pipeline/slim-analysis-dah.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 2.2.1     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.2.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 1.4.1     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 0.7.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 0.7.2     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.2.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.1.1     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.2.0\n",
      "\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",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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",
      "The following object is masked from ‘package:tidyr’:\n",
      "\n",
      "    extract\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(raster)\n",
    "library(gdalUtils)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rm(list=ls())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all the rasters aso tiles\n",
    "#list files in a specific folder\n",
    "veg_aso <- list.files(path = \"~/dah/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180846_1002_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[2]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180847_1002_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[3]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181108_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[4]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181109_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[5]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181110_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[6]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181111_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[7]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181112_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[8]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181113_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[9]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181320_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[10]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181322_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[11]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181323_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "\n",
      "[[1]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180846_1002_3B_AnalyticMS_SR_clip/preds/20180528_180846_1002_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[2]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180847_1002_3B_AnalyticMS_SR_clip/preds/20180528_180847_1002_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[3]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181108_1025_3B_AnalyticMS_SR_clip/preds/20180528_181108_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[4]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181109_1025_3B_AnalyticMS_SR_clip/preds/20180528_181109_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[5]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181110_1025_3B_AnalyticMS_SR_clip/preds/20180528_181110_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[6]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181111_1025_3B_AnalyticMS_SR_clip/preds/20180528_181111_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[7]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181112_1025_3B_AnalyticMS_SR_clip/preds/20180528_181112_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[8]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181113_1025_3B_AnalyticMS_SR_clip/preds/20180528_181113_1025_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[9]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181320_1005_3B_AnalyticMS_SR_clip/preds/20180528_181320_1005_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[10]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181322_1005_3B_AnalyticMS_SR_clip/preds/20180528_181322_1005_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n",
      "[[11]]\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181323_1005_3B_AnalyticMS_SR_clip/preds/20180528_181323_1005_3B_AnalyticMS_SR_clip_merged.tif\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "asos <- list()\n",
    "preds <-list()\n",
    "for (vaso in veg_aso)\n",
    "{\n",
    "    #print(vaso)\n",
    "    recur = list.files(path=paste(\"~/dah/\",vaso,sep=\"\"),recursive = TRUE)\n",
    "    #print(recur)\n",
    "    for (filename in recur)\n",
    "        {\n",
    "          if (startsWith(filename,'mask/') & endsWith(filename,\"merged.tif\"))\n",
    "              asos <- append(asos,paste(\"~/dah/\",vaso,\"/\",filename,sep=\"\"))\n",
    "              \n",
    "           if (startsWith(filename,'preds/') & endsWith(filename,\"merged.tif\"))\n",
    "              preds <- append(preds,paste(\"~/dah/\",vaso,\"/\",filename,sep=\"\"))    \n",
    "        }\n",
    "}\n",
    "\n",
    "print(asos) \n",
    "print(preds)        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 11\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n",
      " $ : chr \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:201805\"| __truncated__\n"
     ]
    }
   ],
   "source": [
    "str(asos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_un <- unlist(asos)\n",
    "pred_un <- unlist(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_un_list <- paste(aso_un, collapse=' ' )\n",
    "pred_un_list <- paste(pred_un, collapse=' ' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "system(\"mkdir aso_dah\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "system(\"mkdir aso_dah_pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
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       " [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file.copy(from=pred_un, to='./aso_dah_pred', \n",
    "          overwrite = TRUE, recursive = FALSE, \n",
    "          copy.mode = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180846_1002_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_180847_1002_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181108_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181109_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181110_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181111_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181112_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181113_1025_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181320_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181322_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n",
      "[1] \"~/dah/planet-snowcover-experiments:planet-snowcover-2020-05-02-21-00-53-062:planet-snowcover-imagery-dah:20180528_181323_1005_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCATE_20180528_binary_merged.tif\"\n"
     ]
    }
   ],
   "source": [
    "for(i in 1:length(aso_un))\n",
    "    {\n",
    "     print(aso_un[i])\n",
    "    file.copy(from=aso_un[i],to=paste('./aso_dah/',i,'-ASO_3M_SD_USCATE_20180528_binary_merged.tif',sep=\"\"))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fo <- list.files(path = \"./aso_dah\", pattern = \".tif$\", full.names = TRUE)\n",
    "rlo <- lapply(fo, raster)\n",
    "\n",
    "aso_dem <- do.call(merge, c(rlo, tolerance = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f <- list.files(path = \"./aso_dah_pred\", pattern = \".tif$\", full.names = TRUE)\n",
    "rl <- lapply(f, raster)\n",
    "\n",
    "aso_dem_pred <- do.call(merge, c(rl, tolerance = 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 32256, 856300032  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpWtNn4j/raster/r_tmp_2020-05-05_043349_9134_34752.grd \n",
       "names       : layer \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EvpKDD1J9XctO2vS/k3EJiTBoRjUK5jr9hoEOwAfzC9IlXbeU615ass+oWxtuwD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\ngmAHAAAgCYIdAACAJP4LdRDthexCrBMAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(aso_dem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26557, 32256, 856622592  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695065e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22956  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpWtNn4j/raster/r_tmp_2020-05-05_043125_9134_88626.grd \n",
       "names       : layer \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+7cuQULFtx7770jR4588sknGxoaGhsb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\n/weidgBAm3KdZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(aso_dem_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the inter,ediate raster\n",
    "writeRaster(aso_dem, filename=\"aso_dah_test_only.tif\", format=\"GTiff\", overwrite=TRUE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeRaster(aso_dem_pred, filename=\"aso_dah_pred_test_only.tif\", format=\"GTiff\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem <- raster(\"aso_dah_test_only.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_pred <- raster(\"aso_dah_pred_test_only.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 32256, 856300032  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/aso_dah_test_only.tif \n",
       "names       : aso_dah_test_only \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26557, 32256, 856622592  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695065e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22956  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/aso_dah_pred_test_only.tif \n",
       "names       : aso_dah_pred_test_only \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reproject aso_dem because it doesn't have the same resolution\n",
    "aso_dem_reproj <- projectRaster(aso_dem, aso_dem_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df <- stack(aso_dem_reproj,aso_dem_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "veg_vegResamp <- raster(\"veg_reasampl2point1by1point6.tif\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "veg_crop<-crop(veg_vegResamp,extent(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 32256, 856300032  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpWtNn4j/raster/r_tmp_2020-05-05_165923_9134_33912.grd \n",
       "names       : veg_reasampl2point1by1point6 \n",
       "values      : 0, 54.08412  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veg_crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeRaster(veg_crop, filename=\"aso_dah_ch_test_only.tif\", format=\"GTiff\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 32256, 856300032  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpWtNn4j/raster/r_tmp_2020-05-05_165923_9134_33912.grd \n",
       "names       : veg_reasampl2point1by1point6 \n",
       "values      : 0, 54.08412  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veg_crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterStack \n",
       "dimensions  : 26557, 32256, 856622592, 2  (nrow, ncol, ncell, nlayers)\n",
       "resolution  : 2.145767e-05, 1.695065e-05  (x, y)\n",
       "extent      : -119.8828, -119.1907, 37.7794, 38.22956  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "names       : aso_dah_test_only, aso_dah_pred_test_only \n",
       "min values  :     -0.0001762735,           0.0000000000 \n",
       "max values  :                 1,                      1 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WUylJSUFN12Zcvp06cJdgAQOF27mnHHscmGN+08L6xFBoTTpUuXkq5Jqv1TbYt7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\nYII=",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(aso_dem_reproj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_matrix <- rasterToPoints(df)\n",
    "#head(data_matrix)\n",
    "#datafra_comb <- data_matrix %>% as.data.frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "veg_crop_aso <- crop(veg_crop, extent(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask all the pixels below 2\n",
    "veg_crop_h <- veg_crop_aso\n",
    "veg_crop_h[veg_crop_h < 2] <- NA\n",
    "aso_demabove2 <- mask(aso_dem, veg_crop_h, filename=\"aso_dahabove2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predabove2 <- mask(aso_dem_pred, veg_crop_h,filename=\"pred_dahabove2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predabove2 <- raster(\"pred_dahabove2_test.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_demabove2 <- raster(\"aso_dahabove2_test.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_pred <- getValues(aso_dem_predabove2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_aso <- getValues(aso_demabove2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_veg_perf<- confusionMatrix(vals_aso, vals_pred,mode = \"prec_recall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_veg_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create below two mask all excpet the ones below .2\n",
    "veg_crop_h <- veg_crop_aso\n",
    "veg_crop_h[veg_crop_h > .2] <- NA\n",
    "aso_vegbpoint2 <- mask(aso_dem, veg_crop_h, filename=\"aso_ndvibelowpoint2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predvegbpoint2 <- mask(aso_dem_pred, veg_crop_h,filename=\"pred_ndvibelowpoint2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_pred <- getValues(aso_dem_predvegbpoint2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_aso <- getValues(aso_vegbpoint2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_veg_perf<- confusionMatrix(vals_aso, vals_pred,mode = \"prec_recall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction        0        1\n",
       "         0  9026149 10949027\n",
       "         1    73676  8734215\n",
       "                                          \n",
       "               Accuracy : 0.617           \n",
       "                 95% CI : (0.6169, 0.6172)\n",
       "    No Information Rate : 0.6838          \n",
       "    P-Value [Acc > NIR] : 1               \n",
       "                                          \n",
       "                  Kappa : 0.3297          \n",
       " Mcnemar's Test P-Value : <2e-16          \n",
       "                                          \n",
       "              Precision : 0.4519          \n",
       "                 Recall : 0.9919          \n",
       "                     F1 : 0.6209          \n",
       "             Prevalence : 0.3162          \n",
       "         Detection Rate : 0.3136          \n",
       "   Detection Prevalence : 0.6940          \n",
       "      Balanced Accuracy : 0.7178          \n",
       "                                          \n",
       "       'Positive' Class : 0               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low_veg_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create below two and above .2\n",
    "veg_crop_h <- veg_crop_aso\n",
    "veg_crop_h[veg_crop_h < .2 &  veg_crop_h > 2] <- NA\n",
    "aso_vegabovepoint2below2 <- mask(aso_dem, veg_crop_h, filename=\"aso_ndviabovepoint2andbelow2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predabovepoint2andbelow2 <- mask(aso_dem_pred, veg_crop_h,filename=\"pred_ndviabovepoint2andbelow2_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_pred <- getValues(aso_dem_predabovepoint2andbelow2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_aso <- getValues(aso_vegabovepoint2below2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "med_veg_perf<- confusionMatrix(vals_aso, vals_pred,mode = \"prec_recall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction        0        1\n",
       "         0 28179704 22939789\n",
       "         1   181878 13806255\n",
       "                                         \n",
       "               Accuracy : 0.6449         \n",
       "                 95% CI : (0.6448, 0.645)\n",
       "    No Information Rate : 0.5644         \n",
       "    P-Value [Acc > NIR] : < 2.2e-16      \n",
       "                                         \n",
       "                  Kappa : 0.3383         \n",
       " Mcnemar's Test P-Value : < 2.2e-16      \n",
       "                                         \n",
       "              Precision : 0.5513         \n",
       "                 Recall : 0.9936         \n",
       "                     F1 : 0.7091         \n",
       "             Prevalence : 0.4356         \n",
       "         Detection Rate : 0.4328         \n",
       "   Detection Prevalence : 0.7852         \n",
       "      Balanced Accuracy : 0.6847         \n",
       "                                         \n",
       "       'Positive' Class : 0              \n",
       "                                         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_veg_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in eval(expr, envir, enclos): object 'med_veg_perf' not found\n",
     "execution_count": 1,
     "output_type": "error",
     "traceback": [
      "Error in eval(expr, envir, enclos): object 'med_veg_perf' not found\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "med_veg_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class <- c(\"< .2m\", \"> .2 & < 2m\",\" > 2m\")\n",
    "f_score <- c(0.6209,0.7091,0.7526)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/hj7vEr6sf/ssXuapIeiie4dLlsPYT0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\nFJIQVUAhCVEF/h+zvKKr/gTGJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "perf<- data.frame(class, f_score)\n",
    "colnames(perf) <- c('class','f_score')\n",
    "perf %>% ggplot() + geom_bar(aes(class,f_score),stat=\"identity\") +\n",
    " labs(x=\"Vegetation class\" , y=\"F-Score\",subtitle=\"+NDVI model\") + theme_minimal(base_size = 12)\n",
    "ggsave(filename = \"gg-ndvi+allclasses.png\",dpi = 300,height=4,width=4,units=\"in\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in eval(expr, envir, enclos): object 'veg_crop_aso' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in eval(expr, envir, enclos): object 'veg_crop_aso' not found\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "# check the result\n",
    "\n",
    "# mask all the pixels below 1\n",
    "veg_crop_h <- veg_crop_aso\n",
    "veg_crop_h[veg_crop_h < 1] <- NA\n",
    "aso_demabove1 <- mask(aso_dem, veg_crop_h, filename=\"aso_ndviabove1_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26544, 31232, 829022208  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695862e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/aso_ndvi_pred_test_only.tif \n",
       "names       : aso_ndvi_pred_test_only \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 31232, 829115904  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpQ4jTi5/raster/r_tmp_2020-05-02_055321_2738_67490.grd \n",
       "names       : layer \n",
       "values      : 1, 54.08412  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veg_crop_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_pred_prj <- projectRaster(aso_dem_pred,veg_crop_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 31232, 829115904  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpQ4jTi5/raster/r_tmp_2020-05-02_061011_2738_85770.grd \n",
       "names       : aso_ndvi_pred_test_only \n",
       "values      : -5.875066e-05, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem_pred_prj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predabove1 <- mask(aso_dem_pred_prj, \n",
    "                           veg_crop_h,filename=\"pred_ndviabove1_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 31232, 829115904  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/pred_ndviabove1_test.tif \n",
       "names       : pred_ndviabove1_test \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem_predabove1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 31232, 829115904  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /tmp/RtmpQ4jTi5/raster/r_tmp_2020-05-02_055321_2738_67490.grd \n",
       "names       : layer \n",
       "values      : 1, 54.08412  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veg_crop_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeRaster(veg_crop_h, filename=\"veg_above_1.tif\", format=\"GTiff\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem <- raster(\"aso_ndvi_test_only.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26544, 31232, 829022208  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695862e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/aso_ndvi_test_only.tif \n",
       "names       : aso_ndvi_test_only \n",
       "values      : 0, 1  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aso_dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class       : RasterLayer \n",
       "dimensions  : 26547, 31232, 829115904  (nrow, ncol, ncell)\n",
       "resolution  : 2.145767e-05, 1.695663e-05  (x, y)\n",
       "extent      : -119.8608, -119.1907, 37.7794, 38.22955  (xmin, xmax, ymin, ymax)\n",
       "coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 \n",
       "data source : /home/ubuntu/planet-snowcover/pipeline/veg_above_1.tif \n",
       "names       : veg_above_1 \n",
       "values      : 1, 54.08412  (min, max)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veg_crop_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_prj <- projectRaster(aso_dem,veg_crop_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask all the pixels below 1\n",
    "\n",
    "aso_demabove1 <- mask(aso_dem_prj, veg_crop_h, filename=\"aso_ndviabove1_test.tif\",overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_above1<- raster('aso_ndviabove1_test.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_dem_predabove1<- raster('pred_ndviabove1_test.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "veg_crop_h <- raster('veg_above_1.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "onem_df <- stack(aso_dem_predabove1,aso_dem_predabove1,veg_crop_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>x</th><th scope=col>y</th><th scope=col>pred_ndviabove1_test.1</th><th scope=col>pred_ndviabove1_test.2</th><th scope=col>veg_above_1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-119.4995</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.981519 </td></tr>\n",
       "\t<tr><td>-119.4995</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.463764 </td></tr>\n",
       "\t<tr><td>-119.4991</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.088179 </td></tr>\n",
       "\t<tr><td>-119.4991</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.055851 </td></tr>\n",
       "\t<tr><td>-119.4989</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.037520 </td></tr>\n",
       "\t<tr><td>-119.4989</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.102266 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " x & y & pred\\_ndviabove1\\_test.1 & pred\\_ndviabove1\\_test.2 & veg\\_above\\_1\\\\\n",
       "\\hline\n",
       "\t -119.4995 & 38.18838  & NA        & NA        & 1.981519 \\\\\n",
       "\t -119.4995 & 38.18838  & NA        & NA        & 1.463764 \\\\\n",
       "\t -119.4991 & 38.18838  & NA        & NA        & 1.088179 \\\\\n",
       "\t -119.4991 & 38.18838  & NA        & NA        & 1.055851 \\\\\n",
       "\t -119.4989 & 38.18838  & NA        & NA        & 1.037520 \\\\\n",
       "\t -119.4989 & 38.18838  & NA        & NA        & 1.102266 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "x | y | pred_ndviabove1_test.1 | pred_ndviabove1_test.2 | veg_above_1 | \n",
       "|---|---|---|---|---|---|\n",
       "| -119.4995 | 38.18838  | NA        | NA        | 1.981519  | \n",
       "| -119.4995 | 38.18838  | NA        | NA        | 1.463764  | \n",
       "| -119.4991 | 38.18838  | NA        | NA        | 1.088179  | \n",
       "| -119.4991 | 38.18838  | NA        | NA        | 1.055851  | \n",
       "| -119.4989 | 38.18838  | NA        | NA        | 1.037520  | \n",
       "| -119.4989 | 38.18838  | NA        | NA        | 1.102266  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     x         y        pred_ndviabove1_test.1 pred_ndviabove1_test.2\n",
       "[1,] -119.4995 38.18838 NA                     NA                    \n",
       "[2,] -119.4995 38.18838 NA                     NA                    \n",
       "[3,] -119.4991 38.18838 NA                     NA                    \n",
       "[4,] -119.4991 38.18838 NA                     NA                    \n",
       "[5,] -119.4989 38.18838 NA                     NA                    \n",
       "[6,] -119.4989 38.18838 NA                     NA                    \n",
       "     veg_above_1\n",
       "[1,] 1.981519   \n",
       "[2,] 1.463764   \n",
       "[3,] 1.088179   \n",
       "[4,] 1.055851   \n",
       "[5,] 1.037520   \n",
       "[6,] 1.102266   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_matrix <- rasterToPoints(onem_df)\n",
    "head(data_matrix)\n",
    "datafra_comb <- data_matrix %>% as.data.frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>x</th><th scope=col>y</th><th scope=col>pred_ndviabove1_test.1</th><th scope=col>pred_ndviabove1_test.2</th><th scope=col>veg_above_1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-119.4995</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.981519 </td></tr>\n",
       "\t<tr><td>-119.4995</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.463764 </td></tr>\n",
       "\t<tr><td>-119.4991</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.088179 </td></tr>\n",
       "\t<tr><td>-119.4991</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.055851 </td></tr>\n",
       "\t<tr><td>-119.4989</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.037520 </td></tr>\n",
       "\t<tr><td>-119.4989</td><td>38.18838 </td><td>NA       </td><td>NA       </td><td>1.102266 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " x & y & pred\\_ndviabove1\\_test.1 & pred\\_ndviabove1\\_test.2 & veg\\_above\\_1\\\\\n",
       "\\hline\n",
       "\t -119.4995 & 38.18838  & NA        & NA        & 1.981519 \\\\\n",
       "\t -119.4995 & 38.18838  & NA        & NA        & 1.463764 \\\\\n",
       "\t -119.4991 & 38.18838  & NA        & NA        & 1.088179 \\\\\n",
       "\t -119.4991 & 38.18838  & NA        & NA        & 1.055851 \\\\\n",
       "\t -119.4989 & 38.18838  & NA        & NA        & 1.037520 \\\\\n",
       "\t -119.4989 & 38.18838  & NA        & NA        & 1.102266 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "x | y | pred_ndviabove1_test.1 | pred_ndviabove1_test.2 | veg_above_1 | \n",
       "|---|---|---|---|---|---|\n",
       "| -119.4995 | 38.18838  | NA        | NA        | 1.981519  | \n",
       "| -119.4995 | 38.18838  | NA        | NA        | 1.463764  | \n",
       "| -119.4991 | 38.18838  | NA        | NA        | 1.088179  | \n",
       "| -119.4991 | 38.18838  | NA        | NA        | 1.055851  | \n",
       "| -119.4989 | 38.18838  | NA        | NA        | 1.037520  | \n",
       "| -119.4989 | 38.18838  | NA        | NA        | 1.102266  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  x         y        pred_ndviabove1_test.1 pred_ndviabove1_test.2 veg_above_1\n",
       "1 -119.4995 38.18838 NA                     NA                     1.981519   \n",
       "2 -119.4995 38.18838 NA                     NA                     1.463764   \n",
       "3 -119.4991 38.18838 NA                     NA                     1.088179   \n",
       "4 -119.4991 38.18838 NA                     NA                     1.055851   \n",
       "5 -119.4989 38.18838 NA                     NA                     1.037520   \n",
       "6 -119.4989 38.18838 NA                     NA                     1.102266   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(datafra_comb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(datafra_comb) <- c('x','y','ndvi_pred','ndvi_aso','veg_height')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "184171620"
      ],
      "text/latex": [
       "184171620"
      ],
      "text/markdown": [
       "184171620"
      ],
      "text/plain": [
       "[1] 184171620"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(datafra_comb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "26971251"
      ],
      "text/latex": [
       "26971251"
      ],
      "text/markdown": [
       "26971251"
      ],
      "text/plain": [
       "[1] 26971251"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafra_comb%>% filter(!is.na(ndvi_pred)) %>% nrow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_not_na <- datafra_comb%>% filter(!is.na(ndvi_pred)) %>% as.data.frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C+VyHJ7BwT4qKkhHZgp0n30pGmTx+SL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\nSIAFhARYQEiABYQEWEBIgAX/H1TUQ5ki9Qh6AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of data_not_na$ndvi_pred”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data_not_na$ndvi_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hVJhbRjfrZI2dqSRq+SVPPHhhqeLmvu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\nnCzdZRW9HNoAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Histogram of data_not_na$ndvi_aso”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data_not_na$ndvi_aso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(data_not_na$ndvi_aso,data_not_na$ndvi_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist(data_not_na$veg_height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in head(new_ndvi_by): object 'new_ndvi_by' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in head(new_ndvi_by): object 'new_ndvi_by' not found\nTraceback:\n",
      "1. head(new_ndvi_by)"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}