TIMESAT & CropPhenology.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(rJava)\n",
    "library (CropPhenology)\n",
    "library(data.table)\n",
    "library(rTIMESAT)\n",
    "library(magrittr)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "path <- \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a\"\n",
    "setwd(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TIMESAT package is used for extracting seasonality parameters. TIMESAT smooths each of the time-series in a defined area and extracts seasonality data such as start and end of the season or the length of the season.\n",
    "\n",
    "TIMESAT need as input the number of seasons and their approximate timing. It uses three smothing methods:\n",
    "i) Savitzky-Golay\n",
    "ii) Least Squares - Assymetric Gaussians\n",
    "iii) Double Logistic functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "TSF_main <- function(d, nptperyear = 25, cache = F){\n",
    "  \n",
    "  ## 1. Prepare inputs\n",
    "  sitename <- \"maria1\"\n",
    "  nyear <- floor(nrow(d)/nptperyear)\n",
    "  npt   <- nyear * nptperyear\n",
    "  \n",
    "  # pls make sure it's complete year in input\n",
    "  file_y   <- sprintf(\"TSM_%s_y.txt\", sitename)\n",
    "  file_w   <- sprintf(\"TSM_%s_w.txt\", sitename)\n",
    "  file_set <- sprintf(\"TSM_%s.set\", sitename)\n",
    "  \n",
    "  write_input(d$NDVI  , file_y, nptperyear)\n",
    "  write_input(rep(1,nrow(d)) %>% as.numeric(), file_w, nptperyear)\n",
    "  \n",
    "  ## 2. Update options\n",
    "  options <- list(\n",
    "    job_name            = \"\",\n",
    "    file_y              = file_y,             # Data file list/name\n",
    "    file_w              = file_w,             # Mask file list/name\n",
    "    nyear_and_nptperear = c(nyear, nptperyear),      # No. years and no. points per year\n",
    "    ylu                 = c(-9999, 9999),     # Valid data range (lower upper)\n",
    "    \n",
    "  ## In TIMESAT cloud classifications and other ancillary data may be used to assign weights to the values in the timeseries series.\n",
    "  ## There are no general rules for converting ancillary data to weights associated with the values in the timeseries.    \n",
    "    has_QC              = 0,              # 1 = use quality data, 0 = do not use quality data. \n",
    "    qc_1                = c(0, 0, 1),     # Quality range 1 and weight\n",
    "    qc_2                = c(1, 1, 0.5),   # Quality range 2 and weight\n",
    "    qc_3                = c(2, 3, 0.2),   # Quality range 3 and weight\n",
    "      \n",
    "  ## Amplitude cuttoff: Minimum Vegetation Index values' range (Works as a threshold)  \n",
    "    A                   = 0.1,            # Amplitude cutoff value\n",
    "    output_type         = c(1, 1, 0),     # Output files (1/0 1/0 1/0), 1: seasonality data; 2: smoothed time-series; 3: original time-series\n",
    "    \n",
    "    spike_meth          = 0,              # (0/1/2/3): (no spike detection/method based on median filtering/weights from STL-decomposition/weights from STL-decomposition).\n",
    "    spike_sd            = 1,              # Numeric, data values that differ from the median value by more than the spike_sd multiplied with the standard deviation of y and that are different from the left and right neighbors are removed (assigned weight 0).      \n",
    "    seasonpar           = 1.0,            # Seasonality parameter (0-1)\n",
    "  \n",
    "  ## No. of envelope iter: How many times fitted function will be applied. Setting 2 or 3 the fit will decrease the weights of the low data \n",
    "  ## values, one to two times respectivelly.    \n",
    "    iters               = 1,              # No. of envelope iterations (3/2/1)\n",
    "    adapt               = 1,              # Adaptation strength. The adaptation strength is a number between 1 and 10 indicating the strength of the upper envelope adaptation.\n",
    "    FUN                 = 1,              # Fitting method (1/2/3): (Savitzky-Golay / Asymmetric Gaussians / Double logistic Functions)\n",
    "    half_win            = 4,              # half Window size for Sav-Gol. In case of Asym.Gaus. or Double.Logist will not be taken into account.\n",
    "    meth_pheno          = 1,              # (1: seasonal amplitude, 2: absolute value, 3: relative amplitude, 4: STL trend)\n",
    "    trs                 = c(0.5, 0.5)     # Season start / end values\n",
    "  )\n",
    "  \n",
    "  options$job_name <- sitename\n",
    "  \n",
    "  opt <- update_setting(options)\n",
    "  write_setting(opt, file_set)\n",
    "  \n",
    "  TSF_process(file_set) # call TSF_process.exe\n",
    "  \n",
    "  file_tts <- sprintf(\"%s_fit.tts\", opt$job_name)\n",
    "  file_tpa <- sprintf(\"%s_TS.tpa\", opt$job_name)\n",
    "  \n",
    "  # note: only suit for ascii\n",
    "  tidy_tts <- function(d_tts){\n",
    "    sites <- d_tts$row %>% paste0(\"v\", .)\n",
    "    npt   <- ncol(d_tts) - 2\n",
    "    d <- d_tts %>% {.[, 3:ncol(.)]} %>% as.matrix() %>% t() %>% data.frame() %>%\n",
    "      set_colnames(sites) %>% cbind(t = 1:npt, .)\n",
    "    d\n",
    "  }\n",
    "  \n",
    "  d_tts <- read_tts(file_tts) %>% tidy_tts()\n",
    "  d_tpa <- read_tpa(file_tpa)\n",
    "  \n",
    "  if (!cache){\n",
    "    status1 <- file.remove(c(file_tts, file_tpa, file_y, file_w, file_set))\n",
    "    status2 <- dir(\".\", \"*.ndx\", full.names = T) %>% file.remove()\n",
    "  }\n",
    "  list(fit = d_tts, pheno = d_tpa)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phenological Parameters in TimeSat:\n",
    "1. Time for the start of the season; time for which the left has increased to a user defined level (often 20%) measured from the left min.\n",
    "2. Time for the end; time for which the right edge has decreased to a user defined from the right minimum.\n",
    "3. Length of the period; time from start to end\n",
    "4. Base level; given as the average of the left and right min values\n",
    "5. Time for the mid of the season; computed as the mean value of the times for which respectively, the left edge has increased to 80% level and the right edge has decreased to the 80% level.\n",
    "6. Largest data value for the fitted function during season\n",
    "7. Seasonal amplitude; difference between the maximal value and the base level\n",
    "8. Rate of increase at the beginning of the season; calculated as the ration between the values evaluated at the season start and at the left 80% level divided by the correct time difference\n",
    "9. Rate of decrease at the end of the season; at the season end at the right 80% level divided by corr. time\n",
    "10. Large Seasonal integral.\n",
    "11. Small Seasonal integral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in fread(pathtofile, header = F): could not find function \"fread\"\n",
     "output_type": "error",
     "traceback": [
      "Error in fread(pathtofile, header = F): could not find function \"fread\"\nTraceback:\n",
      "1. as.data.frame(fread(pathtofile, header = F))"
     ]
    }
   ],
   "source": [
    "pathtofile <- \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a/interpolated_df_nn_fulltime.csv\" \n",
    "rice <- as.data.frame(fread(pathtofile, header=F))\n",
    "colnames(rice) <- rice[1,]\n",
    "rice <- rice[-1,]\n",
    "rice <- rbind(rice,rice)\n",
    "rice <- rice[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TIMESAT needs as input timeseries per year and large amplitude, many decades. If dataset has timeseries only for one year, then dubplicate or triplecate it to work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>2019-03-10</th><th scope=col>2019-03-20</th><th scope=col>2019-03-30</th><th scope=col>2019-04-10</th><th scope=col>2019-04-20</th><th scope=col>2019-04-30</th><th scope=col>2019-05-10</th><th scope=col>2019-05-20</th><th scope=col>2019-05-30</th><th scope=col>2019-06-10</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td>221.4937 </td><td>158.4921 </td><td>139.9091 </td><td>155.7404 </td><td>189.4430 </td><td>213.8895 </td><td>185.0671 </td><td>211.3226 </td><td>371.2179 </td><td>517.6092 </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>212.82404</td><td>109.53846</td><td> 88.59296</td><td>124.17668</td><td>176.65710</td><td>197.20308</td><td>113.33114</td><td> 55.83492</td><td>147.71278</td><td>302.41820</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>238.39798</td><td>136.69881</td><td>118.08474</td><td>155.66540</td><td>208.14684</td><td>221.17180</td><td>111.27477</td><td> 45.71994</td><td>168.12143</td><td>320.75305</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>201.5028 </td><td>116.9077 </td><td>107.5394 </td><td>149.6917 </td><td>204.4011 </td><td>224.7080 </td><td>138.3899 </td><td> 83.5635 </td><td>184.4179 </td><td>330.6723 </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>180.7478 </td><td>171.6532 </td><td>173.4408 </td><td>181.2109 </td><td>187.4011 </td><td>178.5188 </td><td>130.8964 </td><td>108.1516 </td><td>170.6409 </td><td>292.4629 </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>189.6896 </td><td>119.4432 </td><td>108.3780 </td><td>139.2420 </td><td>183.4065 </td><td>218.3921 </td><td>217.4083 </td><td>180.7077 </td><td>143.0232 </td><td>221.6167 </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>182.7741 </td><td>156.0872 </td><td>152.5695 </td><td>164.8150 </td><td>180.9197 </td><td>186.9277 </td><td>161.8629 </td><td>143.2156 </td><td>177.3407 </td><td>286.8962 </td></tr>\n",
       "\t<tr><th scope=row>9</th><td>236.78650</td><td>210.72689</td><td>191.10183</td><td>172.98125</td><td>156.05531</td><td>127.90696</td><td> 69.41510</td><td> 38.07293</td><td> 97.67516</td><td>264.65884</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>231.91905</td><td>205.93345</td><td>193.96003</td><td>188.19285</td><td>181.69823</td><td>154.27718</td><td> 72.52431</td><td> 29.35146</td><td>110.81448</td><td>268.95438</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>190.51706</td><td> 90.42965</td><td> 76.21068</td><td>120.23456</td><td>178.57579</td><td>199.66238</td><td>106.39157</td><td> 35.00954</td><td>118.50332</td><td>283.91652</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & 2019-03-10 & 2019-03-20 & 2019-03-30 & 2019-04-10 & 2019-04-20 & 2019-04-30 & 2019-05-10 & 2019-05-20 & 2019-05-30 & 2019-06-10\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t2 & 221.4937  & 158.4921  & 139.9091  & 155.7404  & 189.4430  & 213.8895  & 185.0671  & 211.3226  & 371.2179  & 517.6092 \\\\\n",
       "\t3 & 212.82404 & 109.53846 &  88.59296 & 124.17668 & 176.65710 & 197.20308 & 113.33114 &  55.83492 & 147.71278 & 302.41820\\\\\n",
       "\t4 & 238.39798 & 136.69881 & 118.08474 & 155.66540 & 208.14684 & 221.17180 & 111.27477 &  45.71994 & 168.12143 & 320.75305\\\\\n",
       "\t5 & 201.5028  & 116.9077  & 107.5394  & 149.6917  & 204.4011  & 224.7080  & 138.3899  &  83.5635  & 184.4179  & 330.6723 \\\\\n",
       "\t6 & 180.7478  & 171.6532  & 173.4408  & 181.2109  & 187.4011  & 178.5188  & 130.8964  & 108.1516  & 170.6409  & 292.4629 \\\\\n",
       "\t7 & 189.6896  & 119.4432  & 108.3780  & 139.2420  & 183.4065  & 218.3921  & 217.4083  & 180.7077  & 143.0232  & 221.6167 \\\\\n",
       "\t8 & 182.7741  & 156.0872  & 152.5695  & 164.8150  & 180.9197  & 186.9277  & 161.8629  & 143.2156  & 177.3407  & 286.8962 \\\\\n",
       "\t9 & 236.78650 & 210.72689 & 191.10183 & 172.98125 & 156.05531 & 127.90696 &  69.41510 &  38.07293 &  97.67516 & 264.65884\\\\\n",
       "\t10 & 231.91905 & 205.93345 & 193.96003 & 188.19285 & 181.69823 & 154.27718 &  72.52431 &  29.35146 & 110.81448 & 268.95438\\\\\n",
       "\t11 & 190.51706 &  90.42965 &  76.21068 & 120.23456 & 178.57579 & 199.66238 & 106.39157 &  35.00954 & 118.50332 & 283.91652\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | 2019-03-10 &lt;chr&gt; | 2019-03-20 &lt;chr&gt; | 2019-03-30 &lt;chr&gt; | 2019-04-10 &lt;chr&gt; | 2019-04-20 &lt;chr&gt; | 2019-04-30 &lt;chr&gt; | 2019-05-10 &lt;chr&gt; | 2019-05-20 &lt;chr&gt; | 2019-05-30 &lt;chr&gt; | 2019-06-10 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2 | 221.4937  | 158.4921  | 139.9091  | 155.7404  | 189.4430  | 213.8895  | 185.0671  | 211.3226  | 371.2179  | 517.6092  |\n",
       "| 3 | 212.82404 | 109.53846 |  88.59296 | 124.17668 | 176.65710 | 197.20308 | 113.33114 |  55.83492 | 147.71278 | 302.41820 |\n",
       "| 4 | 238.39798 | 136.69881 | 118.08474 | 155.66540 | 208.14684 | 221.17180 | 111.27477 |  45.71994 | 168.12143 | 320.75305 |\n",
       "| 5 | 201.5028  | 116.9077  | 107.5394  | 149.6917  | 204.4011  | 224.7080  | 138.3899  |  83.5635  | 184.4179  | 330.6723  |\n",
       "| 6 | 180.7478  | 171.6532  | 173.4408  | 181.2109  | 187.4011  | 178.5188  | 130.8964  | 108.1516  | 170.6409  | 292.4629  |\n",
       "| 7 | 189.6896  | 119.4432  | 108.3780  | 139.2420  | 183.4065  | 218.3921  | 217.4083  | 180.7077  | 143.0232  | 221.6167  |\n",
       "| 8 | 182.7741  | 156.0872  | 152.5695  | 164.8150  | 180.9197  | 186.9277  | 161.8629  | 143.2156  | 177.3407  | 286.8962  |\n",
       "| 9 | 236.78650 | 210.72689 | 191.10183 | 172.98125 | 156.05531 | 127.90696 |  69.41510 |  38.07293 |  97.67516 | 264.65884 |\n",
       "| 10 | 231.91905 | 205.93345 | 193.96003 | 188.19285 | 181.69823 | 154.27718 |  72.52431 |  29.35146 | 110.81448 | 268.95438 |\n",
       "| 11 | 190.51706 |  90.42965 |  76.21068 | 120.23456 | 178.57579 | 199.66238 | 106.39157 |  35.00954 | 118.50332 | 283.91652 |\n",
       "\n"
      ],
      "text/plain": [
       "   2019-03-10 2019-03-20 2019-03-30 2019-04-10 2019-04-20 2019-04-30 2019-05-10\n",
       "2  221.4937   158.4921   139.9091   155.7404   189.4430   213.8895   185.0671  \n",
       "3  212.82404  109.53846   88.59296  124.17668  176.65710  197.20308  113.33114 \n",
       "4  238.39798  136.69881  118.08474  155.66540  208.14684  221.17180  111.27477 \n",
       "5  201.5028   116.9077   107.5394   149.6917   204.4011   224.7080   138.3899  \n",
       "6  180.7478   171.6532   173.4408   181.2109   187.4011   178.5188   130.8964  \n",
       "7  189.6896   119.4432   108.3780   139.2420   183.4065   218.3921   217.4083  \n",
       "8  182.7741   156.0872   152.5695   164.8150   180.9197   186.9277   161.8629  \n",
       "9  236.78650  210.72689  191.10183  172.98125  156.05531  127.90696   69.41510 \n",
       "10 231.91905  205.93345  193.96003  188.19285  181.69823  154.27718   72.52431 \n",
       "11 190.51706   90.42965   76.21068  120.23456  178.57579  199.66238  106.39157 \n",
       "   2019-05-20 2019-05-30 2019-06-10\n",
       "2  211.3226   371.2179   517.6092  \n",
       "3   55.83492  147.71278  302.41820 \n",
       "4   45.71994  168.12143  320.75305 \n",
       "5   83.5635   184.4179   330.6723  \n",
       "6  108.1516   170.6409   292.4629  \n",
       "7  180.7077   143.0232   221.6167  \n",
       "8  143.2156   177.3407   286.8962  \n",
       "9   38.07293   97.67516  264.65884 \n",
       "10  29.35146  110.81448  268.95438 \n",
       "11  35.00954  118.50332  283.91652 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rice[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates <- rice$daily\n",
    "obj <- colnames(rice[2:ncol(rice)])\n",
    "ricen <- rice[2:ncol(rice)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>NDVI</th><th scope=col>NDVI.1</th><th scope=col>NDVI.2</th><th scope=col>NDVI.3</th><th scope=col>NDVI.4</th><th scope=col>NDVI.5</th><th scope=col>NDVI.6</th><th scope=col>NDVI.7</th><th scope=col>NDVI.8</th><th scope=col>NDVI.9</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>158.4921 </td><td>139.9091 </td><td>155.7404 </td><td>189.4430 </td><td>213.8895 </td><td>185.0671 </td><td>211.3226 </td><td>371.2179 </td><td>517.6092 </td><td>596.9302 </td></tr>\n",
       "\t<tr><td>109.53846</td><td> 88.59296</td><td>124.17668</td><td>176.65710</td><td>197.20308</td><td>113.33114</td><td> 55.83492</td><td>147.71278</td><td>302.41820</td><td>445.93172</td></tr>\n",
       "\t<tr><td>136.69881</td><td>118.08474</td><td>155.66540</td><td>208.14684</td><td>221.17180</td><td>111.27477</td><td> 45.71994</td><td>168.12143</td><td>320.75305</td><td>436.78388</td></tr>\n",
       "\t<tr><td>116.9077 </td><td>107.5394 </td><td>149.6917 </td><td>204.4011 </td><td>224.7080 </td><td>138.3899 </td><td> 83.5635 </td><td>184.4179 </td><td>330.6723 </td><td>456.1974 </td></tr>\n",
       "\t<tr><td>171.6532 </td><td>173.4408 </td><td>181.2109 </td><td>187.4011 </td><td>178.5188 </td><td>130.8964 </td><td>108.1516 </td><td>170.6409 </td><td>292.4629 </td><td>424.9826 </td></tr>\n",
       "\t<tr><td>119.4432 </td><td>108.3780 </td><td>139.2420 </td><td>183.4065 </td><td>218.3921 </td><td>217.4083 </td><td>180.7077 </td><td>143.0232 </td><td>221.6167 </td><td>377.4910 </td></tr>\n",
       "\t<tr><td>156.0872 </td><td>152.5695 </td><td>164.8150 </td><td>180.9197 </td><td>186.9277 </td><td>161.8629 </td><td>143.2156 </td><td>177.3407 </td><td>286.8962 </td><td>422.7822 </td></tr>\n",
       "\t<tr><td>210.72689</td><td>191.10183</td><td>172.98125</td><td>156.05531</td><td>127.90696</td><td> 69.41510</td><td> 38.07293</td><td> 97.67516</td><td>264.65884</td><td>460.69321</td></tr>\n",
       "\t<tr><td>205.93345</td><td>193.96003</td><td>188.19285</td><td>181.69823</td><td>154.27718</td><td> 72.52431</td><td> 29.35146</td><td>110.81448</td><td>268.95438</td><td>432.15820</td></tr>\n",
       "\t<tr><td> 90.42965</td><td> 76.21068</td><td>120.23456</td><td>178.57579</td><td>199.66238</td><td>106.39157</td><td> 35.00954</td><td>118.50332</td><td>283.91652</td><td>447.21621</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       " NDVI & NDVI.1 & NDVI.2 & NDVI.3 & NDVI.4 & NDVI.5 & NDVI.6 & NDVI.7 & NDVI.8 & NDVI.9\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 158.4921  & 139.9091  & 155.7404  & 189.4430  & 213.8895  & 185.0671  & 211.3226  & 371.2179  & 517.6092  & 596.9302 \\\\\n",
       "\t 109.53846 &  88.59296 & 124.17668 & 176.65710 & 197.20308 & 113.33114 &  55.83492 & 147.71278 & 302.41820 & 445.93172\\\\\n",
       "\t 136.69881 & 118.08474 & 155.66540 & 208.14684 & 221.17180 & 111.27477 &  45.71994 & 168.12143 & 320.75305 & 436.78388\\\\\n",
       "\t 116.9077  & 107.5394  & 149.6917  & 204.4011  & 224.7080  & 138.3899  &  83.5635  & 184.4179  & 330.6723  & 456.1974 \\\\\n",
       "\t 171.6532  & 173.4408  & 181.2109  & 187.4011  & 178.5188  & 130.8964  & 108.1516  & 170.6409  & 292.4629  & 424.9826 \\\\\n",
       "\t 119.4432  & 108.3780  & 139.2420  & 183.4065  & 218.3921  & 217.4083  & 180.7077  & 143.0232  & 221.6167  & 377.4910 \\\\\n",
       "\t 156.0872  & 152.5695  & 164.8150  & 180.9197  & 186.9277  & 161.8629  & 143.2156  & 177.3407  & 286.8962  & 422.7822 \\\\\n",
       "\t 210.72689 & 191.10183 & 172.98125 & 156.05531 & 127.90696 &  69.41510 &  38.07293 &  97.67516 & 264.65884 & 460.69321\\\\\n",
       "\t 205.93345 & 193.96003 & 188.19285 & 181.69823 & 154.27718 &  72.52431 &  29.35146 & 110.81448 & 268.95438 & 432.15820\\\\\n",
       "\t  90.42965 &  76.21068 & 120.23456 & 178.57579 & 199.66238 & 106.39157 &  35.00954 & 118.50332 & 283.91652 & 447.21621\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| NDVI &lt;chr&gt; | NDVI.1 &lt;chr&gt; | NDVI.2 &lt;chr&gt; | NDVI.3 &lt;chr&gt; | NDVI.4 &lt;chr&gt; | NDVI.5 &lt;chr&gt; | NDVI.6 &lt;chr&gt; | NDVI.7 &lt;chr&gt; | NDVI.8 &lt;chr&gt; | NDVI.9 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 158.4921  | 139.9091  | 155.7404  | 189.4430  | 213.8895  | 185.0671  | 211.3226  | 371.2179  | 517.6092  | 596.9302  |\n",
       "| 109.53846 |  88.59296 | 124.17668 | 176.65710 | 197.20308 | 113.33114 |  55.83492 | 147.71278 | 302.41820 | 445.93172 |\n",
       "| 136.69881 | 118.08474 | 155.66540 | 208.14684 | 221.17180 | 111.27477 |  45.71994 | 168.12143 | 320.75305 | 436.78388 |\n",
       "| 116.9077  | 107.5394  | 149.6917  | 204.4011  | 224.7080  | 138.3899  |  83.5635  | 184.4179  | 330.6723  | 456.1974  |\n",
       "| 171.6532  | 173.4408  | 181.2109  | 187.4011  | 178.5188  | 130.8964  | 108.1516  | 170.6409  | 292.4629  | 424.9826  |\n",
       "| 119.4432  | 108.3780  | 139.2420  | 183.4065  | 218.3921  | 217.4083  | 180.7077  | 143.0232  | 221.6167  | 377.4910  |\n",
       "| 156.0872  | 152.5695  | 164.8150  | 180.9197  | 186.9277  | 161.8629  | 143.2156  | 177.3407  | 286.8962  | 422.7822  |\n",
       "| 210.72689 | 191.10183 | 172.98125 | 156.05531 | 127.90696 |  69.41510 |  38.07293 |  97.67516 | 264.65884 | 460.69321 |\n",
       "| 205.93345 | 193.96003 | 188.19285 | 181.69823 | 154.27718 |  72.52431 |  29.35146 | 110.81448 | 268.95438 | 432.15820 |\n",
       "|  90.42965 |  76.21068 | 120.23456 | 178.57579 | 199.66238 | 106.39157 |  35.00954 | 118.50332 | 283.91652 | 447.21621 |\n",
       "\n"
      ],
      "text/plain": [
       "   NDVI      NDVI.1    NDVI.2    NDVI.3    NDVI.4    NDVI.5    NDVI.6   \n",
       "1  158.4921  139.9091  155.7404  189.4430  213.8895  185.0671  211.3226 \n",
       "2  109.53846  88.59296 124.17668 176.65710 197.20308 113.33114  55.83492\n",
       "3  136.69881 118.08474 155.66540 208.14684 221.17180 111.27477  45.71994\n",
       "4  116.9077  107.5394  149.6917  204.4011  224.7080  138.3899   83.5635 \n",
       "5  171.6532  173.4408  181.2109  187.4011  178.5188  130.8964  108.1516 \n",
       "6  119.4432  108.3780  139.2420  183.4065  218.3921  217.4083  180.7077 \n",
       "7  156.0872  152.5695  164.8150  180.9197  186.9277  161.8629  143.2156 \n",
       "8  210.72689 191.10183 172.98125 156.05531 127.90696  69.41510  38.07293\n",
       "9  205.93345 193.96003 188.19285 181.69823 154.27718  72.52431  29.35146\n",
       "10  90.42965  76.21068 120.23456 178.57579 199.66238 106.39157  35.00954\n",
       "   NDVI.7    NDVI.8    NDVI.9   \n",
       "1  371.2179  517.6092  596.9302 \n",
       "2  147.71278 302.41820 445.93172\n",
       "3  168.12143 320.75305 436.78388\n",
       "4  184.4179  330.6723  456.1974 \n",
       "5  170.6409  292.4629  424.9826 \n",
       "6  143.0232  221.6167  377.4910 \n",
       "7  177.3407  286.8962  422.7822 \n",
       "8   97.67516 264.65884 460.69321\n",
       "9  110.81448 268.95438 432.15820\n",
       "10 118.50332 283.91652 447.21621"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ricen[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(ricen) <- rep(\"NDVI\",ncol(ricen))\n",
    "row.names(ricen) <- 1:nrow(rice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in names(x) <- value: 'names' attribute [16] must be the same length as the vector [0]\n",
     "output_type": "error",
     "traceback": [
      "Error in names(x) <- value: 'names' attribute [16] must be the same length as the vector [0]\nTraceback:\n",
      "1. TSF_main(ricen[i])",
      "2. read_tpa(file_tpa)   # at line 69 of file <text>",
      "3. do.call(rbind.data.frame, lst) %>% set_colnames(Colnames)",
      "4. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))",
      "5. eval(quote(`_fseq`(`_lhs`)), env, env)",
      "6. eval(quote(`_fseq`(`_lhs`)), env, env)",
      "7. `_fseq`(`_lhs`)",
      "8. freduce(value, `_function_list`)",
      "9. withVisible(function_list[[k]](value))",
      "10. function_list[[k]](value)",
      "11. set_colnames(., Colnames)"
     ]
    }
   ],
   "source": [
    "#TimeSat\n",
    "l <- list()\n",
    "for (i in 1: ncol(ricen)){\n",
    "  l[i] <- list(TSF_main(ricen[i]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenolist <- list()\n",
    "for (i in 1:length(l)){\n",
    "  phenolist[i] <- list(l[[i]]$pheno)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 16</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>row</th><th scope=col>col</th><th scope=col>season</th><th scope=col>time_start</th><th scope=col>time_end</th><th scope=col>time_peak</th><th scope=col>len</th><th scope=col>val_start</th><th scope=col>val_end</th><th scope=col>val_peak</th><th scope=col>val_base</th><th scope=col>ampl</th><th scope=col>der_l</th><th scope=col>der_r</th><th scope=col>integ_large</th><th scope=col>integ_small</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>109</th><td>1</td><td>1</td><td>1</td><td>10.30691</td><td>22.06402</td><td>16.54910</td><td>11.757109</td><td>500.1835</td><td>500.1835</td><td>842.1780</td><td>158.1891</td><td>683.9888</td><td> 87.20371</td><td>110.4130</td><td>9541.217</td><td>7326.570</td></tr>\n",
       "\t<tr><th scope=row>110</th><td>1</td><td>1</td><td>1</td><td>11.41991</td><td>22.48041</td><td>17.01886</td><td>11.060491</td><td>495.5674</td><td>495.5674</td><td>871.0121</td><td>120.1226</td><td>750.8895</td><td>126.73415</td><td>116.2375</td><td>9192.518</td><td>7630.924</td></tr>\n",
       "\t<tr><th scope=row>111</th><td>1</td><td>1</td><td>1</td><td>11.63543</td><td>22.37831</td><td>16.95647</td><td>10.742872</td><td>511.7146</td><td>511.7146</td><td>890.6611</td><td>132.7682</td><td>757.8929</td><td>128.01216</td><td>107.8696</td><td>9222.448</td><td>7496.462</td></tr>\n",
       "\t<tr><th scope=row>112</th><td>1</td><td>1</td><td>1</td><td>11.45145</td><td>21.87033</td><td>16.60111</td><td>10.418884</td><td>508.3320</td><td>501.5473</td><td>869.3218</td><td>140.5575</td><td>728.7643</td><td>122.62640</td><td>107.0461</td><td>8506.385</td><td>6819.695</td></tr>\n",
       "\t<tr><th scope=row>113</th><td>1</td><td>1</td><td>1</td><td>11.68167</td><td>21.46642</td><td>16.57726</td><td> 9.784752</td><td>517.0316</td><td>517.0316</td><td>898.8629</td><td>135.2003</td><td>763.6626</td><td>130.70656</td><td>123.2175</td><td>8543.068</td><td>6920.665</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 16\n",
       "\\begin{tabular}{r|llllllllllllllll}\n",
       "  & row & col & season & time\\_start & time\\_end & time\\_peak & len & val\\_start & val\\_end & val\\_peak & val\\_base & ampl & der\\_l & der\\_r & integ\\_large & integ\\_small\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t109 & 1 & 1 & 1 & 10.30691 & 22.06402 & 16.54910 & 11.757109 & 500.1835 & 500.1835 & 842.1780 & 158.1891 & 683.9888 &  87.20371 & 110.4130 & 9541.217 & 7326.570\\\\\n",
       "\t110 & 1 & 1 & 1 & 11.41991 & 22.48041 & 17.01886 & 11.060491 & 495.5674 & 495.5674 & 871.0121 & 120.1226 & 750.8895 & 126.73415 & 116.2375 & 9192.518 & 7630.924\\\\\n",
       "\t111 & 1 & 1 & 1 & 11.63543 & 22.37831 & 16.95647 & 10.742872 & 511.7146 & 511.7146 & 890.6611 & 132.7682 & 757.8929 & 128.01216 & 107.8696 & 9222.448 & 7496.462\\\\\n",
       "\t112 & 1 & 1 & 1 & 11.45145 & 21.87033 & 16.60111 & 10.418884 & 508.3320 & 501.5473 & 869.3218 & 140.5575 & 728.7643 & 122.62640 & 107.0461 & 8506.385 & 6819.695\\\\\n",
       "\t113 & 1 & 1 & 1 & 11.68167 & 21.46642 & 16.57726 &  9.784752 & 517.0316 & 517.0316 & 898.8629 & 135.2003 & 763.6626 & 130.70656 & 123.2175 & 8543.068 & 6920.665\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 16\n",
       "\n",
       "| <!--/--> | row &lt;dbl&gt; | col &lt;dbl&gt; | season &lt;dbl&gt; | time_start &lt;dbl&gt; | time_end &lt;dbl&gt; | time_peak &lt;dbl&gt; | len &lt;dbl&gt; | val_start &lt;dbl&gt; | val_end &lt;dbl&gt; | val_peak &lt;dbl&gt; | val_base &lt;dbl&gt; | ampl &lt;dbl&gt; | der_l &lt;dbl&gt; | der_r &lt;dbl&gt; | integ_large &lt;dbl&gt; | integ_small &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 109 | 1 | 1 | 1 | 10.30691 | 22.06402 | 16.54910 | 11.757109 | 500.1835 | 500.1835 | 842.1780 | 158.1891 | 683.9888 |  87.20371 | 110.4130 | 9541.217 | 7326.570 |\n",
       "| 110 | 1 | 1 | 1 | 11.41991 | 22.48041 | 17.01886 | 11.060491 | 495.5674 | 495.5674 | 871.0121 | 120.1226 | 750.8895 | 126.73415 | 116.2375 | 9192.518 | 7630.924 |\n",
       "| 111 | 1 | 1 | 1 | 11.63543 | 22.37831 | 16.95647 | 10.742872 | 511.7146 | 511.7146 | 890.6611 | 132.7682 | 757.8929 | 128.01216 | 107.8696 | 9222.448 | 7496.462 |\n",
       "| 112 | 1 | 1 | 1 | 11.45145 | 21.87033 | 16.60111 | 10.418884 | 508.3320 | 501.5473 | 869.3218 | 140.5575 | 728.7643 | 122.62640 | 107.0461 | 8506.385 | 6819.695 |\n",
       "| 113 | 1 | 1 | 1 | 11.68167 | 21.46642 | 16.57726 |  9.784752 | 517.0316 | 517.0316 | 898.8629 | 135.2003 | 763.6626 | 130.70656 | 123.2175 | 8543.068 | 6920.665 |\n",
       "\n"
      ],
      "text/plain": [
       "    row col season time_start time_end time_peak len       val_start val_end \n",
       "109 1   1   1      10.30691   22.06402 16.54910  11.757109 500.1835  500.1835\n",
       "110 1   1   1      11.41991   22.48041 17.01886  11.060491 495.5674  495.5674\n",
       "111 1   1   1      11.63543   22.37831 16.95647  10.742872 511.7146  511.7146\n",
       "112 1   1   1      11.45145   21.87033 16.60111  10.418884 508.3320  501.5473\n",
       "113 1   1   1      11.68167   21.46642 16.57726   9.784752 517.0316  517.0316\n",
       "    val_peak val_base ampl     der_l     der_r    integ_large integ_small\n",
       "109 842.1780 158.1891 683.9888  87.20371 110.4130 9541.217    7326.570   \n",
       "110 871.0121 120.1226 750.8895 126.73415 116.2375 9192.518    7630.924   \n",
       "111 890.6611 132.7682 757.8929 128.01216 107.8696 9222.448    7496.462   \n",
       "112 869.3218 140.5575 728.7643 122.62640 107.0461 8506.385    6819.695   \n",
       "113 898.8629 135.2003 763.6626 130.70656 123.2175 8543.068    6920.665   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phenolist <- data.frame(matrix(unlist(phenolist), nrow=length(phenolist), byrow=T))\n",
    "colnames(phenolist) <- c(\"row\", \"col\", \"season\", \"time_start\", \"time_end\", \"time_peak\", \"len\", \"val_start\", \"val_end\",\"val_peak\", \"val_base\", \"ampl\", \"der_l\", \"der_r\", \"integ_large\", \"integ_small\")\n",
    "row.names(phenolist) <- obj\n",
    "phenolist[1:5,1:ncol(phenolist)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>date</th><th scope=col>109</th><th scope=col>110</th><th scope=col>111</th><th scope=col>112</th><th scope=col>113</th><th scope=col>114</th><th scope=col>115</th><th scope=col>116</th><th scope=col>117</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>X1</th><td>2019-03-10</td><td>183.3217</td><td>156.9489</td><td>179.2540</td><td>148.2959</td><td>168.2081</td><td>142.1075</td><td>166.0582</td><td>199.75111</td><td>202.45958</td></tr>\n",
       "\t<tr><th scope=row>X2</th><td>2019-03-20</td><td>166.8784</td><td>128.9249</td><td>154.4576</td><td>133.7728</td><td>176.0298</td><td>136.1862</td><td>162.3961</td><td>196.57433</td><td>191.44746</td></tr>\n",
       "\t<tr><th scope=row>X3</th><td>2019-03-30</td><td>161.3805</td><td>129.8558</td><td>162.5190</td><td>147.3422</td><td>181.3359</td><td>138.2491</td><td>163.4542</td><td>194.64597</td><td>198.54153</td></tr>\n",
       "\t<tr><th scope=row>X4</th><td>2019-04-10</td><td>165.5746</td><td>141.5263</td><td>173.5704</td><td>165.4471</td><td>180.3862</td><td>150.8810</td><td>166.9984</td><td>180.49870</td><td>194.75922</td></tr>\n",
       "\t<tr><th scope=row>X5</th><td>2019-04-20</td><td>158.1891</td><td>131.5856</td><td>154.0773</td><td>158.3149</td><td>168.3521</td><td>173.3941</td><td>166.9223</td><td>138.85234</td><td>154.55865</td></tr>\n",
       "\t<tr><th scope=row>X6</th><td>2019-04-30</td><td>173.6629</td><td>125.4199</td><td>140.9192</td><td>153.9119</td><td>147.5174</td><td>187.7252</td><td>159.6030</td><td> 95.29352</td><td>110.86060</td></tr>\n",
       "\t<tr><th scope=row>X7</th><td>2019-05-10</td><td>215.1492</td><td>120.1226</td><td>132.7682</td><td>150.9187</td><td>135.2003</td><td>181.6364</td><td>154.0752</td><td> 66.63705</td><td> 81.62102</td></tr>\n",
       "\t<tr><th scope=row>X8</th><td>2019-05-20</td><td>282.1911</td><td>133.6237</td><td>143.7051</td><td>164.8191</td><td>147.1236</td><td>175.6640</td><td>166.7095</td><td> 77.92503</td><td> 88.41434</td></tr>\n",
       "\t<tr><th scope=row>X9</th><td>2019-05-30</td><td>369.5294</td><td>184.8503</td><td>188.2021</td><td>212.0232</td><td>196.2190</td><td>198.1065</td><td>213.2633</td><td>121.01205</td><td>146.91707</td></tr>\n",
       "\t<tr><th scope=row>X10</th><td>2019-06-10</td><td>469.1628</td><td>284.0493</td><td>278.2152</td><td>303.5041</td><td>288.0864</td><td>266.9739</td><td>298.6404</td><td>265.42642</td><td>258.22372</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & date & 109 & 110 & 111 & 112 & 113 & 114 & 115 & 116 & 117\\\\\n",
       "  & <date> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tX1 & 2019-03-10 & 183.3217 & 156.9489 & 179.2540 & 148.2959 & 168.2081 & 142.1075 & 166.0582 & 199.75111 & 202.45958\\\\\n",
       "\tX2 & 2019-03-20 & 166.8784 & 128.9249 & 154.4576 & 133.7728 & 176.0298 & 136.1862 & 162.3961 & 196.57433 & 191.44746\\\\\n",
       "\tX3 & 2019-03-30 & 161.3805 & 129.8558 & 162.5190 & 147.3422 & 181.3359 & 138.2491 & 163.4542 & 194.64597 & 198.54153\\\\\n",
       "\tX4 & 2019-04-10 & 165.5746 & 141.5263 & 173.5704 & 165.4471 & 180.3862 & 150.8810 & 166.9984 & 180.49870 & 194.75922\\\\\n",
       "\tX5 & 2019-04-20 & 158.1891 & 131.5856 & 154.0773 & 158.3149 & 168.3521 & 173.3941 & 166.9223 & 138.85234 & 154.55865\\\\\n",
       "\tX6 & 2019-04-30 & 173.6629 & 125.4199 & 140.9192 & 153.9119 & 147.5174 & 187.7252 & 159.6030 &  95.29352 & 110.86060\\\\\n",
       "\tX7 & 2019-05-10 & 215.1492 & 120.1226 & 132.7682 & 150.9187 & 135.2003 & 181.6364 & 154.0752 &  66.63705 &  81.62102\\\\\n",
       "\tX8 & 2019-05-20 & 282.1911 & 133.6237 & 143.7051 & 164.8191 & 147.1236 & 175.6640 & 166.7095 &  77.92503 &  88.41434\\\\\n",
       "\tX9 & 2019-05-30 & 369.5294 & 184.8503 & 188.2021 & 212.0232 & 196.2190 & 198.1065 & 213.2633 & 121.01205 & 146.91707\\\\\n",
       "\tX10 & 2019-06-10 & 469.1628 & 284.0493 & 278.2152 & 303.5041 & 288.0864 & 266.9739 & 298.6404 & 265.42642 & 258.22372\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | date &lt;date&gt; | 109 &lt;dbl&gt; | 110 &lt;dbl&gt; | 111 &lt;dbl&gt; | 112 &lt;dbl&gt; | 113 &lt;dbl&gt; | 114 &lt;dbl&gt; | 115 &lt;dbl&gt; | 116 &lt;dbl&gt; | 117 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| X1 | 2019-03-10 | 183.3217 | 156.9489 | 179.2540 | 148.2959 | 168.2081 | 142.1075 | 166.0582 | 199.75111 | 202.45958 |\n",
       "| X2 | 2019-03-20 | 166.8784 | 128.9249 | 154.4576 | 133.7728 | 176.0298 | 136.1862 | 162.3961 | 196.57433 | 191.44746 |\n",
       "| X3 | 2019-03-30 | 161.3805 | 129.8558 | 162.5190 | 147.3422 | 181.3359 | 138.2491 | 163.4542 | 194.64597 | 198.54153 |\n",
       "| X4 | 2019-04-10 | 165.5746 | 141.5263 | 173.5704 | 165.4471 | 180.3862 | 150.8810 | 166.9984 | 180.49870 | 194.75922 |\n",
       "| X5 | 2019-04-20 | 158.1891 | 131.5856 | 154.0773 | 158.3149 | 168.3521 | 173.3941 | 166.9223 | 138.85234 | 154.55865 |\n",
       "| X6 | 2019-04-30 | 173.6629 | 125.4199 | 140.9192 | 153.9119 | 147.5174 | 187.7252 | 159.6030 |  95.29352 | 110.86060 |\n",
       "| X7 | 2019-05-10 | 215.1492 | 120.1226 | 132.7682 | 150.9187 | 135.2003 | 181.6364 | 154.0752 |  66.63705 |  81.62102 |\n",
       "| X8 | 2019-05-20 | 282.1911 | 133.6237 | 143.7051 | 164.8191 | 147.1236 | 175.6640 | 166.7095 |  77.92503 |  88.41434 |\n",
       "| X9 | 2019-05-30 | 369.5294 | 184.8503 | 188.2021 | 212.0232 | 196.2190 | 198.1065 | 213.2633 | 121.01205 | 146.91707 |\n",
       "| X10 | 2019-06-10 | 469.1628 | 284.0493 | 278.2152 | 303.5041 | 288.0864 | 266.9739 | 298.6404 | 265.42642 | 258.22372 |\n",
       "\n"
      ],
      "text/plain": [
       "    date       109      110      111      112      113      114      115     \n",
       "X1  2019-03-10 183.3217 156.9489 179.2540 148.2959 168.2081 142.1075 166.0582\n",
       "X2  2019-03-20 166.8784 128.9249 154.4576 133.7728 176.0298 136.1862 162.3961\n",
       "X3  2019-03-30 161.3805 129.8558 162.5190 147.3422 181.3359 138.2491 163.4542\n",
       "X4  2019-04-10 165.5746 141.5263 173.5704 165.4471 180.3862 150.8810 166.9984\n",
       "X5  2019-04-20 158.1891 131.5856 154.0773 158.3149 168.3521 173.3941 166.9223\n",
       "X6  2019-04-30 173.6629 125.4199 140.9192 153.9119 147.5174 187.7252 159.6030\n",
       "X7  2019-05-10 215.1492 120.1226 132.7682 150.9187 135.2003 181.6364 154.0752\n",
       "X8  2019-05-20 282.1911 133.6237 143.7051 164.8191 147.1236 175.6640 166.7095\n",
       "X9  2019-05-30 369.5294 184.8503 188.2021 212.0232 196.2190 198.1065 213.2633\n",
       "X10 2019-06-10 469.1628 284.0493 278.2152 303.5041 288.0864 266.9739 298.6404\n",
       "    116       117      \n",
       "X1  199.75111 202.45958\n",
       "X2  196.57433 191.44746\n",
       "X3  194.64597 198.54153\n",
       "X4  180.49870 194.75922\n",
       "X5  138.85234 154.55865\n",
       "X6   95.29352 110.86060\n",
       "X7   66.63705  81.62102\n",
       "X8   77.92503  88.41434\n",
       "X9  121.01205 146.91707\n",
       "X10 265.42642 258.22372"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotlistv <- list()\n",
    "for (i in 1:length(l)){\n",
    "  plotlistv[i] <- list(l[[i]]$fit$v1)\n",
    "}\n",
    "plotlistv <- data.frame(matrix(unlist(plotlistv), nrow=length(plotlistv), byrow=T))\n",
    "plotlistv <- as.data.frame(t(plotlistv))\n",
    "plotlistd <- as.data.frame(as.Date(dates))\n",
    "plotl <- cbind(plotlistd,plotlistv)\n",
    "colnames(plotl)[1] <-\"date\"\n",
    "colnames(plotl)[2:ncol(plotl)] <- obj\n",
    "plotl[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j2DGIJsNjthwoR8vcUIdvFBcrlZt+j/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\n4WC0Bbsbb7yx99H5Occff/z69euLX0/OEAW7Ib+lGABAUnLHd/X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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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kWpVNqxY0dra2tra2v88aJFi7Zs2TJr1qyXXnqpanEBABjMUMXujjvuiKKovE9u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\nZpLwfzAz3DYoGmzOAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = plotl, mapping = aes(x = date, y = plotl$`109`)) +\n",
    "  geom_point()\n",
    "ggplot(data = rice, mapping = aes(x = daily, y= rice$`109`)) +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(phenolist, \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a/phenolist.csv\",row.names = T)\n",
    "write.csv(plotl, \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a/plotl.csv\",row.names = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CropPhenology package computes 15 phenological metrics based on satellite-based NDVI measurements over the season which can be easily visualised and used for successive spatio-temporal analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathtofile <- \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a/interpolated_df_nn_fulltime2.csv\" \n",
    "ricepn <- as.data.frame(fread(pathtofile, header=T))\n",
    "ricepn <- ricepn[-c(1,2)]\n",
    "ricepn <- as.data.frame(t(ricepn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>109</th><td>221.4937</td><td>158.49211</td><td>139.90911</td><td>155.7404</td><td>189.4430</td><td>213.8895</td><td>185.06713</td><td>211.32260</td><td>371.21790</td><td>517.6092</td></tr>\n",
       "\t<tr><th scope=row>110</th><td>212.8240</td><td>109.53846</td><td> 88.59296</td><td>124.1767</td><td>176.6571</td><td>197.2031</td><td>113.33114</td><td> 55.83492</td><td>147.71278</td><td>302.4182</td></tr>\n",
       "\t<tr><th scope=row>111</th><td>238.3980</td><td>136.69881</td><td>118.08474</td><td>155.6654</td><td>208.1468</td><td>221.1718</td><td>111.27477</td><td> 45.71994</td><td>168.12143</td><td>320.7530</td></tr>\n",
       "\t<tr><th scope=row>112</th><td>201.5028</td><td>116.90770</td><td>107.53939</td><td>149.6917</td><td>204.4011</td><td>224.7080</td><td>138.38987</td><td> 83.56350</td><td>184.41790</td><td>330.6723</td></tr>\n",
       "\t<tr><th scope=row>113</th><td>180.7478</td><td>171.65324</td><td>173.44076</td><td>181.2109</td><td>187.4011</td><td>178.5188</td><td>130.89638</td><td>108.15160</td><td>170.64090</td><td>292.4629</td></tr>\n",
       "\t<tr><th scope=row>114</th><td>189.6896</td><td>119.44316</td><td>108.37799</td><td>139.2420</td><td>183.4065</td><td>218.3921</td><td>217.40827</td><td>180.70765</td><td>143.02320</td><td>221.6167</td></tr>\n",
       "\t<tr><th scope=row>115</th><td>182.7741</td><td>156.08719</td><td>152.56949</td><td>164.8150</td><td>180.9197</td><td>186.9277</td><td>161.86288</td><td>143.21560</td><td>177.34070</td><td>286.8962</td></tr>\n",
       "\t<tr><th scope=row>116</th><td>236.7865</td><td>210.72689</td><td>191.10183</td><td>172.9813</td><td>156.0553</td><td>127.9070</td><td> 69.41510</td><td> 38.07293</td><td> 97.67516</td><td>264.6588</td></tr>\n",
       "\t<tr><th scope=row>117</th><td>231.9190</td><td>205.93345</td><td>193.96003</td><td>188.1928</td><td>181.6982</td><td>154.2772</td><td> 72.52431</td><td> 29.35146</td><td>110.81448</td><td>268.9544</td></tr>\n",
       "\t<tr><th scope=row>118</th><td>190.5171</td><td> 90.42965</td><td> 76.21068</td><td>120.2346</td><td>178.5758</td><td>199.6624</td><td>106.39157</td><td> 35.00954</td><td>118.50332</td><td>283.9165</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t109 & 221.4937 & 158.49211 & 139.90911 & 155.7404 & 189.4430 & 213.8895 & 185.06713 & 211.32260 & 371.21790 & 517.6092\\\\\n",
       "\t110 & 212.8240 & 109.53846 &  88.59296 & 124.1767 & 176.6571 & 197.2031 & 113.33114 &  55.83492 & 147.71278 & 302.4182\\\\\n",
       "\t111 & 238.3980 & 136.69881 & 118.08474 & 155.6654 & 208.1468 & 221.1718 & 111.27477 &  45.71994 & 168.12143 & 320.7530\\\\\n",
       "\t112 & 201.5028 & 116.90770 & 107.53939 & 149.6917 & 204.4011 & 224.7080 & 138.38987 &  83.56350 & 184.41790 & 330.6723\\\\\n",
       "\t113 & 180.7478 & 171.65324 & 173.44076 & 181.2109 & 187.4011 & 178.5188 & 130.89638 & 108.15160 & 170.64090 & 292.4629\\\\\n",
       "\t114 & 189.6896 & 119.44316 & 108.37799 & 139.2420 & 183.4065 & 218.3921 & 217.40827 & 180.70765 & 143.02320 & 221.6167\\\\\n",
       "\t115 & 182.7741 & 156.08719 & 152.56949 & 164.8150 & 180.9197 & 186.9277 & 161.86288 & 143.21560 & 177.34070 & 286.8962\\\\\n",
       "\t116 & 236.7865 & 210.72689 & 191.10183 & 172.9813 & 156.0553 & 127.9070 &  69.41510 &  38.07293 &  97.67516 & 264.6588\\\\\n",
       "\t117 & 231.9190 & 205.93345 & 193.96003 & 188.1928 & 181.6982 & 154.2772 &  72.52431 &  29.35146 & 110.81448 & 268.9544\\\\\n",
       "\t118 & 190.5171 &  90.42965 &  76.21068 & 120.2346 & 178.5758 & 199.6624 & 106.39157 &  35.00954 & 118.50332 & 283.9165\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | V1 &lt;dbl&gt; | V2 &lt;dbl&gt; | V3 &lt;dbl&gt; | V4 &lt;dbl&gt; | V5 &lt;dbl&gt; | V6 &lt;dbl&gt; | V7 &lt;dbl&gt; | V8 &lt;dbl&gt; | V9 &lt;dbl&gt; | V10 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 109 | 221.4937 | 158.49211 | 139.90911 | 155.7404 | 189.4430 | 213.8895 | 185.06713 | 211.32260 | 371.21790 | 517.6092 |\n",
       "| 110 | 212.8240 | 109.53846 |  88.59296 | 124.1767 | 176.6571 | 197.2031 | 113.33114 |  55.83492 | 147.71278 | 302.4182 |\n",
       "| 111 | 238.3980 | 136.69881 | 118.08474 | 155.6654 | 208.1468 | 221.1718 | 111.27477 |  45.71994 | 168.12143 | 320.7530 |\n",
       "| 112 | 201.5028 | 116.90770 | 107.53939 | 149.6917 | 204.4011 | 224.7080 | 138.38987 |  83.56350 | 184.41790 | 330.6723 |\n",
       "| 113 | 180.7478 | 171.65324 | 173.44076 | 181.2109 | 187.4011 | 178.5188 | 130.89638 | 108.15160 | 170.64090 | 292.4629 |\n",
       "| 114 | 189.6896 | 119.44316 | 108.37799 | 139.2420 | 183.4065 | 218.3921 | 217.40827 | 180.70765 | 143.02320 | 221.6167 |\n",
       "| 115 | 182.7741 | 156.08719 | 152.56949 | 164.8150 | 180.9197 | 186.9277 | 161.86288 | 143.21560 | 177.34070 | 286.8962 |\n",
       "| 116 | 236.7865 | 210.72689 | 191.10183 | 172.9813 | 156.0553 | 127.9070 |  69.41510 |  38.07293 |  97.67516 | 264.6588 |\n",
       "| 117 | 231.9190 | 205.93345 | 193.96003 | 188.1928 | 181.6982 | 154.2772 |  72.52431 |  29.35146 | 110.81448 | 268.9544 |\n",
       "| 118 | 190.5171 |  90.42965 |  76.21068 | 120.2346 | 178.5758 | 199.6624 | 106.39157 |  35.00954 | 118.50332 | 283.9165 |\n",
       "\n"
      ],
      "text/plain": [
       "    V1       V2        V3        V4       V5       V6       V7        V8       \n",
       "109 221.4937 158.49211 139.90911 155.7404 189.4430 213.8895 185.06713 211.32260\n",
       "110 212.8240 109.53846  88.59296 124.1767 176.6571 197.2031 113.33114  55.83492\n",
       "111 238.3980 136.69881 118.08474 155.6654 208.1468 221.1718 111.27477  45.71994\n",
       "112 201.5028 116.90770 107.53939 149.6917 204.4011 224.7080 138.38987  83.56350\n",
       "113 180.7478 171.65324 173.44076 181.2109 187.4011 178.5188 130.89638 108.15160\n",
       "114 189.6896 119.44316 108.37799 139.2420 183.4065 218.3921 217.40827 180.70765\n",
       "115 182.7741 156.08719 152.56949 164.8150 180.9197 186.9277 161.86288 143.21560\n",
       "116 236.7865 210.72689 191.10183 172.9813 156.0553 127.9070  69.41510  38.07293\n",
       "117 231.9190 205.93345 193.96003 188.1928 181.6982 154.2772  72.52431  29.35146\n",
       "118 190.5171  90.42965  76.21068 120.2346 178.5758 199.6624 106.39157  35.00954\n",
       "    V9        V10     \n",
       "109 371.21790 517.6092\n",
       "110 147.71278 302.4182\n",
       "111 168.12143 320.7530\n",
       "112 184.41790 330.6723\n",
       "113 170.64090 292.4629\n",
       "114 143.02320 221.6167\n",
       "115 177.34070 286.8962\n",
       "116  97.67516 264.6588\n",
       "117 110.81448 268.9544\n",
       "118 118.50332 283.9165"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ricepn[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CropPhenology\n",
    "l = list()\n",
    "for (i in 1:nrow(ricepn)){\n",
    "    l[i] <- list(SinglePhenology(as.numeric(ricepn[i,]),Percentage=20))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoothing in most cases eliminates spatial and temporal variability that may be an important information source to understand enviromental conditions that affect crop development. An obvious consequence of smoothing is the reduction of NDVI peaks, hence potentially removing crop-relevant information and affecting the calculation of the metrics, particullary in the identification of the start, maximum and end of growth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 15</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>OnsetV</th><th scope=col>OnsetT</th><th scope=col>MAxV</th><th scope=col>MaxT</th><th scope=col>OffsetV</th><th scope=col>OffsetT</th><th scope=col>LengthGS</th><th scope=col>BeforeMaxT</th><th scope=col>AfterMaxT</th><th scope=col>GreenUpSlope</th><th scope=col>BrownDownSlope</th><th scope=col>TINDVI</th><th scope=col>TINDVIBeforeMax</th><th scope=col>TINDVIAfeterMax</th><th scope=col>Assymetry</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>109</th><td>167.77013</td><td>36</td><td>855.7260</td><td>155</td><td>214.4254</td><td>246</td><td>210</td><td>119</td><td>91</td><td> 5.781142</td><td>7.047259</td><td>58492.66</td><td>55907.66</td><td>114376.99</td><td>  2584.996</td></tr>\n",
       "\t<tr><th scope=row>110</th><td> 63.40426</td><td>74</td><td>883.9883</td><td>155</td><td>205.4246</td><td>246</td><td>172</td><td> 81</td><td>91</td><td>10.130667</td><td>7.456743</td><td>44158.56</td><td>59340.70</td><td>103428.25</td><td>-15182.133</td></tr>\n",
       "\t<tr><th scope=row>111</th><td> 50.59565</td><td>73</td><td>904.2549</td><td>154</td><td>218.9646</td><td>246</td><td>173</td><td> 81</td><td>92</td><td>10.539003</td><td>7.448807</td><td>43987.32</td><td>60501.03</td><td>104404.17</td><td>-16513.711</td></tr>\n",
       "\t<tr><th scope=row>112</th><td> 93.03092</td><td>74</td><td>883.0690</td><td>154</td><td>180.2379</td><td>246</td><td>172</td><td> 80</td><td>92</td><td> 9.875476</td><td>7.639468</td><td>44491.10</td><td>55642.50</td><td>100089.99</td><td>-11151.406</td></tr>\n",
       "\t<tr><th scope=row>113</th><td>123.88783</td><td>76</td><td>918.8240</td><td>153</td><td>202.6775</td><td>246</td><td>170</td><td> 77</td><td>93</td><td>10.323847</td><td>7.700500</td><td>43204.46</td><td>55314.56</td><td> 98479.63</td><td>-12110.102</td></tr>\n",
       "\t<tr><th scope=row>114</th><td>127.88068</td><td>29</td><td>927.0844</td><td>153</td><td>195.3630</td><td>241</td><td>212</td><td>124</td><td>88</td><td> 6.445191</td><td>8.315016</td><td>50461.16</td><td>55054.51</td><td>105481.92</td><td> -4593.347</td></tr>\n",
       "\t<tr><th scope=row>115</th><td>170.93003</td><td>81</td><td>881.1470</td><td>154</td><td>207.1829</td><td>246</td><td>165</td><td> 73</td><td>92</td><td> 9.729000</td><td>7.325697</td><td>41551.86</td><td>53699.65</td><td> 95233.38</td><td>-12147.787</td></tr>\n",
       "\t<tr><th scope=row>116</th><td> 41.85173</td><td>74</td><td>916.6509</td><td>151</td><td>188.1520</td><td>246</td><td>172</td><td> 77</td><td>95</td><td>11.361029</td><td>7.668410</td><td>42666.17</td><td>59301.35</td><td>101894.37</td><td>-16635.177</td></tr>\n",
       "\t<tr><th scope=row>117</th><td> 32.02429</td><td>73</td><td>933.4703</td><td>154</td><td>194.9326</td><td>246</td><td>173</td><td> 81</td><td>92</td><td>11.128963</td><td>8.027583</td><td>44039.49</td><td>62300.80</td><td>106258.83</td><td>-18261.307</td></tr>\n",
       "\t<tr><th scope=row>118</th><td> 40.00018</td><td>74</td><td>895.2584</td><td>154</td><td>195.1855</td><td>246</td><td>172</td><td> 80</td><td>92</td><td>10.690727</td><td>7.609488</td><td>43338.79</td><td>56284.69</td><td> 99545.89</td><td>-12945.898</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 15\n",
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & OnsetV & OnsetT & MAxV & MaxT & OffsetV & OffsetT & LengthGS & BeforeMaxT & AfterMaxT & GreenUpSlope & BrownDownSlope & TINDVI & TINDVIBeforeMax & TINDVIAfeterMax & Assymetry\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t109 & 167.77013 & 36 & 855.7260 & 155 & 214.4254 & 246 & 210 & 119 & 91 &  5.781142 & 7.047259 & 58492.66 & 55907.66 & 114376.99 &   2584.996\\\\\n",
       "\t110 &  63.40426 & 74 & 883.9883 & 155 & 205.4246 & 246 & 172 &  81 & 91 & 10.130667 & 7.456743 & 44158.56 & 59340.70 & 103428.25 & -15182.133\\\\\n",
       "\t111 &  50.59565 & 73 & 904.2549 & 154 & 218.9646 & 246 & 173 &  81 & 92 & 10.539003 & 7.448807 & 43987.32 & 60501.03 & 104404.17 & -16513.711\\\\\n",
       "\t112 &  93.03092 & 74 & 883.0690 & 154 & 180.2379 & 246 & 172 &  80 & 92 &  9.875476 & 7.639468 & 44491.10 & 55642.50 & 100089.99 & -11151.406\\\\\n",
       "\t113 & 123.88783 & 76 & 918.8240 & 153 & 202.6775 & 246 & 170 &  77 & 93 & 10.323847 & 7.700500 & 43204.46 & 55314.56 &  98479.63 & -12110.102\\\\\n",
       "\t114 & 127.88068 & 29 & 927.0844 & 153 & 195.3630 & 241 & 212 & 124 & 88 &  6.445191 & 8.315016 & 50461.16 & 55054.51 & 105481.92 &  -4593.347\\\\\n",
       "\t115 & 170.93003 & 81 & 881.1470 & 154 & 207.1829 & 246 & 165 &  73 & 92 &  9.729000 & 7.325697 & 41551.86 & 53699.65 &  95233.38 & -12147.787\\\\\n",
       "\t116 &  41.85173 & 74 & 916.6509 & 151 & 188.1520 & 246 & 172 &  77 & 95 & 11.361029 & 7.668410 & 42666.17 & 59301.35 & 101894.37 & -16635.177\\\\\n",
       "\t117 &  32.02429 & 73 & 933.4703 & 154 & 194.9326 & 246 & 173 &  81 & 92 & 11.128963 & 8.027583 & 44039.49 & 62300.80 & 106258.83 & -18261.307\\\\\n",
       "\t118 &  40.00018 & 74 & 895.2584 & 154 & 195.1855 & 246 & 172 &  80 & 92 & 10.690727 & 7.609488 & 43338.79 & 56284.69 &  99545.89 & -12945.898\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 15\n",
       "\n",
       "| <!--/--> | OnsetV &lt;dbl&gt; | OnsetT &lt;dbl&gt; | MAxV &lt;dbl&gt; | MaxT &lt;dbl&gt; | OffsetV &lt;dbl&gt; | OffsetT &lt;dbl&gt; | LengthGS &lt;dbl&gt; | BeforeMaxT &lt;dbl&gt; | AfterMaxT &lt;dbl&gt; | GreenUpSlope &lt;dbl&gt; | BrownDownSlope &lt;dbl&gt; | TINDVI &lt;dbl&gt; | TINDVIBeforeMax &lt;dbl&gt; | TINDVIAfeterMax &lt;dbl&gt; | Assymetry &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 109 | 167.77013 | 36 | 855.7260 | 155 | 214.4254 | 246 | 210 | 119 | 91 |  5.781142 | 7.047259 | 58492.66 | 55907.66 | 114376.99 |   2584.996 |\n",
       "| 110 |  63.40426 | 74 | 883.9883 | 155 | 205.4246 | 246 | 172 |  81 | 91 | 10.130667 | 7.456743 | 44158.56 | 59340.70 | 103428.25 | -15182.133 |\n",
       "| 111 |  50.59565 | 73 | 904.2549 | 154 | 218.9646 | 246 | 173 |  81 | 92 | 10.539003 | 7.448807 | 43987.32 | 60501.03 | 104404.17 | -16513.711 |\n",
       "| 112 |  93.03092 | 74 | 883.0690 | 154 | 180.2379 | 246 | 172 |  80 | 92 |  9.875476 | 7.639468 | 44491.10 | 55642.50 | 100089.99 | -11151.406 |\n",
       "| 113 | 123.88783 | 76 | 918.8240 | 153 | 202.6775 | 246 | 170 |  77 | 93 | 10.323847 | 7.700500 | 43204.46 | 55314.56 |  98479.63 | -12110.102 |\n",
       "| 114 | 127.88068 | 29 | 927.0844 | 153 | 195.3630 | 241 | 212 | 124 | 88 |  6.445191 | 8.315016 | 50461.16 | 55054.51 | 105481.92 |  -4593.347 |\n",
       "| 115 | 170.93003 | 81 | 881.1470 | 154 | 207.1829 | 246 | 165 |  73 | 92 |  9.729000 | 7.325697 | 41551.86 | 53699.65 |  95233.38 | -12147.787 |\n",
       "| 116 |  41.85173 | 74 | 916.6509 | 151 | 188.1520 | 246 | 172 |  77 | 95 | 11.361029 | 7.668410 | 42666.17 | 59301.35 | 101894.37 | -16635.177 |\n",
       "| 117 |  32.02429 | 73 | 933.4703 | 154 | 194.9326 | 246 | 173 |  81 | 92 | 11.128963 | 8.027583 | 44039.49 | 62300.80 | 106258.83 | -18261.307 |\n",
       "| 118 |  40.00018 | 74 | 895.2584 | 154 | 195.1855 | 246 | 172 |  80 | 92 | 10.690727 | 7.609488 | 43338.79 | 56284.69 |  99545.89 | -12945.898 |\n",
       "\n"
      ],
      "text/plain": [
       "    OnsetV    OnsetT MAxV     MaxT OffsetV  OffsetT LengthGS BeforeMaxT\n",
       "109 167.77013 36     855.7260 155  214.4254 246     210      119       \n",
       "110  63.40426 74     883.9883 155  205.4246 246     172       81       \n",
       "111  50.59565 73     904.2549 154  218.9646 246     173       81       \n",
       "112  93.03092 74     883.0690 154  180.2379 246     172       80       \n",
       "113 123.88783 76     918.8240 153  202.6775 246     170       77       \n",
       "114 127.88068 29     927.0844 153  195.3630 241     212      124       \n",
       "115 170.93003 81     881.1470 154  207.1829 246     165       73       \n",
       "116  41.85173 74     916.6509 151  188.1520 246     172       77       \n",
       "117  32.02429 73     933.4703 154  194.9326 246     173       81       \n",
       "118  40.00018 74     895.2584 154  195.1855 246     172       80       \n",
       "    AfterMaxT GreenUpSlope BrownDownSlope TINDVI   TINDVIBeforeMax\n",
       "109 91         5.781142    7.047259       58492.66 55907.66       \n",
       "110 91        10.130667    7.456743       44158.56 59340.70       \n",
       "111 92        10.539003    7.448807       43987.32 60501.03       \n",
       "112 92         9.875476    7.639468       44491.10 55642.50       \n",
       "113 93        10.323847    7.700500       43204.46 55314.56       \n",
       "114 88         6.445191    8.315016       50461.16 55054.51       \n",
       "115 92         9.729000    7.325697       41551.86 53699.65       \n",
       "116 95        11.361029    7.668410       42666.17 59301.35       \n",
       "117 92        11.128963    8.027583       44039.49 62300.80       \n",
       "118 92        10.690727    7.609488       43338.79 56284.69       \n",
       "    TINDVIAfeterMax Assymetry \n",
       "109 114376.99         2584.996\n",
       "110 103428.25       -15182.133\n",
       "111 104404.17       -16513.711\n",
       "112 100089.99       -11151.406\n",
       "113  98479.63       -12110.102\n",
       "114 105481.92        -4593.347\n",
       "115  95233.38       -12147.787\n",
       "116 101894.37       -16635.177\n",
       "117 106258.83       -18261.307\n",
       "118  99545.89       -12945.898"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnam <- c('OnsetV', 'OnsetT','MAxV', 'MaxT', 'OffsetV', 'OffsetT','LengthGS', 'BeforeMaxT', 'AfterMaxT', 'GreenUpSlope', 'BrownDownSlope',  'TINDVI', 'TINDVIBeforeMax', 'TINDVIAfeterMax', 'Assymetry')\n",
    "cnam <- as.numeric(rownames(rice))\n",
    "crph <- as.data.frame(l, row.names = rnam, col.names=cnam)\n",
    "crph <- t(as.data.frame(crph))\n",
    "row.names(crph) <- obj\n",
    "crph <- as.data.frame(crph)\n",
    "crph[1:10,1:ncol(crph)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(crph, \"C:/Users/User/Documents/NOA_e-shape/Korea-Data_Thanassis/wetransfer-09e80a/croppheno.csv\",row.names = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract DOYs from TIMESAT output\n",
    "space <- abs(as.numeric(format(as.Date(rice$daily[1],format=\"%Y-%m-%d\"), format = \"%d\"))-as.numeric(format(as.Date(rice$daily[2],format=\"%Y-%m-%d\"), format = \"%d\")))\n",
    "first_doy <- as.numeric(strftime(rice$daily[1], format = \"%j\"))\n",
    "phenolist$time_start <- round(phenolist$time_start * space + first_doy,0)\n",
    "phenolist$time_end <- round(phenolist$time_end * space + first_doy,0)\n",
    "phenolist$time_peak <- round(phenolist$time_peak * space + first_doy,0)\n",
    "phenolist$len <- phenolist$time_end - phenolist$time_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds <- format(strptime(phenolist$time_start, format=\"%j\"), format=\"%m-%d\")\n",
    "de <- format(strptime(phenolist$time_end, format=\"%j\"), format=\"%m-%d\")\n",
    "d <- cbind(ds,de)\n",
    "colnames(d) <- c('day_start','day_end')\n",
    "phenolistn <- cbind(d,phenolist)\n",
    "phenolistn <- phenolistn[-c(3:6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>day_start</th><th scope=col>day_end</th><th scope=col>time_end</th><th scope=col>time_peak</th><th scope=col>len</th><th scope=col>val_start</th><th scope=col>val_end</th><th scope=col>val_peak</th><th scope=col>val_base</th><th scope=col>ampl</th><th scope=col>der_l</th><th scope=col>der_r</th><th scope=col>integ_large</th><th scope=col>integ_small</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>109</th><td>06-21</td><td>10-17</td><td>290</td><td>234</td><td>118</td><td>500.1835</td><td>500.1835</td><td>842.1780</td><td>158.18912</td><td>683.9888</td><td> 87.20371</td><td>110.41296</td><td>9541.217</td><td>7326.570</td></tr>\n",
       "\t<tr><th scope=row>110</th><td>07-02</td><td>10-21</td><td>294</td><td>239</td><td>111</td><td>495.5674</td><td>495.5674</td><td>871.0121</td><td>120.12262</td><td>750.8895</td><td>126.73415</td><td>116.23748</td><td>9192.518</td><td>7630.924</td></tr>\n",
       "\t<tr><th scope=row>111</th><td>07-04</td><td>10-20</td><td>293</td><td>239</td><td>108</td><td>511.7146</td><td>511.7146</td><td>890.6611</td><td>132.76816</td><td>757.8929</td><td>128.01216</td><td>107.86961</td><td>9222.448</td><td>7496.462</td></tr>\n",
       "\t<tr><th scope=row>112</th><td>07-03</td><td>10-15</td><td>288</td><td>235</td><td>104</td><td>508.3320</td><td>501.5473</td><td>869.3218</td><td>140.55751</td><td>728.7643</td><td>122.62640</td><td>107.04610</td><td>8506.385</td><td>6819.695</td></tr>\n",
       "\t<tr><th scope=row>113</th><td>07-05</td><td>10-11</td><td>284</td><td>235</td><td> 98</td><td>517.0316</td><td>517.0316</td><td>898.8629</td><td>135.20030</td><td>763.6626</td><td>130.70656</td><td>123.21746</td><td>8543.068</td><td>6920.665</td></tr>\n",
       "\t<tr><th scope=row>114</th><td>07-07</td><td>10-12</td><td>285</td><td>236</td><td> 97</td><td>515.9840</td><td>515.9840</td><td>895.7818</td><td>136.18622</td><td>759.5956</td><td>136.83629</td><td>121.18666</td><td>8519.909</td><td>6885.674</td></tr>\n",
       "\t<tr><th scope=row>115</th><td>07-04</td><td>10-13</td><td>286</td><td>235</td><td>101</td><td>500.0210</td><td>500.0210</td><td>845.9667</td><td>154.07523</td><td>691.8915</td><td>116.03899</td><td>105.77312</td><td>8215.723</td><td>6366.820</td></tr>\n",
       "\t<tr><th scope=row>116</th><td>06-29</td><td>10-17</td><td>290</td><td>232</td><td>110</td><td>480.5396</td><td>480.5396</td><td>894.4420</td><td> 66.63705</td><td>827.8050</td><td>165.72737</td><td> 99.19761</td><td>9204.847</td><td>8338.564</td></tr>\n",
       "\t<tr><th scope=row>117</th><td>07-02</td><td>10-23</td><td>296</td><td>239</td><td>113</td><td>495.0048</td><td>495.0048</td><td>908.3886</td><td> 81.62102</td><td>826.7676</td><td>145.48515</td><td>112.61842</td><td>9536.738</td><td>8475.665</td></tr>\n",
       "\t<tr><th scope=row>118</th><td>07-01</td><td>10-16</td><td>289</td><td>235</td><td>107</td><td>489.2664</td><td>489.2664</td><td>869.4349</td><td>109.09793</td><td>760.3369</td><td>136.97014</td><td>105.69049</td><td>8914.155</td><td>7495.882</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 14\n",
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & day\\_start & day\\_end & time\\_end & time\\_peak & len & val\\_start & val\\_end & val\\_peak & val\\_base & ampl & der\\_l & der\\_r & integ\\_large & integ\\_small\\\\\n",
       "  & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t109 & 06-21 & 10-17 & 290 & 234 & 118 & 500.1835 & 500.1835 & 842.1780 & 158.18912 & 683.9888 &  87.20371 & 110.41296 & 9541.217 & 7326.570\\\\\n",
       "\t110 & 07-02 & 10-21 & 294 & 239 & 111 & 495.5674 & 495.5674 & 871.0121 & 120.12262 & 750.8895 & 126.73415 & 116.23748 & 9192.518 & 7630.924\\\\\n",
       "\t111 & 07-04 & 10-20 & 293 & 239 & 108 & 511.7146 & 511.7146 & 890.6611 & 132.76816 & 757.8929 & 128.01216 & 107.86961 & 9222.448 & 7496.462\\\\\n",
       "\t112 & 07-03 & 10-15 & 288 & 235 & 104 & 508.3320 & 501.5473 & 869.3218 & 140.55751 & 728.7643 & 122.62640 & 107.04610 & 8506.385 & 6819.695\\\\\n",
       "\t113 & 07-05 & 10-11 & 284 & 235 &  98 & 517.0316 & 517.0316 & 898.8629 & 135.20030 & 763.6626 & 130.70656 & 123.21746 & 8543.068 & 6920.665\\\\\n",
       "\t114 & 07-07 & 10-12 & 285 & 236 &  97 & 515.9840 & 515.9840 & 895.7818 & 136.18622 & 759.5956 & 136.83629 & 121.18666 & 8519.909 & 6885.674\\\\\n",
       "\t115 & 07-04 & 10-13 & 286 & 235 & 101 & 500.0210 & 500.0210 & 845.9667 & 154.07523 & 691.8915 & 116.03899 & 105.77312 & 8215.723 & 6366.820\\\\\n",
       "\t116 & 06-29 & 10-17 & 290 & 232 & 110 & 480.5396 & 480.5396 & 894.4420 &  66.63705 & 827.8050 & 165.72737 &  99.19761 & 9204.847 & 8338.564\\\\\n",
       "\t117 & 07-02 & 10-23 & 296 & 239 & 113 & 495.0048 & 495.0048 & 908.3886 &  81.62102 & 826.7676 & 145.48515 & 112.61842 & 9536.738 & 8475.665\\\\\n",
       "\t118 & 07-01 & 10-16 & 289 & 235 & 107 & 489.2664 & 489.2664 & 869.4349 & 109.09793 & 760.3369 & 136.97014 & 105.69049 & 8914.155 & 7495.882\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 14\n",
       "\n",
       "| <!--/--> | day_start &lt;fct&gt; | day_end &lt;fct&gt; | time_end &lt;dbl&gt; | time_peak &lt;dbl&gt; | len &lt;dbl&gt; | val_start &lt;dbl&gt; | val_end &lt;dbl&gt; | val_peak &lt;dbl&gt; | val_base &lt;dbl&gt; | ampl &lt;dbl&gt; | der_l &lt;dbl&gt; | der_r &lt;dbl&gt; | integ_large &lt;dbl&gt; | integ_small &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 109 | 06-21 | 10-17 | 290 | 234 | 118 | 500.1835 | 500.1835 | 842.1780 | 158.18912 | 683.9888 |  87.20371 | 110.41296 | 9541.217 | 7326.570 |\n",
       "| 110 | 07-02 | 10-21 | 294 | 239 | 111 | 495.5674 | 495.5674 | 871.0121 | 120.12262 | 750.8895 | 126.73415 | 116.23748 | 9192.518 | 7630.924 |\n",
       "| 111 | 07-04 | 10-20 | 293 | 239 | 108 | 511.7146 | 511.7146 | 890.6611 | 132.76816 | 757.8929 | 128.01216 | 107.86961 | 9222.448 | 7496.462 |\n",
       "| 112 | 07-03 | 10-15 | 288 | 235 | 104 | 508.3320 | 501.5473 | 869.3218 | 140.55751 | 728.7643 | 122.62640 | 107.04610 | 8506.385 | 6819.695 |\n",
       "| 113 | 07-05 | 10-11 | 284 | 235 |  98 | 517.0316 | 517.0316 | 898.8629 | 135.20030 | 763.6626 | 130.70656 | 123.21746 | 8543.068 | 6920.665 |\n",
       "| 114 | 07-07 | 10-12 | 285 | 236 |  97 | 515.9840 | 515.9840 | 895.7818 | 136.18622 | 759.5956 | 136.83629 | 121.18666 | 8519.909 | 6885.674 |\n",
       "| 115 | 07-04 | 10-13 | 286 | 235 | 101 | 500.0210 | 500.0210 | 845.9667 | 154.07523 | 691.8915 | 116.03899 | 105.77312 | 8215.723 | 6366.820 |\n",
       "| 116 | 06-29 | 10-17 | 290 | 232 | 110 | 480.5396 | 480.5396 | 894.4420 |  66.63705 | 827.8050 | 165.72737 |  99.19761 | 9204.847 | 8338.564 |\n",
       "| 117 | 07-02 | 10-23 | 296 | 239 | 113 | 495.0048 | 495.0048 | 908.3886 |  81.62102 | 826.7676 | 145.48515 | 112.61842 | 9536.738 | 8475.665 |\n",
       "| 118 | 07-01 | 10-16 | 289 | 235 | 107 | 489.2664 | 489.2664 | 869.4349 | 109.09793 | 760.3369 | 136.97014 | 105.69049 | 8914.155 | 7495.882 |\n",
       "\n"
      ],
      "text/plain": [
       "    day_start day_end time_end time_peak len val_start val_end  val_peak\n",
       "109 06-21     10-17   290      234       118 500.1835  500.1835 842.1780\n",
       "110 07-02     10-21   294      239       111 495.5674  495.5674 871.0121\n",
       "111 07-04     10-20   293      239       108 511.7146  511.7146 890.6611\n",
       "112 07-03     10-15   288      235       104 508.3320  501.5473 869.3218\n",
       "113 07-05     10-11   284      235        98 517.0316  517.0316 898.8629\n",
       "114 07-07     10-12   285      236        97 515.9840  515.9840 895.7818\n",
       "115 07-04     10-13   286      235       101 500.0210  500.0210 845.9667\n",
       "116 06-29     10-17   290      232       110 480.5396  480.5396 894.4420\n",
       "117 07-02     10-23   296      239       113 495.0048  495.0048 908.3886\n",
       "118 07-01     10-16   289      235       107 489.2664  489.2664 869.4349\n",
       "    val_base  ampl     der_l     der_r     integ_large integ_small\n",
       "109 158.18912 683.9888  87.20371 110.41296 9541.217    7326.570   \n",
       "110 120.12262 750.8895 126.73415 116.23748 9192.518    7630.924   \n",
       "111 132.76816 757.8929 128.01216 107.86961 9222.448    7496.462   \n",
       "112 140.55751 728.7643 122.62640 107.04610 8506.385    6819.695   \n",
       "113 135.20030 763.6626 130.70656 123.21746 8543.068    6920.665   \n",
       "114 136.18622 759.5956 136.83629 121.18666 8519.909    6885.674   \n",
       "115 154.07523 691.8915 116.03899 105.77312 8215.723    6366.820   \n",
       "116  66.63705 827.8050 165.72737  99.19761 9204.847    8338.564   \n",
       "117  81.62102 826.7676 145.48515 112.61842 9536.738    8475.665   \n",
       "118 109.09793 760.3369 136.97014 105.69049 8914.155    7495.882   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phenolistn[1:10,1:ncol(phenolistn)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract DOYs from CropPhenology output\n",
    "crph$OnsetT\t <- round(crph$OnsetT + first_doy,0)\n",
    "crph$OffsetT <- round(crph$OffsetT + first_doy,0)\n",
    "crph$MaxT <- round(crph$MaxT + first_doy,0)\n",
    "crph$LengthGS <- crph$OffsetT - crph$OnsetT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds <- format(strptime(crph$OnsetT, format=\"%j\"), format=\"%m-%d\")\n",
    "de <- format(strptime(crph$OffsetT, format=\"%j\"), format=\"%m-%d\")\n",
    "d <- cbind(ds,de)\n",
    "colnames(d) <- c('day_start','day_end')\n",
    "crph <- cbind(d,crph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 17</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>day_start</th><th scope=col>day_end</th><th scope=col>OnsetV</th><th scope=col>OnsetT</th><th scope=col>MAxV</th><th scope=col>MaxT</th><th scope=col>OffsetV</th><th scope=col>OffsetT</th><th scope=col>LengthGS</th><th scope=col>BeforeMaxT</th><th scope=col>AfterMaxT</th><th scope=col>GreenUpSlope</th><th scope=col>BrownDownSlope</th><th scope=col>TINDVI</th><th scope=col>TINDVIBeforeMax</th><th scope=col>TINDVIAfeterMax</th><th scope=col>Assymetry</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>109</th><td>04-15</td><td>11-11</td><td>167.77013</td><td>105</td><td>855.7260</td><td>224</td><td>214.4254</td><td>315</td><td>210</td><td>119</td><td>91</td><td> 5.781142</td><td>7.047259</td><td>58492.66</td><td>55907.66</td><td>114376.99</td><td>  2584.996</td></tr>\n",
       "\t<tr><th scope=row>110</th><td>05-23</td><td>11-11</td><td> 63.40426</td><td>143</td><td>883.9883</td><td>224</td><td>205.4246</td><td>315</td><td>172</td><td> 81</td><td>91</td><td>10.130667</td><td>7.456743</td><td>44158.56</td><td>59340.70</td><td>103428.25</td><td>-15182.133</td></tr>\n",
       "\t<tr><th scope=row>111</th><td>05-22</td><td>11-11</td><td> 50.59565</td><td>142</td><td>904.2549</td><td>223</td><td>218.9646</td><td>315</td><td>173</td><td> 81</td><td>92</td><td>10.539003</td><td>7.448807</td><td>43987.32</td><td>60501.03</td><td>104404.17</td><td>-16513.711</td></tr>\n",
       "\t<tr><th scope=row>112</th><td>05-23</td><td>11-11</td><td> 93.03092</td><td>143</td><td>883.0690</td><td>223</td><td>180.2379</td><td>315</td><td>172</td><td> 80</td><td>92</td><td> 9.875476</td><td>7.639468</td><td>44491.10</td><td>55642.50</td><td>100089.99</td><td>-11151.406</td></tr>\n",
       "\t<tr><th scope=row>113</th><td>05-25</td><td>11-11</td><td>123.88783</td><td>145</td><td>918.8240</td><td>222</td><td>202.6775</td><td>315</td><td>170</td><td> 77</td><td>93</td><td>10.323847</td><td>7.700500</td><td>43204.46</td><td>55314.56</td><td> 98479.63</td><td>-12110.102</td></tr>\n",
       "\t<tr><th scope=row>114</th><td>04-08</td><td>11-06</td><td>127.88068</td><td> 98</td><td>927.0844</td><td>222</td><td>195.3630</td><td>310</td><td>212</td><td>124</td><td>88</td><td> 6.445191</td><td>8.315016</td><td>50461.16</td><td>55054.51</td><td>105481.92</td><td> -4593.347</td></tr>\n",
       "\t<tr><th scope=row>115</th><td>05-30</td><td>11-11</td><td>170.93003</td><td>150</td><td>881.1470</td><td>223</td><td>207.1829</td><td>315</td><td>165</td><td> 73</td><td>92</td><td> 9.729000</td><td>7.325697</td><td>41551.86</td><td>53699.65</td><td> 95233.38</td><td>-12147.787</td></tr>\n",
       "\t<tr><th scope=row>116</th><td>05-23</td><td>11-11</td><td> 41.85173</td><td>143</td><td>916.6509</td><td>220</td><td>188.1520</td><td>315</td><td>172</td><td> 77</td><td>95</td><td>11.361029</td><td>7.668410</td><td>42666.17</td><td>59301.35</td><td>101894.37</td><td>-16635.177</td></tr>\n",
       "\t<tr><th scope=row>117</th><td>05-22</td><td>11-11</td><td> 32.02429</td><td>142</td><td>933.4703</td><td>223</td><td>194.9326</td><td>315</td><td>173</td><td> 81</td><td>92</td><td>11.128963</td><td>8.027583</td><td>44039.49</td><td>62300.80</td><td>106258.83</td><td>-18261.307</td></tr>\n",
       "\t<tr><th scope=row>118</th><td>05-23</td><td>11-11</td><td> 40.00018</td><td>143</td><td>895.2584</td><td>223</td><td>195.1855</td><td>315</td><td>172</td><td> 80</td><td>92</td><td>10.690727</td><td>7.609488</td><td>43338.79</td><td>56284.69</td><td> 99545.89</td><td>-12945.898</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 17\n",
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       "  & day\\_start & day\\_end & OnsetV & OnsetT & MAxV & MaxT & OffsetV & OffsetT & LengthGS & BeforeMaxT & AfterMaxT & GreenUpSlope & BrownDownSlope & TINDVI & TINDVIBeforeMax & TINDVIAfeterMax & Assymetry\\\\\n",
       "  & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t109 & 04-15 & 11-11 & 167.77013 & 105 & 855.7260 & 224 & 214.4254 & 315 & 210 & 119 & 91 &  5.781142 & 7.047259 & 58492.66 & 55907.66 & 114376.99 &   2584.996\\\\\n",
       "\t110 & 05-23 & 11-11 &  63.40426 & 143 & 883.9883 & 224 & 205.4246 & 315 & 172 &  81 & 91 & 10.130667 & 7.456743 & 44158.56 & 59340.70 & 103428.25 & -15182.133\\\\\n",
       "\t111 & 05-22 & 11-11 &  50.59565 & 142 & 904.2549 & 223 & 218.9646 & 315 & 173 &  81 & 92 & 10.539003 & 7.448807 & 43987.32 & 60501.03 & 104404.17 & -16513.711\\\\\n",
       "\t112 & 05-23 & 11-11 &  93.03092 & 143 & 883.0690 & 223 & 180.2379 & 315 & 172 &  80 & 92 &  9.875476 & 7.639468 & 44491.10 & 55642.50 & 100089.99 & -11151.406\\\\\n",
       "\t113 & 05-25 & 11-11 & 123.88783 & 145 & 918.8240 & 222 & 202.6775 & 315 & 170 &  77 & 93 & 10.323847 & 7.700500 & 43204.46 & 55314.56 &  98479.63 & -12110.102\\\\\n",
       "\t114 & 04-08 & 11-06 & 127.88068 &  98 & 927.0844 & 222 & 195.3630 & 310 & 212 & 124 & 88 &  6.445191 & 8.315016 & 50461.16 & 55054.51 & 105481.92 &  -4593.347\\\\\n",
       "\t115 & 05-30 & 11-11 & 170.93003 & 150 & 881.1470 & 223 & 207.1829 & 315 & 165 &  73 & 92 &  9.729000 & 7.325697 & 41551.86 & 53699.65 &  95233.38 & -12147.787\\\\\n",
       "\t116 & 05-23 & 11-11 &  41.85173 & 143 & 916.6509 & 220 & 188.1520 & 315 & 172 &  77 & 95 & 11.361029 & 7.668410 & 42666.17 & 59301.35 & 101894.37 & -16635.177\\\\\n",
       "\t117 & 05-22 & 11-11 &  32.02429 & 142 & 933.4703 & 223 & 194.9326 & 315 & 173 &  81 & 92 & 11.128963 & 8.027583 & 44039.49 & 62300.80 & 106258.83 & -18261.307\\\\\n",
       "\t118 & 05-23 & 11-11 &  40.00018 & 143 & 895.2584 & 223 & 195.1855 & 315 & 172 &  80 & 92 & 10.690727 & 7.609488 & 43338.79 & 56284.69 &  99545.89 & -12945.898\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 17\n",
       "\n",
       "| <!--/--> | day_start &lt;fct&gt; | day_end &lt;fct&gt; | OnsetV &lt;dbl&gt; | OnsetT &lt;dbl&gt; | MAxV &lt;dbl&gt; | MaxT &lt;dbl&gt; | OffsetV &lt;dbl&gt; | OffsetT &lt;dbl&gt; | LengthGS &lt;dbl&gt; | BeforeMaxT &lt;dbl&gt; | AfterMaxT &lt;dbl&gt; | GreenUpSlope &lt;dbl&gt; | BrownDownSlope &lt;dbl&gt; | TINDVI &lt;dbl&gt; | TINDVIBeforeMax &lt;dbl&gt; | TINDVIAfeterMax &lt;dbl&gt; | Assymetry &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 109 | 04-15 | 11-11 | 167.77013 | 105 | 855.7260 | 224 | 214.4254 | 315 | 210 | 119 | 91 |  5.781142 | 7.047259 | 58492.66 | 55907.66 | 114376.99 |   2584.996 |\n",
       "| 110 | 05-23 | 11-11 |  63.40426 | 143 | 883.9883 | 224 | 205.4246 | 315 | 172 |  81 | 91 | 10.130667 | 7.456743 | 44158.56 | 59340.70 | 103428.25 | -15182.133 |\n",
       "| 111 | 05-22 | 11-11 |  50.59565 | 142 | 904.2549 | 223 | 218.9646 | 315 | 173 |  81 | 92 | 10.539003 | 7.448807 | 43987.32 | 60501.03 | 104404.17 | -16513.711 |\n",
       "| 112 | 05-23 | 11-11 |  93.03092 | 143 | 883.0690 | 223 | 180.2379 | 315 | 172 |  80 | 92 |  9.875476 | 7.639468 | 44491.10 | 55642.50 | 100089.99 | -11151.406 |\n",
       "| 113 | 05-25 | 11-11 | 123.88783 | 145 | 918.8240 | 222 | 202.6775 | 315 | 170 |  77 | 93 | 10.323847 | 7.700500 | 43204.46 | 55314.56 |  98479.63 | -12110.102 |\n",
       "| 114 | 04-08 | 11-06 | 127.88068 |  98 | 927.0844 | 222 | 195.3630 | 310 | 212 | 124 | 88 |  6.445191 | 8.315016 | 50461.16 | 55054.51 | 105481.92 |  -4593.347 |\n",
       "| 115 | 05-30 | 11-11 | 170.93003 | 150 | 881.1470 | 223 | 207.1829 | 315 | 165 |  73 | 92 |  9.729000 | 7.325697 | 41551.86 | 53699.65 |  95233.38 | -12147.787 |\n",
       "| 116 | 05-23 | 11-11 |  41.85173 | 143 | 916.6509 | 220 | 188.1520 | 315 | 172 |  77 | 95 | 11.361029 | 7.668410 | 42666.17 | 59301.35 | 101894.37 | -16635.177 |\n",
       "| 117 | 05-22 | 11-11 |  32.02429 | 142 | 933.4703 | 223 | 194.9326 | 315 | 173 |  81 | 92 | 11.128963 | 8.027583 | 44039.49 | 62300.80 | 106258.83 | -18261.307 |\n",
       "| 118 | 05-23 | 11-11 |  40.00018 | 143 | 895.2584 | 223 | 195.1855 | 315 | 172 |  80 | 92 | 10.690727 | 7.609488 | 43338.79 | 56284.69 |  99545.89 | -12945.898 |\n",
       "\n"
      ],
      "text/plain": [
       "    day_start day_end OnsetV    OnsetT MAxV     MaxT OffsetV  OffsetT LengthGS\n",
       "109 04-15     11-11   167.77013 105    855.7260 224  214.4254 315     210     \n",
       "110 05-23     11-11    63.40426 143    883.9883 224  205.4246 315     172     \n",
       "111 05-22     11-11    50.59565 142    904.2549 223  218.9646 315     173     \n",
       "112 05-23     11-11    93.03092 143    883.0690 223  180.2379 315     172     \n",
       "113 05-25     11-11   123.88783 145    918.8240 222  202.6775 315     170     \n",
       "114 04-08     11-06   127.88068  98    927.0844 222  195.3630 310     212     \n",
       "115 05-30     11-11   170.93003 150    881.1470 223  207.1829 315     165     \n",
       "116 05-23     11-11    41.85173 143    916.6509 220  188.1520 315     172     \n",
       "117 05-22     11-11    32.02429 142    933.4703 223  194.9326 315     173     \n",
       "118 05-23     11-11    40.00018 143    895.2584 223  195.1855 315     172     \n",
       "    BeforeMaxT AfterMaxT GreenUpSlope BrownDownSlope TINDVI   TINDVIBeforeMax\n",
       "109 119        91         5.781142    7.047259       58492.66 55907.66       \n",
       "110  81        91        10.130667    7.456743       44158.56 59340.70       \n",
       "111  81        92        10.539003    7.448807       43987.32 60501.03       \n",
       "112  80        92         9.875476    7.639468       44491.10 55642.50       \n",
       "113  77        93        10.323847    7.700500       43204.46 55314.56       \n",
       "114 124        88         6.445191    8.315016       50461.16 55054.51       \n",
       "115  73        92         9.729000    7.325697       41551.86 53699.65       \n",
       "116  77        95        11.361029    7.668410       42666.17 59301.35       \n",
       "117  81        92        11.128963    8.027583       44039.49 62300.80       \n",
       "118  80        92        10.690727    7.609488       43338.79 56284.69       \n",
       "    TINDVIAfeterMax Assymetry \n",
       "109 114376.99         2584.996\n",
       "110 103428.25       -15182.133\n",
       "111 104404.17       -16513.711\n",
       "112 100089.99       -11151.406\n",
       "113  98479.63       -12110.102\n",
       "114 105481.92        -4593.347\n",
       "115  95233.38       -12147.787\n",
       "116 101894.37       -16635.177\n",
       "117 106258.83       -18261.307\n",
       "118  99545.89       -12945.898"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crph[1:10,1:ncol(crph)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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