nga | ä¿„ç½—æ–¯ | Russian Federation | Do not use \"Russia\" | ECA | 1645.53615 | 2925.332609 |\n",
+ "| 7 | 48 | Admin-0 country | 2 | Sovereign country | Federal Republic of Germany | NA | 80594017 | 16 | 3979000 | ⋯ | Ð“ÐµÑ€Ð¼Ð°Ð½Ð¸Ñ | Tyskland | Almanya | Äức | 德国 | Germany | None | Other | 68.23519 | 45.930027 |\n",
+ "| 8 | 51 | Admin-0 country | 2 | Sovereign country | Kingdom of Norway | NA | 5320045 | 13 | 364700 | ⋯ | ÐÐ¾Ñ€Ð²ÐµÐ³Ð¸Ñ | Norge | Norveç | Na Uy | æŒªå¨ | Norway | None | Other | 552.73323 | 85.597443 |\n",
+ "| 9 | 75 | Admin-0 country | 2 | Sovereign country | Republic of Poland | NA | 38476269 | 15 | 1052000 | ⋯ | Польша | Polen | Polonya | Ba Lan | 波兰 | Poland | None | ECA | 37.81876 | 41.141773 |\n",
+ "| 10 | 76 | Admin-0 country | 2 | Sovereign country | Ireland | NA | 5011102 | 13 | 322000 | ⋯ | Ð˜Ñ€Ð»Ð°Ð½Ð´Ð¸Ñ | Irland | Ä°rlanda | Cá»™ng hòa Ireland | 爱尔兰共和国 | Ireland | None | Other | 48.24202 | 9.334048 |\n",
+ "| 11 | 77 | Admin-0 country | 2 | Country | United Kingdom of Great Britain and Northern Ireland | NA | 64769452 | 16 | 2788000 | ⋯ | Ð’ÐµÐ»Ð¸ÐºÐ¾Ð±Ñ€Ð¸Ñ‚Ð°Ð½Ð¸Ñ | Storbritannien | BirleÅŸik Krallık | VÆ°Æ¡ng quốc Liên hiệp Anh và Bắc Ireland | 英国 | United Kingdom | None | Other | 155.61409 | 33.545736 |\n",
+ "| 12 | 150 | Admin-0 country | 2 | Country | United States of America | NA | 326625791 | 17 | 18560000 | ⋯ | Соединённые Штаты Ðмерики | USA | Amerika BirleÅŸik Devletleri | Hoa Kỳ | 美国 | United States of America | None | Other | 948.72432 | 1116.361761 |\n",
+ "| 13 | 151 | Admin-0 country | 2 | Sovereign country | Canada | NA | 35623680 | 15 | 1674000 | ⋯ | Канада | Kanada | Kanada | Canada | åŠ æ‹¿å¤§ | Canada | None | Other | 2573.71248 | 1691.869076 |\n",
+ "| 14 | 238 | Admin-0 country | 2 | Country | Kingdom of the Netherlands | NA | 17084719 | 14 | 870800 | ⋯ | Ðидерланды | Nederländerna | Hollanda | Hà Lan | è·è˜ | Netherlands | None | Other | 22.81639 | 4.884540 |\n",
"\n"
],
"text/plain": [
- " class cell_area region_area region_cell_count region_mean sample_var \n",
- "1 BLR 5.184e+09 2.629739e+09 11 96.868149 2.176783e-01\n",
- "2 CAN 5.184e+09 2.288319e+12 627 53.244565 6.916580e-04\n",
- "3 CHN 5.184e+09 9.305738e+12 2016 41.130567 3.444781e-04\n",
- "4 DEU 5.184e+09 2.109578e+11 65 89.174784 7.463370e-03\n",
- "5 GBR 5.184e+09 3.915120e+10 23 40.225928 2.147697e-02\n",
- "6 IRL 5.184e+09 5.616566e+08 3 28.307549 5.722809e-01\n",
- "7 KAZ 5.184e+09 2.362658e+12 553 3.948623 6.183252e-05\n",
- "8 MNG 5.184e+09 1.560160e+12 362 7.887330 6.998089e-05\n",
- "9 NLD 5.184e+09 6.720407e+09 10 44.256891 2.469888e-02\n",
- "10 NOR 5.184e+09 5.520980e+07 1 0.000000 0.000000e+00\n",
- "11 POL 5.184e+09 1.220705e+11 45 73.172129 7.738243e-03\n",
- "12 RUS 5.184e+09 1.807355e+12 578 57.198421 1.158709e-03\n",
- "13 UKR 5.184e+09 5.770285e+11 165 38.097908 1.260510e-03\n",
- "14 USA 5.184e+09 7.960166e+12 1734 54.412356 2.989194e-04\n",
- " model_var sample_cov model_cov region_var region_stderr region_rel_stderr\n",
- "1 8.5113520 4.142518e-02 NA 8.7704554 2.9614955 3.057244 \n",
- "2 0.4370265 2.473388e-04 NA 0.4379655 0.6617896 1.242924 \n",
- "3 0.2889541 1.976979e-04 NA 0.2894963 0.5380486 1.308148 \n",
- "4 1.7981588 1.852809e-03 NA 1.8074750 1.3444237 1.507628 \n",
- "5 2.0322501 2.738371e-03 NA 2.0564654 1.4340381 3.564960 \n",
- "6 7.1520627 3.202226e-03 NA 7.7275459 2.7798464 9.820159 \n",
- "7 0.1944473 2.403520e-05 NA 0.1945332 0.4410592 11.169949 \n",
- "8 0.1674246 2.348949e-05 NA 0.1675181 0.4092897 5.189205 \n",
- "9 3.1750318 2.569909e-03 NA 3.2023006 1.7894973 4.043432 \n",
- "10 0.0000000 0.000000e+00 NA 0.0000000 0.0000000 0.000000 \n",
- "11 2.1043726 2.185907e-03 NA 2.1142968 1.4540622 1.987180 \n",
- "12 0.5529233 4.315925e-04 NA 0.5545136 0.7446567 1.301883 \n",
- "13 0.9436175 3.496119e-04 NA 0.9452276 0.9722282 2.551920 \n",
- "14 0.3659962 8.492571e-05 NA 0.3663800 0.6052933 1.112419 \n",
- " nshots ntracks nonresponse_cells nmodels geom \n",
- "1 454160 NA 0 3 MULTIPOLYGON (((2277299 573...\n",
- "2 205016836 NA 9 3 MULTIPOLYGON (((-6332280 50...\n",
- "3 331396373 NA 3 5 MULTIPOLYGON (((10682088 25...\n",
- "4 17299568 NA 0 3 MULTIPOLYGON (((1330146 550...\n",
- "5 5069694 NA 0 3 MULTIPOLYGON (((-259910.5 5...\n",
- "6 101090 NA 0 3 MULTIPOLYGON (((-839409.2 5...\n",
- "7 226954869 NA 0 4 MULTIPOLYGON (((4889731 517...\n",
- "8 144092338 NA 0 3 MULTIPOLYGON (((11238039 55...\n",
- "9 937626 NA 0 3 MULTIPOLYGON (((362358.5 57...\n",
- "10 0 NA 1 0 MULTIPOLYGON (((335559.4 -5...\n",
- "11 13172276 NA 0 3 MULTIPOLYGON (((1817554 557...\n",
- "12 137759303 NA 1 5 MULTIPOLYGON (((15227973 57...\n",
- "13 37904754 NA 1 3 MULTIPOLYGON (((2868630 523...\n",
- "14 384983129 NA 25 6 MULTIPOLYGON (((-9176117 55..."
+ " fid OBJECTID featurecla LEVEL TYPE \n",
+ "1 1 9 Admin-0 country 2 Country \n",
+ "2 2 32 Admin-0 country 2 Sovereign country\n",
+ "3 3 33 Admin-0 country 2 Sovereign country\n",
+ "4 4 40 Admin-0 country 2 Sovereign country\n",
+ "5 5 45 Admin-0 country 2 Sovereign country\n",
+ "6 6 46 Admin-0 country 2 Sovereign country\n",
+ "7 7 48 Admin-0 country 2 Sovereign country\n",
+ "8 8 51 Admin-0 country 2 Sovereign country\n",
+ "9 9 75 Admin-0 country 2 Sovereign country\n",
+ "10 10 76 Admin-0 country 2 Sovereign country\n",
+ "11 11 77 Admin-0 country 2 Country \n",
+ "12 12 150 Admin-0 country 2 Country \n",
+ "13 13 151 Admin-0 country 2 Sovereign country\n",
+ "14 14 238 Admin-0 country 2 Country \n",
+ " FORMAL_EN FORMAL_FR POP_EST \n",
+ "1 People's Republic of China NA 1379302771\n",
+ "2 Ukraine NA 44033874\n",
+ "3 Republic of Belarus NA 9549747\n",
+ "4 Republic of Kazakhstan NA 18556698\n",
+ "5 Mongolia NA 3068243\n",
+ "6 Russian Federation NA 142257519\n",
+ "7 Federal Republic of Germany NA 80594017\n",
+ "8 Kingdom of Norway NA 5320045\n",
+ "9 Republic of Poland NA 38476269\n",
+ "10 Ireland NA 5011102\n",
+ "11 United Kingdom of Great Britain and Northern Ireland NA 64769452\n",
+ "12 United States of America NA 326625791\n",
+ "13 Canada NA 35623680\n",
+ "14 Kingdom of the Netherlands NA 17084719\n",
+ " POP_RANK GDP_MD_EST ⋯ NAME_RU NAME_SV \n",
+ "1 18 21140000 ⋯ КитайÑÐºÐ°Ñ ÐÐ°Ñ€Ð¾Ð´Ð½Ð°Ñ Ð ÐµÑпублика Kina \n",
+ "2 15 352600 ⋯ Украина Ukraina \n",
+ "3 13 165400 ⋯ БелоруÑÑÐ¸Ñ Vitryssland \n",
+ "4 14 460700 ⋯ КазахÑтан Kazakstan \n",
+ "5 12 37000 ⋯ ÐœÐ¾Ð½Ð³Ð¾Ð»Ð¸Ñ Mongoliet \n",
+ "6 17 3745000 ⋯ РоÑÑÐ¸Ñ Ryssland \n",
+ "7 16 3979000 ⋯ Ð“ÐµÑ€Ð¼Ð°Ð½Ð¸Ñ Tyskland \n",
+ "8 13 364700 ⋯ ÐÐ¾Ñ€Ð²ÐµÐ³Ð¸Ñ Norge \n",
+ "9 15 1052000 ⋯ Польша Polen \n",
+ "10 13 322000 ⋯ Ð˜Ñ€Ð»Ð°Ð½Ð´Ð¸Ñ Irland \n",
+ "11 16 2788000 ⋯ Ð’ÐµÐ»Ð¸ÐºÐ¾Ð±Ñ€Ð¸Ñ‚Ð°Ð½Ð¸Ñ Storbritannien\n",
+ "12 17 18560000 ⋯ Соединённые Штаты Ðмерики USA \n",
+ "13 15 1674000 ⋯ Канада Kanada \n",
+ "14 14 870800 ⋯ Ðидерланды Nederländerna \n",
+ " NAME_TR NAME_VI \n",
+ "1 Çin Halk Cumhuriyeti Cộng hòa Nhân dân Trung Hoa \n",
+ "2 Ukrayna Ukraina \n",
+ "3 Beyaz Rusya Belarus \n",
+ "4 Kazakistan Kazakhstan \n",
+ "5 Moğolistan Mông Cổ \n",
+ "6 Rusya Nga \n",
+ "7 Almanya Äức \n",
+ "8 Norveç Na Uy \n",
+ "9 Polonya Ba Lan \n",
+ "10 İrlanda Cộng hòa Ireland \n",
+ "11 Birleşik Krallık Vương quốc Liên hiệp Anh và Bắc Ireland\n",
+ "12 Amerika Birleşik Devletleri Hoa Kỳ \n",
+ "13 Kanada Canada \n",
+ "14 Hollanda HÃ Lan \n",
+ " NAME_ZH WB_NAME WB_RULES WB_REGION\n",
+ "1 ä¸åŽäººæ°‘共和国 China None EAP \n",
+ "2 乌克兰 Ukraine None ECA \n",
+ "3 白罗斯 Belarus None ECA \n",
+ "4 哈è¨å…‹æ–¯å¦ Kazakhstan None ECA \n",
+ "5 è’™å¤å›½ Mongolia None EAP \n",
+ "6 ä¿„ç½—æ–¯ Russian Federation Do not use \"Russia\" ECA \n",
+ "7 德国 Germany None Other \n",
+ "8 æŒªå¨ Norway None Other \n",
+ "9 波兰 Poland None ECA \n",
+ "10 爱尔兰共和国 Ireland None Other \n",
+ "11 英国 United Kingdom None Other \n",
+ "12 美国 United States of America None Other \n",
+ "13 åŠ æ‹¿å¤§ Canada None Other \n",
+ "14 è·è˜ Netherlands None Other \n",
+ " Shape_Leng Shape_Area \n",
+ "1 376.66709 951.364850\n",
+ "2 84.13558 73.649890\n",
+ "3 33.71343 28.122390\n",
+ "4 158.82012 329.367573\n",
+ "5 85.72407 184.602987\n",
+ "6 1645.53615 2925.332609\n",
+ "7 68.23519 45.930027\n",
+ "8 552.73323 85.597443\n",
+ "9 37.81876 41.141773\n",
+ "10 48.24202 9.334048\n",
+ "11 155.61409 33.545736\n",
+ "12 948.72432 1116.361761\n",
+ "13 2573.71248 1691.869076\n",
+ "14 22.81639 4.884540"
]
},
"metadata": {},
@@ -526,13 +498,13 @@
}
],
"source": [
- "GEDIv21"
+ "BOREALv01"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "55e70c13-47bb-4529-bf6d-37a2151d2993",
+ "id": "1ac85db0-3261-429b-a0fa-bff779b83ac0",
"metadata": {},
"outputs": [],
"source": []
diff --git a/country_summaries/IPCC_classes_DPS/ESA_WC_and_FH.ipynb b/country_summaries/IPCC_classes_DPS/ESA_WC_and_FH.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cf920890013b4f0dd2e9e57cc08a5a41bb38cbc9
--- /dev/null
+++ b/country_summaries/IPCC_classes_DPS/ESA_WC_and_FH.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0515b949-02d6-4193-831f-5607f9f5fd5b",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: rasterio in /opt/conda/lib/python3.10/site-packages (1.3.9)\n",
+ "Requirement already satisfied: affine in /opt/conda/lib/python3.10/site-packages (from rasterio) (2.4.0)\n",
+ "Requirement already satisfied: attrs in /opt/conda/lib/python3.10/site-packages (from rasterio) (23.1.0)\n",
+ "Requirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from rasterio) (2023.7.22)\n",
+ "Requirement already satisfied: click>=4.0 in /opt/conda/lib/python3.10/site-packages (from rasterio) (8.1.7)\n",
+ "Requirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.10/site-packages (from rasterio) (0.7.2)\n",
+ "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rasterio) (1.26.1)\n",
+ "Requirement already satisfied: snuggs>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from rasterio) (1.4.7)\n",
+ "Requirement already satisfied: click-plugins in /opt/conda/lib/python3.10/site-packages (from rasterio) (1.1.1)\n",
+ "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from rasterio) (68.2.2)\n",
+ "Requirement already satisfied: pyparsing>=2.1.6 in /opt/conda/lib/python3.10/site-packages (from snuggs>=1.4.1->rasterio) (3.1.1)\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0mRequirement already satisfied: rio_cogeo in /opt/conda/lib/python3.10/site-packages (5.2.0)\n",
+ "Requirement already satisfied: click>=7.0 in /opt/conda/lib/python3.10/site-packages (from rio_cogeo) (8.1.7)\n",
+ "Requirement already satisfied: rasterio>=1.3.3 in /opt/conda/lib/python3.10/site-packages (from rio_cogeo) (1.3.9)\n",
+ "Requirement already satisfied: numpy~=1.15 in /opt/conda/lib/python3.10/site-packages (from rio_cogeo) (1.26.1)\n",
+ "Requirement already satisfied: morecantile<6.0,>=5.0 in /opt/conda/lib/python3.10/site-packages (from rio_cogeo) (5.3.0)\n",
+ "Requirement already satisfied: pydantic~=2.0 in /opt/conda/lib/python3.10/site-packages (from rio_cogeo) (2.6.1)\n",
+ "Requirement already satisfied: attrs in /opt/conda/lib/python3.10/site-packages (from morecantile<6.0,>=5.0->rio_cogeo) (23.1.0)\n",
+ "Requirement already satisfied: pyproj~=3.1 in /opt/conda/lib/python3.10/site-packages (from morecantile<6.0,>=5.0->rio_cogeo) (3.6.1)\n",
+ "Requirement already satisfied: annotated-types>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from pydantic~=2.0->rio_cogeo) (0.6.0)\n",
+ "Requirement already satisfied: pydantic-core==2.16.2 in /opt/conda/lib/python3.10/site-packages (from pydantic~=2.0->rio_cogeo) (2.16.2)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /opt/conda/lib/python3.10/site-packages (from pydantic~=2.0->rio_cogeo) (4.8.0)\n",
+ "Requirement already satisfied: affine in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (2.4.0)\n",
+ "Requirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (2023.7.22)\n",
+ "Requirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (0.7.2)\n",
+ "Requirement already satisfied: snuggs>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (1.4.7)\n",
+ "Requirement already satisfied: click-plugins in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (1.1.1)\n",
+ "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from rasterio>=1.3.3->rio_cogeo) (68.2.2)\n",
+ "Requirement already satisfied: pyparsing>=2.1.6 in /opt/conda/lib/python3.10/site-packages (from snuggs>=1.4.1->rasterio>=1.3.3->rio_cogeo) (3.1.1)\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0mRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (2.2.0)\n",
+ "Requirement already satisfied: numpy<2,>=1.22.4 in /opt/conda/lib/python3.10/site-packages (from pandas) (1.26.1)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas) (2023.3.post1)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas) (2024.1)\n",
+ "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_5644/2900853303.py:16: DeprecationWarning: \n",
+ "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
+ "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
+ "but was not found to be installed on your system.\n",
+ "If this would cause problems for you,\n",
+ "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
+ " \n",
+ " import pandas as pd\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install rasterio\n",
+ "!pip install rio_cogeo\n",
+ "!pip install pandas\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import rasterio\n",
+ "import argparse\n",
+ "from rasterio.io import MemoryFile\n",
+ "from rasterio.rio import options\n",
+ "from rasterio.shutil import copy, delete\n",
+ "from rasterio.vrt import WarpedVRT\n",
+ "from rio_cogeo.cogeo import cog_translate\n",
+ "from rio_cogeo.profiles import cog_profiles\n",
+ "import copy\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bb8cbd8-e707-43ce-af3e-4a3c635e2795",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "TILES = [\"00N_000E\",\"00N_010E\",\"00N_020E\",\"00N_030E\",\"00N_040E\",\"00N_040W\",\"00N_050W\",\"00N_060W\",\"00N_070E\",\"00N_070W\",\"00N_080W\",\"00N_090E\",\"00N_090W\",\"00N_100E\",\"00N_100W\",\"00N_110E\",\"00N_120E\",\"00N_130E\",\"00N_140E\",\"00N_150E\",\"00N_160E\",\"00N_170E\",\"10N_000E\",\"10N_010E\",\"10N_010W\",\"10N_020E\",\"10N_020W\",\"10N_030E\",\"10N_040E\",\"10N_050E\",\"10N_050W\",\"10N_060W\",\"10N_070E\",\"10N_070W\",\"10N_080E\",\"10N_080W\",\"10N_090E\",\"10N_090W\",\"10N_100E\",\"10N_100W\",\"10N_110E\",\"10N_120E\",\"10N_130E\",\"10N_150E\",\"10N_160E\",\"10N_170E\",\"10S_010E\",\"10S_020E\",\"10S_030E\",\"10S_040E\",\"10S_040W\",\"10S_050E\",\"10S_050W\",\"10S_060W\",\"10S_070W\",\"10S_080W\",\"10S_110E\",\"10S_120E\",\"10S_130E\",\"10S_140E\",\"10S_150E\",\"10S_160E\",\"10S_170E\",\"10S_180W\",\"20N_000E\",\"20N_010E\",\"20N_010W\",\"20N_020E\",\"20N_020W\",\"20N_030E\",\"20N_030W\",\"20N_040E\",\"20N_050E\",\"20N_060W\",\"20N_070E\",\"20N_070W\",\"20N_080E\",\"20N_080W\",\"20N_090E\",\"20N_090W\",\"20N_100E\",\"20N_100W\",\"20N_110E\",\"20N_110W\",\"20N_120E\",\"20N_120W\",\"20N_160W\",\"20S_010E\",\"20S_020E\",\"20S_030E\",\"20S_040E\",\"20S_050E\",\"20S_050W\",\"20S_060W\",\"20S_070W\",\"20S_080W\",\"20S_110E\",\"20S_120E\",\"20S_130E\",\"20S_140E\",\"20S_150E\",\"20S_160E\",\"20S_180W\",\"30N_000E\",\"30N_010E\",\"30N_010W\",\"30N_020E\",\"30N_020W\",\"30N_030E\",\"30N_040E\",\"30N_050E\",\"30N_060E\",\"30N_070E\",\"30N_080E\",\"30N_080W\",\"30N_090E\",\"30N_090W\",\"30N_100E\",\"30N_100W\",\"30N_110E\",\"30N_110W\",\"30N_120E\",\"30N_120W\",\"30N_130E\",\"30N_160W\",\"30N_170W\",\"30S_010E\",\"30S_020E\",\"30S_030E\",\"30S_060W\",\"30S_070W\",\"30S_080W\",\"30S_110E\",\"30S_120E\",\"30S_130E\",\"30S_140E\",\"30S_150E\",\"30S_170E\",\"40N_000E\",\"40N_010E\",\"40N_010W\",\"40N_020E\",\"40N_020W\",\"40N_030E\",\"40N_040E\",\"40N_050E\",\"40N_060E\",\"40N_070E\",\"40N_070W\",\"40N_080E\",\"40N_080W\",\"40N_090E\",\"40N_090W\",\"40N_100E\",\"40N_100W\",\"40N_110E\",\"40N_110W\",\"40N_120E\",\"40N_120W\",\"40N_130E\",\"40N_130W\",\"40N_140E\",\"40S_070W\",\"40S_080W\",\"40S_140E\",\"40S_160E\",\"40S_170E\",\"50N_000E\",\"50N_010E\",\"50N_010W\",\"50N_020E\",\"50N_030E\",\"50N_040E\",\"50N_050E\",\"50N_060E\",\"50N_060W\",\"50N_070E\",\"50N_070W\",\"50N_080E\",\"50N_080W\",\"50N_090E\",\"50N_090W\",\"50N_100E\",\"50N_100W\",\"50N_110E\",\"50N_110W\",\"50N_120E\",\"50N_120W\",\"50N_130E\",\"50N_130W\",\"50N_140E\",\"50N_150E\",\"50S_060W\",\"50S_070W\",\"50S_080W\",\"60N_000E\",\"60N_010E\",\"60N_010W\",\"60N_020E\",\"60N_020W\",\"60N_030E\",\"60N_040E\",\"60N_050E\",\"60N_060E\",\"60N_060W\",\"60N_070E\",\"60N_070W\",\"60N_080E\",\"60N_080W\",\"60N_090E\",\"60N_090W\",\"60N_100E\",\"60N_100W\",\"60N_110E\",\"60N_110W\",\"60N_120E\",\"60N_120W\",\"60N_130E\",\"60N_130W\",\"60N_140E\",\"60N_140W\",\"60N_150E\",\"60N_150W\",\"60N_160E\",\"60N_160W\",\"60N_170E\",\"60N_170W\",\"60N_180W\",\"70N_000E\",\"70N_010E\",\"70N_020E\",\"70N_030E\",\"70N_040E\",\"70N_050E\",\"70N_060E\",\"70N_070E\",\"70N_070W\",\"70N_080E\",\"70N_080W\",\"70N_090E\",\"70N_090W\",\"70N_100E\",\"70N_100W\",\"70N_110E\",\"70N_110W\",\"70N_120E\",\"70N_120W\",\"70N_130E\",\"70N_130W\",\"70N_140E\",\"70N_140W\",\"70N_150E\",\"70N_150W\",\"70N_160E\",\"70N_160W\",\"70N_170E\",\"70N_170W\",\"70N_180W\",\"80N_010E\",\"80N_020E\",\"80N_030E\",\"80N_070E\",\"80N_080E\",\"80N_090E\",\"80N_100E\",\"80N_110E\",\"80N_120E\",\"80N_130E\",\"80N_130W\",\"80N_140E\",\"80N_140W\",\"80N_150E\",\"80N_150W\",\"80N_160E\",\"80N_160W\",\"80N_170E\",\"80N_170W\"]\n",
+ "TILES = [\"00N_000E\",\"00N_010E\",\"00N_020E\",\"00N_030E\",\"00N_040E\",\"00N_040W\",\"00N_050W\",\"00N_060W\",\"00N_070W\",\"00N_080W\",\"00N_090E\",\"00N_090W\",\"00N_100E\",\"00N_110E\",\"00N_120E\",\"00N_130E\",\"00N_140E\",\"00N_150E\",\"00N_160E\",\"10N_000E\",\"10N_010E\",\"10N_010W\",\"10N_020E\",\"10N_020W\",\"10N_030E\",\"10N_040E\",\"10N_050E\",\"10N_050W\",\"10N_060W\",\"10N_070E\",\"10N_070W\",\"10N_080E\",\"10N_080W\",\"10N_090E\",\"10N_090W\",\"10N_100E\",\"10N_110E\",\"10N_120E\",\"10N_130E\",\"10S_010E\",\"10S_020E\",\"10S_030E\",\"10S_040E\",\"10S_040W\",\"10S_050E\",\"10S_050W\",\"10S_060W\",\"10S_070W\",\"10S_080W\",\"10S_110E\",\"10S_120E\",\"10S_130E\",\"10S_140E\",\"10S_150E\",\"10S_160E\",\"10S_170E\",\"20N_000E\",\"20N_010E\",\"20N_010W\",\"20N_020E\",\"20N_020W\",\"20N_030E\",\"20N_040E\",\"20N_050E\",\"20N_060W\",\"20N_070E\",\"20N_070W\",\"20N_080E\",\"20N_080W\",\"20N_090E\",\"20N_090W\",\"20N_100E\",\"20N_100W\",\"20N_110E\",\"20N_110W\",\"20N_120E\",\"20N_160W\",\"20S_010E\",\"20S_020E\",\"20S_030E\",\"20S_040E\",\"20S_050W\",\"20S_060W\",\"20S_070W\",\"20S_080W\",\"20S_110E\",\"20S_120E\",\"20S_130E\",\"20S_140E\",\"20S_150E\",\"20S_160E\",\"30N_000E\",\"30N_010E\",\"30N_010W\",\"30N_020E\",\"30N_020W\",\"30N_030E\",\"30N_040E\",\"30N_050E\",\"30N_060E\",\"30N_070E\",\"30N_080E\",\"30N_080W\",\"30N_090E\",\"30N_090W\",\"30N_100E\",\"30N_100W\",\"30N_110E\",\"30N_110W\",\"30N_120E\",\"30N_120W\",\"30N_130E\",\"30N_160W\",\"30N_170W\",\"30S_010E\",\"30S_020E\",\"30S_030E\",\"30S_060W\",\"30S_070W\",\"30S_080W\",\"30S_110E\",\"30S_120E\",\"30S_130E\",\"30S_140E\",\"30S_150E\",\"30S_170E\",\"40N_000E\",\"40N_010E\",\"40N_010W\",\"40N_020E\",\"40N_020W\",\"40N_030E\",\"40N_040E\",\"40N_050E\",\"40N_060E\",\"40N_070E\",\"40N_080E\",\"40N_080W\",\"40N_090E\",\"40N_090W\",\"40N_100E\",\"40N_100W\",\"40N_110E\",\"40N_110W\",\"40N_120E\",\"40N_120W\",\"40N_130E\",\"40N_130W\",\"40N_140E\",\"40S_070W\",\"40S_080W\",\"40S_140E\",\"40S_160E\",\"40S_170E\",\"50N_000E\",\"50N_010E\",\"50N_010W\",\"50N_020E\",\"50N_030E\",\"50N_040E\",\"50N_050E\",\"50N_060E\",\"50N_060W\",\"50N_070E\",\"50N_070W\",\"50N_080E\",\"50N_080W\",\"50N_090E\",\"50N_090W\",\"50N_100E\",\"50N_100W\",\"50N_110E\",\"50N_110W\",\"50N_120E\",\"50N_120W\",\"50N_130E\",\"50N_130W\",\"50N_140E\",\"50N_150E\",\"50S_070W\",\"50S_080W\",\"60N_000E\",\"60N_010E\",\"60N_010W\",\"60N_020E\",\"60N_020W\",\"60N_030E\",\"60N_040E\",\"60N_050E\",\"60N_060E\",\"60N_060W\",\"60N_070E\",\"60N_070W\",\"60N_080E\",\"60N_080W\",\"60N_090E\",\"60N_090W\",\"60N_100E\",\"60N_100W\",\"60N_110E\",\"60N_110W\",\"60N_120E\",\"60N_120W\",\"60N_130E\",\"60N_130W\",\"60N_140E\",\"60N_140W\",\"60N_150E\",\"60N_150W\",\"60N_160E\",\"60N_160W\",\"60N_170E\",\"60N_170W\",\"60N_180W\",\"70N_000E\",\"70N_010E\",\"70N_020E\",\"70N_030E\",\"70N_040E\",\"70N_050E\",\"70N_060E\",\"70N_070E\",\"70N_070W\",\"70N_080E\",\"70N_080W\",\"70N_090E\",\"70N_090W\",\"70N_100E\",\"70N_100W\",\"70N_110E\",\"70N_110W\",\"70N_120E\",\"70N_120W\",\"70N_130E\",\"70N_130W\",\"70N_140E\",\"70N_140W\",\"70N_150E\",\"70N_150W\",\"70N_160E\",\"70N_160W\",\"70N_170E\",\"70N_170W\",\"80N_010E\",\"80N_020E\",\"80N_030E\",\"80N_070E\",\"80N_080E\",\"80N_090E\",\"80N_100E\",\"80N_110E\",\"80N_120E\",\"80N_130E\",\"80N_130W\",\"80N_140E\",\"80N_140W\",\"80N_150E\",\"80N_150W\",\"80N_160E\",\"80N_160W\",\"80N_170W\"]\n",
+ "len(TILES)\n",
+ "df = pd.DataFrame(columns=['TILE','ESA_eq_10','ESA_10_and_3m',\"ESA_10_and_5m\",\"FH_ge_5\",\"FH_ge_5_and_ESA\",\"FH_lt_5_and_ESA\"])\n",
+ "for i in range(0,len(TILES)):\n",
+ " tile = TILES[i]\n",
+ " ESA = rasterio.open('/projects/my-public-bucket/Data/Harris_et_al_PAPER/ESA_WorldCover2021_v2_C/WC_'+tile+'_C.tif').read(1)\n",
+ " FH = rasterio.open('/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_height_2020/2020_'+tile+'.tif').read(1)\n",
+ " FH[ESA == 0] = 0\n",
+ " df.loc[i, ['TILE']] = tile\n",
+ " df.loc[i, ['ESA_eq_10']] = (len(ESA[(ESA == 10)]))\n",
+ " df.loc[i, ['ESA_10_and_3m']] = (len(ESA[(ESA == 10) & (FH < 5) & (FH > 0)]))\n",
+ " df.loc[i, ['ESA_10_and_5m']] = (len(ESA[(ESA == 10) & (FH >= 5)]))\n",
+ " df.loc[i, ['FH_ge_5']] = (len(FH[(FH >= 5)]))\n",
+ " df.loc[i, ['FH_ge_5_and_ESA']] = (len(FH[(FH >= 5) & (ESA == 10)]))\n",
+ " df.loc[i, ['FH_lt_5_and_ESA']] = (len(FH[(ESA == 10) & (FH < 5) & (FH > 0)]))\n",
+ " df.to_csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_height_2020/AREA_3m_or_5m.csv', index=False) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23643e48-425c-4d9e-87c2-fff61baa7dcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "CSV = read.csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_height_2020/AREA_3m_or_5m.csv')\n",
+ "print(nrow(CSV))\n",
+ "sum(CSV$FH_ge_5_and_ESA)/sum(CSV$FH_ge_5)*100"
+ ]
+ }
+ ],
+ "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": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/country_summaries/IPCC_classes_DPS/Generate_presigned_URLS.ipynb b/country_summaries/IPCC_classes_DPS/Generate_presigned_URLS.ipynb
index 55f3dd1f9a7ef10d5fc8f8e7475412ca6251f5f0..1a1b2a97d0d508c4bb3b5f4762fe43a684cd5b63 100644
--- a/country_summaries/IPCC_classes_DPS/Generate_presigned_URLS.ipynb
+++ b/country_summaries/IPCC_classes_DPS/Generate_presigned_URLS.ipynb
@@ -40,7 +40,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_452/950621156.py:5: DeprecationWarning: \n",
+ "/tmp/ipykernel_195/950621156.py:5: DeprecationWarning: \n",
"Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
"(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
"but was not found to be installed on your system.\n",
@@ -162,7 +162,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
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"yes\n"
]
},
diff --git a/country_summaries/IPCC_classes_DPS/IPCC_raster_post_processing.ipynb b/country_summaries/IPCC_classes_DPS/IPCC_raster_post_processing.ipynb
index c530564ecba9f3d1255b5f59645d38153ce75023..7cffa8c3e00cb8be84fd027d0d56160765f2c2a6 100644
--- a/country_summaries/IPCC_classes_DPS/IPCC_raster_post_processing.ipynb
+++ b/country_summaries/IPCC_classes_DPS/IPCC_raster_post_processing.ipynb
@@ -161,7 +161,7 @@
"# packages <- c(\"raster\", \"rgdal\", \"terra\", \"sf\", \"sp\")\n",
"package.check <- lapply(packages, FUN = function(x) {\n",
" if (!require(x, character.only = TRUE)) {\n",
- " install.packages(x, dependencies = TRUE)\n",
+ " # install.packages(x, dependencies = TRUE)\n",
" library(x, character.only = TRUE, quietly=TRUE)\n",
" }\n",
"})\n",
@@ -289,7 +289,7 @@
},
"outputs": [],
"source": [
- "# ############## ESA WORLD COVER FACT CHECK ##############\n",
+ "# ############## ESA WORLD COVER FACT CHECK ######################################################\n",
"# files <- list.files(path=\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/ESA_WorldCover2021_v2\", pattern=\"*C.tif\", full.names=TRUE, recursive=FALSE)\n",
"# for (file in rev(files)){\n",
"# print(file)\n",
@@ -298,7 +298,7 @@
"# }\n",
"# }\n",
"\n",
- "# ######################### ESTIMATING FOREST AREA GLOBALLY ##########\n",
+ "# ######################### ESTIMATING FOREST AREA GLOBALLY ########################################\n",
"# Table <- data.frame(matrix(ncol=3,nrow=0, dimnames=list(NULL, c('FILE','RES','ESA_AREA'))))\n",
"# files <- list.files(path=\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/ESA_WorldCover_2021_v2_CRS\", pattern=\"*C.tif\", full.names=TRUE, recursive=FALSE)\n",
"# files <- files[rev(1:length(files))]\n",
@@ -309,6 +309,22 @@
"# STR = c(file,toString(res(raster)[1]),toString(fre$count[fre$value == 10]*((res(raster)[1])^2)/10000000000))\n",
"# Table[nrow(Table) + 1,] = STR\n",
"# write.csv(Table,\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/ESA_WorldCover_2021_v2_CRS/AREA_per_ESA_TILE_REV.csv\", row.names=FALSE)\n",
+ "# }\n",
+ "\n",
+ "# ######################### ESTIMATING HEIGHTS ABOVE AND BELOW 5 M WITH ESA WC MASK ##############################\n",
+ "# Table <- data.frame(matrix(ncol=3,nrow=0, dimnames=list(NULL, c('FILE','3m_AREA','5m_AREA'))))\n",
+ "# files <- list.files(path=\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/ESA_WorldCover2021_v2_C\", pattern=\"*C.tif\", full.names=TRUE, recursive=FALSE)\n",
+ "# for (file in files){\n",
+ "# tile <-strsplit(strsplit(basename(file), split = \"WC_\")[[1]][2],split = \"_C.tif\")[[1]]\n",
+ "# FH <- rast(paste0('/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/',tile,'.tif'))\n",
+ "# ESA <- rast(file)\n",
+ "# FH[ESA == 0] = 0\n",
+ "# FH[FH < 5] = 3\n",
+ "# FH[FH >= 5] = 5\n",
+ "# fre = freq(FH)\n",
+ "# STR = c(file,toString(fre$count[fre$value == 3]),toString(fre$count[fre$value == 5]))\n",
+ "# Table[nrow(Table) + 1,] = STR\n",
+ "# write.csv(Table,\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/AREA_3m_or_5m.csv\", row.names=FALSE)\n",
"# }"
]
},
@@ -873,143 +889,23 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "id": "bf817c12-282a-468d-8d4a-8db5c390fdc6",
+ "execution_count": 15,
+ "id": "4c4ba68c-ebf6-44d2-99fb-38c6a00f1936",
"metadata": {},
- "outputs": [],
- "source": [
- "IPCC_guidelines <- read.csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/IPCC_Table_GIBBS_for_JOIN.csv')\n",
- "# IPCC_guidelines$Uncertainty[IPCC_guidelines$Uncertainty_TYPE == 'default'] = 0.9/1.96*IPCC_guidelines$AGB[IPCC_guidelines$Uncertainty_TYPE == 'default']#NA\n",
- "IPCC_Table <- merge(x=IPCC_Table,y=IPCC_guidelines,by.x='IPCC_Table_NAME',by.y='NAME',all.x=TRUE)\n",
- "IPCC_Table <- IPCC_Table[!is.na(IPCC_Table$region_mean),]\n",
- "sum(IPCC_Table$area,na.rm=TRUE)\n",
- "\n",
- "MAP_EXT <- read.csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/AGB_Extract_IPCC_Classes.csv')\n",
- "IPCC_Table <- merge(IPCC_Table,MAP_EXT,by.x=c(\"IPCC_classes_1km.x\"),by.y=c(\"IPCC_classes_1km\"))\n",
- "\n",
- "IPCC_Table_W <- IPCC_Table[,c(\"Continent\",\"GEZ_NAME\",\"AGE_CLASS.x\",\"AGB\",\"Uncertainty\",\"Uncertainty_TYPE\",\"area_T\",\"region_mean\",\"region_stderr\",\"GEDI_ICESat_Q2\",\"GEDI_ICESat_Q3\",\"GEDI_ICESat_Q4\",\"GEDI_SD\",\"CCI_mean\",\"CCI_stdev\",\"CCI_Q2\",\"CCI_Q3\",\"CCI_Q4\",\"CCI_SD\",\"IPCC_classes_1km.x\")]\n",
- "colnames(IPCC_Table_W) <- c(\"Continent\",\"Ecological zone\",\"Status/condition\",\"IPCC_AGB_mean\",\"IPCC_AGB_uncertainty\",\"IPCC_AGB_uncertainty_type\",\"Area_year_2020\",\"GEDI_AGBD_mean\",\"GEDI_AGBD_uncertainty\",\"GEDI_DIST_Q1\",\"GEDI_DIST_Q2\",\"GEDI_DIST_Q3\",\"GEDI_DIST_SD\",\"CCI_AGBD_mean\",\"CCI_AGBD_uncertainty\",\"CCI_DIST_Q1\",\"CCI_DIST_Q2\",\"CCI_DIST_Q3\",\"CCI_DIST_SD\",\"ID\")\n",
- "IPCC_guidelines$Uncertainty[IPCC_guidelines$Uncertainty_TYPE == 'default'] = 0.9/1.96*IPCC_guidelines$AGB[IPCC_guidelines$Uncertainty_TYPE == 'default']#NA\n",
- "IPCC_Table_W[''] <- ''\n",
- "IPCC_Table_W['Description'] <- c(\"Columns 'Continent' to 'IPCC_ABG_uncertainty_type': Replica of Table 4.7 from IPCC 2019 Guidelines\"\n",
- " \"Column 'Area_year_2020': Area identified in this study [Mha]\",\n",
- " \"Column 'GEDI_AGBD_mean': Estimated AGBD by GEDI/ICESat-2 [Mg/ha]\",\n",
- " \"Column 'GEDI_AGBD_uncertainty': Estimated uncertainty GEDI/ICESat-2 [Mg/ha]\",\n",
- " \"Column 'GEDI_DIST_Q1' to 'GEDI_DIST_Q3': GEDI/ICESat-2 25th, 50th and 75th quartiles of AGBD distribution across ecoregion [Mg/ha]\",\n",
- " \"Column 'GEDI_DIST_SD': GEDI/ICESat-2 standard deviation of AGBD distribution across ecoregion [Mg/ha]\",\n",
- " \"Column 'CCI_AGBD_mean': Estimated AGBD by CCI Biomass [Mg/ha]\",\n",
- " \"Column 'CCI_AGBD_uncertainty': Estimated uncertainty CCI Biomass [Mg/ha]\",\n",
- " \"Column 'CCI_DIST_Q1' to 'CCI_DIST_Q3': CCI Biomass 25th, 50th and 75th quartiles of AGBD distribution across ecoregion [Mg/ha]\", \n",
- " \"Column 'CCI_DIST_SD': CCI Biomass standard deviation of AGBD distribution across ecoregion [Mg/ha]\",\n",
- " \"Column 'ID': Unique ID\",\n",
- " rep(\"\", nrow(IPCC_Table_W)-11))\n",
- "\n",
- "write.csv(IPCC_Table_W,\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/IPCC_Table.csv\", row.names=FALSE)\n",
- "\n",
- "IPCC_Table$Uncertainty[IPCC_Table$Uncertainty_TYPE == 'default' & !is.na(IPCC_Table$AGB)] = 0.9/1.96*IPCC_Table$AGB[IPCC_Table$Uncertainty_TYPE == 'default' & !is.na(IPCC_Table$AGB)]#NA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "022676d4-c87a-44f7-9223-e9cc37152891",
- "metadata": {
- "tags": []
- },
"outputs": [
{
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- ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
- "</style>\n",
- "<ol class=list-inline><li>332.2</li><li>162</li><li>51.6</li><li>301.1</li><li>214.7</li><li>27.8</li><li>126.1</li><li>153.9</li><li>22.3</li><li>1.9</li><li>104.2</li><li>104.2</li></ol>\n"
- ],
- "text/latex": [
- "\\begin{enumerate*}\n",
- "\\item 332.2\n",
- "\\item 162\n",
- "\\item 51.6\n",
- "\\item 301.1\n",
- "\\item 214.7\n",
- "\\item 27.8\n",
- "\\item 126.1\n",
- "\\item 153.9\n",
- "\\item 22.3\n",
- "\\item 1.9\n",
- "\\item 104.2\n",
- "\\item 104.2\n",
- "\\end{enumerate*}\n"
- ],
- "text/markdown": [
- "1. 332.2\n",
- "2. 162\n",
- "3. 51.6\n",
- "4. 301.1\n",
- "5. 214.7\n",
- "6. 27.8\n",
- "7. 126.1\n",
- "8. 153.9\n",
- "9. 22.3\n",
- "10. 1.9\n",
- "11. 104.2\n",
- "12. 104.2\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- " [1] 332.2 162.0 51.6 301.1 214.7 27.8 126.1 153.9 22.3 1.9 104.2 104.2"
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- "</style>\n",
- "<ol class=list-inline><li>104.2</li><li>1.9</li><li>104.2</li><li>126.1</li><li>332.2</li><li>301.1</li><li>22.3</li><li>51.6</li><li>27.8</li><li>153.9</li><li>162</li><li>214.7</li></ol>\n"
+ "3259.18191464996"
],
"text/latex": [
- "\\begin{enumerate*}\n",
- "\\item 104.2\n",
- "\\item 1.9\n",
- "\\item 104.2\n",
- "\\item 126.1\n",
- "\\item 332.2\n",
- "\\item 301.1\n",
- "\\item 22.3\n",
- "\\item 51.6\n",
- "\\item 27.8\n",
- "\\item 153.9\n",
- "\\item 162\n",
- "\\item 214.7\n",
- "\\end{enumerate*}\n"
+ "3259.18191464996"
],
"text/markdown": [
- "1. 104.2\n",
- "2. 1.9\n",
- "3. 104.2\n",
- "4. 126.1\n",
- "5. 332.2\n",
- "6. 301.1\n",
- "7. 22.3\n",
- "8. 51.6\n",
- "9. 27.8\n",
- "10. 153.9\n",
- "11. 162\n",
- "12. 214.7\n",
- "\n",
- "\n"
+ "3259.18191464996"
],
"text/plain": [
- " [1] 104.2 1.9 104.2 126.1 332.2 301.1 22.3 51.6 27.8 153.9 162.0 214.7"
+ "[1] 3259.182"
]
},
"metadata": {},
@@ -1017,13 +913,48 @@
}
],
"source": [
- "IPCC_guidelines$AGB[IPCC_guidelines$Uncertainty_TYPE == 'default']\n",
- "IPCC_Table$AGB[IPCC_Table$Uncertainty_TYPE == 'default' & !is.na(IPCC_Table$AGB)]"
+ "IPCC_guidelines <- read.csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/IPCC_Table_GIBBS_for_JOIN.csv')\n",
+ "# IPCC_guidelines$Uncertainty[IPCC_guidelines$Uncertainty_TYPE == 'default'] = 0.9/1.96*IPCC_guidelines$AGB[IPCC_guidelines$Uncertainty_TYPE == 'default']#NA\n",
+ "IPCC_Table <- merge(x=IPCC_Table,y=IPCC_guidelines,by.x='IPCC_Table_NAME',by.y='NAME',all.x=TRUE)\n",
+ "IPCC_Table <- IPCC_Table[!is.na(IPCC_Table$region_mean),]\n",
+ "sum(IPCC_Table$area,na.rm=TRUE)\n",
+ "\n",
+ "MAP_EXT <- read.csv('/projects/my-public-bucket/Data/Harris_et_al_PAPER/AGB_Extract_IPCC_Classes.csv')\n",
+ "IPCC_Table <- merge(IPCC_Table,MAP_EXT,by.x=c(\"IPCC_classes_1km.x\"),by.y=c(\"IPCC_classes_1km\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "1ced6789-b5e2-4297-a881-7d82686d6453",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "IPCC_Table_W <- IPCC_Table[,c(\"Continent\",\"GEZ_NAME\",\"AGE_CLASS.x\",\"AGB\",\"Uncertainty_IPCC\",\"Uncertainty_TYPE\",\"area_T\",\"region_mean\",\"region_stderr\",\"GEDI_ICESat_Q2\",\"GEDI_ICESat_Q3\",\"GEDI_ICESat_Q4\",\"GEDI_SD\",\"CCI_mean\",\"CCI_stdev\",\"CCI_Q2\",\"CCI_Q3\",\"CCI_Q4\",\"CCI_SD\",\"IPCC_classes_1km.x\")]\n",
+ "colnames(IPCC_Table_W) <- c(\"Continent\",\"Ecological zone\",\"Status/condition\",\"IPCC_AGB_mean\",\"IPCC_AGB_uncertainty\",\"IPCC_AGB_uncertainty_type\",\"Area_year_2020\",\"GEDI_AGBD_mean\",\"GEDI_AGBD_SE\",\"GEDI_DIST_Q1\",\"GEDI_DIST_Q2\",\"GEDI_DIST_Q3\",\"GEDI_DIST_SD\",\"CCI_AGBD_mean\",\"CCI_AGBD_SE\",\"CCI_DIST_Q1\",\"CCI_DIST_Q2\",\"CCI_DIST_Q3\",\"CCI_DIST_SD\",\"ID\")\n",
+ "# IPCC_guidelines$Uncertainty[IPCC_guidelines$Uncertainty_TYPE == 'default'] = 0.9/1.96*IPCC_guidelines$AGB[IPCC_guidelines$Uncertainty_TYPE == 'default']#NA\n",
+ "# IPCC_Table_W[''] <- ''\n",
+ "IPCC_Table_W['Description'] <- c(\"Columns 'Continent' to 'IPCC_ABG_uncertainty_type': Replica of Table 4.7 from IPCC 2019 Guidelines\",\n",
+ " \"Column 'Area_year_2020': Area identified in this study [Mha]\",\n",
+ " \"Column 'GEDI_AGBD_mean': Estimated AGBD by GEDI/ICESat-2 [Mg/ha]\",\n",
+ " \"Column 'GEDI_AGBD_SE': Estimated uncertainty GEDI/ICESat-2 [Mg/ha]\",\n",
+ " \"Column 'GEDI_DIST_Q1' to 'GEDI_DIST_Q3': GEDI/ICESat-2 25th, 50th and 75th quartiles of AGBD distribution across ecoregion extent [Mg/ha]\",\n",
+ " \"Column 'GEDI_DIST_SD': GEDI/ICESat-2 standard deviation of AGBD distribution across ecoregion extent [Mg/ha]\",\n",
+ " \"Column 'CCI_AGBD_mean': Estimated AGBD by CCI Biomass [Mg/ha]\",\n",
+ " \"Column 'CCI_AGBD_SE': Estimated uncertainty CCI Biomass [Mg/ha]\",\n",
+ " \"Column 'CCI_DIST_Q1' to 'CCI_DIST_Q3': CCI Biomass 25th, 50th and 75th quartiles of AGBD distribution across ecoregion extent [Mg/ha]\", \n",
+ " \"Column 'CCI_DIST_SD': CCI Biomass standard deviation of AGBD distribution across ecoregion extent [Mg/ha]\",\n",
+ " \"Column 'ID': Unique ID\",\n",
+ " rep(\"\", nrow(IPCC_Table_W)-11))\n",
+ "\n",
+ "write.csv(IPCC_Table_W,\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/IPCC_Table.csv\", row.names=FALSE)\n",
+ "\n",
+ "IPCC_Table$Uncertainty[IPCC_Table$Uncertainty_TYPE == 'default' & !is.na(IPCC_Table$AGB)] = 0.9/1.96*IPCC_Table$AGB[IPCC_Table$Uncertainty_TYPE == 'default' & !is.na(IPCC_Table$AGB)]#NA"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 11,
"id": "48b60e80-115a-478a-a1f5-b7ed52d21b91",
"metadata": {},
"outputs": [],
@@ -1048,7 +979,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 12,
"id": "0d5d0fe7-db96-480d-9859-fe564160c889",
"metadata": {},
"outputs": [],
@@ -1062,7 +993,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 32,
"id": "72b500c2-3535-4390-87d8-d9f5b8cfea16",
"metadata": {},
"outputs": [
@@ -1073,41 +1004,24 @@
"Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
+ "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n",
+ "Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
+ "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n",
+ "Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n"
- ]
- },
- {
- "data": {
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Pv9dE8+lxhMt/YJzrHGSx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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
+ "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n",
"Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n"
+ "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n"
]
},
{
"data": {
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i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\nvLwFCxYwmUyiJ7S0tIRdZPrC3t5eKpVu2rRJdXPtAfSE4HVyc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Vq1avH3VvihjY3Noxrz8/NND+Pi4kJC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\nQugyambUanVrayubzfbx8cFDAkzs4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"text/plain": [
"plot without title"
]
@@ -1127,16 +1041,16 @@
"Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 5 rows containing missing values (`geom_point()`).â€\n",
+ "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n",
"Warning message:\n",
"“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 5 rows containing missing values (`geom_point()`).â€\n"
+ "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n"
]
},
{
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I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\nR0fHpEmT9uzZQ3WwYADBGkJABlhqpRW//vrr4sWLVSslWCyWt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3h5eb3++uvnzp2Li4sTQixdutRoNIaF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Tp09tbW2dnJxu3LhhYmIy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GBkdHR3d393Pnzu3atcs4I4VCsXfv3hs3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\neuaZZ/gY9WDq7e0tLCzMy8urqamRyWRr1qzJyMjge1C/can0gzhftMNrW3C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iXPrWbl28q+5xq1swAAAAAQytzyNGjQoPDw8IL0zMnJGTRo0I0bNxQZFwAAAADw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\nUBACAAAAAACMU/8DgCTcxnt5LvcAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
@@ -1177,32 +1095,119 @@
}
},
"output_type": "display_data"
- },
+ }
+ ],
+ "source": [
+ "IPCC_Table$DIFF_CCI_IPCC <- IPCC_Table$CCI_mean - IPCC_Table$AGB\n",
+ "IPCC_Table$SD_CCI_IPCC <- sqrt((IPCC_Table$CCI_stdev)^2 + (IPCC_Table$Uncertainty)^2)\n",
+ "IPCC_Table$IN_AGB_CI_CCI <- 1\n",
+ "IPCC_Table$IN_AGB_CI_CCI[abs(IPCC_Table$DIFF_CCI_IPCC) > 2*IPCC_Table$SD_CCI_IPCC] = 0\n",
+ "IPCC_Table$Tval_CCI_IPCC <- IPCC_Table$DIFF_CCI_IPCC/IPCC_Table$SD_CCI_IPCC # plot the t-values \n",
+ "\n",
+ "SUBSET <- 'Continent'\n",
+ "SUBSET_AGE <- 'AGE'\n",
+ "for (i in 1:nrow(unique(IPCC_Table[SUBSET]))){\n",
+ " if (i==7) { for (f in 1:nrow(unique(IPCC_Table[SUBSET_AGE]))){\n",
+ " WIDTH <- c(2.8,4,3.5,4,2.8,3.3,2)\n",
+ " HEIGHT <- c(3.1,3.1,3.1,3.1,3.1,3.1,3.1)\n",
+ " W <- WIDTH[i]\n",
+ " H <- HEIGHT[i]\n",
+ " # if (as.character(unique(IPCC_Table[SUBSET])[i,1])==\"Oceania\") {ll = 1.5}\n",
+ " # else{ll = 1}\n",
+ " IPCC_Table_SUBSET <- IPCC_Table[(IPCC_Table[SUBSET] == as.character(unique(IPCC_Table[SUBSET])[i,1])) & (IPCC_Table[SUBSET_AGE] == as.character(unique(IPCC_Table[SUBSET_AGE])[f,1])),]\n",
+ " IPCC_Table_SUBSET$GEZ_CODE <- as.numeric(IPCC_Table_SUBSET$GEZ_CODE)\n",
+ " IPCC_Table_SUBSET <- IPCC_Table_SUBSET %>% group_by(AGE) %>% arrange(GEZ_CODE, .by_group=TRUE) %>% arrange(desc(AGB), .by_group=TRUE) #arrange(desc(region_mean), .by_group=TRUE) #%>% group_by(GEZ_CODE)\n",
+ " IPCC_Table_SUBSET$AGE[IPCC_Table_SUBSET$AGE== 'Sec_gt20'] = 'Old secondary'\n",
+ " IPCC_Table_SUBSET$AGE[IPCC_Table_SUBSET$AGE == 'Sec_lt20'] = 'Young secondary'\n",
+ " IPCC_Table_SUBSET$'IPCC' = 'Tier 1 estimates'\n",
+ " CONT <- gsub(\"_\",\" \",as.character(unique(IPCC_Table[SUBSET])[i,1]))\n",
+ " AGEY <- as.character(unique(IPCC_Table[SUBSET_AGE])[f,1])\n",
+ " # if (CONT == \"Oceania\") {CONT <- \"Oc.\"}\n",
+ " if (CONT == \"Oceania\" & AGEY == \"Primary\") {AGEY <- \"Primary\"}\n",
+ " if (CONT == \"Oceania\" & AGEY == \"Old secondary\") {AGEY <- \"Old sec.\"}\n",
+ " if (CONT == \"Oceania\" & AGEY == \"Young secondary\") {AGEY <- \"Young sec.\"}\n",
+ " p <- ggplot() +\n",
+ " ggtitle(paste0(CONT,\",\\n\",AGEY)) +\n",
+ " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=AGB, color=\"black\", size=IPCC),alpha=0.35,color=\"black\") + \n",
+ " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=AGB-Uncertainty, ymax=AGB+Uncertainty, color=\"black\",alpha=IPCC),alpha=0.35,color=\"black\", width=0.5) +\n",
+ " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=region_mean), size=2, shape=8, color=\"darkorchid4\") + \n",
+ " # geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=region_mean-region_stderr, ymax=region_mean+region_stderr), width=0.5,color=\"darkorchid4\") +\n",
+ " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=region_mean-GEDI_SD, ymax=region_mean+GEDI_SD), width=0.5,color=\"darkorchid4\") +\n",
+ " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=CCI_mean), size=2, shape=8,color=\"darkorange2\") + \n",
+ " # geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=CCI_mean-CCI_stdev, ymax=CCI_mean+CCI_stdev), width=0.5, color=\"darkorange2\") +\n",
+ " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=CCI_mean-CCI_SD, ymax=CCI_mean+CCI_SD), width=0.5, color=\"darkorange2\") +\n",
+ " theme_bw() +\n",
+ " coord_cartesian(ylim=c(0,600)) +\n",
+ " # ylim(0, 600) +\n",
+ " ylab(\"AGBD [Mg/ha]\") +\n",
+ " scale_x_continuous(breaks=1:nrow(IPCC_Table_SUBSET) , labels=IPCC_Table_SUBSET$GEZ_NAME) +\n",
+ " # xlab(\"Various ecozones in different continents, displayed in groups of forest age classes\") +\n",
+ " # theme(legend.position=\"bottom\") +\n",
+ " # guides(color = guide_legend(override.aes = list(size=4),title = \"Space-lidar estimates\")) +\n",
+ " # scale_color_brewer(palette=\"Dark2\") +\n",
+ " theme(\n",
+ " legend.position=\"none\",\n",
+ " text=element_text(size=13),\n",
+ " legend.text=element_text(size=13),\n",
+ " legend.title=element_text(size=13),\n",
+ " axis.title.y=element_text(size=11),\n",
+ " axis.title.x=element_blank(),\n",
+ " axis.ticks.x=element_blank(),\n",
+ " panel.grid.major = element_blank(), \n",
+ " panel.grid.minor = element_blank(),\n",
+ " panel.background = element_blank(),\n",
+ " axis.text.x=element_text(angle=45,hjust=1, vjust=1,size=11.75),\n",
+ " plot.margin = margin(t=-1.4,r=0,b=0,l=0.5, unit = \"cm\"),\n",
+ " plot.title=element_text(hjust=0.95, vjust=-1.8, margin=margin(t=40,b=-20),size=12),\n",
+ " ) \n",
+ " \n",
+ " # options(repr.plot.width=12, repr.plot.height=5)\n",
+ " print(p)\n",
+ " ggsave(p, file=paste0(\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/_PLOTS/\",as.character(unique(IPCC_Table[SUBSET])[i,1]),\"_\",as.character(unique(IPCC_Table[SUBSET_AGE])[f,1]),\"_age_classes.png\"), width=W, height=H)\n",
+ " }\n",
+ "}}\n",
+ "nrow(IPCC_Table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "73140caf-9527-4b42-938b-74e99bfab0fd",
+ "metadata": {},
+ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
+ "Warning message in geom_errorbarh(aes(xmin = AGB - Uncertainty, xmax = AGB + Uncertainty), :\n",
+ "“\u001b[1m\u001b[22mIgnoring unknown parameters: `width`â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
+ "Warning message:\n",
+ "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 2 rows containing missing values (`geom_point()`).â€\n",
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
+ "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
+ "Warning message in geom_errorbarh(aes(xmin = AGB - Uncertainty, xmax = AGB + Uncertainty), :\n",
+ "“\u001b[1m\u001b[22mIgnoring unknown parameters: `width`â€\n",
+ "Warning message:\n",
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 2 rows containing missing values (`geom_point()`).â€\n"
+ "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n"
]
},
{
"data": {
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enTt5YrFYR8fmZMotioqr/9xBs3t4+Gl4Z63i4uKrV6/27NmzTruJ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0KTk5ediwYVSb5hhggg6NRnPkyBEOhzNn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\nTMf4gPCXDDOYXEzXs+g5NSYkb+V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+ "image/png": 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RkZGhpKTxgTQgiplxiAWq0+ePDg+vXr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dpvgUxCG93Jrb0OfnEHnw3oMc88kh511N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\noK4rG3bvy2pQee655wThnjXUR44cCYCNI5rS6XRffvnlihUrnn322fj4+Fm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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
- "height": 180,
- "width": 600
+ "height": 270,
+ "width": 270
}
},
"output_type": "display_data"
@@ -1212,26 +1217,24 @@
"output_type": "stream",
"text": [
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n",
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
+ "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
"Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n"
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n"
]
},
{
"data": {
- "image/png": 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8h/L7rmKRv/2Qj6v3BVNGtoJ3V76ZPN96nfs4ScTSho4+uP6YUkqs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\niNwqN+o+M/oYMDtgGPbs2bOjR49+9dVXZWVl2lOgtKhtcOLEiSdPniQlJek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15v0vnz5+fNm6fZBXLFihUBAQHWD0qI9cXExCQnJ0dGRgrT44uHKhcNaWhIB9ch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\nZAACaRhRk9DkdNQ6gGkYDZgekJAdSPY4rmlQ+wG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7HczW2zBacv8DIKlk/nc3P4EgCEIv6WhJthDFMNeXi9gAAB0dSURBVIz58+c/8sgj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\nAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
- "height": 180,
- "width": 600
+ "height": 270,
+ "width": 270
}
},
"output_type": "display_data"
@@ -1241,592 +1244,20 @@
"output_type": "stream",
"text": [
"Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n"
+ "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n"
]
},
{
"data": {
- "image/png": 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cawXhhg0bDAwMfv/9d2dn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06dOJnrCpqYnqiIYiDofj5+dXVVX18OFDqmOhEsX8DqcAACAASURBVOQBEBhYKlXV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\nuTmFAQ80xp5SDlnQEL5c11tLW1paPn36NDs7OzExMTc3FyG0a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nPnTolEolarb926tWvXLqlUymZCmUxG0lF456cGkfOCcaXlOj8TspVm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\n6n/Kq2svxVwyFAAAAABJRU5ErkJggg==",
+ "image/png": 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+c+//zzcsRJ1FSUHamUUQCUnYDSHwpfGYLZunVrTk6OLUsC1l8GA65ckTZmZMDf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\nsmXLYmN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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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c3Irys++VtuAbmDe3sU9NTTU1NfXz89N1dtpwcXFxcXHRuvvz58+F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fk0gkKpXKyckJACAWiwsLC8+fP3/06FHD\nkgUA/PTTTyqVSv90m5T0qYB5gHkw1NTUZG1t7ejo+NSr+eHDhxMTEz/99FOhUEjKNQNhPXRA\n8XrAMCwnJ6e8vHzgwIGjR4+m0+n4qLDn7Lc+YsQIgoLtCxSvB9iEwD1r13X92fHw4cM9e/bg\nb+bz+cuXL3/llVeIjbk3wDzg4HnxVPAJYf/V2Ni4bdu2q1ev+vj4CAQCrVabmJiYkZEBADAs\n2djY2FOnTqEoumXLFv3CuCRjmAozMzOYB4rnwVBKSkpVVRWLxTp8+HCHq/nhw4cLCwt9fHzG\njBlDbJC9BNZDZ1Suh9ra2l27dp09e/bevXs3b968ffu2m5ublZUVBfdb16NyPcAmBO75u67r\nzw65XO7r6xsSEvLWW2+5uroSHXXPg3nAwfPiWeCiMv3XsWPHpFKpo6OjtbU1AGDGjBn4g3tb\nW9uJEycCAJRK5bFjxyIjIwEA69evF4lExAbcewxTAfNAwTzk5+djf633IJFIdu3ahU92BwBM\nmTJFqVT+8MMPHVp7sbGxV69eRVF0wYIFxATd+2A9AFgPf2lqatq2bVthYaFQKFy0aFFgYGBN\nTU1ISEh6ejowWD2ClKunwHp4KtiEwP3444/19fXu7u4HDhw4f/783r173d3d8/LywsLCVCoV\nMDg7kpOTf//9d5JtLqcH84CD58WzwCeE/ZFUKuVwOJGRkQKBYPfu3SiKAgAQBPHx8cnKynr8\n+PGFCxeuX7/+yy+/5OTkIAiycuXKWbNmER11r+icCpgHquUhPT09NDS0srLSz8+voqIiJCSk\ntLS0ra1t3LhxAIAhQ4ZkZmZKpVJbW9uVK1dyOBylUnny5Mmff/4ZALBp0yYPDw+i/4KeB+sB\n1kMHERERxcXFHh4eu3fv9vHxqa2tTU1N1Wg0iYmJLi4uNjY2hs8JXVxc8KUFSQDWQ2ewCYHD\n83Do0CELC4s9e/YMGDAAQRArK6uAgID8/Py8vDydTjdy5EhgMI+OlE/RYR5w8Lx4Ptgh7HcM\nN4+aPXs2fpbiUBQNCAjAMEwikdTX1+t0Oi8vr82bN5Nvf0zcs1IB84C/TpE88Hi89PT0jIyM\nsrKyM2fOyGQyLy+vjRs3MhgMQMmrOawHKteDRqMxXBACl5+fHxUVJRKJdu/ezefzr1y58t13\n32EYNm3atKKiog59QpKtnkLxeugMNiFwHfbhNBwYTKfTvby8oqOjS0pKFixYgD8KI2tfCOYB\nB8+LF4KLyvQ7+s2jAACdH9mjKLps2bK3335bLpdzOBwmk0lEjH3kOamAecBRIQ/m5uaff/75\njh077t69CwDw8vLauXMnm83Wv0EgEERERJw5c+bq1avV1dUIgnh5eb355pukvPcPYD1QuB40\nGk1ERAQAYPv27Yb/9Dk5OQCANWvWmJubJyUlGa6l2d7enpCQgK8YMWbMGPKtnkLlengq2ITA\nGeahM5FI5ODgUFJSUlZW5ubmhr+oX1slMTFx0aJFgwYN6sN4ewvMAw6eFy8E5xD2O4ZbhV6/\nfh2f/tsBgiB8Pp/0JfvCVMA84EifB4VC0djYiP8sFApZLFaHN+BX86ioqOPHj589e/aLL74g\na2sPwHqgcD1oNBq5XJ6SkrJ7927Df/fXXnstKCjIx8enubn54MGDGIYtWbIEny9na2srFAq1\nWm14eDhZN6CnbD08FWxC4IzbdR0/6osvviBHLwjAPPwFnhcvBIeM9kf6R/YSiaSurs7X15cc\nj+yNAFOBo3geWCxWbm6uSCTi8XiZmZnV1dV+fn6dM4AgCJvNJutUeEOwHqhZDwwGY9KkSbm5\nuVlZWaWlpf7+/vjYUQRBxowZQ6PRYmJiUlNTR48evX79evyQ48ePoyi6bt26QYMG+fn5ERp+\nb6FsPTwLxa8Pevo8VFZW1tTUGObh0qVLt2/f5nK5K1euxEcXGx5laWlJRLy9BeYBB8+L54Md\nwn5KX7jZ2dlkGsZtBJgKHGXzIJPJlErljBkzAgICJk+enJmZmZGR0aHNl5yczOfzDceJkR6s\nB2rWw7P6hLjr168XFxe//vrr+BZbMTExsbGx7u7ub7zxBln3G6R4PTwLZa8PHVB813U9mAcc\nPC+eA3YI+wWNRnP9+vWLFy+mpKQ0NzcPHjyYwWCQdWrvczw1D4C8s5yfBeYB19DQcODAgW+/\n/fbOnTsTJkwQCARsNtvf31/f5vPx8aHRaDdu3Ni7d29aWtr06dM73OMkB1gPOFgPuOf0CZub\nm5OTk2Uy2YABA6Kjo0+ePIkgyLp16/AF1kkG1oMebELgFArFqVOnvv/++3PnzuXn59va2lpY\nWOjzUFpampCQcP369YKCAj6fv3btWlKuKgRgHv4Cz4suQUi2H5Epqqqq+uKLL8rLy/WvWFtb\nf/zxx0OHDgUAyGSy4ODgioqKGTNmrF+/nsSF+/w8AMqkAuYBV1VVFRwcXF9fLxAIgoKCpk6d\nqt8OSC6X79y5s6SkxM3NbdCgQfHx8QCAJUuWLFmyhMiIewesBxyshw6USuWuXbsePHjg4+Oj\nX2NGq9Xu2rUrOztb/7YVK1YsXLiQuDB7C6wHPdiEwFVWVoaGhtbW1gIAOBxOW1sbg8HYsGFD\nQEAAMMiDr6/v8uXLxWIxWe8OwDzg4HnRVfAJIcHw3YSrqqpsbGwWLVrk4+PT3t5eWlp68+bN\n4cOHW1tbk3XzqA5emAfw9+chZE0FzANOpVIFBwfX1NS4u7uHh4d7e3tzuVz9b9ls9qRJkwoL\nC/Py8srKymg02ooVKxYvXkxgwL0E1gMO1kNnT31OSKPRJk6cyGAw1Gq1k5PTmjVrpk2bRnSk\nPQ/Wgx5sQuCUSuW2bdtqamqcnZ3DwsLWrFnT0NBQWFh49+7diRMnCgQCfR4ePHjQ3t7+1Fmm\nJADzgIPnhTEwqK+o1erIyMiamhrDFyMjIwMDA7ds2dLW1qZ/8ddffw0MDHzzzTebm5vxVxoa\nGqKjo/s03F7TnTxgJEoFzMNzXLp0KTAwcM2aNQqFQv9iZmbmzz//HBMTo9FoMAzT6XS3b98+\nceJEaWkpYYH2HFgPz0HBenhJbW1tn3zySWBg4Oeff47ngQqoWQ+wCfEcp06dCgwM3LBhA56H\ny5cvBwUFBQYGnj9/3vBtDQ0N7733XmBg4IEDB3Q6HUHB9iIK5gGeFz0FPiHsIzqdbs+ePTdu\n3Lh///6sWbP0t2T279+vUqlCQkIM53gMGzasoqKioKCARqPhu2dyOBz9FjEmrZt5AGRJBczD\n88XExJSWlr7xxhuenp4AAIlEEhERcfr06YcPH6ampubl5U2bNg1BEHt7e09PTwsLC6Lj7S5Y\nD89HtXp4Fp1O12FX+uevMUNWFKwH2IR4vqNHjzY0NHz22WcikSg2NtZwK04AQFxcnK2tLRXm\nj1EtD/C86EHk/+boJ86fP5+UlMTj8QwHK2MY1tLSAgCwt7fv8P65c+cCANLT0/s4zt4G84CD\neehMIpEUFRXhPw8ePBgAkJWVVV5efuLEiY0bN2IYtn///hMnTojF4pycnMLCQkKD7WGwHp5K\nXxJUqwetVov9fXq/VCr997///frrry9cuPCf//znxYsX9fuGoSgaFhbm4eHReX9CkqFsPQB4\niXiRpqYma2trR0fHuLi4yMhIw16QXC4/fPhwREQE/k79fnSJiYlVVVWERt3zqJYHeF70INgh\n7CN//vknAGDjxo1OTk4SieTu3bsAAARBbGxsAACdv71QFAUAtLa29nmkvQvmAQfz0IFSqQwJ\nCfnjjz/w/50/f76Hh0daWto///nPmJiYVatWhYeHOzk5oSiKL57RYRddUwfroTPDkqBUPWg0\nmt27d3/99df6PqFMJvv4448TEhJUKhWGYeXl5UeOHAkODpbL5fgbDPuEiYmJxMXeiyhbDzh4\niQAA5Ofn608KiUSya9cufFN1AIBYLG5ubj5//vy3335r2AsCAPz0008qlcrOzk7/Oaa+6zrM\ngx48L3oQ7BD2EXzKO5PJlEgkISEh//73v/GF4GbOnAkA+OGHH1QqleH7b968CQAYMmQIEcH2\nIpgHHMxDByiKikSipKSkpqYm/H/Dw8N37Nixffv2I0eOzJ07F7/5Fx0dXVFRIRQKXV1diQ65\nJ8F66MywJChVDwqFoqKi4tq1a/o+4Y8//lhfX+/u7n7gwIHz58/v3bvX3d09Ly8vLCxMXxh4\nn3DDhg2TJk0iNPzeQtl6wMFLRHp6+vbt2/ft24dhGJ6EjIyMX375Bf/tlClTlErlDz/80KEX\nFBsbe/XqVRRFFyxYYPhpQqHQxcWlr/+GngDzYAieFz0IziHsI0Kh8NatW2lpafHx8TKZzNPT\nc+HChQwGw9XVNT09vaio6P79+15eXmZmZhiGxcTEnDhxAkGQ9evX65fSJgeYBxzMQ2dsNjsh\nIcHc3HzYsGEAABqNZmtra2dnx2QyAQAYhv32228//fQTAGD9+vWOjo6EBtvDYD08lWFJUKce\nUBTtML3n0KFDFhYWe/bsGTBgAIIgVlZWAQEB+fn5eXl5Op1OP4+UwWDg+9GTFTXrAQcvETwe\nLz09PSMjo6ys7MyZMzKZzMvLa+PGjfiuCUOGDMnMzJRKpba2titXruRwOEql8uTJkz///DMA\nYNOmTR4eHkT/BT0D5sEQPC96ENyHsO9ERUWdPXsWAODu7v7555+z2Wz89aampl27dpWUlNBo\nNHt7+6amJplMBgBYuXLlq6++SmTEvQPmAQfz0IFGo3nnnXeYTOaRI0c6THDPyMg4e/ZsTk4O\ngiDLly8n5b5qsB46e1ZJUKEeDLfJSk9Pnzlz5tKlSw3fIJVK16xZw2Kxjh07xmKxiIqzL1G5\nHgC8RAAgl8t37NhRWloKAPDy8tq5c6c+CeDvebC2tm5oaE1QRgYAACAASURBVFCpVAiCrFix\nAuaBlHnAwfOip8AnhH2ksrLyyJEjSqUSANDe3j527FihUIj/CkXRgIAAlUpVWlpaX1+vVCot\nLS0/+OCDWbNmERpyr4B5wME8dEaj0ZRKZXJyMr6dtP71xsbG3bt3l5SUiMXiTz75ZOrUqQQG\n2UtgPTzVU0uCCvUA/r69ZFtb24gRI/AVNfW4XO7du3fr6up8fHysrKyIirMvUbke4CUCACCT\nyaKjo/EkuLu7T5w40fC+AJ4HfCBlfX29Tqfz8vLavHkz+QZRwzzowfOiB8EnhH2ktbU1NDQU\nRdFRo0ZFRUWZm5t//vnnHYb3KJXK8vJyJpPp4OBgoksAvxDMAw7mQSKRlJeX+/r6Gq6S39jY\nuGrVqtGjR+/cudPwzVKptKCgYPz48eTLAw7WA+hKSZC+HvT0zwkHDRr0zTff4KPCcBiGvfPO\nO1KpdM+ePe7u7gQG2UtgPRiClwgAgEqlCg8PV6vVCoWipKQkICBg06ZNnf9SDMPkcjmHw8GH\nE5MPzIMePC96EHxC2EeYTObEiRMDAgK8vLzwO7sJCQmjR4/W38wAADAYDCsrKwsLCxKXLMwD\njuJ5aGxs/Pjjj69evXr9+nWNRmNnZ4ePeUNRtLKyMjExcfr06WZmZvr3c7lcOzs78uVBj+L1\nALpYEiSuh8rKSjqdrm/A6Z8TVlZW1tTU+Pr66v/qS5cu3b59m8vlrly50rCjSA6wHjqAlwiZ\nTKZUKmfMmBEQEDB58uTMzMyMjIzq6mo/Pz/935ucnMzn81EUZbPZ+Hqz5APzYAieFz0Idgj7\nDpPJxL+23d3dn1W4VADzgKNyHlAUHTduHIIg+EbS0dHRdXV1YrFYIBAMGDAgNjaWzWbrl8og\nN3yMBoIgVK4HAEsCAABAbW3t1q1bU1JSJk6c2LlPmJOTk5GRweVym5qaLly4cPLkSQDA6tWr\nSfl4ENZDZ5S9RDQ0NBw4cODbb7+9c+fOhAkTBAIBm8329/fX94V8fHxoNNqNGzf27t2blpY2\nffp08t0iATAPz0DZ86LHwQ4hMWDh4mAecJTKg0wmUygUYrHY29t7/vz51tbWNTU1aWlply5d\nysvLs7e3r6mpyc7ODgoKMhwqRj51dXVfffXV/v37z507V1dX5+HhoV8ahFL1AGBJ/AVF0YKC\ngszMzOzs7Kf2CUtLSxMSEq5fv15QUMDn89euXUvK+TCwHl6IOpeIqqqqrVu34gU/f/58Z2dn\nfKcBw75QRkZGbm7uqVOnMAybO3fuqFGjiI6658E8vAzqnBe9AXYICUOFwpVIJLW1tZaWls95\nDxXy8DKokAfDG5y+vr48Ho/BYLi4uMyePXv06NFqtTo9PR1fPLqtrc3BwcHe3p7okHuLTCb7\n6KOPioqKMAxTq9VFRUUJCQnjxo3j8Xj4G6hQDwCWxN/RaLTx48eXl5c/p08ol8t9fX1DQkLe\neust8u22B+vh5VHhEqFSqYKDg2tqatzd3cPDw729vfFeEI7NZk+aNKmwsDAvL6+srIxGo61Y\nsWLx4sUEBtxLYB5wsEnZq2CHkEj6whWLxSTbHAYAoFQqN23a1NDQ4O/v//x3kjsPL4/ceXjW\nDU6cSCQaP3787Nmzzc3NKysrFQpFU1PT9OnTCQy4V/3www+5ubmurq47dux47bXX2trasrOz\nk5KS8EYw/h5y1wOAJfE0L9MnfPDgwejRo+3s7IgNtcfBeugq0l8i4uLirl+/LhaLIyIi+Hw+\n/mJWVlZcXFxFRYWTkxObzZ46daq9vb29vf2aNWvGjx9PbMC9BOYBwCZl74OrjBLv4cOHQ4cO\nJTqKXrFly5bS0tIff/xRIBC88M0kzkOXkDIPKpVq48aNEonE3d19+/btz79ph2FYZGRkbGzs\n/v37ybfRtlQqtbKyWrNmjU6nO3jwoL77d/LkyZMnT4pEovDwcLFYrH8/KesBwJL4i0KhMFw/\nCafVar/88svExEQ3N7fPPvvMsF8kk8kSExPnzZvXt2H2OmrWg0ajaW9v71wAXULWSwQAYP/+\n/devX3/nnXcWLFgAAJBIJJGRkbm5uXQ6XavVenp6fvHFF1RYKQTmAQeblL0KPiEknkgkIjqE\n3sJmsxMSEszNzYcNG/bCN5M4D11Cyjy88Aan4VwgBEGEQmFcXByNRhs7dixBIfcAjUaj0+kM\n/7SKioqtW7eWl5fX1NTMmDHD29tb/yt8l7mUlJQOzwlJWQ+AqiXRgUQi2bJlC4PB6NBwwZ8T\npqenFxYWdn5O6ObmRkSwvYuC9aDVaiMiImJiYiZOnKifP2wEsl4iAAASiSQrK4tOpzs5OcXE\nxOzbt8/S0jIkJGTFihV37twpKSkZO3YsFTbhhHnAwSZlryL/AkQQgSZOnPjjjz9euXLltdde\no8LtK+hZHj58CACYN28e/qyjww3OhISEDjc4zc3NAQAPHjwgKuDu02g0ERERAIDt27frF/7m\ncrlcLvfatWsAAA6H0+GQJUuWAABOnjwZHBzc4Tkh+VCwJDqQSCQFBQVarfbIkSMAgMDAQMPf\n0un0xYsXh4eHFxQUhIaGdnhOSD4UrAcEQTgcTnFx8c6dOz///HP9PSBIb/78+ampqWlpaWlp\naebm5qtWrZozZw6CIBiG4RdVnU5HdIx9AeYBB5uUvYqiK3RBfYPBYMyePbu2tvbevXtExwIR\nafDgwQCArKys8vLyEydObNy4EcOw/fv3nzhxQiwW5+TkFBYW6t+s0+l++uknAIBJ94g0Go1c\nLs/Ly6uurta/KBQKw8PDbW1tAQDx8fFarbbDUUuWLFmyZIlUKk1OTu7TcPscBUvCUGNjY2ho\n6A8//PDuu+/y+fwjR45cvHixw3vwkYQ+Pj4FBQV37twhIsy+Q8F6oNFomzZtmjJlCt4nbGlp\nITqifgdF0fDw8B07dmzfvv3IkSNz587FuwHR0dEVFRVCoZB86yo9FcwDDjYpexV8Qgj1GIlE\nUl5e7uvrazi2Z86cOb/++uvly5dNd2CPEfSbyxEdSH/RpRucxcXFycnJXC532bJlxIXcXSiK\nhoWF1dbW2tra1tTUiEQi/C/F+4TBwcElJSXffvvt+vXrO9TJkiVLPD09R4wYQVDgfYSCJWHo\n2LFjUqnU09PTx8fH3t4+JCSk83PCmzdvAgA++OCD+/fvv3AdBVNHzXrA+4QAgJs3b8LnhE9F\np9N9fHz0/4th2G+//Xbs2DEAwOrVq8m967ohCuYBNin7GFxUpgdoNJr4+Pj79+8jCOLh4TF5\n8mQ2m935bRUVFfiTAVJqbGz88MMPZTKZtbX13LlzZ86cqf9i27dvX3x8/JEjR6ytrYkNssdp\ntVoajWbYoK+rq/vuu+/S09PZbPaUKVPefvvtZ33Bk7seOtNqtffu3dNqtSNHjtQPfrt48eKR\nI0eEQuHRo0cNv9JSUlIsLCzIMVeqqqpq27Ztrq6uhmNHZTJZcHBwRUXFjBkzOvcJKYKaJYGv\nKrRixQoWi3Xw4EF85PCjR49CQkKam5vfeOONpUuXIgiC50EkEh09epTokPsINesBAKDT6fbt\n23fz5k1nZ+dn9Qmp9n3xVBkZGWfPns3JyUEQZPny5QsXLiQ6ImJQIQ/UbFISCy4q011VVVXB\nwcFXr14tLS0tKSlJSUm5efPm0KFDO8xnjY+PDw0NNTMzI+uqR1KpdMiQIZaWlg8fPkxNTY2O\njq6rqxOLxQKBYMCAAbGxsWw2e+TIkUSH2ZPwSWJZWVk+Pj54g/6Fm8vpkb4eOqPRaLa2tnZ2\ndvjyGPgNTnzc1/r16x0dHQ3fbGtrS5op8kwmMy0tLSsrq7S01N/fH7/Zqd9CICsrSyqV6kuI\nUihYEoarCs2ePVt/SbSwsPD29k5KSrp3796lS5eio6PxMaJr164dMmQIoSH3HQrWg0wmO3z4\n8JEjR6RSaWtrq0wmy8zM7LzGDAW/LzprbGzcvXt3SUmJWCz+5JNPpk6dSnRExKBIHijYpCQc\n7BB2S1NT07Zt26qqqmxsbBYtWuTj49Pe3l5aWnrz5s3hw4cb3r24d+9eZmbm0KFD8bUESaax\nsXHbtm3Jycnr1q178803ra2ta2pq0tLSLl26lJeXZ29vX1NTk52dHRQUZPjo39TJ5fI//vjD\nsEH/MpvL4chdDy+UkZHxzTffXL16FUGQFStWzJ49m+iIehGDwZg0aVJubi7sEz4HRUpCq9Xe\nunUrKyurra1tzJgxhntkWVhY+Pv7l5aWlpeXt7a2slisd955Z9asWQRGSyAq1INUKv3oo4/u\n37/P4/ECAgKGDRsmlUolEknnPiHFvy9wKIqOHz/ew8Nj3bp1NjY2RIdDGCrkgZpNSsLBIaPd\ncujQocuXL7u5uX3xxRcoiuIvnj17Nioqis/nHzp0CF8JDZeXl/cyS+Waoq+//vrq1auenp6h\noaH64bL5+fmXLl1KSEhQq9U0Gk2n033yyScTJ04kNtSe1WHg38tvLgdIXQ/P19jY+Mknn1RX\nV4vF4vfff3/UqFFER9QXlErlrl27Hjx44OPj89Sxozt27DCcIkIplCoJ/b+4vb39gQMHOk/+\nefz4cX19vYuLi+HXB6VQpB52796dlJTk7u4eFhaGjxxWqVT79u1LSEjoPHaUst8XEAVRtklJ\nLPiEsFv279+vUqlCQkIMHwYOGzasoqKioKCARqMZPtEeMGAAETH2LqlUyuFwIiMjBQLB7t27\n9b1iAIBIJBo/fvzs2bPNzc0rKysVCkVTU9P06dMJjLbHdXjI8/KbywGS1sPLIP0NTgzDcnJy\n0tLSmpubBw4ciN/CfP5zwoEDB5J15M/LIH1JGNJfNCQSSV1dna+vb4cnwwKBwMbG5qkT0SmC\nCvWg1WoPHDig0+nCwsL0Y1/pdPr48ePT0tKKi4s7PCek7PcFRCkUb1ISC3YIjYdhWFRUFABg\nzZo1He7yWlhYXLt2TalUkmmgi0ajaWtrMxzH8qz5MIZQFB02bFhgYKBMJsM7RUKhsA+j7nWG\nfUKFQuHj4+Pu7m74huf0CSmLy+Xa2dmRcoRkbW3trl27zp49e+/evZs3b96+fdvNzQ1v8D2n\nT0iOtTG6g8Ql0Zn+opGdnQ1HCz8V6etBo9GcOnWKwWCsXbvW8HUajYaiaFJS0rPmE5q0/Px8\nKysr/J9VIpH85z//8fb2pvK9D8rq3J4EsElJNDj61ngIguA3Lw33R8LhdzVaW1sJCKt3aLXa\niIiIHTt2GO6VpN9lu76+/vmrHiMIMnPmTABAXFxcr8fa5+DmchAOn1RcWFgoFAoXLVoUGBhY\nU1MTEhKSnp6OvwHfi8LDwyMlJWX37t2dSwUiHwzDOk/N0F80rl279vXXX8O5G1TDYrFsbGw0\nGk1ZWVmHX+EN3HHjxhUXFyckJBAQXO9IT0/fvn37vn37MAyTSCQhISEZGRm//PIL0XFBfe2p\n7UkAm5REg08Iu0WlUmVmZj569Gjq1KmG5Xvu3Ln8/HxPT89JkyYRGF7PSktLy8jIMLxnqb/P\nLZfLGxoaZs2a9ZwJvmq1+uLFixqNZs6cOX0YdR/RpwKf/9P5lr+np6enp+fkyZOJihDqAxER\nEcXFxR4eHrt37/bx8amtrU1NTdVoNImJiS4uLvj9I8PnhPb29g4ODkRHDfUMrVaLIEiHTWi+\n+uqr/fv3nzt3rq6uzsPDw/COOFxViOI0Gk1mZmZ5eXlAQIDhV+f58+cLCws//fTTESNGBAQE\nEBdgD+PxeOnp6RkZGWVlZWfOnJHJZF5eXhs3bmQwKLohtlQq/e67737++eeUlBQej0epbUU6\ntycBbFISDXYIu8XV1TU9Pb2oqOj+/fteXl5mZmYYhsXExJw4cQJBkPXr13fYfMJ0IQji5+dX\nVVX1rD7hs+bD4HQ63bffflteXu7h4UGmTrKhFzbv4J455Jafnx8VFSUSiXbv3s3n869cufLd\nd99hGDZt2rSioqLOfUIbGxsqzxskGeM2oTG8aLi4uFCqRQi5ubmlp6fn5+cXFRWNGjUKH1h0\n+fLlkydPWlhYLF261N7enugYexKbzfb398/IyMjNzVUqlV5eXjt37nzWeNGKigo+n9/HEfal\nxsbGLVu2PHjwQC6XV1dX37p1q7Gx0dvbu3MLinypeFZ7EsAmJaFgh7BbaDSan59fVlZWQUFB\ndHR0YmLi6dOn8TEeK1euJFmZvrBP+Jz5MEVFRT/99BOHw/nkk09IdmkzRLVb/lS+wdnZjRs3\nsrOzP/zwQ2dn56SkpP3792MYtnr16uXLlz9+/LisrKxDn9DJyYnokHseZUvC6E1oyL2qEGXr\n4WXo2w95eXkxMTH37t379ddf4+PjAQBr1651cXEhOsCeJ5PJoqOjlUolAMDd3X3ixIlP/Yqk\nwr6Lhw8fvn//vrOz8/r168eOHVtYWJidnV1dXe3n52eYE7Km4mX6hLBJ2cdgh7C7UBQNCAhQ\nqVSlpaX19fVKpdLS0vKDDz4g5f5RL+wTPqsjZGVl5eTkNGfOHNJvskydPiGVb3B2IJFIpFLp\nhAkTWltb58+f39LSsnPnTpVKtWTJkkWLFgEAysrKKisrlUplQkLC5MmTybqw0EuWBCnrAUXR\nDif+kSNHOBzOnj17xGIxj8fz9fUFz1hciqyrClG5Hl6Svv1QXFxcXV3d0tLC5XJXr15NmvaD\nVqtNS0sbNGgQ/o/OYrFyc3NFIhGPx8vMzOzc/8FRYd/FyMhIPp+/d+9eBwcHR0fHgICA9PT0\nrKysDjkhcSpepk8Im5R9CXYIewCDwRgzZkxQUJCfn9/8+fOXL19OynlBMpns8OHDR44ckUql\nra2tHdZAe+EJbGtrq19cm9woMgyM4jc49fAtdK9everr6zt16lQajRYTE5Oamjp69Oj169fj\n7zl+/DiKouvWrRs0aJCfnx+xAfeelykJEtdDdzahISXK1kOX1tLUtx/Gjh07Y8aMlf/f3r0H\nRXWefwB/9waIKKKiUGXFpYAiKIIQFhXB+xLrxBlSJ3WiGEtsKswU26kpiEmnXhhjJ9o2SqoT\nW2AKadqMHSUYZBCJl1ER2c1yERW0sCh2cbnEsCy77u+PM78zJ7uwLIoc9j3fz3+uMPMA77k8\n5zzv82zfPn/+/LGN91WprKw8ePBgaWkpe0sgkUji4+MTExMTEhJqa2tv375tsx6uX78+efLk\nxYsXL1q0aOXKlfzG/0p9+eWXGzZsYBtpMk+U7HPCsLAwKn8Vju8nCW4p+YCEcNRIpdJp06ZN\nmTKFyjdCer3+N7/5TV1dnZeXV2JiYlhYmF6vb2trGyonpDgRcgbdZWAMPOBknDx5UqvVhoaG\nJicnM90RKioq7t+//9Of/pQpCi0pKfn666/nzZu3efPm8PBwvuN9hZxZEnSvBwyh4RLmeqip\nqdm3b197e3tcXJxOp8vOzm5paenr64uJiXHwXVKp1NfX19fXl44OKxaLJS8vr6Cg4NmzZ0ql\n8o033uDOWpRIJMx+QjYnjI2NFYvFFy9ePHLkSHV19apVq6icP2kwGE6dOlVYWMhMqQ0PD+c+\nBxkqJ6RvBKUz95MEt5RjDgkhOOXYsWNNTU3z5s07fPhwdHT0okWL1q9fr9Pp1Gq1fU5IdyLk\nJFrLwFgCf8BJhh6h29PTc/36dYPB4Ovre+7cuaKiIpFI9N5771HfVciZJUHxemBwG+V1d3ev\nWbPGplEemxPOmDHDJl2kjDDXA3ppEkKOHTtWXl7u4eGRmZm5ZcuWqVOn2n8NNydkOs0UFxdb\nrdbk5OTIyMixj/lVMxgMu3fv1mq13d3d7e3tfX199ucH7jEyd+7cgIAAHgN+dZy8nyS4pRxb\nSAhheBaL5dixY8+fP//973/Pfc6nVCqrq6vv379vkxPSnQgJGR5wshyM0J0zZ05DQ0N9fX1l\nZWVTUxMhJDU1dcWKFfwF+wq9wJKgcj0QQgwGw7Nnzzw9PYU8hAbrYUS9NKl07dq1goICqVS6\nf/9+bsm0PXd39+XLl9+9e7e+vv7BgwdisTg1NfXNN98cs1DHUl5eXn19vUKhSE9PX7x4cVNT\nU3t7u/35gTlG/Pz8aE2BRnQ/SXBLOYaQEMLwzGZzcXGxVCp99913uZ+LxWIPD49r167Z13+7\nuhFtAhEIPODkslgsVVVVarW6r68vKiqKu+1HLBYvW7ZMKpUODAwoFIq0tDSaXoBwYUkwnj59\neuzYsU8++eTy5ctMIagwh9BgPTCc7KVJqxMnTjx58mTz5s32KU1ra2tDQ4PJZPLx8WE+cXNz\nS0pKksvlcrk8LS1NqVSOebyjz2w29/X1sbdDTC1JXl4eU0EdGBioUChWrFgx1PnBw8MjODiY\np9hfOQHeT7oKJIS20CbbnkQiqays7OnpUSqVU6ZM4f5Xd3f3xYsXY2JitFqtn58fHZ2yX2wT\nCPXwgJPL8QhdiUQSHh6+Zs2ahIQEKjfDMLAkCCGPHj3as2dPU1PT5MmTN2zYEBQU5OnpSYTU\ncJiF9cBwspcmrT777DOTybRjxw5upWhjY2Nubm5BQcE333xz/vz5pqammJgY5o5fJBLJ5fKI\niAibuwsXxYwkLSkpYVIatpbkyZMnKpWKrSUR4PmBIbT7SReChPAH0El/KGazuba2trW1NTEx\nkXvj+5///Ofu3bsffvhheHh4YmIifwGOJmwCsYEHnINycoQulbAkGCaTKSsrq6OjY968eQcP\nHoyOjmayQYZw7vmwHlgGg8FoNK5evXrYXpq0lpxUVFT09PSEhIQEBQURQoxG4+nTp48fP97Z\n2Tlr1iymg0hra+u9e/foK51gssEbN24MDAwwCQ9bS/L999/HxMRwK6iFc36wIaj7SReChPAH\n0El/KCEhITU1NY2Njffu3YuMjGT6Z5SWlhYVFU2ZMuVnP/uZXC7nO8ZRM9JNIHQ/HcADTgec\nGaFLHywJVllZWUVFhZ+fX25uLnsSUKvVZWVlOp1OoVB4enpS3ygP64HBrRyOj4/39vYetpcm\nrQ8Zb926pdForFZrQ0PD0aNHa2trvb29d+3alZ6enpCQoFQqy8vL29vbFyxYMHPmTL6DHTVs\nNujl5bV//35mRJ7jLlPCbKQpqPtJF4KE8AfQSX8oYrE4Li5OrVbX19eXlJTcunXriy++qKys\nJIS8++679L3Zd34TCPVPB/CA0zEB/hKwJFglJSUtLS2bN29mrgVtbW25ubmff/75nTt3bt68\nWV9fv3LlSuob5WE9kKErh4XWS5MQEhwc/PTp0zt37mg0GmaXdUJCQk5ODttT19vb+9tvv+3o\n6AgKCqLmummTDTIzhxjsUfDw4UP7Cmrqzw/2hHY/6SqQEP4AOuk74OHhkZiYaDKZ7t+///jx\n4++++87T0/PnP//5unXr+A5tFFgslurq6h/96EfMn9j5TSDUPx3AA85hCeeul4ElwWpra1Or\n1RKJRKFQlJSUfPzxx1OnTs3Ozk5NTb18+XJzc/OSJUumTZtGd6M8rAfHlcOC6qVJCBGJRLGx\nsaGhod7e3kuWLNm5c6dKpWKn8hBCzGZzfn6+0WhUqVSzZ8/mMdTRwmaDMpns0KFDTK0sl+Nr\nBN3nh0HRfT/pokRWq5XvGHhmMBgKCwubmpp8fX1bWlo2bdq0ceNG7hd0d3fv3bv34cOHiYmJ\nmZmZdN/qOcNoNLa0tFitVoVCwT3Lu67KysrCwsInT56sXr06IyOD+RObTCZCSH9/f05OTnNz\ns81f//r16/Pnz2eKxOrr68PCwniMfwwYDIasrCydTsf9FXH/9+rVq6+//jpf4Y0H7K9o7969\nsbGxfIfzymFJEEKMRuMHH3zQ0NBACJk0adKWLVtUKpVIJLJarb/85S91Ot3hw4fpHjbIEsh6\nMJvNhBCbUs/S0tITJ074+fkdPXqUTQXVarVarZ4+ffq6deskEonVar1y5Upra6tSqQwMDBz7\nyMePoqKioqIiHx+fU6dOyWQyvsN5WWw2yPwzJSVl69atg36l42NEmOi7n3RdQn9DiDbZL0Aq\nlfr6+vr6+lKw/8FiseTl5RUUFDx79kypVL7xxhvcwTgSicSZTSCUzdEaFB5wDktolT9YEoQQ\nqVS6cuXK4ODgpUuXpqWlhYWFMb+Ec+fOVVZW+vj4vPPOOzaD6WklhPXA3Ppfvnx56dKl3D+r\nM5XDlPXSfGGlpaV/+9vfCCG7d++eM2cO3+G8LG6l6Ntvv63VarVa7cDAAHcyLUtotSTOoOl+\n0tUJPSFEm2yBO3bsWHl5uYeHR2Zm5pYtW7htslkC3AQyKOovZjbDowghbW1ter2eHZk1LDru\nep1H/ZJwhlgsnjVrVkBAAPOuw2q1/vvf/2ZueTMyMgT1Loj69WAymb7++uv79+/Hx8dzG4k5\nWTnMY+TjQX9//6efflpcXEwI2bZt29q1a/mO6GXZ7BtUKpXBwcFXrlxxMieko4La/rpJRn7p\nhPFAQAkhRoWCjWvXrhUUFEil0v3790dHRzv4SqFtAhkKxTd8NsOjCCFdXV3vv//+hQsXYmNj\nvb29+Q5wnKJ4SbyA27dv/+Uvf7lw4YJIJEpNTV2/fj3fEY01uteDVCpdvny5UqkMCAjo6OiY\nMGEC855QoVBotVqNRvPVV189fPhw27Ztv/jFL6ZOnSqVSktLS3t7e1evXj19+nS+w+eNxWIp\nKSnJzc2tq6tzd3fPzMxUqVR8BzUKysrKzpw5w+0i4+/v72ROSEctif11k+DS6bKEkhBiVCir\nsbFx2rRpzM/V1tb2xz/+MTo6mtaBSI6dOHHiyZMnmzdvtj8vt7a2NjQ0mEwm9hGXm5tbUlKS\nXC6Xy+VpaWlKpXLM4x0X6HvASQYbHkUIOXnypFarDQ0NTU5ORjWLA1QuiRfQ1dV16NCh5uZm\nPz+/3/72txTc7b0YuteDVCr19vZmeorW19cztaOoHHZMLBZXVVVpNBqlUrlnzx5qGrAFBQWZ\nTKbt27dze4o6mRNSUEsy6HWT4NLpsgSREGJUKKumj4uwwQAADuRJREFUpmbfvn3t7e1xcXE6\nnS47O7ulpaWvry8mJobv0Hjw2WefmUymHTt2cCtFGxsbc3NzCwoKvvnmm/Pnzzc1NcXExDCP\nvrAJhEHTA04y2PAopnzg+PHj3t7ehw4dwk73YVG2JGw4Wf7k4eGhVCrnz5//3nvv+fv7j01s\n4xPd64EQIpPJqqur1Wp1S0sLkxOictix6OjohISE5ORkmmb2ikSiyMhI+zODMzmhqxt06CIu\nnS6N/oQQo0K5vLy8ampqbt++/eDBg3/+858Gg2HhwoW/+tWvhPkUp6KioqenJyQkhGkSbTQa\nT58+ffz48c7OzlmzZoWFhen1+tbW1nv37lE2YuTli/7peMBJBhsexZYPdHR0rF+/nspr+atA\nx5KwPzRGVP7k6ekZEBBA0zPEF0bHehgKUzuq1Wq5OSH7v6gcHhRNqeCw6M4JBx26iEunq6M8\nIcSoUBtMfxSmM4rRaFy4cGFOTo6DelGdTkf3SfzWrVsajcZqtTY0NBw9erS2ttbb23vXrl3p\n6ekJCQlKpbK8vLy9vX3BggUzZ87kO9jRgaJ/1qDDo9jygb6+vqioqPnz59t/I/XHhTANemig\n/AkGNVROiMphYNCaEw41dBGXTldHc0KIUaEMm5HrBoPh3LlzRqOREDJv3rxly5YN9Ty7srJy\n3759EydO5JbU0iQ4OPjp06d37tzRaDTMWSwhISEnJ4edG+bt7f3tt992dHQEBQXR8UtA0T+L\nOzzq+fPnkyZNYi7Y3PKBp0+frlu3zmbzD/XHhTDZHxoofwLHBs0JUTkMLPpywqGumwSXTtdH\nbULoYNVy0b1jkBBSWVl58ODB0tJS9qdzc3PTarXTp0/38vKqra19/PhxXFzcoD/1rVu3amtr\nQ0NDqdkCbkMkEsXGxoaGhnp7ey9ZsmTnzp0qlYp722c2m/Pz841Go0qlmj17No+hjgoU/bMc\nD49iTwttbW3/+9//XnvtNe4BQt9xgUZT9ocGyp+Ay2w2V1RUnD179saNGz09PbNnz2YenA2a\nE6JyGFhsThgREeHql4xhhy4K7dJJGToTQowKJUOPXJdIJPHx8YmJiQkJCey8dW5OeP369cmT\nJ7u7u4eFhS1atIiy7XP2/P39o6KiwsPD7UslP//88+rqah8fn507d0okEl7CGy0o+mc5MzyK\nPS1oNBqb0wJlxwUaTQ16aKD8iRCi1+vz8vL+/ve/M78cmrbTj8ijR4+ysrIuXLjQ0tLS3Nx8\n48aNS5cuhYaGMpMkHO8nBPD392d2oPAdyEtxcuiicC6d9KEwIcSoUIaDkesSiUQikXDnrT9+\n/Dg2NlYsFl+8ePHIkSPV1dWrVq2SSqW+vr48/gj8Ki0tZXrE7d69e86cOXyH81JQ9M/l5PAo\nB4+KaDouRtpoirL1MNShgfKnrq6uX//61w0NDb29vY8fP66qqurq6oqOjrZ5YErZerDX3d39\n/vvvP3r0yN/fPyUlJTY2tr+/v6Wl5dKlSwsWLJgxYwb5YU4ol8td/XoBo27SpEl8h/CynB+6\nKJBLJ30oTAgxKpQ4PXKdmxMynWaKi4utVmtycnJkZORYBjyu9Pf3f/rpp8XFxYSQbdu2rV27\nlu+IXgqK/m04PzyK1vIBrhE1mqJsPTjeWSDw8qe//vWvdXV1QUFBGRkZS5YsuXv3rkajsSkn\noWw9DOr06dNqtTokJOSjjz6KiIgICQlZtWqVTCarqam5efPmmjVrmIOFyQn9/f3puIUAsDGi\noYtCuHTSh8KEUOCjQhnOj1x3d3dfvnz53bt36+vrHzx4IBaLU1NT33zzTT6i5p/FYikpKcnN\nza2rq3N3d8/MzFSpVHwH9VJQ9G9vRMOjqCwfeOFGUzStB2d2Fgi5/On48eOTJ08+cuTInDlz\nAgMDExMTa2pq1Go1NyekaT0M5ejRoyaTKTs7m3kZyAgLC9PpdE1NTWKxmF0wUqmUe9cBQJOR\nDl2k8tJJNwoTQiGPCmWNaOS6m5tbUlKSXC6Xy+VpaWmuXun+MsRicVVVlUajUSqVe/bscfW7\nHBT9j5SDnJCa8oGXaTRFzXpwfmeBYMufvvzyyw0bNrC/Cg8Pj6VLl9rkhNSsh6FYrdb8/HxC\nSFpams028ilTppSXlxuNRowZBIFznBNSc+mkHoUJoQPCyQlHOnJdJBLJ5fKIiAh2DoFgRUdH\nJyQkJCcnU7AxBkX/L2ConJCC8oGXbzRFaFkPI9pZIJzyJ4PBcOrUqcLCwurq6p6envDwcG4t\n6KA5IR3rYSgikejSpUu9vb2LFy/mviEkhPT29p4/f97d3f0nP/kJX+EBjBMObi0ouHQKhLAS\nQiKknFCAI9dHCwWpIANF/y+G1rPEqDSa4jH+UTTSnQVCKH8yGAy7d+/WarXd3d3t7e19fX3d\n3d1r1qzh7ivm5oRz584NCAjgMeCxYTKZamtrHz58mJSUxH1JeObMmcbGxoiIiOXLl/MYHsA4\nQet1UzgElxASYaxaAY5cB3so+n9hNA2PYqDRFNcL7CygvvwpLy+vvr5eoVCkp6cvXry4qamp\nvb29s7PT5tkQkxP6+flR+Xuwn7QRHBxcU1Nz7969urq6hQsXTpw40Wq1lpSU/OMf/xCJRBkZ\nGczwCQCg77opKCKr1cp3DPyoqak5cOBASkrKW2+9xXcsrwrTTd7Lyys+Pt7mUa7ZbH7nnXe6\nurqys7Nfe+01viIEHjGHwMDAQEpKytatW9nPDQbD1atXX3/9dR5jGyeYXvN8RzE6srKytFrt\nW2+9ZX/Ga21t1el0M2bMYF+XPXv27NChQxqNhhAiFou3bdu2adOmsY6YP0MdGnQwm839/f0T\nJ05kP9Hr9dOmTUtNTZXJZH/60588PT0JIU+fPs3OztbpdKtXr87IyBBCvUBXV1dmZmZnZyf7\niUql2rlzZ29v7wcffNDc3CwWi+VyeXd3t8FgIIRs375dUMcFgDNoum4KihDfEDLoGBXqmEBG\nrsOLQdH/sCgYHsVCoynnUVxFwnTTKSkpWbZsmZubGyFEp9Pt2bOntbX1yZMnKpVKyDXkQ03a\nSExMTEpKMplMLS0tnZ2dRqNx6tSp6enp69at4ztkgHGHpuumoAg3ISQCXrU0jVyHl0HxjS/Y\nQKOpEaHy0GB7qw4MDCiVSuYva7FYqqqq1Gr1999/HxMTw91BILSc0MGkjWXLlkVFRW3cuDEu\nLm7Dhg3btm3DpRMAaCLohFCAKBu5Di8PRf/CgUZTI0LZoWEzaWPu3LnM52zW19vba99FRlD7\nioedtCGTyaZNmzZlyhS6E2MAECDh7iEUGovF8tVXX33xxRddXV3u7u4ZGRkJCQl8BwXjBYr+\nqWe1Wj/55JOysjLmnyKRaPny5Wlpadx68pycHLVanZaWhk76LDoODZts0H5+usFgyMrKGmrH\nIMX7ig0GQ2FhYVNTk6+vb0tLy6ZNmzZu3Mj9gu7u7r179z58+DAxMTEzMxOpIABQCQmhgJw8\nefLs2bNKpXLr1q10P+gFgEGh0ZQAsdmgTCY7fPgwUzNsz3FOSCVm0ga3i0xQUNCRI0ds9tWz\nOeHvfvc7oe2nBQCBQEIoLDqdDqkgANgrKioqKiry8fE5deqUTCbjOxwYHWw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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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+fe/evb169Zo+fTpbl3vtleZVZN97okHHp4VVquOC1DLVcUWRuKlD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\njMHQfvMq0i372k/TU+LO3L9yMGHKF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PwRvCflJeXh4eHi6VShFC7e3t48ePNzY2Jn7F4c+wRe4AACAASURBVHB8fHxkMllx\ncXFtba1UKjUxMfnwww9nz55Nash9AvLQFYPBkEqliYmJxLa5qs/r6+t37dpVVFQkFAo/++wz\nX19fEoPsB3TOA1wXBMgDQSKRREREEHlwcnLy8vJS3e8SeSBGSdXW1iqVSldX148//pgyI8E6\ngSJBgDx09dQmgw7tBfrfZYTb2tpGjx5NrLCqwuPx7t27V1NT4+7ubmpqSlacfeo59SSiX1XZ\nc/CGsJ+0trYGBwdzOJyxY8ceP35cX19/586dnV7fS6XS0tJSHR2d4cOHU3XpW8iDSCQqLS31\n8PBQX/25vr7+7bffHjdu3LZt29QPFovFeXl5EydOhDxQNQ8EuC4IkAeCTCYLCQnp6OhoaWkp\nKiry8fFZv359pz8Wx/GmpiYul0uMkaMqKBIEyEP3mwzKtxcqqveEQ4cO/f7774nRkgQcx995\n5x2xWLxnzx4nJycSg+w73aknEW2qyp6DN4T9REdHx8vLy8fHx9XVlXhyExcXN27cONVDPoQQ\ni8UyNTU1MjKicC1G8zzU19d/+umn169fj4mJkcvlVlZWxFgODodTXl4eHx8/ffp0PT091fE8\nHs/KygryQNU8qND8ulCBPCCEJBKJVCqdMWOGj4/PlClTMjIy0tPTKysrPT09VX9yYmKigYGB\ngYEBsYgihUGRINA8Dxo1GdRuL8rLy5lMJtG3Ub0nLC8vr6qq8vDwUP3VV65cuXPnDo/HW7ly\npXpHkTK6X09yOBw2m035qrLnoEPYf3R0dIjL0snJ6VkVOoUR76IxDKNzHjgczoQJEzAMIzbM\njYiIqKmpEQqFhoaGZmZmUVFRbDZbNQWcwiAPXdH5ulBH5zzU1dXt37//4MGDd+/enTRpkqGh\nIZvNnjx5supex93dncFg3Lx5c+/evSkpKdOnT6fkrV4ndC4S6uicB2gyCNXV1Rs3bkxKSvLy\n8urUJ7x//356ejqPx2toaPjjjz9OnTqFEFq1ahX1Xg9CPdlHoENIDlpV6DU1Nd9++21oaOjF\nixdramqcnZ1VU5xplQeJRNLS0iIUCt3c3Pz9/c3NzauqqlJSUq5cuZKTk2NtbV1VVZWVlTV/\n/nz1ITHUA3l4IVpdF89BqzxUVFRs3LgxLy/PwMDA39/fzs6O2GlA/V4nPT09Ozv79OnTOI7P\nnTt37NixZEfd32hVJJ6DVnmAJkOFw+Hk5eVlZGRkZWV17RMWFxfHxcXFxMQQ1ci7775LvXmk\nUE/2HegQkoYmFbpEIvnkk08KCgpwHO/o6CgoKIiLi5swYQKfzycOoEMe1B9oeXh48Pl8Fotl\nb2/v5+c3bty4jo6OtLQ0YjHxtra24cOHW1tbkx1yn4A8dB8drguEkEgkqq6uNjExedYBNMmD\nTCbbsmVLVVWVk5NTSEiIm5sbcZdDYLPZ3t7e+fn5OTk5JSUlDAZjxYoVixcvJjHgPvLC8oBo\nUyReiA55gCajEwaDMXHixNLS0mf1CZuamjw8PIKCgt58800q7b5IgHqyT0GHkEyqCl0oFFJ1\nU5SjR49mZ2c7ODhs3br11VdfbWtry8rKSkhIICp34hhq5+FZD7QIAoFg4sSJfn5++vr65eXl\nLS0tDQ0N06dPJzHgPgJ50BS1rwuEkFQqXb9+fV1d3eTJk59zGOXzgBCKjo6OiYkRCoW7d+82\nMDAgPszMzIyOji4rKxsxYgSbzfb19bW2tra2tg4MDJw4cSK5AfeFbpYHRI8i0R3UzgM0GU/1\nwj7hw4cPx40bZ2VlRXakvQ/qyT4Fq4yS79GjRyNHjiQ7it4nFotNTU0DAwOVSuWBAwdU3b9T\np06dOnVKIBCEhIQIhULV8ZTMg0wmW7dunUgkcnJy2rx58/Mf4uI4HhYWFhUVFRoaSqUNZBGN\n8yCXy9vb29UXyNEUJa8LlQ0bNhQXF//444+GhobPP5LaeQgNDY2JiXnnnXcWLFiAEBKJRGFh\nYdnZ2UwmU6FQuLi4fPnll1RdJENd98sDonqR6D5K5oG2TUZXLS0tXVsQhULxf//3f/Hx8Y6O\njl988YWqqyyRSOLj4+fNm9fvYfYHqCf7FLwhJJ9AICA7hJ6Sy+VKpVJ9+H5ZWdnGjRtLS0ur\nqqpmzJjh5uam+hWxW05SUlKn94QUyENXL3ygpZ40DMOMjY2jo6MZDMb48eNJCrlP0DMPCoVi\n9+7dkZGRXl5eWm8NTMnrQoXNZsfFxenr648aNer5R1I7DyKRKDMzk8lkjhgxIjIyct++fSYm\nJkFBQStWrLh7925RUdH48eOpupmYuu6XB0T1ItF9lMwDPZuMrkQi0YYNG1gsVqc+P/GeMC0t\nLT8/v9N7QkdHR5KC7XNQT/YpWHgH9JRcLt+9ezdCaPPmzaqFfXk8Ho/Hu3HjBkKIy+V2OmXp\n0qUIoVOnTm3ZsqXTe0KKefToEUJo3rx5xAO8Tg+04uLiOj3Q0tfXRwg9fPiQrID7CD3zgGEY\nl8stLCzctm3bzp07Vc8+gIqXl9ePP/547dq1V199lc5Pdv39/ZOTk1NSUlJSUvT19d9+++05\nc+ZgGIbjOFGpKpVKsmPsD1AeAIGeTYY6kUgkk8mIvzc8PBwhFBAQoH4Ak8lcvHhxSEhIXl5e\ncHCw+ntCqoJ6sk9RfEUm0A/kcnlTU1NOTk5lZaXqQ2Nj45CQEEtLS4RQbGysQqHodNbSpUuX\nLl0qFosTExP7Ndz+NWzYMIRQZmZmaWnpyZMn161bh+N4aGjoyZMnhULh/fv38/PzVQcrlcqf\nfvoJIUS9HjI988BgMNavXz916lSiT9jc3Ex2RAMOi8Xy8/Orrq5OTU0lOxYycTickJCQrVu3\nbt68OTw8fO7cucTNbkRERFlZmbGxMfXWh3gqKA+AQM8mQ6W+vj44OHjbtm0MBuOrr74yMDAI\nDw+/fPlyp8OIoaTu7u55eXl3794lI9J+BfVkn4I3hKCnOBzOjh07qqurLS0tq6qqBAIB8aiG\n6BNu2bKlqKjo4MGDa9as6fTEd+nSpS4uLqNHjyYp8P6g0QOtwsLCxMREHo+3bNky8kLuE7TN\nA9EnRAjdunUL3hOKRKLS0lIPDw/14V5z5sw5d+7c1atXKTbWS1NMJtPd3V31TxzHL1y4cOLE\nCYTQqlWrKLmrMpQHFdU+vWQHMlDQtskgnDhxQiwWu7i4mJubs9nsr776KigoqOt7wlu3biGE\nPvzwwwcPHrxwKSZqoGE92W9gUZleIJfLY2NjHzx4gGGYs7PzlClT2Gx218PKysqIN2ZUVVFR\nsWnTJgcHB/WxoxKJZMuWLWVlZTNmzOjaJ6QDhUKRmpqqUCjGjBmjGtFx+fLl8PBwY2PjY8eO\nqVdhSUlJRkZGlJwDQOc8KJXKffv23bp1y87O7ll9QsrXD/X19WvXrpVIJObm5nPnzp01a5Yq\nD/v27YuNjQ0PDzc3Nyc3yAEiPT39/Pnz9+/fxzBs+fLlCxcuJDui3kfP8qBQKBgMhno7WFNT\nc/jw4bS0NDabPXXq1Lfeeoue9UNX9GwyiNX4VqxYoaure+DAAdWMm8ePHwcFBTU2Ni5ZsuT1\n11/HMIxIhUAgOHbsGLkxk4UO9WR/gkVleqqiomLLli3Xr18vLi4uKipKSkq6devWyJEjO83z\njo2NDQ4O1tPTo95qYCo6OjopKSmZmZnFxcWTJ08mHvqqlkLOzMwUi8Xu7u506xMyGAxLS0sr\nKytizjfxQIsY37JmzRobGxv1gy0tLak6JZqeeZBIJEeOHAkPDxeLxa2trRKJJCMjo+saM5Sv\nH0QiUXNz86xZszAMe/ToUXJyckRERE1NjVAoNDQ0NDMzi4qKYrPZY8aMITtS8tXX1+/atauo\nqEgoFH722We+vr5kR9T76FkeiPn2mZmZqnbwhfv0EihfPzwVDZsM9dX4/Pz81Mu/kZGRm5tb\nQkJCamrqlStXIiIiiDGi7777rq2tLXkhk4YO9WQ/gw5hjzQ0NGzatKmiosLCwmLRokXu7u7t\n7e3FxcW3bt166aWX1J9upqamZmRkjBw5klhjk5JYLJa3t3d2djb0CZ8lPT39+++/v379OoZh\nK1as8PPzIzsictAkD2Kx+JNPPnnw4AGfz/fx8Rk1apRYLBaJRF37hNSuH+rr6zdt2nT9+vUZ\nM2ZMmzbN39/f3Ny8qqoqJSXlypUrOTk51tbWVVVVWVlZ8+fPVx89SE8cDmfixInOzs6rV6+2\nsLAgO5zeR9vy0NTU9Pvvv6u3g93ZpxdRvX7oDpo0GQqF4vbt25mZmW1tbS+//HKnvSWNjIwm\nT55cXFxcWlra2tqqq6v7zjvvzJ49m6xoyUX5erL/wZDRHjl06NDVq1cdHR2//PJLDodDfHj+\n/Pnjx48bGBgcOnSIWPmKkJOT052ltAc7qVS6ffv2hw8furu7P3Xs6NatW9WHgNNHfX39Z599\nVllZKRQKP/jgg7Fjx5IdETnok4ddu3YlJCQ4OTnt2LGDGPkjk8n27dsXFxfXdewoheuH7777\n7vr16y4uLsHBwerD6XNzc69cuRIXF9fR0cFgMJRK5Weffebl5UViqKAf0Lk8dJpD0f19eilc\nP7wQfZoMpFZCrK2t9+/f/9RJcU+ePKmtrbW3t1e/wwSgh+ANYY+EhobKZLKgoCD1l4GjRo0q\nKyvLy8tjMBjqb/zNzMzIiLEP4Th+//79lJSUxsbGIUOGEI9yn/+ecMiQIbR9sw8PtAg0yYNC\nodi/f79SqdyxY4dqLBOTyZw4cWJKSkphYWGn94TUqx8QQmKxmMvlhoWFGRoa7tq1S/XUjCAQ\nCCZOnOjn56evr19eXt7S0tLQ0DB9+nSyogV9DcpDp/Ey3d+nl5L1QzfRpMkgqEqISCSqqanx\n8PDoOqLK0NDQwsLiqWtVAKA16BBqD8fx48ePI4QCAwM7PcUxMjK6ceOGVCql6sAGhFB1dfX2\n7dvPnz+fmpp669atO3fuODo6Eje+z+kTUmDOd0/weDwrKyvajphVoUMe5HL56dOnWSzWu+++\nq/45g8HgcDgJCQnPmk84SMnl8ra2NvW/5TnzYdRxOJxRo0YFBARIJBLiJtjY2Li/ogZ9BcrD\ns6j3CVtaWtzd3Z2cnNQPeFafkM7o0GSoqEpIVlYWtWfZ5ObmmpqaEn+dSCT65ptv3NzcoKNL\nFuqMzu9/GIYRD6vU98MhEA8+W1tbSQirXxCTJ/Pz842NjRctWhQQEFBVVRUUFJSWlkYcQOxF\n4ezsnJSUtGvXrq77EAJAbbq6uhYWFnK5vKSkpNOviBvcCRMmFBYWxsXFkRBcb1MoFLt37966\ndav6Xos8Ho/H4924caO2tvaFq4FjGDZr1iyEUHR0dN/GCvoelIfng316gTocxzvN3lKVkBs3\nbnz33XeUnNuVlpa2efPmffv24TguEomCgoLS09N//fVXsuOiL3hD2CMymSwjI+Px48e+vr7q\nLdzFixdzc3NdXFy8vb1JDK/v7N69u7Cw0NnZedeuXe7u7tXV1cnJyXK5PD4+3t7enugnq78n\ntLa2Hj58ONlRA9Cv5HJ5RkZGaWmpj4+P+toYly5dys/P//zzz0ePHu3j40NegL0pJSUlPT1d\n/Z2n6jl3U1NTXV3d7Nmzn79ASEdHx+XLl+Vy+Zw5c/or6r4lFosPHz78888/JyUl8fl8Wm0b\nAOXh+VTZIOaDdX0L5OLi4uLiMmXKFLIiBL1OoVBgGNZp05Fvv/02NDT04sWLNTU1zs7Oqpfq\nlF+Nj8/np6Wlpaenl5SUnD17ViKRuLq6rlu3jsWCDdLJAR3CHnFwcEhLSysoKHjw4IGrq6ue\nnh6O45GRkSdPnsQwbM2aNZ02n6CG3Nzc48ePCwSCXbt2GRgYXLt27fDhwziOT5s2raCgoGuf\n0MLCgrbzBgGdOTo6pqWl5ebmFhQUjB07lhg4cPXq1VOnThkZGb3++uvW1tZkx9g7MAzz9PSs\nqKh4Vh/gOfNhCEql8uDBg6Wlpc7OztR4jlZfX79hw4aHDx82NTVVVlbevn27vr7ezc2tUwbK\nysoMDAzICrKPQHnojhfe8VNvG0Y602LTEfUSYm9vT7EnSmw2e/Lkyenp6dnZ2VKp1NXVddu2\nbc8ZL0rJqnJAgQ5hjzAYDE9Pz8zMzLy8vIiIiPj4+DNnzhBjwFauXEnVZuzmzZtZWVlr1661\ns7NLSEgIDQ3FcXzVqlXLly9/8uRJSUlJpz7hiBEjyA65l9H5wb86yMPzqeqHnJycyMjI1NTU\nc+fOxcbGIoTeffdde3t7sgPsTS/sAzx/PkxBQcFPP/3E5XI/++wzarT6R44cefDggZ2d3Zo1\na8aPH5+fn5+VlVVZWenp6anKAIX3l4Py0B2UfwvUCZ2bDO02HaH2anwSiSQiIkIqlSKEnJyc\nvLy8nlX+KVxVDhzQIewpDofj4+Mjk8mKi4tra2ulUqmJicmHH35Iyc1hRCKRWCyeNGlSa2ur\nv79/c3Pztm3bZDLZ0qVLFy1ahBAqKSkpLy+XSqVxcXFTpkyh5Gz4bj74R1R/oAV56A5V/VBY\nWFhZWdnc3Mzj8VatWkXJ+uGFfYDn3PWampqOGDFizpw5lNlkOSwszMDAYO/evcOHD7exsfHx\n8UlLS8vMzFTvE1J7fzkoD91Bnz4hnd+ZI4Q4HE6n/9Hh4eFcLnfPnj1CoZDP53t4eKCnLSZE\npdX4FApFSkrK0KFDif/purq62dnZAoGAz+dnZGR0el6mjtpV5QABHcJewGKxXn755fnz53t6\nevr7+y9fvpyS8+VUuwl7eHj4+voyGIzIyMjk5ORx48atWbOGOOaXX37hcDirV68eOnSop6cn\nuQH3ke48+Ec0eKBF5zxotDaaqn4YP378jBkzVq5c2Wm7YWqQSCRHjhwJDw8Xi8Wtra2d1lDt\nzl2vpaWlan8OCvjtt9/8/f1Va2kSt4Od+oSjRo0aM2bMtGnTyA21L0B56D5qjwxUofk7c9SD\nTUeoITY2NiQk5OrVq6pLnslkTpo0ycfHZ8qUKRkZGenp6Z3KQ2JiooGBAZvNpnBVOXBAh7DX\nsFgsU1NTIyMjqj7eCw8Pz87OHjly5Ny5c4lZvzExMYWFha+99hoxKDQyMjIqKsrJyWnJkiWj\nR48mO96+0p0H/4gGD7Rom4e0tLTg4ODy8nJPT8+ysrKgoKDi4uK2trYJEyY85ywWi2VmZmZm\nZkbJGfNisfiTTz558OABn8/38fEZNWqUWCwWiUTP6gNQ9a5XIpH88MMPv/zyC7E76+jRo9Xv\na5/aJ6Tk/nJQHjRF7ZGBBHhnjui66YhCoTh8+PCJEydaWlomTpz4yiuvqO/Ny2QyifmEqj6h\nu7s7g8G4efPm/7d370FRnecfwM/eWER0QblWWXEpoCiKIMT1guB9iXXqDBkndSIYS2wqzBQ7\nU1MuJp16YaydSNsoaZyYgFNI03bsKMEig0i8jIqE3SwXUUEKC0KBZSGGZVnc3x9nfmdOdmFZ\nEDnse76f/1xx5tnj8r77nPO8z3P69OmqqqrNmzfTGyi374J4SAhhfGNNE+7v7797965er/f2\n9r5y5UphYaFAIHj33XfJPgrvyI1/iqKIv6HF2+uA3mi2cnNzGxsblyxZcurUqaioqJUrV+7Y\nsUOn06nVatscgNRvvXq9/vDhw1qt1mAwtLe3Dw4OGgyGrVu3sntpsn9NFi9eHBAQwGHArw4+\nD5NAUmXgqHj+zJzBbrdru0RQrJzQx8fHKl10Urm5uWVlZa6urunp6Xv37p03b57tz7BzQrrT\nTFFRkcViSUhIiIiImP6YeQgJIYzDzjThRYsW1dfX19XVVVRUNDY2UhSVnJy8ceNG7oJ9VSZx\n45+iKPJuaOE6UBPvjUa8kZGR3NzcFy9e/O53v2Pf91UqlVVVVU+ePLHKAUj91puXl1dXV6dQ\nKFJTU1etWtXY2Nje3m47UYD+NfHz8yM1C8LnARh4Zs6m1+ufP3/u5ubGq6Ejd+7cKSgoEIvF\nx44dY9fH2pJKpRs2bHj06FFdXd3Tp0+FQmFycvIbb7wxbaHyHBJCGMfIyEhlZaVarR4cHIyM\njGQffxIKhevXrxeLxcPDwwqFIiUlhcgbe7jxT8N1YDjeG40PzGZzUVGRWCx+55132K8LhUJX\nV9c7d+5YnR9zdmazeXBwkP1e6BqKvLw8uiIuMDBQoVBs3LhxrDNyrq6uwcHBXMQ+Hfj2eaBN\n6FwxT2DLYPT29ubm5n700Uc3b96kC0H5M3Tk3LlzXV1de/bssb0F1traWl9fbzKZPD096Vdc\nXFzi4+PlcrlcLk9JSVEqldMeL38hIbTG57bIo7I/TVgkEi1fvnzr1q2xsbH0nAny4MY/DdeB\n4XhvND4QiUQVFRX9/f1KpdLDw4P9VwaD4fr169HR0Vqt1s/Pj4BJG/QwseLiYiafYWoourq6\nVCoVU0PBn+6RVnj1eaBN7lwx8bBl0Do6Oo4cOdLY2Dh37tydO3cGBQW5ublRvFkiPv30U5PJ\ndODAAXalaENDQ05OTkFBwddff3316tXGxsbo6Gh6RRUIBHK5PDw83Gr1gFcNCeEPoJP+qByf\nJkwY3Pin4Tqw6fV6o9G4ZcsWR3qjcRvqtDGbzTU1Na2trXFxcewbRv/+978fPXr0wQcfLF++\nPC4ujrsApwadDd67d294eJjJdpgaiu+//z46OppdEceTL3y2ePJ5YOBcsRVsGQyTyZSRkdHZ\n2blkyZITJ05ERUXR2SCND0tEeXl5f39/SEhIUFAQRVFGo/HChQtnz57t6elZsGAB3XGqtbX1\n8ePHRJaYOREkhD/A50769jk4TZgkuPFPw3VgsMt+1q5dK5PJHOmNxnXU0yEkJKS6urqhoeHx\n48cRERF036mSkpLCwkIPD4+f/exncrmc6xhfFpMNuru7Hzt2jJmPZ79FBD97afLh88A20XPF\nZN9QxpbBVlpaWl5e7ufnl5OTw/ynq9Xq0tJSnU6nUCjc3NyIXyIePHig0WgsFkt9ff2ZM2dq\nampkMtmhQ4dSU1NjY2OVSmVZWVl7e/uyZct8fX25Dpa/kBD+AG876TuCb6s5bvzTcB1oY5X9\noDcaRVFCoXDNmjVqtbqurq64uPjBgwdffvllRUUFRVHvvPMOAZWBVtkgPWiHwfwitLS02FbE\n8bCXJvGfB1uOnysm/oYytgy24uLi5ubmPXv20F8X29racnJyvvjii4cPH96/f7+urm7Tpk1k\nLxHBwcG9vb0PHz7UaDR0N4rY2Njs7GymgapMJvv22287OzuDgoJI/aVwCkgIf4C3nfQdxKvV\nHDf+abgO1HhlP+iNRlGUq6trXFycyWR68uTJs2fPvvvuOzc3t5///Ofbt2/nOrSXxWSDEonk\n5MmTdOGTFftrIw97aRL8eaCNjIxUVVX96Ec/ov+jHT9XTPwNZWwZbG1tbWq1WiQSKRSK4uLi\nDz/8cN68eZmZmcnJyTdv3mxqalq9evX8+fMJXiIEAkFMTExoaKhMJlu9evXBgwdVKhUzvYyi\nKLPZnJ+fbzQaVSrVwoULOQyV5wQWi4XrGDim1+svXrzY2Njo7e3d3Ny8e/fuXbt2sX/AYDBk\nZWW1tLTExcWlp6cTnAI5SK/XZ2Rk6HS6rKysmJgYrsN5tZg3u2XLlrS0NKv/fb1ef/v27ddf\nf52r8KYNT66D2WymKMq2zrOkpOTcuXN+fn5nzpxhUkG1Wq1Wq728vLZv3y4SiSwWy61bt1pb\nW5VKZWBg4DRHPnMYj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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 5 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 5 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
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ajI6Ojo2NDQ8PNzc3Hzdu\nnFwuT0hIeOedd/z8/CZNmqT/NauqqgghNjY2mpfp6ekcDsfHx0cb4OXlxefzMzIy9AxoQWr3\nzlfcOWJQF2VtKcm7RgiRHF5BDq/Qs5dowrf8Ppi/9zQeppaUF+hVtBNCyh8+iix7UPOoJb+6\nPF+v7ozaVFZtOfHdZUnVo2cgO/Z1u/DrDUKItFrhMdL52JZHH+sd3K31vNMIoJAptf+iGkHR\nFM2hKwprBCK+tFZOEULRlLRWTnNoiqb1uYJCpmIjXwAAgFaItYJw06ZNIpHol19+8fT0XLRo\n0axZszw8PC5cuKCZKXr//v0ePXp88803+heEeXl5Fy5cCAwM1BaEZWVlYrGYw+FoYyiKsrGx\nKS0t1TNAo7a2VqV69OFAJjPRFT6IrKZ5xmHktc0zUOvz4YVFjQcoZEqFVKk5vnsh+/tXY1VK\nNc2hw157YcC07pp2Lp/TUpbHYBhSWyGtq370I6OUqwh59ACktFpGcx5NFpVUSY2TH7RAg2b3\nGDS7hz6RDEN+XXPyetw9tYrHqBlzsbBLsNe8jaO0//AAAADg6bBWEF69ejUiImLMmDGEkHXr\n1g0bNmz16tXa5wa9vLymTZv266+/6nk1iUTy0Ucf8Xi8+uvByGQyHq/hR2c+n68t6nQGaCxY\nsCA9PV1z/Nxzz/n6+uqZVXMSDl/HK7qrZ7AyN1GetIu2cCQ8M3VNkXDAa7RVez378joMe9oc\nQYeN037LupHfoFGtUsdsSIjZkKB5KbIWbkhaTpnKyjKNoSgSvipYc1xXJftq7u9CS15NqYon\n4FQW1i76blwzzGuFtqauWpZ9s0BzHDCiY0VB9c3TmRQhdq5W/Sb5p13K0Zzy6OpsZiUwXpoA\nAAAtGGsf4AoKCrRzNTWP/Lm7u9cP8PDwqKys1OdSUql03bp1hYWFa9eudXb+Z8kNgUBQV1fX\nIFgulwuFQj0DNPr06ePp6ak5FgqFmmmupobrNZDrNVCfSPmNX+XJP4oidsuTvud2HKkuuy+7\n9K1l5B8cZ/+mThIa9/IPk2orHt0uS0/M/enNEyqFmubQo17q+/z4zpp2oQXfRKpBtUp98PPz\n+kQqZaqko6k0TcxsBDWlUgdvcdatgvdG7AwY0UGf2zVmFvyRS/s8c74sKysru3379uXLlx88\neMDlcv38/Dp37lx/ugEYxb0ruXvfj9e+VCnVhCEMIUVZ5Xve+ecR3IlvDgoY3sEYCTZUkFFW\nofdM8mfR/jkHK3vzZhgIAABaPdYKQqVSqb07x+fzCSFc7r8uzuVy9Sm9ZDLZ+++/n56e/s47\n73Tp0qX+KVtb2+zsbJVKpf2UxjBMeXm5v7+/ngEaK1b883xdYmLikSOGPapnUuRXd9fuf1E0\n9Wd+14nypO8JRZmP+5qolTXbh1osPsNx8DN2gq3ZeyN3VRbpNbNXpVTLJIpHK3BS5MiXf8Zt\nvcLl61tsfPznYp6wyW++qZTquK1XnqJj5pUCQkhted2pqCR94q0cRaZWED58+PD8+fNpaWk1\nNTVSqTQzMzMpKWnw4MFDhgxp8HsMmlm3IT7dhjz6qrE0t3LTjGiaS1OEUSvVS3+Y3M7Xzrjp\nPe70rqvn9txohoEWfjW25+jnmmEgAABo9Uzrs45cLv/ggw9SUlJWr14dENBwRTgfH5+kpKTM\nzMwOHR59E3z//n25XK69M6kzoPVRpB3XVIP/NFGU+YRv6/jmyozTKAiblG17S55Ad1EnrZEX\nZ1fYOFsILLkFaeXW7c05FLfkQaXIRmhpq98X/M1yC5GiSbd59jrDpBWqohsS134WNI8qya14\neFLpPcnMwlKkkKgfXqpx6WfBFeq4SSg0N62pfSqV6vLly7m5uT4+PhkZGbW1tfb29i4uLvHx\n8e3atevWDQvkNKHCzLK8u8X6RFaXSmK/uujoaS0pq2QYlaufw2eTfh71cj8bZ0t9urfvaO/c\nLNVjpwGeQksD/oUzaubk9kSbdpZBYZ10R9fj7GNrYGoAAABPxmZBuHfv3rt37xJCJBIJIeTr\nr7+OiYnRnr1161bj3RUKxYcffnjr1q033nijd+/ejwcMGDAgOjr68OHDr732mqbl8OHDFEUN\nGDBAz4DWRzRtzxNaKcpszMZmz6XNWf79RJ0xNWWSNwdsm7wmJGR24IPsBx8Njg5dFdh/RO+b\npzK2v3Ro8Xfj3bs4NUOq+qA5dK8RXXTHEUIiHv23Ik+y/2TigPG9Ht38nKBX78cf9DWuwsLC\nhISEXr161W/kcDiurq7Z2dkoCJvUzT8y9n98Vv/46jJJaNcjHI7qyPlxhJDf15/Rs+O4lf1H\nNktBGDC8g0GTV1VK9cntiXau4glv6PWMAAAAAOvYLAivXLly5co/U87i4uIM6r5169bk5OSO\nHTvm5OT89ttv2vaBAwe2a9eOEOLu7j5q1KjY2FiFQuHv75+SkpKQkDBy5EjtA4E6A1o3fu8X\nOY6GfccMTc3C1vzNg7MevzXRbajPmuPzbF2sjJLVE9E0HRQUZFAXZYBKpLDt07drE6XUPGpr\na83MzKjHHuU0Nzevra1lGObxU8CWDr1d9SmEck/HShTWHYb2o2hKdP0kxSgnvDGQYZj0Py6Z\n0SWug8fqvIJvb2xeD9BqKaTKnJQi70B9l9MDgAZYKwgTExOf8QqFhYWEkLS0tLS0tPrt3t7e\nmoKQEBIZGWlnZxcXF3f58mU7O7tZs2aFh4fXD9YZ0Irxu002dgrwBNpqkKIooQ1HYP7oh87B\nw9p4SbGDy+P0m9yyq0FCCI/HUyqVj7crFAoul4tqsEl5dm/n2b2dzrA6t9/l17+3nDKWtvO9\n8ZY5pZb3W9RbXXa/T/lGfteJZmOeMKMEANqO9MTcH1ed0LkXFAD8F9YKwgYTrp7C+++/rzOG\npulJkyY1spmhzgAAY6FoqvdKZ5GdUHcoNCMHB4e6ujqJRGJu/q9HOktKShqsawXGYhb6sbqm\nqHpriOWi02IHEVHz1OXZ1duHcN37mI361NjZAYAR5KQUpV58MHRBL0KImmHUfy9bWFUiORWV\nOOGNgRSNr/MA9IUtfQGgTROJRLNnz757925NzaM1YxmGyc3N7dixY9euLf7+ZytB0aLJ33N9\nh1RvDXFoRzu041VvC+G6Bomm/Uxo01oaDQCaB4/POf7NpQYPIVeVSDbN+C0/vZRgcgeAIdj8\nX+mxY8domh4xYgQhpKioaP78+fXPduvW7cMPP2RxOAAAVgQGBlIUde/evbNnz1ZXVyuVypEj\nR/bp08fausVP6zVx0tMfSc983GgIQ9Qq7SGjksuTfyKEEJpWVBdWvCt+dIriNP75TzBgpdnQ\nNc+eMAA0tZoyyUn9NjHqNsT3zO5raZdz7FzF0hrZb+v+SDx8V2jBd/axjdlwTmd3OxergTMa\nLmgP0DaxVhDeuHFj9OjR3377realRCKJjY2tHxAbGztx4sSePXuyNSIAACtomu7Zs2fnzp2t\nrKzu3bs3ZswYLy8vmsYEiianfJjMSKuepqdaxagl+oer8q4+zSgA0OxqyqUGbYqbfbMg+2YB\nIeTM7mvEkE1xvQPboyAE0GCtINyxY4eDg8O8efPqN+7cuXPkyJGEEKVS2a1btx9++AEFIbQa\nBw8e1OywoqeKiorMzMzCwsIbNwzbt3rKlCkcjr672MNTMzMzc3BwqKystLOzQzXYPAS9X6TN\n9N1Pj5HVKO4cJhRFCEXUSl6nsZRQ33V6eV3GP22OzaGuWmbsFABMhZ2r1eqDsxoJUKvUSbGp\naqVa87KmTJIUm8ao1WJHUcCwDjT30W9vR0+bxtcdFZib1h5IAEbEWkF45syZYcOG8fn8+o3W\n1tbOzs6a47Fjx547p/sOPkBL4eDgIJMZ8DHO2dnZz8/vKQbCQpfNhlEzsiqV7jhgSZF55yyn\n2fpE8iQFHrdektoFKQXWlFpJqRUWafFZ/b6Si/TaT8JD5GHK+05Ia+XGTgEas+OVIxFrh1jY\nmBk7kTaBJ+C6+ze2Q69MoqguvaaUP/pdrZSrKJowakJzOdXlddowBw/rxq8DAFqsFYT379+f\nOLGxfbo9PT3r71MP0NL169fP2CkAy/L/qrgZVTI+wth5tBnV1dUPHjzQGWYmLwlKf7/M3OeG\nwzynv77iENXDriv9a2pczy1N7PBOLV/3Zz5ra2tXVxMqCb+cFd0l2Hvown+tzq1UqL5fEevW\n2TF0WR9jJQZajJrRLlOZdOTuyCXPawrC+u3QFAoyytYN//4pOpY/rCp/+M8U9OSjqdHvxTcS\n7x3Y/v/2Tn+KgQBaH9YKQqlUyuP9c/Pdw8OjurrazOyfr9PMzc3r6uqe1BUAwGiKH1QUpJd2\nHexDCFEp1cyjWUhEJlEkHbn7whQsNNqEOnTo4O3trTNMtm8B8RviMPF7H5qTWrCHUiv6TJxE\nmHDZgRdDVJcEE3frvIKpTboe/XK/zfP3KeXKYS8+2kRRqVBtX36oIL0sYs1g4+YGhJDaCunb\nwdte3DKuU3+P+u0FGWVfzPwtcnOYT08XY+XW6gnMeX4veOiOI0SlUGX/VcgTcK2dLQozytw6\nOz74q1DsaOHoZaNP93Z/7xIMAKwVhLa2tnl5edqXFEVZWFjUD8jNzbWzw88eADQTtYqR1uie\n01uYWbZt6aHw1cHPj++skKoIw9RVyRhp9Y6XDstlyoDhHXTO2KW5tFDE1xEET8LhcPQp1fhT\nfyDUo+eCaJqmCK15PIE/dTdh1NpTLYhvkOvy7ydunr9PrWYIIYQhUS8dLkgve3VPhNjJQldv\neCZlD6vUKkZn2OB5vb558cCMD4b5BrkSQioKaqpKJTtfi+3Yx83KwaIkp1LnFexcrHAv8SnY\ntLN8ZfdknWHVpZKN03/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zZs2LBhwwaG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\n12m1WqFQOHHiRPwNValU5ubmqlQqDMP8/f3nzp070CIMslc0qH5jQjabzeV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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
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Rubm5lZWVhoZmQqGQ1FpXv+Dq6xv/fvNZVkKtOqtJGZjwVH/oZiUQ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L6WcjmjkR0FEjmjkR0NFGoHacRiKR9O/f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\ncePGlStXDLPIeHp6WhgT0n05mvrDgiWGh4crKyvb29tXrFhhuLP6p59+Egq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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
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XVpKO3g8Y11WvNvuB149B4AeyrLO1O4eZLDirbqxP+FxyfYdxeEIz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\n1Dt8S0nat4m8++EngKo5IYPBCAgIePfdd+Pi4hISElavXk3AQvHjncgjY9w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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
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nOWVR3//BdRXM6DKipUvr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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
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XlEBKh33jhSEAwWrYOkkJbqDHbv3j25XK76LoZh3d3d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\nm5ubv74mBMNILpf39fUZGhoqX3n9FjUlJpM5Zcq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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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HrpbMtJryWnbzCnW1OFzq1fMAAEA1aAibqcEzuncfod53MCi5N0dS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\nWL6rN28yfSGYwQZZUlJSdnY2g8HYunXrypUrR40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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
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XpXaSh/fwE0II6div/Vu/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\nu0mZhu/le4YYuornQHe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KWllZSUvHF24201IZgYSMvvBIfKKJ6enp79+/dnZWV5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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 6 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
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w8enDp1KiUlBSG0YcMGBoNBXrCaDQYYRFJepPrmm2/k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\n544dOx4+fNjd3d3a2nr79u3Ozk4/Pz/lPgw3H4y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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
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Mw/r7+0tKSigUiq+vL1z7wGQDDSHQbkwm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\nezo6Omx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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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yZnvLZmoJM7tzcDAAD8Q/6ZNlxdXSdNmnTkyJGYmJhhw4b99NNPTz311KJFi2Q/\nEADczzMDGwx+t8PaubuEENcupF1PSJvy/SAXL0elcwEAAFiWUpwksHbt2o0bNz548ODhw4dv\n375degcCAJNdqw5HbTplflnJzUHKTM66bdRUr7Rs3AZTo41O89IXvVyqOCmUEQAAwIKUSkG4\nf//+VatWrVu3Lisrq1WrVqGhoYMGDSqNAwFAQU+0rGnnqDNtG/ON4Wui+zdcHXHmWbdmTWvU\nvTttr8ZG7ehWomVyAAAAKjw5C8IrV66sWbPm66+/PnPmjJeX14QJE8aMGVO3bl0ZDwEAD1Cr\nfpVa9avoD69WBfZc+UaEIc+gURldPG33rzs+9rOe9Xzi1S7emir1lY4JAABgKWR7hrB37941\na9acOXOmv7//L7/8kpSUtGjRIqpBAGUvN/qHS+89nZGUPPqTHkIIOyfd4Hc7HPpgXubqnsZb\niUqnA+4hSUonAABYL9muEG7atMnOzq5Pnz7e3t4HDhw4cOBAkd2YXQZAafvx8NhntO9O6LDS\n4NjdtPRNszqn6jRft+HIwDa96z2hcDqgMEOeUekIAADrJectozk5OWvXrn1wHwpCoGLIO7cz\nP+5PpVMUrV3g1ap+HcW5MPUPTzvb32lgty3rp+W2dbp2rOlod25RdpqFrjxh3+ldodEpnQJl\nYe8PsXVa1ari7256ab4+eGJPvMZGUzfYR6lgKEuGfGNull7pFBWKnZNOrZF//nygwpOtIIyK\nipJrVwAsX/7FfTl7PlA6RdG8hDDeuLvtoBMO4oIwirxTW5yFEELkKBfsweyem6WiILQOKXE3\nf//iwOs/DPL0/mcplOjfz6ye9rvpPmdYg7//uvDlS78pnaJCef3HQU88XVPpFED5I1tB2KxZ\nM7l2BcDyqe3dbWq1UDpFYVJGivFO6j8vDXnCoBdCqHQOQqhMjSqVUHs+ITRaZSI+gEqldAKU\nkednP2c0SIsHr331mz6mC9bR286unvb70AWdmnQOVDgcyoprFaem3eoonaJ4x/+M02hU9doF\nKB2keJXcmUEaeBSluA4hgArsxvkEh0uRSqcoKUl/559tIYzJMQqGuR9DnsHG8qpUlAaVSrww\nr4MQYumoX99sKySjtHrq1qELOrXsyxS4VqRWUJVxn/dUOkXxtg951qhx6vr5FKWDACgt8hSE\n33zzTZcuXapWrVpsT4PB8N1333Xv3r1y5cqyHBqAIo6m9ti1yU3pFPdVt/rffeYpdEQAACAA\nSURBVJr+uuVo72cC9h2Kb9Wg5lF7m+zv/jsyO89CHyAUQiycbcuf6Cq85DM3/t4Tb9p2r+7s\n7u0qhBBGfaOOtTOuZe1YHimEUGvUrQYEObraKZgTVs6QclTtUkPl4CGEsLPJyVdpTO3G21el\nrJuaKvUUTQdAZvJ8/Rg1alR4eHhJCsK8vLxRo0YdOHCAghAo15r0aFi1jrfSKYrmeH1r5dO/\n3ajzf43a93A93Ktli9ZZnrNdjo9/xXvzlUarjTbOSgcsmtaOerDiSzl349T+BPPLnJw8IYSj\n7nbq5cysW3efb1WpVUHt/CgIoaDc/Z/lJ0ZWGvenysnL3GhMT8pc3l5bt4dDzyUKZgMgO9m+\nf5w8edLOrvjfXno9E2oBFUG1QM9qgZ5KpyhaxieD7AZ94954sBAi44ytW+NquiZNpM67s9b0\nrVvtuG3z0UoHhPVq1uPJZj2ezAlfqK3T5ehRxzXTNokgoVarbibemvLDII/b21U6R2293krH\nRFnIv3QwZ8cspVPch2SUMq+kf+CvqdHU3+OkJDSZX7UxJB9RaR2MV47dDu2odL6i2XdfpKnW\nSOkUQPkjW0H48ssvy7UrAHgczlNi721U6Zycxu4s+zDAvST97bRl7bfvGjPo3eEiSqjUqsad\nnwh/47Uu9dZXGrlJ6XQoI1LW9bxzu5ROUYz8+AgnnRBC5F+IEEJI+jvGuN0KZ7o/u+w0pSMA\n5ZI8BeHnn3/+UP39/PxkOS4UJ925aXrGALBMusaDNdWbKJ0C+JdTxhGJJyNfem5lpaf6ZUUJ\nIUTfHgm3c9eFHRvdXduiitLxUDa0T3RxnZtafL+yJeXnGq+fuvvCkJe9c27exQNCpdL6h9g/\nN0uoTQ8TqjRedS1w3VSVbSWlIwDlkjwF4eTJk2XZD8qdjE8aOQ5db+PTSukgQNHs2r+ldASg\nsJhtZ4OGf+rg8MOdb3sIIXSanOyNLzsP/9nxV+e/98RX8XtK6YAoExqtyt7ipubKi/4ua90I\nIUnmFpVKCCHlx+/NjP/fnaIqtdOwX7RBfRVJCEB2zGGAhyRJ+qM/6RoMNC3jJuXliPy7EyHk\nndupqVJf7Vxd0XwAYLnyLx3K+/u3IV2FEPuEUag96xgSD9nb3NY92c+QeKhXCyGEyN7+m23r\nV9ROXCms4KSsG/mXi7i/XVmqStWcxuzYsSLyZmJ6n1frG3e/ZchIkYRK6+ottXl/w5LjtepX\naTu8sRDCAu93tanxlAXW2IDloyDEQzLmZ++cqz+23mnouoJLe+dGfX0nbFKl8XsoCAHgfqTb\nV42p8f+8Nubfbc9N/6ddpRa5mYKCsKLLv3Tg9je9lE5RtGfUQvgIw0YhhFCrhBCSMT1RbB7R\nt7YQueJ2qMLx7qfS+HAb/3ZKpwDKHwpCCyVlXZdyM5VOUTTHF76789Ow26s6OfT9UkgGKTMl\nZ88H2TtnO/b6TO3k9a/vOpZDpVa7+SodAoC109brpa13twbIjfpaHzZJCKGXHFRJR5zG7rCp\n0VzRdChTao/adu2mK52iaFJupj7mx/Tb9vHpTappYw2SzVV9vdrusc5Oel2TwSqdk9IBi6Z2\nraV0BKBcoiC0UNl/zMyNtNQ/wQkhhDDcjEtfVFcIcfunoaaWrA0TFE30ICo7F9d5t5ROAQB3\nme6qcBj4/Z2fBuYanJxbjb0d2oma0KpovOrad/0/pVMUlp9nuHXltnrbCOEdomu74vA7u5uo\nb+Tk257Vjaz3UhvjzuE5t9KNz853r15JrVErHRaAPCgILZSNXxuhsrihVsq5JeVm3X2Rn5t/\nab+kzxZCpfFuqq5U9W67WvPPtuXQ2iudAADuyo1adSfsZaeh69QBnU0t9p3ni3z97VVdKr20\nm4XUoKCoTafW/OcPnU3zPINO+uB7IUSTZpKQxLnIxHldf1CrOtlo8vXvrnxpaa8mXZ5QOiwA\neVAQWihtg/7auj2UTlFY9uap+Zci/3mt1gqRLVQqKeuG4c5NU5tKa+vQ+wuVraXdT6JSOgAA\n3GW4dMhp6DptvV6G3Fxzo333j4TOIf/SIQpCKKhV/6BW/YNM2xk37nwybN3VjOq5+bY16lZ+\n7bvnndz46ypQAZViQWgwGDQaTentv2LL3vSahd8y+g/JaLyVULAhfaHF3cTPLaMALIdD/xVF\nttt3nFfGSYD7MVWD7tUqHTzUTqvV1Gpg/+nwddSEQIUkc0GYmpr66aefbtmy5cyZM1lZWY6O\njnXq1OnZs+drr73m5sZEwA9B7VVXG9hB6RT3Zcy4bLh+2qZqA8PVkxqvuoab51Vae021hhZ4\nm+tdWkelEwBA0XKNDFCwLOZqcPxXvac2+UKoxKSVfZeN+42aEKiQ5CwIjx492rlz56tXrwoh\nKlWq5O3tnZGRER0dHR0dvXLlyj/++KNBgwYyHq5iswuZKkKmKp2iaKa5EJyG/aqt3+fWPE/7\nnp+oPZ/IXN5O2DoXWosCAFCsfMlW6QjAv/w870+PGs7jl/W20d291Utnr524ou+ysRvCPtw7\nbGFnZeMBkJds13Oys7P79+9//fr1qVOnxsXFZWRkJCUlZWRknD17dsqUKSkpKQMGDMgt8LAE\nyitDXvbvbzoNWaut38fcpnbxrjR+j/HqybyzfygYDQAAPL5h8ztNXN7HXA2a2DpoX/l2wIC3\n2yuVCkApke0K4c8//3z+/PmlS5dOmjSpYHtgYOCSJUv8/Pxee+219evXDxs2TK4jQhkareuc\nm/c2q128nd88U/ZxAACAvOyd/7lqrdaoNdq71w9stBobLdNDABWNbFcIN23a5OvrO2FC0SvR\nTZ48uVatWhs3bpTrcLAQuoYD1e7+SqcAAAClQmdnY+/EXc1ARSbbFcJjx44999xzanXRFaZa\nre7QocPevXvlOhwshEPfL5WOAAAAAOARyXaF8OrVqz4+Pg/oUKtWrWvXrsl1OAAAAADAY5Kt\nIMzKyrK3f9A0xI6OjpmZmXIdDgAAAADwmGQrCCVJkqUPAAAAAKBsyLkO4fr160+fPn2/d48f\nPy7jsQAAAMqvuKik9e+HK52ieHfSc3Ju5y7s/Z3SQYo3dH6nWkFVlE4BlD9yFoSRkZGRkZEy\n7hAAAKBCys3Ou5GYrnSK4tk66oQQ5SKqPidf6QhQgCHlmJR13ab2c0oHKcdkKwijoqLk2hWE\nEGEfRRzZet/LrXhYdk66t7eMUDoFAAB31W/j93H0ZKVTAOWe/vh64/WzFISPQ7aCsFmzZnLt\nCuVFvt5w6+pt+0q2jq52SmcBAACAVcjd94lkNNi1mVaoXX/8F0PCAfseHyuSqvyS85ZRyKjP\nmyF93gxROkUx4qMvfzTwx6e61xk6v5PSWQAAAGAVNDWa3V7VVeTn2j0709yoP7Yu6+cXHQeu\nVjBYOVW6BWFubu6pU6cyMjIaNmzo6upaqseCUrIzcpWOAAAAAGth4xvsNOaP2193FZLB1KI/\n/kvWzy869l+pazxY2WzlkZwF4bZt27755hudTjdu3Lg2bdrs2LFj9OjRycnJQgidTjdr1qx3\n3nnnAR9PSkras2fPkSNHUlJSbGxsatas2adPn6effrpgH6PRGBYWtn379uvXr3t6enbq1Klf\nv35qtbrkHfCYDPnGt0OWD1vYOaidf8H2jOtZn41Y3+fNNkHt/e/3WQAAAJQLhpSjmcvbK52i\nGNk75giVRgihP7ZOpXO8s+m1O5teUzrUfTn0X6FrMEDpFEWQrSD866+/unfvblppcN26dVu3\nbu3Xr5+Dg0Pv3r31en1ERMSsWbOefPLJAQPu+6+wbt26ffv2NWrUqEmTJrm5ufv27Zs/f/7g\nwYMHD/6n0A8NDd2yZcszzzzTq1evkydPrlmz5saNGxMmTCh5BzwmjY262+RWKyZtemlZLwfn\nu48OZlzPWjJsnWcNlydb+ygbDwBMrl+6lXjiqtIpiiHl5/kLYcw3Rv9+Ruksxavfzt/WQat0\nCgBlRaVR2bspHeIeBr2UfUuI/61tbmMn8rOFEMLG1vTu3XZbZ5XWXol8D6LS6JSOUDTZCsIl\nS5Y4Ojr+9NNPvr6+48ePHz58uI+Pz/79+013il64cKFJkybLli17QEHYtm3bMWPGuLi4mF4O\nHjx4ypQp69ev7927t4ODgxAiMTFx69atbdu2nTZtmhCie/fuWq1227ZtXbt29fHxKUkHyKLN\n0MZqjXrFpE09XmsthMjX538yfL1HdeeXlvWy0WmUTgcAQghxal/CT7N2Kp2iGBqV4b3+Qp+d\nt/KVzUpnKd68P8d4+Vret0MApUQySNlpSoe4h2SUjAXWF/nfLaPCaJCEytys0mdJ+Tllm6x4\nkrletTCyFYRHjhwZNGhQjx49hBDz5s3r2LHjW2+9ZX5u0M/Pb/DgwWvXrn3AHp566qmCL52c\nnFq2bLlp06YrV674+/sLISIiIiRJ6tmzp7lPr169du/evXfv3uHDh5ekA+QS/EJDIcTP8/4U\nQpw9lOjfxHv8V721tsxRBMBSPNGihuXPd6WS8kXsDC9fN8uPKoSo5OGgdAQAZUijU7tb9HNA\nUnaaMfWCys5VSAZJf0fl4K6uVE3pUA+i0jkpHaFosn2Dv3LlSkBAgGnbVL/VqlWrYAcfH5/0\n9Idb1TQjI0MI4eZ29++RcXFxGo3GfBQhhJ+fn06nO3/+fAk74DGtnbPr778umF9qbW3y9Ybc\nrLwr8Tff7Xx3TidbB+2bvwzltiIAyqpa26NqbQ+lUxTHkJcWK5wrO5r+ygYAlkPjVdf51cNK\np7gv/fFfstYOcxz0reHGWeP1s7atX739dVddywl2z81SOlr5I1tBmJ+fr9XerQF0Op0Qwsbm\nXzu3sbExPWFYQsnJyfv372/atKm5IExNTXVxcdFo/rkpUaVSubm53bx5s4QdTFauXHn9+nXT\nNvPNPJSQwY2eeLqmaTs7M3fjx/tM2091e9KnQRXTts5Bq7OnGgQAAECpyPs7LGvtMMcBobom\nw7J3zBJC2Pi2dhq19fbX3YTW4d71CfFgFnqP3507dxYuXKjVagvOB5Obm2uuOc10Ol1ubm4J\nO5j8+eefcXFxpu06derUrl1b5vQVl/eTlb2frCyEyLhx55Nh6yrXdM28eadmfa/wb6PHftaz\nYYeAYvcAAGVDf/zXnPCFSqcojiQJIfKTYzI+a6Z0lOI5vfib2rWm0ikAQEj6LMdB3+kaDizY\naOMX4jTmj/xLB5VKVX7JWRCuX7/+9OnTQog7d+4IIT7//POwsDDzu8ePHy/hfnJycubNm3f1\n6tW5c+dWrVrV3G5ra5udnV2os16vt7OzK2EHkwULFphLxPj4+MjIyBIGg4mpGnSvVqnThBZL\nhvzsWdM1ZHDj0Fc3UxMCsBxS1nVD8hGlU5SM/nb5iGp5MzQAsE66JkMLvFIJ1d3pZGx8W9v4\ntlYkUrkmZ0EYGRlZsLjasWPHI+wkNzf3vffei4uLmzVrVv369Qu+5e7unpCQYDAYzDeFSpKU\nlpYWFBRUwg4mpkccTW7fvv0IIa2Z0WBcMvRnz5ou45f1vvS/Kd1bD2pgyDeEvrp56k+DfBtZ\n9OO8AKyEbcsJti1ZcwgAKjjb5mOknIebpgSFyFYQRkVFPf5O9Hr9+++/f/Lkybfeeqtx48aF\n3g0ICDh8+HB8fHxgYKCp5cKFC3q93jyLTLEd8PhUanWncc2b96pbaIWJNkMbu1at5FLFQmdP\nAgAAQMWjdmNtucclW0HYrNnjPv+Ql5e3YMGC48eP/+c//2nRosW9HUJCQtatW7d58+apU6ea\nWjZv3qxSqUJCQkrYAY9PpRKtBvzriqtKffcyfcPnKLwBAACA8sSCJpVZvnx5dHT0E088kZiY\n+PPPP5vb27RpU61aNSFErVq1unXrtnXr1ry8vKCgoJMnT0ZERHTp0sXX19fUs9gO5ci5yKSr\n8alKpyjGjUu3hBBZt3L2rT2mdJZiaLTqVv2Diu8HAAAAWBM5C8Jt27ap1erOnTsLIa5duzZ6\n9OiC7zZs2HDBggUP+PjVq1eFEGfPnj179mzBdn9/f1NBKIQYN26ch4fHjh07Dh065OHhMXz4\n8H79+hXsXGyH8uJQ2N/7fy7pNDzKOrXv4ql9F5VOUQz7SrYUhAAAAEAhqodaG/ABjh492qRJ\nky+//HL8+PFCiIsXL/r5+RXqc/jw4aeeekqWw8klKipqy5Yt8+bNUzpIYQnHr9xM5AFZ2Wi0\nmkYdWV8EAACgRPTZeVfOW/rdauWLRw0XR1e74vuVrW7dusl2hXDVqlWVK1ceNWpUwcbVq1d3\n6dJFCJGfn9+wYcNvv/3W0gpCi+XToKpPg6rF9wMAAADkduV86sLe3ymdokIZvaR78151lU5R\nBNkKwj179nTs2FGn0xVsdHV1NS8k2LNnz71798p1OAAAAAClxMndPviFhkqnKF5k2El9bn7w\noHIQtbKPq9IRiiZbQXjhwoX+/fs/oIOvr2/BdeoBAAAAWCb36s5D53dSOkXRti09mHNb33d6\nGyHEsV1xeXkGU9SIn45ePHpl+P91VjpgOaOWa0c5OTlardb80sfHJzMzs2fPnuYWBweH7Oxs\nuQ4HAAAAwAo1eDZg/7rj698PL9j41/ex698Lb9r1CaVSlV+yXSF0d3dPTk42v1SpVE5O/1qj\nPCkpycPDQ67DAQAAALBCNepWnvL9858OX2du2bf22K8L9oz9vGf9toVntUSxZLtC2KRJk+3b\ntxuNxiLfNRqN27dvb9KkiVyHAwAAAGCdatSt/Np3z0eGnczJ0gtJrHt399jPezZ8LkDpXOWS\nbFcIBw0aNHr06CVLlkybNu3ed5csWXLu3LmZM2fKdTgAAAAAVuXS31fDPtxrXjWvso/rhdgU\nIYSXv3v4t9Hh30ab2tu/2LRhB4rDkpKtIBw2bNjSpUvfeOONv//+e9KkSY0bN7axscnPz4+N\njV22bNnq1aubNWs2dOhQuQ4HAAAAwKq4VqlUN9hXMt6tCC8eTTFt2Dtqn3zGR6USQgihElUC\n3BUKWC7JVhBqtdqNGzf27Nlz9erVq1evVqlUDg4Od+7cMS1837Rp040bNxacdQYAAAAASs7Z\n06HjuOam7X1rj53Yc8HB2Tb7tv7K+dRaDaoOeLv93ZoQD0O2ZwiFEN7e3ocOHQoNDe3cuXP1\n6tVVKlX16tU7d+68atWqgwcPVq9eXcZjAQAAALBO+9YeMz03aKPTCJUwPU/4y/xw892kKDnZ\nrhCaaLXaMWPGjBkzpsh3Y2JimFcGAAAAwCM7uOHvde/ufmlZr6B2/j8IIf43x8wnw9bZOmh7\nTQ1WOF95I+cVwvtJT0//8ssvn3rqqaZNm5bB4QAAAABUVA4udpNC+wW18y/YWKNu5dd/HORZ\n00WpVOWXzFcIC9m3b19oaOj69evv3Lnj6Og4cODAUj0cAAAAgMd3+eyNL1/6TekUxbudmi0Z\npVntVppbti09qGCeBxj4zrOWOfdpqRSE169fX7NmTWho6OnTp4UQnTt3Hj9+fJcuXezt7Uvj\ncAAAAABkZDRIdzJylU5RPFsnnZBEuYian2dQOkLR5CwIjUbjrl27QkNDN27cqNfrmzZt+vbb\nb8+fP3/ChAl9+vSR8UAAAAAASk+NupU/jp6sdAqUBdmeIXz33Xf9/f07d+68Z8+eSZMmHT16\n9MiRI2PHjpVr/wAAAAAAecl2hXDOnDm1a9fesGFDjx49WG8QAAAAACyfbFcIPT094+LiZs6c\nuXjx4suXL8u1WwAAAABAKZGtIExOTl67dq23t/dbb71Vq1at7t27//LLL3q9Xq79AwAAAADk\nJVtBqNPpBg0atGvXrri4uOnTp8fGxg4cONC08CAXDAEAAADAAsm/ML2/v//8+fMvXbq0cePG\n9u3bazSal19+2d/f/z//+U9UVJTshwMAAAAAPBr5C0ITjUbTq1evzZs3JyQkvPfee5IkffTR\nRy1atCilwwEAAAAAHlZpFYRm3t7e77zzTnx8/I4dOwYOHFjahwMAAAAAlJCcC9M/gEql6tix\nY8eOHcvmcAAAAACAYpX6FUIAAAAAgGWiIAQAAAAAK0VBCAAAAABWioIQAAAAAKwUBSEAAAAA\nWCkKQgAAAACwUhSEAAAAAGClKAgBAAAAwEpREAIAAACAlaIgBAAAAAArRUEIAAAAAFaKghAA\nAAAArBQFIQAAAABYKQpCAAAAALBSFIQAAAAAYKUoCAEAAADASlEQAgAAAICVoiAEAAAAACtF\nQQgAAAAAVoqCEAAAAACsFAUhAAAAAFgpCkIAAAAAsFIUhAAAAABgpSgIAQAAAMBKURACAAAA\ngJWiIAQAAAAAK0VBCAAAAABWioIQAAAAAKwUBSEAAAAAWCkKQgAAAACwUhSEAAAAAGClbJQO\nIDOj0RgWFrZ9+/br1697enp26tSpX79+ajV1LwAAAAAUVtEqpdDQ0G+++cbPz2/MmDGBgYFr\n1qxZsWKF0qEAAAAAwBJVqCuEiYmJW7dubdu27bRp04QQ3bt312q127Zt69q1q4+Pj9LpAAAA\nAMCyVKgrhBEREZIk9ezZ09zSq1cvSZL27t2rYCoAAAAAsEwVqiCMi4vTaDQBAQHmFj8/P51O\nd/78eQVTAQAAAIBlqlC3jKamprq4uGg0GnOLSqVyc3O7efNmwW6bNm1KS0szbd+5c6dMIwIA\nAACAxahQBWFubq5Wqy3UqNPpcnNzC7b8+OOPcXFxpu06derUrl27jPIBAAAAgCWpUAWhra1t\ndnZ2oUa9Xm9nZ1ewZcqUKbdv3zZtX7t27cyZM2WUDwAAAAAsSYUqCN3d3RMSEgwGg/muUUmS\n0tLSgoKCCnZr2bKleTsqKoqCEAAAAIB1qlCTygQEBBgMhvj4eHPLhQsX9Hp9wWlmAAAAAAAm\nFaogDAkJUalUmzdvNrds3rxZpVKFhIQomAoAAAAALFOFumW0Vq1a3bp127p1a15eXlBQ0MmT\nJyMiIrp06eLr66t0NAAAAACwOBWqIBRCjBs3zsPDY8eOHYcOHfLw8Bg+fHi/fv2UDgUAAAAA\nlqiiFYRqtXrAgAEDBgxQOggAAAAAWLoK9QwhAAAAAKDkKtoVwkeQl5eXkZGhdAoAAAAAKFOS\nJKkkSVI6hpJOnjy5cOFCpVMAAAAAgAKsvSAEAAAAAKvFM4QAAAAAYKUoCAEAAADASlEQAgAA\nAICVoiAEAAAAACtFQQgAAAAAVoqCEAAAAACsFAUhAAAAAFgpCkIAAAAAsFIUhAAAAABgpSgI\nAQAAAMBKURDiUZw+fVqSJNN2UlLSnDlzMjIylI0EFMQpCgCPg1EUsB6auXPnKp0B5Ux0dPTs\n2bMvX77csmXL5OTkt99++8KFC9nZ2c2bN1c6GiAEpyjKjxs3bnz11VfffvttZGSkk5OTt7e3\n0okAIRhFUa4wkD4+G6UDoPwJDAz08fHZs2dPTk7OmTNn0tLSGjZsOHr0aKVzAXdxiqJcuHXr\n1ptvvnnz5k0hxOXLl2NjY7t27Tp+/Hi1mpt3oDBGUZQXDKSy4AohHpqtrW3r1q1jYmJOnDiR\nk5PTsGHDWbNm2draKp0LuItTFOXCihUr/v7774CAgFdeeaVZs2bnzp07duzYlStXWrZsqVKp\nlE4Hq8YoivKCgVQWXCHEo8jKyrp165Zp283NTafTKZsHKIRTFJbvyJEjXl5e8+fPd3BwEEI0\nbtz4nXfe2bNnjxDi9ddf56sMlMUoinKBgVQWXCHEo9DpdCdOnPD09HRycoqNjeUvMbA0nKKw\nfBs2bOjRo0ejRo1ML+3s7Fq3bh0dHX306FHOWCiOURTlAgOpLCgI8dDS0tJycnI6dOjQrl27\nNm3axMbGxsTEFPq/7tChQ87OztxeAkVwisJipaWlhYaGfv/994cPH87IyAgKCqpTp475Xb7K\nwEIwisKSMZDKjoIQDyE1NfXTTz9dunTpvn37nnnmGRcXF9NjBuZfFS1atFCr1eHh4YsWLTp8\n+PBzzz1nY8NtySg7nKKwZGlpaVOnTj1x4kR6evrly5ezs7PT09M7duxYcPKDgl9l/Pz8atas\nqWBgWCFGUVg4BtLSQEGIkkpJSZk+ffrZs2ednZ179OgREBBgul274K8K0wPoa9eulSSpW7du\njRs3Vjo1rAinKCzcV199dfLkSX9//8mTJzdp0uTs2bOXL1++efNmixYtCv4B2/RVpmrVqu3b\nt1cwLawQoygsHwNpaVCZVx0FHkCv10+ZMiUpKenJJ59866233NzcCnXIyspauHDhsWPHhBBq\ntXrEiBF9+/ZVIimsFKcoLNmNGzc8PDxGjhyp1Wo/++wz05fs1NTUt99+Ozk5uUOHDq+88go3\nNUFZjKKwcAykpYcrhCiRHTt27N69u2rVqv/3f//n7Oxsajx69OiOHTuSk5P9/f1tbW3bt29f\nq1atWrVqjRs3rlWrVsoGhrXhFIXFSk5Onj59emJi4rVr17p27Wqe/MDe3r5169ZRUVFHjx69\nceNGoT9vA2WMURSWjIG0VHHbN0rkzJkzQoju3bub/h6TlJS0bNmyEydOaDQag8Gwf//+999/\nX6VSBQcHK50UVopTFBbLwcHBwcFh165dQohCc/e7ubktWLBg5syZpnf58zYUxCgKS8ZAWqrU\nxXcBhKhRo4YQ4ujRo4mJiT/++OOUKVMkSfrkk09+/PHHqlWrHj9+/Ny5c0pnhFXjFIXFMn1Z\n8fb2FkKEh4cbDIYi3921a1dUVJRCGQFGUVg0BtJSxS2jKBF/f/8TJ04cO3bs999/T0hIGDFi\nxIQJE9zd3W1sbLZt25aZmdmhQwdPT0+lY8J6cYrCkplvakpISLh38gPTu1WqVGHyAyiIURQW\njoG09DCpDIqQlZX166+/RkVF5ebmBgYGDhw40NfX12AwHDlyxGAwNGrUyHQ/iRBi8+bNK1eu\ndHNz+/rrrzUajbKxYT04RVEepaWlzZw5k8kPYCHuHUhr1qzJKAoLx0Babyu97wAAIABJREFU\nGigIUdjly5dnz5597do1IYS9vX12draNjc2rr77arl27gt0kSfr111+/++47SZLefPPNkJAQ\nZeLC+nCKwvIV+TcLwVcZWIySDKSMolDQ/UZRwUBaCrhlFP+Sk5MzY8aMq1evBgQEzJs3b9y4\ncampqefOnTt48GBwcLCLi4upW0xMzBdffLFz506VSjVy5MguXbooGxvWg1MUlu/y5cvTp0+P\niopKT083GAznz5/fuXNnlSpVfH19mRAPlqAkAymjKBT0gFFUMLNoKWBSGfzLxo0bU1JS/Pz8\nFi5c6Ovr+8cff+zYsUMIMWbMmJo1a5r63Lp168svvzx+/HjVqlXnzZvXr18/RSPDunCKwsLl\n5OTMmzfv2rVrAQEBn3322c8//9y5c+f8/PwlS5YkJiYKJj+ABSh2IGUUhYKKHUUFA6ncWHYC\n/3Lo0CEhxOuvv25nZ7d9+/Yvv/xSkqSxY8f26tVLCLFjx442bdq4urouWLDg7NmzrVq14k8y\nKGOcorBwBb9q29nZFfk3C9NXmf/+978tWrRQNCysVEkGUkZRKKUko6hgIJUVVwjxL+np6V5e\nXr6+vjt27Fi2bFnB3xCZmZkrVqz4v//7PyGEp6fnM888wy8JlD1OUVi4Yr9q5+TkCCHc3Ny6\nd++ucFZYq5IMpIyiUEoJR1HBQCofCkIIIURSUlJ8fLwQomrVqhkZGRs3bly6dGnB//2EEN98\n841ery/4txmgzHCKorwo4d8sgDJmHkUFAyksG6No2aMghLh169bs2bNnzZqVmJjYtm3bnJyc\nVatWFfoNsX379p07d9rZ2fXu3VvZtLBCnKIoR/iqDQtUcBQVQjCQwpIxipY9CkKI77777saN\nG76+vl5eXh06dHjyySeFEN7e3sHBwUKInJyc7777btmyZUKIV155hUVpUfY4RWFpTp8+bV60\nKSkpac6cORkZGaaXfNWGBSo4igohGEihOEZRi8I6hFbtxo0bHh4eI0eO1Ol0n332mb29vRAi\nPT19zpw58fHxarXay8srNTVVr9ebZp3u27ev0pFhXThFYYGio6Pfe++9kJCQ119/PTk5+e23\n305LS+vatevEiROFEEajccaMGadPn/b29p4/f767u3tOTs769et/+eUXFnND2StyFBUMpFAU\no6iloSC0XsnJyTNnznzqqadiYmK6des2cOBA81s5OTnr1q3buXNnenq6SqVq0KDB0KFD69at\nq2BaWCFOUVimzMzMWbNmxcfHt2zZ8syZM2lpaQ0bNpw1a5atra2pA1+1YSEeMIoKBlIoh1HU\n0lAQWq+0tLSZM2cmJycLIUaOHHnvKkOSJGVmZtrb22u1WiUCwtpxisJiZWZmvvPOOxcuXBBC\nFPoeY8JXbViCYkdRwUAKhTCKWhQKQqtm/lVRq1atTz/9VKPRKJ0I+BdOUVimK1euTJ8+PS0t\nTQjRtm3bqVOnFjk7P1+1oThGUVgmRlGLopk7d67SGaAYe3v71q1bR0VFJSUlXb9+/emnn2bF\nIVgUTlFYJp1Od+LECU9PTycnp9jY2CtXrrRs2fLek1OlUtna2vIVHApiFIVlYhS1KBSEViQ/\nP3/37t2bN2+OjIzMyMioUaOGjY2N+VfFsWPHbty40aJFC35VQCmcoigX0tLScnJyOnTo0K5d\nuzZt2sTGxsbExBT6NnPo0CFnZ+dCd0ABZeDegdTJyYlRFBaFUdTSUBBai5SUlJkzZ+7cufPC\nhQvx8fGRkZF//fVXnTp1PD09zV+4jx49yq8KKIVTFJYvNTX1008/Xbp06b59+5555hkXFxdb\nW9vWrVubv820aNFCrVaHh4cvWrTo8OHDzz33nI2NjdKpYUXuN5DWqFGDURSWgFHUMlEQWoX0\n9PQZM2akpKRUq1ZtwIABLVq0yM3NvXDhwl9//fX/7d17UFNn+gfwN4GQcDfKJRS5CIKAIAIC\npiiK4h3b3W53dpiOVt1lHLcwq9bbgqI7pYF13RZXS6lWa9VFu7Pt2llpXaAaqWgFBBIDKlDR\nknANJoAIhoT8/jjzO7/8AlrWS05Cvp+/2pNj53Hm7XPe57zv+5yZM2d6eHhgwg3MwhAF89fe\n3r5z587GxkYXF5eUlJTAwEAHBwdCiOFspra2ViaTnT17Vq/Xr1y5cvbs2UxHDVbk6YnU19cX\nWRSYhSxqtlAQTjRarfaTTz7x8/NzdHSkL3722WcSiSQ4OPgvf/lLREREcHDw4sWLORxOTU1N\nVVXVkiVLuFyu4YR7+vTp3t7eDP4tYALDEAVLpNFoMjMzOzs7Q0JCRCJRTEwMNY+hcLnc+fPn\nNzU1NTQ03Lt3j81mr1u3zqjFP8AL9GyJ1NXVFVkUmIIsas5QEE4oIyMj+/fvv3TpUn19/bJl\ny+iXf/n5+RqNJisry8PDg745LCxMoVA0Njay2ezIyEjyv0fPPT09k5KSmPkLwESHIQoWqqSk\n5OLFiwKBIC8vz8XFhbookUhKSkoUCkVAQACXy01KSvL19fX19U1LSxMKhcwGDBPY8yRSZFFg\nCrKoOcOu3Anl66+/vnbtmpOTU0ZGBv2E0Ov1Dx8+JIT4+voa3b9y5cry8vKampq1a9dSV/h8\n/qpVq0wZM1gVDFGwUHfu3CGErFq1inqlLZfLCwoKZDKZjY2NTqerqKjIyclhsVjz5s1jOlKY\n+J4zkSKLAiOQRc0Zm+kA4EX67rvvCCGbN28OCAiQy+U//PADIYTFYnl5eRFCmpqajO7n8XiE\nkEePHpk8UrBSGKJgWeRyeXNzMyFk6tSphBCJRNLa2lpUVLR582a9Xp+fn19UVCQQCG7evDl6\n9AK8JEikYEGQRS0CCsIJhXrpwuFw5HJ5VlbWn//8Z6lUSghZunQpIeTYsWMajcbw/suXLxNC\npk2bxkSwYI0wRMGCDA0NZWVl/etf/yKEpKSkhIaGVldXv/POO8XFxRs2bBCJRAEBATwej/pA\n1sjICNPxgrVAIgVLgSxqKXCGcELh8/nl5eXV1dVisVilUkVERLzxxhu2trZBQUE1NTXNzc31\n9fWzZs1ydHTU6/XFxcVFRUUsFisjI8PNzY3p2MEqYIiCBbG1tb1y5crNmzeXL1/u5OS0aNGi\noKCghISEtLS0sLAwaqve+fPnxWIxn8/fsGEDm413rGAKSKRgKZBFLQUKwgnllVdeGR4erqur\nGxoaCgkJ2bdvH/VBTzabPXfuXIlE0tjYeP78+atXr37xxRcVFRWEkPXr18+fP5/pwMFaYIiC\nZeFyuRUVFc7OzmFhYWw229vb28fHh8PhEEL0ev2XX3554sQJQkhGRoa/vz+zoYL1QCIFC4Is\nahFQEE4obW1tR48eHRoaIoQ8fvx4zpw5fD6f+onH4y1cuFCj0bS0tPT09AwNDU2ePDk9PX3Z\nsmWMhgzWBUMULMvUqVNLSkpaWlpWr15t+NG22traw4cPl5aWslisdevWLV++nMEgwdogkYIF\nQRa1CCy9Xs90DPDCPHr0KDs7m8fjzZ49++TJk87Ozu+9915AQIDhPUNDQ62trRwOx8/PDx+l\nBRPDEAWLc+bMmTNnzmRnZ8+ZM4e6olard+zY0dHRIRAIfv/73+O7yWBiSKRgWZBFzR8Kwonm\n0aNHNjY2XC7366+/Pnbs2JjPCQAGYYiC2ZLL5a2trfHx8YbnWNRq9YYNG6Kiovbs2UNfVCqV\njY2NQqEQU21gBBIpmCdkUQuFLaMTDYfDsbW1JYSEhIQ4ODj88MMPFRUVUVFR9H4SAEZQ755Y\nLBaGKJgntVq9ffv20tLSixcvarVaHx8fOzs7QgiPx2tra7t69erixYsdHR2pmx0cHHx8fDCP\nAaYgkYIZQha1XCgIJzI8J8AcdHd3f/DBB/n5+efOnevu7g4NDaWeEARDFMwJj8eLjY1lsVh3\n7typqqo6f/58d3e3QCBwdXV1d3f/z3/+w+VyIyMjmQ4TwBgSKZgJZFHLhYJwgsNzApilUqm2\nbdvW3Nys1+uHh4ebm5srKipiY2OdnJyoGzBEwRyoVKqBgQGBQBATE5OSkuLh4dHZ2VldXf3N\nN980NDT4+vp2dnZKpdLXXnsNXdHBDCGRAuOQRS0aCsKJj35OCASC0NBQpsMB63Ls2DGZTBYU\nFLR79+5f/epXg4ODUqn02rVr8fHxo2tCDFEwvQcPHhw8ePCjjz66cuUKNSxtbW2nT5++fPny\nqKio4eHhmpoa6mtvg4ODfn5+vr6+TIcMMAYkUmAKsugEgKYy1uLOnTszZsxgOgqwIkqlcsqU\nKWlpaSMjI3/729/o8o/qNubm5iYSiQQCAX0/hiiYXnt7e2ZmZk9Pj6ur62uvvZaUlDT6y929\nvb2lpaUXLlzo6uoKDw8XiUSMhAowHkikYGLIohMDVgitxej/PwFeHoVCsXPnztbW1s7OzuTk\n5JiYGPqniIgIQkhlZaXROiGGKJiYRqPJzMzs7OwMCQkRiUQxMTEODg6jb+PxeGFhYatXr1ap\nVNSgxX48MFtIpGBKyKITBnbxAsDz0mq1Wq3W8IqDg4ODg0NZWVlXV5e9vb3R/ampqampqUql\nMjMzs6Ojw4SRAvyf7777Ti6XCwSCffv20bMTiURy8uTJb775RqfTGd7MYrGWLl1KCCkpKWEg\nVgAA84MsOmHYMh0AAFg2rVabl5dHCPnjH/9oY2NDXeTz+SKRKDMzU6FQiMXi1atX0z9RUlNT\nCSFnzpy5fv3666+/bvqwAe7cuUMIWbVqFfVKWy6XFxQUyGQyGxsbnU5XUVGRk5Nj2BLd2dmZ\nEHLr1i2mAgYAMCvIohMGVggB4Llotdr+/v6GhgajtT6qJvT29r579+5HH300+rhyamqqSCRC\nNQhMmTp1KiFEIpG0trYWFRVt3rxZr9fn5+cXFRUJBIKbN282NTXRN4+MjJw4cYIQYnjwFQDA\nmiGLThhYIbQYWq1WLBbX19ezWKzQ0NDExEQul8t0UACEx+P96U9/6urq8vb27uzsdHNzG71O\nWFZWRgjJyMgw+gRteHg4AxEDEEIISUlJqaqqqq6urq6udnZ23rBhw4oVK1gsll6vp8bwyMgI\nffOPP/54/fp1BweHtWvXMhcyWAXq9Rk+2A3mD1l0wkCXUcvQ3t6ek5PT2tpKX/Hw8Ni+ffvo\nZmIKhcLb29u00QEQQkh7e/uuXbuCgoIM944SQlQqFbV3NDk5eXRNCMAgnU5348YNnU4XGRlJ\n90L497//ffToUT6ff/z4ccORXFlZOWnSpODgYIaChYlGp9Ox2WzDlNjd3V1YWFhTU8Plchcs\nWLBmzRq67ZYhPOjBfCCLTgzoMmoBent7d+3a1d7e7uXl9eabb8bFxT1+/LilpeXy5cszZ870\n8PCg7xSLxdnZ2Y6Ojug6DabH4XCqq6slEklLS0tCQgL95Vl7e/uEhISqqiqJRKJUKuPi4lAT\ngplgs9ne3t4+Pj4cDocQotfrv/zyS2pTU0ZGhr+/v+HN3t7eU6ZMYSJMmICo09cSiYROiSqV\natu2bc3NzXq9fnh4uLm5uaKiIjY21qgmxIMezAqy6MSAM4QWoKioqKurKzg4+ODBg2+88UZK\nSopIJFq7dq1Go8nNze3v76fv7OnpGRkZefjwIYPRgtWi9o6GhoZWVlbm5uYathejzxOWlZVV\nVVUxGCTAk9TW1u7evfvkyZOEkHXr1s2fP5/piGAiGxgYUCgUZWVlhw4dovZq/f3vf+/p6QkK\nCvrggw+OHDmSnJzc0dExuhUzHvRgtpBFLRdWCC1Afn6+RqPJysoyXAwMCwtTKBSNjY1sNjsy\nMpK+GBkZuWjRIoYiBWtna2s7f/58mUz2pHVCT0/PpKQkZoMEGE2tVufm5t69e1cgEOzYsQOj\nFF42Ho9ntHXi6NGj9vb2+/fvFwgETk5O8fHxZKxPtuJBD+YJWdSioSA0d3q9nnrXkpaWZtS4\nf9KkSWVlZUNDQ8uXL6cvuru7mzpEsFZ6vf7mzZvV1dV9fX2enp5U7ff0mhAnB8A88Xg8oVAY\nGhq6adMmLy8vpsMBq2C0nb6zszM5OTkmJoa+ISIigoxVE+JBD2YIWdSioSA0dywW6/Lly/39\n/VFRUYYrhISQ/v7+CxcucLnc1atXMxUeWK2urq69e/f+85//vHHjxuXLl7///vvg4GDqbMBT\nakIAs+Xg4ODj44MDrmBKhjXhwMBAXFxcSEiI4Q1PqgkBzBCyqOVCQWgBNBpNXV3d/fv3k5KS\nDBcJz507d/v27YiICOzSBhPr7e3dvn27XC7n8/kpKSmBgYF1dXVisXj69OnUe0HUhAAA40HX\nhP39/b29vUuWLDHKlnRN6OHhYVQuAgC8ECgILUBQUFBNTU1zc3N9ff2sWbMcHR31en1xcXFR\nURGLxcrIyHBzc2M6RrAueXl5P/74Y2hoaG5ublxcXFdXV1VVlVarvXr16pg1oa+vr5+fH9NR\nAwCYI7om/Omnn3p6eka3Yo6IiIiIiEhMTGQqQgCY2PAdQsvQ29u7d+/eu3fvstlsX1/f3t5e\nlUpFCFm/fv0vf/lLpqMD63L79u0dO3a4ubkdPHjQ2dn5woULH3/8sV6vX7Ro0cWLF+3s7DIz\nM6Ojo6mbh4aGrly5kpyczGzMAABmDp9sBQCmYIXQMvB4vIULF2o0mpaWlp6enqGhocmTJ6en\npy9btozp0MDqXLp0SSqV/uEPfwgMDLx27Vp+fr5er//d73739ttv//TTT/fu3TNaJwwICGA6\nZLAiSqWysLDw888/r6ysdHJywve7wVLgk61gJpBFrRBWCC3M0NBQa2srh8Px8/PDowJMTC6X\nazQaf3//48ePr1+/fmBgYOPGjQMDA6mpqampqYSQ06dPl5aW9vX12djYHD58WCAQMB0yWBe1\nWr1ly5aenh76yooVKzZu3Gh0KEuhUGCKA+YJ64TALGRR64QVQgtja2s7ZcqUSZMm4SEBJqZW\nq3ft2lVaWhofH5+UlMRms4uLi6uqqqKiojIyMqh7Tp8+zePxNm3a9Morr8ydO5fZgMEKHTly\npL6+PjAwMCMjY86cOU1NTVKptKOjY+7cuXTOFIvF2dnZjo6OM2bMYDZagNEM1wmnT5+OOTeY\nGLKodbJlOgAAsAynTp1SKpURERH050/a29sJIXST2+Li4tu3b7/66qtxcXFxcXGMBQpW7MaN\nGx4eHu+//76DgwMhZPbs2bt37xaLxYSQLVu2ULOZnp6ekZGRhw8fMhsqwJPw+XyRSHT16lUk\nUjA9ZFHrhBVCAPgZSqXS3t6+oKDA1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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mUsing \u001b[32msize\u001b[39m for a discrete variable is not advised.â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 3 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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0aPHu3p6SnXkQFAdjprTd2mNZROIb+Um2m5D/S1G1dXqSSls8jMsYa90hEAACiH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PDaa69NmjQpKSlJ2YEAAAAAAAWkzML0JmfOnGnduvXdu3eFEGXKlKlcuXJKSkpk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\n6v8AEBI7U4sQDIwAAAAASUVORK5CYII=",
- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 180,
- "width": 600
- }
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "IPCC_Table$DIFF_CCI_IPCC <- IPCC_Table$CCI_mean - IPCC_Table$AGB\n",
- "IPCC_Table$SD_CCI_IPCC <- sqrt((IPCC_Table$CCI_stdev)^2 + (IPCC_Table$Uncertainty)^2)\n",
- "IPCC_Table$IN_AGB_CI_CCI <- 1\n",
- "IPCC_Table$IN_AGB_CI_CCI[abs(IPCC_Table$DIFF_CCI_IPCC) > 2*IPCC_Table$SD_CCI_IPCC] = 0\n",
- "IPCC_Table$Tval_CCI_IPCC <- IPCC_Table$DIFF_CCI_IPCC/IPCC_Table$SD_CCI_IPCC # plot the t-values \n",
- "\n",
- "SUBSET <- 'Continent'\n",
- "SUBSET_AGE <- 'AGE'\n",
- "for (i in 1:nrow(unique(IPCC_Table[SUBSET]))){\n",
- " if (i>0) { for (f in 1:nrow(unique(IPCC_Table[SUBSET_AGE]))){\n",
- " WIDTH <- c(2.8,4,2.8,3.2,4,2,3.5)\n",
- " HEIGHT <- c(3.1,3.1,3.1,3.1,3.1,3.1,3.1)\n",
- " W <- WIDTH[i]\n",
- " H <- HEIGHT[i]\n",
- " # if (as.character(unique(IPCC_Table[SUBSET])[i,1])==\"Oceania\") {ll = 1.5}\n",
- " # else{ll = 1}\n",
- " IPCC_Table_SUBSET <- IPCC_Table[(IPCC_Table[SUBSET] == as.character(unique(IPCC_Table[SUBSET])[i,1])) & (IPCC_Table[SUBSET_AGE] == as.character(unique(IPCC_Table[SUBSET_AGE])[f,1])),]\n",
- " IPCC_Table_SUBSET$GEZ_CODE <- as.numeric(IPCC_Table_SUBSET$GEZ_CODE)\n",
- " IPCC_Table_SUBSET <- IPCC_Table_SUBSET %>% group_by(AGE) %>% arrange(GEZ_CODE, .by_group=TRUE) %>% arrange(desc(AGB), .by_group=TRUE) #arrange(desc(region_mean), .by_group=TRUE) #%>% group_by(GEZ_CODE)\n",
- " IPCC_Table_SUBSET$AGE[IPCC_Table_SUBSET$AGE== 'Sec_gt20'] = 'Old secondary'\n",
- " IPCC_Table_SUBSET$AGE[IPCC_Table_SUBSET$AGE == 'Sec_lt20'] = 'Young secondary'\n",
- " IPCC_Table_SUBSET$'IPCC' = 'Tier 1 estimates'\n",
- " CONT <- gsub(\"_\",\" \",as.character(unique(IPCC_Table[SUBSET])[i,1]))\n",
- " AGEY <- as.character(unique(IPCC_Table[SUBSET_AGE])[f,1])\n",
- " # if (CONT == \"Oceania\") {CONT <- \"Oc.\"}\n",
- " if (CONT == \"Oceania\" & AGEY == \"Primary\") {AGEY <- \"Primary\"}\n",
- " if (CONT == \"Oceania\" & AGEY == \"Old secondary\") {AGEY <- \"Old sec.\"}\n",
- " if (CONT == \"Oceania\" & AGEY == \"Young secondary\") {AGEY <- \"Young sec.\"}\n",
- " p <- ggplot() +\n",
- " ggtitle(paste0(CONT,\",\\n\",AGEY)) +\n",
- " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=AGB, color=\"black\", size=IPCC),alpha=0.35,color=\"black\") + \n",
- " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=AGB-Uncertainty, ymax=AGB+Uncertainty, color=\"black\",alpha=IPCC),alpha=0.35,color=\"black\", width=0.5) +\n",
- " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=region_mean), size=2, shape=8, color=\"darkorchid4\") + \n",
- " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=region_mean-region_stderr, ymax=region_mean+region_stderr), width=0.5,color=\"darkorchid4\") +\n",
- " geom_point(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), y=CCI_mean), size=2, shape=8,color=\"darkorange2\") + \n",
- " geom_errorbar(data=IPCC_Table_SUBSET, aes(x=1:nrow(IPCC_Table_SUBSET), ymin=CCI_mean-CCI_stdev, ymax=CCI_mean+CCI_stdev), width=0.5, color=\"darkorange2\") +\n",
- " theme_bw() +\n",
- " coord_cartesian(ylim=c(0,600)) +\n",
- " # ylim(0, 600) +\n",
- " ylab(\"AGBD [Mg/ha]\") +\n",
- " scale_x_continuous(breaks=1:nrow(IPCC_Table_SUBSET) , labels=IPCC_Table_SUBSET$GEZ_NAME) +\n",
- " # xlab(\"Various ecozones in different continents, displayed in groups of forest age classes\") +\n",
- " # theme(legend.position=\"bottom\") +\n",
- " # guides(color = guide_legend(override.aes = list(size=4),title = \"Space-lidar estimates\")) +\n",
- " # scale_color_brewer(palette=\"Dark2\") +\n",
- " theme(\n",
- " legend.position=\"none\",\n",
- " text=element_text(size=13),\n",
- " legend.text=element_text(size=13),\n",
- " legend.title=element_text(size=13),\n",
- " axis.title.y=element_text(size=11),\n",
- " axis.title.x=element_blank(),\n",
- " axis.ticks.x=element_blank(),\n",
- " panel.grid.major = element_blank(), \n",
- " panel.grid.minor = element_blank(),\n",
- " panel.background = element_blank(),\n",
- " axis.text.x=element_text(angle=45,hjust=1, vjust=1,size=12),\n",
- " plot.margin = margin(t=-1.4,r=0,b=0,l=0.5, unit = \"cm\"),\n",
- " plot.title=element_text(hjust=0.95, vjust=-1.8, margin=margin(t=40,b=-20),size=12),\n",
- " ) \n",
- " \n",
- " # options(repr.plot.width=12, repr.plot.height=5)\n",
- " print(p)\n",
- " ggsave(p, file=paste0(\"/projects/my-public-bucket/Data/Harris_et_al_PAPER/_PLOTS/\",as.character(unique(IPCC_Table[SUBSET])[i,1]),\"_\",as.character(unique(IPCC_Table[SUBSET_AGE])[f,1]),\"_age_classes.png\"), width=W, height=H)\n",
- " }\n",
- "}}\n",
- "nrow(IPCC_Table)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "73140caf-9527-4b42-938b-74e99bfab0fd",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message in geom_errorbarh(aes(xmin = AGB - Uncertainty, xmax = AGB + Uncertainty), :\n",
- "“\u001b[1m\u001b[22mIgnoring unknown parameters: `width`â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
- "Warning message in geom_errorbarh(aes(xmin = AGB - Uncertainty, xmax = AGB + Uncertainty), :\n",
- "“\u001b[1m\u001b[22mIgnoring unknown parameters: `width`â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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WrTps3t27fpATBCGurtt9+WyWTr16+nC1+kNXhQVE6ePPnzzz9v3bpVJpNptjz2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dpvgUxCG93Jrb0OfnEHnw3oMc88kh511N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\nGkIIIfc1usVJCCHEb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- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 270,
- "width": 270
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 93 rows containing missing values (`geom_errorbarh()`).â€\n",
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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15v0vnz5+fNm6fZBXLFihUBAQHWD0qI9cXExCQnJ0dGRgrT44uHKhcNaWhIB9ch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\nZAACaRhRk9DkdNQ6gGkYDZgekJAdSPY4rmlQ+wG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c6YMWPy+/Cqgc1fGAzmlaaxsXHZsmU0Gs3BwcHGxkYkEjU2NspkMisr\nq4qKiqVLl77sDmL+xbSjR4++7D5gMBiMRoyMjKhUqlgs7u7u5vF4/f39ixcvjoiIOHfu3Jiz\nOGMmDrxSwWAwGIzewBv1GAwGg9EbWKlgMBgMRm9gpYLBYDAYvYGVCgaDwWD0BlYqGAwGg9Eb\nWKlgMBgMRm9gpYLBYDAYvYGVCgaDwWD0BlYqGAwGg9EbWKlgMBgMRm/8D8w1Ihg2+9jyAAAA\nAElFTkSuQmCC",
- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 270,
- "width": 270
- }
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Warning message:\n",
- "“\u001b[1m\u001b[22mRemoved 80 rows containing missing values (`geom_point()`).â€\n"
- ]
- },
- {
- "data": {
- "image/png": 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+c+//zzcsRJ1FSUHamUUQCUnYDSHwpfGYLZunVrTk6OLUsC1l8GA65ckTZmZMDf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\nsmXLYmN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OOPn3vuOdOISPvU8o6W5Omn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\nDIIKAAAw8wsDhvIYziNadAAAAABJRU5ErkJggg==",
- "text/plain": [
- "plot without title"
- ]
- },
- "metadata": {
- "image/png": {
- "height": 270,
- "width": 270
+ "height": 270,
+ "width": 270
}
},
"output_type": "display_data"
@@ -1904,7 +1335,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 21,
"id": "3f908519-8578-455f-862d-692a091bf131",
"metadata": {
"tags": []
@@ -1948,7 +1379,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 22,
"id": "365291c3-2c18-4f9a-b58d-7ccfdc49a573",
"metadata": {},
"outputs": [
@@ -2107,7 +1538,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 34,
"id": "0cb6103f-a430-4620-bbd1-35f6c48f29ad",
"metadata": {},
"outputs": [
@@ -2118,50 +1549,6 @@
" MAP - NFI ~ null: Coefficient SE t-value p-value\n",
"1 Intercept -1.3 4.5 -0.29 0.77\n"
]
- },
- {
- "data": {
- "text/plain": [
- "\n",
- "Call:\n",
- "lm(formula = IPCC_Table$region_mean ~ IPCC_Table$CCI_mean)\n",
- "\n",
- "Residuals:\n",
- " Min 1Q Median 3Q Max \n",
- "-73.082 -26.009 -3.178 21.187 157.885 \n",
- "\n",
- "Coefficients:\n",
- " Estimate Std. Error t value Pr(>|t|) \n",
- "(Intercept) 36.20943 5.92930 6.107 9.73e-09 ***\n",
- "IPCC_Table$CCI_mean 0.69355 0.05771 12.018 < 2e-16 ***\n",
- "---\n",
- "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
- "\n",
- "Residual standard error: 39.26 on 138 degrees of freedom\n",
- "Multiple R-squared: 0.5114,\tAdjusted R-squared: 0.5078 \n",
- "F-statistic: 144.4 on 1 and 138 DF, p-value: < 2.2e-16\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "140"
- ],
- "text/latex": [
- "140"
- ],
- "text/markdown": [
- "140"
- ],
- "text/plain": [
- "[1] 140"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
}
],
"source": [
@@ -2171,7 +1558,7 @@
"# IPCC_Table$region_stderr = 0\n",
"# IPCC_Table$Uncertainty[is.na(IPCC_Table$Uncertainty)] <- 0\n",
"NFI = IPCC_Table$AGB # Mean AGBD estimate from IPCC\n",
- "NFI.var = (IPCC_Table$Uncertainty)^2 # Variance of AGBD estimate from IPCC\n",
+ "NFI.var = ((IPCC_Table$Uncertainty)^2) # Variance of AGBD estimate from IPCC\n",
"# NFI = IPCC_Table$CCI_mean # Mean AGBD estimate from IPCC\n",
"# NFI.var = (IPCC_Table$CCI_stdev)^2 # Variance of AGBD estimate from IPCC\n",
"MAP = IPCC_Table$region_mean # Mean AGBD estimate from Spaceborne lidar estimates map\n",
@@ -2190,13 +1577,13 @@
"Table['MAP - NFI ~ null:'] <- c('Intercept')\n",
"print(Table)\n",
"\n",
- "summary(lm(IPCC_Table$region_mean~IPCC_Table$CCI_mean))\n",
- "nrow(IPCC_Table)"
+ "# summary(lm(IPCC_Table$region_mean~IPCC_Table$CCI_mean))\n",
+ "# nrow(IPCC_Table)"
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 28,
"id": "b571bd45-dff2-423b-b4b0-63c97f07d39f",
"metadata": {},
"outputs": [
@@ -2204,32 +1591,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " GEZ_NAME N Coefficient SE t.value p.value\n",
- "1 Tr. rainforest 12 -0.36 15.13 -0.02 0.982\n",
- "2 Tr. moist d. forest 12 -13.61 14.22 -0.96 0.359\n",
- "3 Tr. dry forest 12 -9.81 14.86 -0.66 0.523\n",
- "4 Tr. shrubland 11 25.39 13.53 1.88 0.090\n",
- "5 Tr. m. system 12 25.00 25.92 0.96 0.355\n",
- "6 St. h. forest 12 -0.85 11.11 -0.08 0.940\n",
- "7 St. dry forest 12 -1.90 11.26 -0.17 0.869\n",
- "8 St. m. system 12 15.30 12.33 1.24 0.241\n",
- "9 St. steppe 11 13.91 10.24 1.36 0.204\n",
- "10 Bo. t. woodland 3 -47.81 22.84 -2.09 0.171\n",
- "11 Tm. o. forest 10 -38.69 33.43 -1.16 0.277\n",
- "12 Tm. c. forest 7 -2.60 11.17 -0.23 0.824\n",
- "13 Tm. steppe 4 -9.14 60.67 -0.15 0.890\n",
- "14 Tm. m. system 8 -27.66 23.04 -1.20 0.269\n"
+ " Continent N Coefficient SE t.value p.value\n",
+ "1 Africa 26 -32.65 12.85 -2.54 0.018\n",
+ "2 North_America 39 -16.53 7.53 -2.20 0.034\n",
+ "3 South_America 33 -26.24 11.38 -2.31 0.028\n",
+ "4 Asia 30 -9.73 14.63 -0.67 0.511\n",
+ "5 Europe 9 -62.07 27.00 -2.30 0.051\n",
+ "6 Oceania 3 -54.72 60.18 -0.91 0.459\n"
]
}
],
"source": [
- "SUBSET <- 'GEZ_NAME' #GEZ_NAME\n",
+ "SUBSET <- 'Continent' #GEZ_NAME\n",
"Table <- data.frame(matrix(ncol=6,nrow=0, dimnames=list(NULL, c(SUBSET,'N','Coefficient','SE','t-value','p-value'))))\n",
"\n",
"for (i in 1:nrow(unique(IPCC_Table[SUBSET]))){\n",
" IPCC_Table_SUBSET <- IPCC_Table[IPCC_Table[SUBSET] == as.character(unique(IPCC_Table[SUBSET])[i,1]),]\n",
" NFI = IPCC_Table_SUBSET$AGB # Mean AGBD estimate from IPCC\n",
- " NFI.var = (IPCC_Table_SUBSET$Uncertainty)^2 # Variance of AGBD estimate from IPCC\n",
+ " NFI.var = ((IPCC_Table_SUBSET$Uncertainty)^2) # Variance of AGBD estimate from IPCC\n",
" MAP = IPCC_Table_SUBSET$region_mean # Mean AGBD estimate from Spaceborne lidar estimates map\n",
" MAP.var = (IPCC_Table_SUBSET$region_stderr)^2 # Variance of AGBD estimate from Spaceborne lidar estimates map\n",
" # MAP = IPCC_Table_SUBSET$CCI_mean # Mean AGBD estimate from Spaceborne lidar estimates map\n",
diff --git a/country_summaries/IPCC_classes_DPS/NOTES.ipynb b/country_summaries/IPCC_classes_DPS/NOTES.ipynb
index 11108ac46882be0114a400603d14adc582e18244..fdb55129f0b1dc4b6b0a4f831672dbcb9ab5aec8 100644
--- a/country_summaries/IPCC_classes_DPS/NOTES.ipynb
+++ b/country_summaries/IPCC_classes_DPS/NOTES.ipynb
@@ -18,7 +18,7 @@
},
{
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- "execution_count": 2,
+ "execution_count": 3,
"id": "925dfbb2-d2a6-4503-8603-70f30acde654",
"metadata": {
"tags": []
@@ -112,16 +112,8561 @@
},
{
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+ "execution_count": 4,
"id": "frequent-cardiff",
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+ "jupyter": {
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+ },
"tags": []
},
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+ "outputs": [
+ {
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+ "output_type": "stream",
+ "text": [
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+ " 33700K .......... .......... .......... .......... .......... 2% 49.9M 1m42s\n",
+ " 33750K .......... .......... .......... .......... .......... 2% 51.3M 1m42s\n",
+ " 33800K .......... .......... .......... .......... .......... 2% 1.07M 1m44s\n",
+ " 33850K .......... .......... .......... .......... .......... 2% 27.5M 1m43s\n",
+ " 33900K .......... .......... .......... .......... .......... 2% 49.7M 1m43s\n",
+ " 33950K .......... .......... .......... .......... .......... 2% 50.0M 1m43s\n",
+ " 34000K .......... .......... .......... .......... .......... 2% 38.9M 1m43s\n",
+ " 34050K .......... .......... .......... .......... .......... 2% 45.7M 1m43s\n",
+ " 34100K .......... .......... .......... .......... .......... 2% 48.9M 1m43s\n",
+ " 34150K .......... .......... .......... .......... .......... 2% 45.8M 1m43s\n",
+ " 34200K .......... .......... .......... .......... .......... 2% 43.1M 1m43s\n",
+ " 34250K .......... .......... .......... .......... .......... 2% 49.8M 1m43s\n",
+ " 34300K .......... .......... .......... .......... .......... 2% 49.3M 1m43s\n",
+ " 34350K .......... .......... .......... .......... .......... 2% 50.1M 1m42s\n",
+ " 34400K .......... .......... .......... .......... .......... 2% 42.1M 1m42s\n",
+ " 34450K .......... .......... .......... .......... .......... 2% 50.7M 1m42s\n",
+ " 34500K .......... .......... .......... .......... .......... 2% 50.5M 1m42s\n",
+ " 34550K .......... .......... .......... .......... .......... 2% 49.2M 1m42s\n",
+ " 34600K .......... .......... .......... .......... .......... 2% 42.8M 1m42s\n",
+ " 34650K .......... .......... .......... .......... .......... 2% 50.2M 1m42s\n",
+ " 34700K .......... .......... .......... .......... .......... 2% 49.9M 1m42s\n",
+ " 34750K .......... .......... .......... .......... .......... 2% 48.6M 1m42s\n",
+ " 34800K .......... .......... .......... .......... .......... 2% 40.6M 1m41s\n",
+ " 34850K .......... .......... .......... .......... .......... 2% 49.5M 1m41s\n",
+ " 34900K .......... .......... .......... .......... .......... 2% 50.4M 1m41s\n",
+ " 34950K .......... .......... .......... .......... .......... 2% 50.9M 1m41s\n",
+ " 35000K .......... .......... .......... .......... .......... 2% 41.5M 1m41s\n",
+ " 35050K .......... .......... .......... .......... .......... 2% 50.3M 1m41s\n",
+ " 35100K .......... .......... .......... .......... .......... 2% 51.0M 1m41s\n",
+ " 35150K .......... .......... .......... .......... .......... 2% 51.0M 1m41s\n",
+ " 35200K .......... .......... .......... .......... .......... 2% 40.9M 1m41s\n",
+ " 35250K .......... .......... .......... .......... .......... 2% 51.0M 1m41s\n",
+ " 35300K .......... .......... .......... .......... .......... 2% 1.09M 1m42s\n",
+ " 35350K .......... .......... .......... .......... .......... 2% 48.7M 1m42s\n",
+ " 35400K .......... .......... .......... .......... .......... 2% 31.0M 1m42s\n",
+ " 35450K .......... .......... .......... .......... .......... 2% 49.5M 1m42s\n",
+ " 35500K .......... .......... .......... .......... .......... 2% 50.8M 1m42s\n",
+ " 35550K .......... .......... .......... .......... .......... 2% 42.9M 1m42s\n",
+ " 35600K .......... .......... .......... .......... .......... 2% 39.4M 1m42s\n",
+ " 35650K .......... .......... .......... .......... .......... 2% 41.6M 1m42s\n",
+ " 35700K .......... .......... .......... .......... .......... 2% 50.4M 1m42s\n",
+ " 35750K .......... .......... .......... .......... .......... 2% 49.0M 1m41s\n",
+ " 35800K .......... .......... .......... .......... .......... 2% 43.9M 1m41s\n",
+ " 35850K .......... .......... .......... .......... .......... 2% 47.7M 1m41s\n",
+ " 35900K .......... .......... .......... .......... .......... 2% 50.7M 1m41s\n",
+ " 35950K .......... .......... .......... .......... .......... 2% 49.0M 1m41s\n",
+ " 36000K .......... .......... .......... .......... .......... 2% 42.9M 1m41s\n",
+ " 36050K .......... .......... .......... .......... .......... 2% 50.2M 1m41s\n",
+ " 36100K .......... .......... .......... .......... .......... 2% 48.2M 1m41s\n",
+ " 36150K .......... .......... .......... .......... .......... 2% 49.5M 1m41s\n",
+ " 36200K .......... .......... .......... .......... .......... 2% 42.4M 1m41s\n",
+ " 36250K .......... .......... .......... .......... .......... 2% 50.0M 1m40s\n",
+ " 36300K .......... .......... .......... .......... .......... 2% 41.9M 1m40s\n",
+ " 36350K .......... .......... .......... .......... .......... 2% 43.3M 1m40s\n",
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+ " 36450K .......... .......... .......... .......... .......... 2% 51.0M 1m40s\n",
+ " 36500K .......... .......... .......... .......... .......... 2% 50.0M 1m40s\n",
+ " 36550K .......... .......... .......... .......... .......... 2% 47.3M 1m40s\n",
+ " 36600K .......... .......... .......... .......... .......... 2% 43.9M 1m40s\n",
+ " 36650K .......... .......... .......... .......... .......... 2% 50.9M 1m40s\n",
+ " 36700K .......... .......... .......... .......... .......... 2% 49.0M 1m40s\n",
+ " 36750K .......... .......... .......... .......... .......... 2% 48.5M 1m40s\n",
+ " 36800K .......... .......... .......... .......... .......... 2% 42.8M 99s\n",
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+ " 37100K .......... .......... .......... .......... .......... 2% 49.1M 1m41s\n",
+ " 37150K .......... .......... .......... .......... .......... 2% 43.1M 1m41s\n",
+ " 37200K .......... .......... .......... .......... .......... 2% 40.9M 1m40s\n",
+ " 37250K .......... .......... .......... .......... .......... 2% 51.2M 1m40s\n",
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+ " 45600K .......... .......... .......... .......... .......... 2% 41.5M 95s\n",
+ " 45650K .......... .......... .......... .......... .......... 2% 51.7M 95s\n",
+ " 45700K .......... .......... .......... .......... .......... 2% 51.4M 95s\n",
+ " 45750K .......... .......... .......... .......... .......... 2% 51.7M 95s\n",
+ " 45800K .......... .......... .......... .......... .......... 2% 41.8M 95s\n",
+ " 45850K .......... .......... .......... .......... .......... 2% 51.8M 94s\n",
+ " 45900K .......... .......... .......... .......... .......... 2% 51.9M 94s\n",
+ " 45950K .......... .......... .......... .......... .......... 2% 51.3M 94s\n",
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+ " 46100K .......... .......... .......... .......... .......... 2% 50.6M 96s\n",
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+ " 46200K .......... .......... .......... .......... .......... 2% 41.6M 95s\n",
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+ " 46300K .......... .......... .......... .......... .......... 2% 50.8M 95s\n",
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+ " 46400K .......... .......... .......... .......... .......... 2% 42.6M 95s\n",
+ " 46450K .......... .......... .......... .......... .......... 2% 49.4M 95s\n",
+ " 46500K .......... .......... .......... .......... .......... 2% 51.1M 95s\n",
+ " 46550K .......... .......... .......... .......... .......... 2% 50.7M 95s\n",
+ " 46600K .......... .......... .......... .......... .......... 2% 43.4M 95s\n",
+ " 46650K .......... .......... .......... .......... .......... 2% 50.8M 95s\n",
+ " 46700K .......... .......... .......... .......... .......... 2% 49.7M 95s\n",
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+ " 46850K .......... .......... .......... .......... .......... 2% 51.5M 94s\n",
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+ " 46950K .......... .......... .......... .......... .......... 2% 41.8M 94s\n",
+ " 47000K .......... .......... .......... .......... .......... 3% 43.5M 94s\n",
+ " 47050K .......... .......... .......... .......... .......... 3% 50.9M 94s\n",
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+ " 47200K .......... .......... .......... .......... .......... 3% 43.5M 94s\n",
+ " 47250K .......... .......... .......... .......... .......... 3% 51.8M 94s\n",
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+ " 47500K .......... .......... .......... .......... .......... 3% 49.3M 94s\n",
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+ " 47750K .......... .......... .......... .......... .......... 3% 48.1M 95s\n",
+ " 47800K .......... .......... .......... .......... .......... 3% 41.5M 95s\n",
+ " 47850K .......... .......... .......... .......... .......... 3% 46.6M 95s\n",
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+ " 82250K .......... .......... .......... .......... .......... 5% 50.0M 84s\n",
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+ "407250K .......... .......... .......... .......... .......... 25% 13.6M 60s\n",
+ "407300K .......... .......... .......... .......... .......... 25% 13.9M 60s\n",
+ "407350K .......... .......... .......... .......... .......... 25% 17.2M 60s\n",
+ "407400K .......... .......... .......... .......... .......... 25% 18.5M 60s\n",
+ "407450K .......... .......... .......... .......... .......... 25% 53.5M 60s\n",
+ "407500K .......... .......... .......... .......... .......... 25% 28.4M 60s\n",
+ "407550K .......... .......... .......... .......... .......... 25% 32.1M 60s\n",
+ "407600K .......... .......... .......... .......... .......... 26% 45.3M 60s\n",
+ "407650K .......... .......... .......... .......... .......... 26% 51.8M 60s\n",
+ "407700K .......... .......... .......... .......... .......... 26% 51.4M 60s\n",
+ "407750K .......... .......... .......... .......... .......... 26% 50.3M 60s\n",
+ "407800K .......... .......... .......... .......... .......... 26% 45.6M 60s\n",
+ "407850K .......... .......... .......... .......... .......... 26% 51.4M 60s\n",
+ "407900K .......... .......... .......... .......... .......... 26% 52.5M 60s\n",
+ "407950K .......... .......... .......... .......... .......... 26% 53.4M 60s\n",
+ "408000K .......... .......... .......... .......... .......... 26% 43.0M 60s\n",
+ "408050K .......... .......... .......... .......... .......... 26% 52.2M 60s\n",
+ "408100K .......... .......... .......... .......... .......... 26% 50.8M 60s\n",
+ "408150K .......... .......... .......... .......... .......... 26% 52.9M 60s\n",
+ "408200K .......... .......... .......... .......... .......... 26% 44.6M 60s\n",
+ "408250K .......... .......... .......... .......... .......... 26% 53.2M 60s\n",
+ "408300K .......... .......... .......... .......... .......... 26% 51.7M 60s\n",
+ "408350K .......... .......... .......... .......... .......... 26% 54.1M 60s\n",
+ "408400K .......... .......... .......... .......... .......... 26% 45.9M 60s\n",
+ "408450K .......... .......... .......... .......... .......... 26% 51.9M 60s\n",
+ "408500K .......... .......... .......... .......... .......... 26% 52.7M 60s\n",
+ "408550K .......... .......... .......... .......... .......... 26% 52.9M 60s\n",
+ "408600K .......... .......... .......... .......... .......... 26% 46.9M 60s\n",
+ "408650K .......... .......... .......... .......... .......... 26% 35.7M 60s\n",
+ "408700K .......... .......... .......... .......... .......... 26% 1.38M 60s\n",
+ "408750K .......... .......... .......... .......... .......... 26% 22.2M 60s\n",
+ "408800K .......... .......... .......... .......... .......... 26% 8.54M 60s\n",
+ "408850K .......... .......... .......... .......... .......... 26% 20.1M 60s\n",
+ "408900K .......... .......... .......... .......... .......... 26% 22.8M 60s\n",
+ "408950K .......... .......... .......... .......... .......... 26% 18.8M 60s\n",
+ "409000K .......... .......... .......... .......... .......... 26% 29.2M 60s\n",
+ "409050K .......... .......... .......... .......... .......... 26% 34.2M 60s\n",
+ "409100K .......... .......... .......... .......... .......... 26% 51.5M 60s\n",
+ "409150K .......... .......... .......... .......... .......... 26% 39.0M 60s\n",
+ "409200K .......... .......... .......... .......... .......... 26% 44.2M 60s\n",
+ "409250K .......... .......... .......... .......... .......... 26% 54.4M 60s\n",
+ "409300K .......... .......... .......... .......... .......... 26% 53.8M 60s\n",
+ "409350K .......... .......... .......... .......... .......... 26% 52.0M 60s\n",
+ "409400K .......... .......... .......... .......... .......... 26% 45.7M 60s\n",
+ "409450K .......... .......... .......... .......... .......... 26% 53.1M 60s\n",
+ "409500K .......... .......... .......... .......... .......... 26% 54.1M 60s\n",
+ "409550K .......... .......... .......... .......... .......... 26% 51.4M 60s\n",
+ "409600K .......... .......... .......... .......... .......... 26% 44.1M 60s\n",
+ "409650K .......... .......... .......... .......... .......... 26% 53.9M 60s\n",
+ "409700K .......... .......... .......... .......... .......... 26% 54.9M 60s\n",
+ "409750K .......... .......... .......... .......... .......... 26% 53.7M 60s\n",
+ "409800K .......... .......... .......... .......... .......... 26% 45.2M 60s\n",
+ "409850K .......... .......... .......... .......... .......... 26% 53.2M 60s\n",
+ "409900K .......... .......... .......... .......... .......... 26% 52.8M 60s\n",
+ "409950K .......... .......... .......... .......... .......... 26% 52.9M 60s\n",
+ "410000K .......... .......... .......... .......... .......... 26% 42.9M 60s\n",
+ "410050K .......... .......... .......... .......... .......... 26% 51.7M 60s\n",
+ "410100K .......... .......... .......... .......... .......... 26% 53.3M 60s\n",
+ "410150K .......... .......... .......... .......... .......... 26% 34.5M 60s\n",
+ "410200K .......... .......... .......... .......... .......... 26% 1.52M 60s\n",
+ "410250K .......... .......... .......... .......... .......... 26% 10.2M 60s\n",
+ "410300K .......... .......... .......... .......... .......... 26% 10.8M 60s\n",
+ "410350K .......... .......... .......... .......... .......... 26% 13.6M 60s\n",
+ "410400K .......... .......... .......... .......... .......... 26% 22.0M 60s\n",
+ "410450K .......... .......... .......... .......... .......... 26% 15.5M 60s\n",
+ "410500K .......... .......... .......... .......... .......... 26% 50.6M 60s\n",
+ "410550K .......... .......... .......... .......... .......... 26% 24.8M 60s\n",
+ "410600K .......... .......... .......... .......... .......... 26% 36.9M 60s\n",
+ "410650K .......... .......... .......... .......... .......... 26% 46.0M 60s\n",
+ "410700K .......... .......... .......... .......... .......... 26% 48.7M 60s\n",
+ "410750K .......... .......... .......... .......... .......... 26% 51.6M 60s\n",
+ "410800K .......... .......... .......... .......... .......... 26% 43.6M 60s\n",
+ "410850K .......... .......... .......... .......... .......... 26% 51.9M 60s\n",
+ "410900K .......... .......... .......... .......... .......... 26% 49.8M 60s\n",
+ "410950K .......... .......... .......... .......... .......... 26% 51.1M 60s\n",
+ "411000K .......... .......... .......... .......... .......... 26% 45.2M 60s\n",
+ "411050K .......... .......... .......... .......... .......... 26% 52.2M 60s\n",
+ "411100K .......... .......... .......... .......... .......... 26% 49.5M 60s\n",
+ "411150K .......... .......... .......... .......... .......... 26% 51.9M 60s\n",
+ "411200K .......... .......... .......... .......... .......... 26% 44.1M 60s\n",
+ "411250K .......... .......... .......... .......... .......... 26% 49.2M 60s\n",
+ "411300K .......... .......... .......... .......... .......... 26% 51.4M 60s\n",
+ "411350K .......... .......... .......... .......... .......... 26% 51.4M 60s\n",
+ "411400K .......... .......... .......... .......... .......... 26% 44.8M 60s\n",
+ "411450K .......... .......... .......... .......... .......... 26% 51.8M 60s\n",
+ "411500K .......... .......... .......... .......... .......... 26% 51.7M 60s\n",
+ "411550K .......... .......... .......... .......... .......... 26% 51.8M 60s\n",
+ "411600K .......... .......... .......... .......... .......... 26% 43.0M 60s\n",
+ "411650K .......... .......... .......... .......... .......... 26% 43.3M 60s\n",
+ "411700K .......... .......... .......... .......... .......... 26% 51.5M 60s\n",
+ "411750K .......... .......... .......... .......... .......... 26% 1.37M 60s\n",
+ "411800K .......... .......... .......... .......... .......... 26% 10.2M 60s\n",
+ "411850K .......... .......... .......... .......... .......... 26% 3.31M 60s\n",
+ "411900K .......... .......... .......... .......... .......... 26% 48.5M 60s\n",
+ "411950K .......... .......... .......... .......... .......... 26% 51.8M 60s\n",
+ "412000K .......... .......... .......... .......... .......... 26% 2.32M 60s\n",
+ "412050K .......... .......... .......... .......... .......... 26% 51.9M 60s\n",
+ "412100K .......... .. 26% 35.5M=21s\n",
+ "\n",
+ "\n",
+ "Cannot write to ‘/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_000E.tif’ (Software caused connection abort).\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_040E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_040W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_050E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_050W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_060W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_070E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_070W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_080W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_090E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_090W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_100E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_100W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_110E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_130E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_140E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_150E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_160E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/00N_170E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_000E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_010W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_020W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_040E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_050E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_050W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_060W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_070E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_070W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_080E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_080W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_090E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_090W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_100E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_100W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_110E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_130E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_150E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_160E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10N_170E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_040E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_040W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_050E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_050W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_060W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_070W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_080W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_110E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_130E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_140E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_150E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_160E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_170E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/10S_180W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_000E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_010W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_020W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_030W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_040E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_050E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_060W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_070E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_070W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_080E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_080W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_090E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_090W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_100E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_100W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_110E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_110W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_120W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20N_160W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20S_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20S_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20S_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20S_040E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/20S_050E.tif: Transport endpoint is not connected\n",
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+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_120W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_130E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_130W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_140E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_140W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_150E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_150W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_160E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_160W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_170E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_170W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/70N_180W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_010E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_020E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_030E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_070E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_080E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_090E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_100E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_110E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_120E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_130E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_130W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_140E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_140W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_150E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_150W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_160E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_160W.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_170E.tif: Transport endpoint is not connected\n",
+ "/projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/80N_170W.tif: Transport endpoint is not connected\n"
+ ]
+ }
+ ],
"source": [
- "# for TILE in TILES: \n",
- "# string = \"wget https://glad.umd.edu/users/Potapov/GLCLUC2020/Forest_extent_2020/\" + TILE + \".tif -O /projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/\" + TILE + \".tif\"\n",
- "# os.system(string)\n",
+ "TILES = [\"00N_000E\",\"00N_010E\",\"00N_020E\",\"00N_030E\",\"00N_040E\",\"00N_040W\",\"00N_050E\",\"00N_050W\",\"00N_060W\",\"00N_070E\",\"00N_070W\",\"00N_080W\",\"00N_090E\",\"00N_090W\",\"00N_100E\",\"00N_100W\",\"00N_110E\",\"00N_120E\",\"00N_130E\",\"00N_140E\",\"00N_150E\",\"00N_160E\",\"00N_170E\",\"10N_000E\",\"10N_010E\",\"10N_010W\",\"10N_020E\",\"10N_020W\",\"10N_030E\",\"10N_040E\",\"10N_050E\",\"10N_050W\",\"10N_060W\",\"10N_070E\",\"10N_070W\",\"10N_080E\",\"10N_080W\",\"10N_090E\",\"10N_090W\",\"10N_100E\",\"10N_100W\",\"10N_110E\",\"10N_120E\",\"10N_130E\",\"10N_150E\",\"10N_160E\",\"10N_170E\",\"10S_010E\",\"10S_020E\",\"10S_030E\",\"10S_040E\",\"10S_040W\",\"10S_050E\",\"10S_050W\",\"10S_060W\",\"10S_070W\",\"10S_080W\",\"10S_110E\",\"10S_120E\",\"10S_130E\",\"10S_140E\",\"10S_150E\",\"10S_160E\",\"10S_170E\",\"10S_180W\",\"20N_000E\",\"20N_010E\",\"20N_010W\",\"20N_020E\",\"20N_020W\",\"20N_030E\",\"20N_030W\",\"20N_040E\",\"20N_050E\",\"20N_060W\",\"20N_070E\",\"20N_070W\",\"20N_080E\",\"20N_080W\",\"20N_090E\",\"20N_090W\",\"20N_100E\",\"20N_100W\",\"20N_110E\",\"20N_110W\",\"20N_120E\",\"20N_120W\",\"20N_160W\",\"20S_010E\",\"20S_020E\",\"20S_030E\",\"20S_040E\",\"20S_050E\",\"20S_050W\",\"20S_060W\",\"20S_070W\",\"20S_080W\",\"20S_110E\",\"20S_120E\",\"20S_130E\",\"20S_140E\",\"20S_150E\",\"20S_160E\",\"20S_180W\",\"30N_000E\",\"30N_010E\",\"30N_010W\",\"30N_020E\",\"30N_020W\",\"30N_030E\",\"30N_040E\",\"30N_050E\",\"30N_060E\",\"30N_070E\",\"30N_080E\",\"30N_080W\",\"30N_090E\",\"30N_090W\",\"30N_100E\",\"30N_100W\",\"30N_110E\",\"30N_110W\",\"30N_120E\",\"30N_120W\",\"30N_130E\",\"30N_160W\",\"30N_170W\",\"30S_010E\",\"30S_020E\",\"30S_030E\",\"30S_060W\",\"30S_070W\",\"30S_080W\",\"30S_110E\",\"30S_120E\",\"30S_130E\",\"30S_140E\",\"30S_150E\",\"30S_170E\",\"40N_000E\",\"40N_010E\",\"40N_010W\",\"40N_020E\",\"40N_020W\",\"40N_030E\",\"40N_040E\",\"40N_050E\",\"40N_060E\",\"40N_070E\",\"40N_070W\",\"40N_080E\",\"40N_080W\",\"40N_090E\",\"40N_090W\",\"40N_100E\",\"40N_100W\",\"40N_110E\",\"40N_110W\",\"40N_120E\",\"40N_120W\",\"40N_130E\",\"40N_130W\",\"40N_140E\",\"40S_070W\",\"40S_080W\",\"40S_140E\",\"40S_160E\",\"40S_170E\",\"50N_000E\",\"50N_010E\",\"50N_010W\",\"50N_020E\",\"50N_030E\",\"50N_040E\",\"50N_050E\",\"50N_060E\",\"50N_060W\",\"50N_070E\",\"50N_070W\",\"50N_080E\",\"50N_080W\",\"50N_090E\",\"50N_090W\",\"50N_100E\",\"50N_100W\",\"50N_110E\",\"50N_110W\",\"50N_120E\",\"50N_120W\",\"50N_130E\",\"50N_130W\",\"50N_140E\",\"50N_150E\",\"50S_060W\",\"50S_070W\",\"50S_080W\",\"60N_000E\",\"60N_010E\",\"60N_010W\",\"60N_020E\",\"60N_020W\",\"60N_030E\",\"60N_040E\",\"60N_050E\",\"60N_060E\",\"60N_060W\",\"60N_070E\",\"60N_070W\",\"60N_080E\",\"60N_080W\",\"60N_090E\",\"60N_090W\",\"60N_100E\",\"60N_100W\",\"60N_110E\",\"60N_110W\",\"60N_120E\",\"60N_120W\",\"60N_130E\",\"60N_130W\",\"60N_140E\",\"60N_140W\",\"60N_150E\",\"60N_150W\",\"60N_160E\",\"60N_160W\",\"60N_170E\",\"60N_170W\",\"60N_180W\",\"70N_000E\",\"70N_010E\",\"70N_020E\",\"70N_030E\",\"70N_040E\",\"70N_050E\",\"70N_060E\",\"70N_070E\",\"70N_070W\",\"70N_080E\",\"70N_080W\",\"70N_090E\",\"70N_090W\",\"70N_100E\",\"70N_100W\",\"70N_110E\",\"70N_110W\",\"70N_120E\",\"70N_120W\",\"70N_130E\",\"70N_130W\",\"70N_140E\",\"70N_140W\",\"70N_150E\",\"70N_150W\",\"70N_160E\",\"70N_160W\",\"70N_170E\",\"70N_170W\",\"70N_180W\",\"80N_010E\",\"80N_020E\",\"80N_030E\",\"80N_070E\",\"80N_080E\",\"80N_090E\",\"80N_100E\",\"80N_110E\",\"80N_120E\",\"80N_130E\",\"80N_130W\",\"80N_140E\",\"80N_140W\",\"80N_150E\",\"80N_150W\",\"80N_160E\",\"80N_160W\",\"80N_170E\",\"80N_170W\"]\n",
+ "len(TILES)\n",
+ "for TILE in TILES: \n",
+ " string = \"wget https://glad.umd.edu/users/Potapov/GLCLUC2020/Forest_height_2020/2020_\" + TILE + \".tif -O /projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_height_2020/2020_\" + TILE + \".tif\"\n",
+ " os.system(string)\n",
+ " # string = \"wget https://glad.umd.edu/users/Potapov/GLCLUC2020/Forest_extent_2020/\" + TILE + \".tif -O /projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2020/\" + TILE + \".tif\"\n",
+ " # os.system(string) \n",
"# string = \"wget https://glad.umd.edu/users/Potapov/GLCLUC2020/Forest_extent_2000/\" + TILE + \".tif -O /projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_extent_2000/\" + TILE + \".tif\"\n",
"# os.system(string)\n",
"# string = \"wget https://glad.umd.edu/users/Potapov/GLCLUC2020/Forest_height_2000/2000_\" + TILE + \".tif -O /projects/my-public-bucket/Data/Harris_et_al_PAPER/Potapov_Forest_height_2000/2000_\" + TILE + \".tif\"\n",
@@ -10289,7 +18834,7 @@
"# Desired_output_folder = '/projects/my-private-bucket/dps_output/run_IPCC_classes_ubuntu/Global_IPCC_classes_ADE_v4/'\n",
"# if not os.path.exists(Desired_output_folder):\n",
"# os.mkdir(Desired_output_folder)\n",
- "# TILES = [\"00N_000E\",\"00N_010E\",\"00N_020E\",\"00N_030E\",\"00N_040E\",\"00N_040W\",\"00N_050E\",\"00N_050W\",\"00N_060W\",\"00N_070E\",\"00N_070W\",\"00N_080W\",\"00N_090E\",\"00N_090W\",\"00N_100E\",\"00N_100W\",\"00N_110E\",\"00N_120E\",\"00N_130E\",\"00N_140E\",\"00N_150E\",\"00N_160E\",\"00N_170E\",\"10N_000E\",\"10N_010E\",\"10N_010W\",\"10N_020E\",\"10N_020W\",\"10N_030E\",\"10N_040E\",\"10N_050E\",\"10N_050W\",\"10N_060W\",\"10N_070E\",\"10N_070W\",\"10N_080E\",\"10N_080W\",\"10N_090E\",\"10N_090W\",\"10N_100E\",\"10N_100W\",\"10N_110E\",\"10N_120E\",\"10N_130E\",\"10N_150E\",\"10N_160E\",\"10N_170E\",\"10S_010E\",\"10S_020E\",\"10S_030E\",\"10S_040E\",\"10S_040W\",\"10S_050E\",\"10S_050W\",\"10S_060W\",\"10S_070W\",\"10S_080W\",\"10S_110E\",\"10S_120E\",\"10S_130E\",\"10S_140E\",\"10S_150E\",\"10S_160E\",\"10S_170E\",\"10S_180W\",\"20N_000E\",\"20N_010E\",\"20N_010W\",\"20N_020E\",\"20N_020W\",\"20N_030E\",\"20N_030W\",\"20N_040E\",\"20N_050E\",\"20N_060W\",\"20N_070E\",\"20N_070W\",\"20N_080E\",\"20N_080W\",\"20N_090E\",\"20N_090W\",\"20N_100E\",\"20N_100W\",\"20N_110E\",\"20N_110W\",\"20N_120E\",\"20N_120W\",\"20N_160W\",\"20S_010E\",\"20S_020E\",\"20S_030E\",\"20S_040E\",\"20S_050E\",\"20S_050W\",\"20S_060W\",\"20S_070W\",\"20S_080W\",\"20S_110E\",\"20S_120E\",\"20S_130E\",\"20S_140E\",\"20S_150E\",\"20S_160E\",\"20S_180W\",\"30N_000E\",\"30N_010E\",\"30N_010W\",\"30N_020E\",\"30N_020W\",\"30N_030E\",\"30N_040E\",\"30N_050E\",\"30N_060E\",\"30N_070E\",\"30N_080E\",\"30N_080W\",\"30N_090E\",\"30N_090W\",\"30N_100E\",\"30N_100W\",\"30N_110E\",\"30N_110W\",\"30N_120E\",\"30N_120W\",\"30N_130E\",\"30N_160W\",\"30N_170W\",\"30S_010E\",\"30S_020E\",\"30S_030E\",\"30S_060W\",\"30S_070W\",\"30S_080W\",\"30S_110E\",\"30S_120E\",\"30S_130E\",\"30S_140E\",\"30S_150E\",\"30S_170E\",\"40N_000E\",\"40N_010E\",\"40N_010W\",\"40N_020E\",\"40N_020W\",\"40N_030E\",\"40N_040E\",\"40N_050E\",\"40N_060E\",\"40N_070E\",\"40N_070W\",\"40N_080E\",\"40N_080W\",\"40N_090E\",\"40N_090W\",\"40N_100E\",\"40N_100W\",\"40N_110E\",\"40N_110W\",\"40N_120E\",\"40N_120W\",\"40N_130E\",\"40N_130W\",\"40N_140E\",\"40S_070W\",\"40S_080W\",\"40S_140E\",\"40S_160E\",\"40S_170E\",\"50N_000E\",\"50N_010E\",\"50N_010W\",\"50N_020E\",\"50N_030E\",\"50N_040E\",\"50N_050E\",\"50N_060E\",\"50N_060W\",\"50N_070E\",\"50N_070W\",\"50N_080E\",\"50N_080W\",\"50N_090E\",\"50N_090W\",\"50N_100E\",\"50N_100W\",\"50N_110E\",\"50N_110W\",\"50N_120E\",\"50N_120W\",\"50N_130E\",\"50N_130W\",\"50N_140E\",\"50N_150E\",\"50S_060W\",\"50S_070W\",\"50S_080W\",\"60N_000E\",\"60N_010E\",\"60N_010W\",\"60N_020E\",\"60N_020W\",\"60N_030E\",\"60N_040E\",\"60N_050E\",\"60N_060E\",\"60N_060W\",\"60N_070E\",\"60N_070W\",\"60N_080E\",\"60N_080W\",\"60N_090E\",\"60N_090W\",\"60N_100E\",\"60N_100W\",\"60N_110E\",\"60N_110W\",\"60N_120E\",\"60N_120W\",\"60N_130E\",\"60N_130W\",\"60N_140E\",\"60N_140W\",\"60N_150E\",\"60N_150W\",\"60N_160E\",\"60N_160W\",\"60N_170E\",\"60N_170W\",\"60N_180W\",\"70N_000E\",\"70N_010E\",\"70N_020E\",\"70N_030E\",\"70N_040E\",\"70N_050E\",\"70N_060E\",\"70N_070E\",\"70N_070W\",\"70N_080E\",\"70N_080W\",\"70N_090E\",\"70N_090W\",\"70N_100E\",\"70N_100W\",\"70N_110E\",\"70N_110W\",\"70N_120E\",\"70N_120W\",\"70N_130E\",\"70N_130W\",\"70N_140E\",\"70N_140W\",\"70N_150E\",\"70N_150W\",\"70N_160E\",\"70N_160W\",\"70N_170E\",\"70N_170W\",\"70N_180W\",\"80N_010E\",\"80N_020E\",\"80N_030E\",\"80N_070E\",\"80N_080E\",\"80N_090E\",\"80N_100E\",\"80N_110E\",\"80N_120E\",\"80N_130E\",\"80N_130W\",\"80N_140E\",\"80N_140W\",\"80N_150E\",\"80N_150W\",\"80N_160E\",\"80N_160W\",\"80N_170E\",\"80N_170W\"]\n",
+ "TILES = [\"00N_000E\",\"00N_010E\",\"00N_020E\",\"00N_030E\",\"00N_040E\",\"00N_040W\",\"00N_050E\",\"00N_050W\",\"00N_060W\",\"00N_070E\",\"00N_070W\",\"00N_080W\",\"00N_090E\",\"00N_090W\",\"00N_100E\",\"00N_100W\",\"00N_110E\",\"00N_120E\",\"00N_130E\",\"00N_140E\",\"00N_150E\",\"00N_160E\",\"00N_170E\",\"10N_000E\",\"10N_010E\",\"10N_010W\",\"10N_020E\",\"10N_020W\",\"10N_030E\",\"10N_040E\",\"10N_050E\",\"10N_050W\",\"10N_060W\",\"10N_070E\",\"10N_070W\",\"10N_080E\",\"10N_080W\",\"10N_090E\",\"10N_090W\",\"10N_100E\",\"10N_100W\",\"10N_110E\",\"10N_120E\",\"10N_130E\",\"10N_150E\",\"10N_160E\",\"10N_170E\",\"10S_010E\",\"10S_020E\",\"10S_030E\",\"10S_040E\",\"10S_040W\",\"10S_050E\",\"10S_050W\",\"10S_060W\",\"10S_070W\",\"10S_080W\",\"10S_110E\",\"10S_120E\",\"10S_130E\",\"10S_140E\",\"10S_150E\",\"10S_160E\",\"10S_170E\",\"10S_180W\",\"20N_000E\",\"20N_010E\",\"20N_010W\",\"20N_020E\",\"20N_020W\",\"20N_030E\",\"20N_030W\",\"20N_040E\",\"20N_050E\",\"20N_060W\",\"20N_070E\",\"20N_070W\",\"20N_080E\",\"20N_080W\",\"20N_090E\",\"20N_090W\",\"20N_100E\",\"20N_100W\",\"20N_110E\",\"20N_110W\",\"20N_120E\",\"20N_120W\",\"20N_160W\",\"20S_010E\",\"20S_020E\",\"20S_030E\",\"20S_040E\",\"20S_050E\",\"20S_050W\",\"20S_060W\",\"20S_070W\",\"20S_080W\",\"20S_110E\",\"20S_120E\",\"20S_130E\",\"20S_140E\",\"20S_150E\",\"20S_160E\",\"20S_180W\",\"30N_000E\",\"30N_010E\",\"30N_010W\",\"30N_020E\",\"30N_020W\",\"30N_030E\",\"30N_040E\",\"30N_050E\",\"30N_060E\",\"30N_070E\",\"30N_080E\",\"30N_080W\",\"30N_090E\",\"30N_090W\",\"30N_100E\",\"30N_100W\",\"30N_110E\",\"30N_110W\",\"30N_120E\",\"30N_120W\",\"30N_130E\",\"30N_160W\",\"30N_170W\",\"30S_010E\",\"30S_020E\",\"30S_030E\",\"30S_060W\",\"30S_070W\",\"30S_080W\",\"30S_110E\",\"30S_120E\",\"30S_130E\",\"30S_140E\",\"30S_150E\",\"30S_170E\",\"40N_000E\",\"40N_010E\",\"40N_010W\",\"40N_020E\",\"40N_020W\",\"40N_030E\",\"40N_040E\",\"40N_050E\",\"40N_060E\",\"40N_070E\",\"40N_070W\",\"40N_080E\",\"40N_080W\",\"40N_090E\",\"40N_090W\",\"40N_100E\",\"40N_100W\",\"40N_110E\",\"40N_110W\",\"40N_120E\",\"40N_120W\",\"40N_130E\",\"40N_130W\",\"40N_140E\",\"40S_070W\",\"40S_080W\",\"40S_140E\",\"40S_160E\",\"40S_170E\",\"50N_000E\",\"50N_010E\",\"50N_010W\",\"50N_020E\",\"50N_030E\",\"50N_040E\",\"50N_050E\",\"50N_060E\",\"50N_060W\",\"50N_070E\",\"50N_070W\",\"50N_080E\",\"50N_080W\",\"50N_090E\",\"50N_090W\",\"50N_100E\",\"50N_100W\",\"50N_110E\",\"50N_110W\",\"50N_120E\",\"50N_120W\",\"50N_130E\",\"50N_130W\",\"50N_140E\",\"50N_150E\",\"50S_060W\",\"50S_070W\",\"50S_080W\",\"60N_000E\",\"60N_010E\",\"60N_010W\",\"60N_020E\",\"60N_020W\",\"60N_030E\",\"60N_040E\",\"60N_050E\",\"60N_060E\",\"60N_060W\",\"60N_070E\",\"60N_070W\",\"60N_080E\",\"60N_080W\",\"60N_090E\",\"60N_090W\",\"60N_100E\",\"60N_100W\",\"60N_110E\",\"60N_110W\",\"60N_120E\",\"60N_120W\",\"60N_130E\",\"60N_130W\",\"60N_140E\",\"60N_140W\",\"60N_150E\",\"60N_150W\",\"60N_160E\",\"60N_160W\",\"60N_170E\",\"60N_170W\",\"60N_180W\",\"70N_000E\",\"70N_010E\",\"70N_020E\",\"70N_030E\",\"70N_040E\",\"70N_050E\",\"70N_060E\",\"70N_070E\",\"70N_070W\",\"70N_080E\",\"70N_080W\",\"70N_090E\",\"70N_090W\",\"70N_100E\",\"70N_100W\",\"70N_110E\",\"70N_110W\",\"70N_120E\",\"70N_120W\",\"70N_130E\",\"70N_130W\",\"70N_140E\",\"70N_140W\",\"70N_150E\",\"70N_150W\",\"70N_160E\",\"70N_160W\",\"70N_170E\",\"70N_170W\",\"70N_180W\",\"80N_010E\",\"80N_020E\",\"80N_030E\",\"80N_070E\",\"80N_080E\",\"80N_090E\",\"80N_100E\",\"80N_110E\",\"80N_120E\",\"80N_130E\",\"80N_130W\",\"80N_140E\",\"80N_140W\",\"80N_150E\",\"80N_150W\",\"80N_160E\",\"80N_160W\",\"80N_170E\",\"80N_170W\"]\n",
"# TILES = [\"70N_030E\"]\n",
"\n",
"# i = 0\n",
@@ -10392,17 +18937,21 @@
],
"metadata": {
"kernelspec": {
- "display_name": "R",
- "language": "R",
- "name": "ir"
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
},
"language_info": {
- "codemirror_mode": "r",
- "file_extension": ".r",
- "mimetype": "text/x-r-source",
- "name": "R",
- "pygments_lexer": "r",
- "version": "4.2.3"
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
}
},
"nbformat": 4,