M2SEME_UE4/UE4_TP2_Kc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lecture des données de Kc au pas de temps mensuel\n",
    "kc.moi <- read.csv('../Data/Tableau_Kc_Ebener1999.csv',header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 12 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>mois</th><th scope=col>Prairies</th><th scope=col>Vigne</th><th scope=col>Foret.de.conifères</th><th scope=col>Mais</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&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><td> 1</td><td>0.45</td><td>0.25</td><td>1.00</td><td>0.20</td></tr>\n",
       "\t<tr><td> 2</td><td>0.45</td><td>0.25</td><td>1.00</td><td>0.20</td></tr>\n",
       "\t<tr><td> 3</td><td>0.50</td><td>0.25</td><td>1.02</td><td>0.20</td></tr>\n",
       "\t<tr><td> 4</td><td>0.61</td><td>0.38</td><td>1.05</td><td>0.32</td></tr>\n",
       "\t<tr><td> 5</td><td>0.78</td><td>0.53</td><td>1.07</td><td>0.68</td></tr>\n",
       "\t<tr><td> 6</td><td>0.78</td><td>0.70</td><td>1.10</td><td>1.20</td></tr>\n",
       "\t<tr><td> 7</td><td>0.78</td><td>0.73</td><td>1.10</td><td>1.20</td></tr>\n",
       "\t<tr><td> 8</td><td>0.78</td><td>0.73</td><td>1.10</td><td>0.91</td></tr>\n",
       "\t<tr><td> 9</td><td>0.65</td><td>0.65</td><td>1.06</td><td>0.65</td></tr>\n",
       "\t<tr><td>10</td><td>0.58</td><td>0.53</td><td>1.03</td><td>0.35</td></tr>\n",
       "\t<tr><td>11</td><td>0.45</td><td>0.25</td><td>1.00</td><td>0.20</td></tr>\n",
       "\t<tr><td>12</td><td>0.45</td><td>0.25</td><td>1.00</td><td>0.20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 12 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " mois & Prairies & Vigne & Foret.de.conifères & Mais\\\\\n",
       " <int> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  1 & 0.45 & 0.25 & 1.00 & 0.20\\\\\n",
       "\t  2 & 0.45 & 0.25 & 1.00 & 0.20\\\\\n",
       "\t  3 & 0.50 & 0.25 & 1.02 & 0.20\\\\\n",
       "\t  4 & 0.61 & 0.38 & 1.05 & 0.32\\\\\n",
       "\t  5 & 0.78 & 0.53 & 1.07 & 0.68\\\\\n",
       "\t  6 & 0.78 & 0.70 & 1.10 & 1.20\\\\\n",
       "\t  7 & 0.78 & 0.73 & 1.10 & 1.20\\\\\n",
       "\t  8 & 0.78 & 0.73 & 1.10 & 0.91\\\\\n",
       "\t  9 & 0.65 & 0.65 & 1.06 & 0.65\\\\\n",
       "\t 10 & 0.58 & 0.53 & 1.03 & 0.35\\\\\n",
       "\t 11 & 0.45 & 0.25 & 1.00 & 0.20\\\\\n",
       "\t 12 & 0.45 & 0.25 & 1.00 & 0.20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 12 × 5\n",
       "\n",
       "| mois &lt;int&gt; | Prairies &lt;dbl&gt; | Vigne &lt;dbl&gt; | Foret.de.conifères &lt;dbl&gt; | Mais &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "|  1 | 0.45 | 0.25 | 1.00 | 0.20 |\n",
       "|  2 | 0.45 | 0.25 | 1.00 | 0.20 |\n",
       "|  3 | 0.50 | 0.25 | 1.02 | 0.20 |\n",
       "|  4 | 0.61 | 0.38 | 1.05 | 0.32 |\n",
       "|  5 | 0.78 | 0.53 | 1.07 | 0.68 |\n",
       "|  6 | 0.78 | 0.70 | 1.10 | 1.20 |\n",
       "|  7 | 0.78 | 0.73 | 1.10 | 1.20 |\n",
       "|  8 | 0.78 | 0.73 | 1.10 | 0.91 |\n",
       "|  9 | 0.65 | 0.65 | 1.06 | 0.65 |\n",
       "| 10 | 0.58 | 0.53 | 1.03 | 0.35 |\n",
       "| 11 | 0.45 | 0.25 | 1.00 | 0.20 |\n",
       "| 12 | 0.45 | 0.25 | 1.00 | 0.20 |\n",
       "\n"
      ],
      "text/plain": [
       "   mois Prairies Vigne Foret.de.conifères Mais\n",
       "1   1   0.45     0.25  1.00               0.20\n",
       "2   2   0.45     0.25  1.00               0.20\n",
       "3   3   0.50     0.25  1.02               0.20\n",
       "4   4   0.61     0.38  1.05               0.32\n",
       "5   5   0.78     0.53  1.07               0.68\n",
       "6   6   0.78     0.70  1.10               1.20\n",
       "7   7   0.78     0.73  1.10               1.20\n",
       "8   8   0.78     0.73  1.10               0.91\n",
       "9   9   0.65     0.65  1.06               0.65\n",
       "10 10   0.58     0.53  1.03               0.35\n",
       "11 11   0.45     0.25  1.00               0.20\n",
       "12 12   0.45     0.25  1.00               0.20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kc.moi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Date pour les valeurs de Kc mensuel\n",
    "kc.dates <- as.Date(c(\"2016-1-1\",paste(\"2016\",kc.moi$mois,\"15\",sep=\"-\"),\"2016-12-31\"))\n",
    "## Passage au jour julien\n",
    "kc.doi <- julian(kc.dates,origin = as.Date(\"2016-01-01\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Kc de la culture d'intérêt\n",
    "kc.prairies <- c(kc.moi$Prairies[1],kc.moi$Prairies,kc.moi$Prairies[12])\n",
    "kc.vigne <- c(kc.moi$Vigne[1],kc.moi$Vigne,kc.moi$Vigne[12])\n",
    "kc.conifere <- c(kc.moi$Foret.de.conifères[1],kc.moi$Foret.de.conifères,kc.moi$Foret.de.conifères[12])\n",
    "kc.mais <- c(kc.moi$Mais[1],kc.moi$Mais,kc.moi$Mais[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6ZqJvOofWr3uSjcBy1RpBB0uEPVTAui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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(kc.mais, type=\"l\") ## Trace le Kc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## transforme les dates calendaires en jours juliens ou day of the year pour une année type\n",
    "mydates <- seq(as.Date(\"2016/1/1\"), as.Date(\"2016/12/31\"), by = \"days\")\n",
    "doi <- julian(mydates,origin = as.Date(\"2016-01-01\"))+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>doi</th><th scope=col>kc.capprox</th><th scope=col>kc.lapprox</th><th scope=col>kc.spline</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0.2</td><td>0.2</td><td>0.1999332</td></tr>\n",
       "\t<tr><td>2</td><td>0.2</td><td>0.2</td><td>0.1998767</td></tr>\n",
       "\t<tr><td>3</td><td>0.2</td><td>0.2</td><td>0.1998305</td></tr>\n",
       "\t<tr><td>4</td><td>0.2</td><td>0.2</td><td>0.1997945</td></tr>\n",
       "\t<tr><td>5</td><td>0.2</td><td>0.2</td><td>0.1997688</td></tr>\n",
       "\t<tr><td>6</td><td>0.2</td><td>0.2</td><td>0.1997534</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{llll}\n",
       " doi & kc.capprox & kc.lapprox & kc.spline\\\\\n",
       "\\hline\n",
       "\t 1 & 0.2 & 0.2 & 0.1999332\\\\\n",
       "\t 2 & 0.2 & 0.2 & 0.1998767\\\\\n",
       "\t 3 & 0.2 & 0.2 & 0.1998305\\\\\n",
       "\t 4 & 0.2 & 0.2 & 0.1997945\\\\\n",
       "\t 5 & 0.2 & 0.2 & 0.1997688\\\\\n",
       "\t 6 & 0.2 & 0.2 & 0.1997534\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| doi | kc.capprox | kc.lapprox | kc.spline |\n",
       "|---|---|---|---|\n",
       "| 1 | 0.2 | 0.2 | 0.1999332 |\n",
       "| 2 | 0.2 | 0.2 | 0.1998767 |\n",
       "| 3 | 0.2 | 0.2 | 0.1998305 |\n",
       "| 4 | 0.2 | 0.2 | 0.1997945 |\n",
       "| 5 | 0.2 | 0.2 | 0.1997688 |\n",
       "| 6 | 0.2 | 0.2 | 0.1997534 |\n",
       "\n"
      ],
      "text/plain": [
       "     doi kc.capprox kc.lapprox kc.spline\n",
       "[1,] 1   0.2        0.2        0.1999332\n",
       "[2,] 2   0.2        0.2        0.1998767\n",
       "[3,] 3   0.2        0.2        0.1998305\n",
       "[4,] 4   0.2        0.2        0.1997945\n",
       "[5,] 5   0.2        0.2        0.1997688\n",
       "[6,] 6   0.2        0.2        0.1997534"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## fonction pour l'interpolation de kc au pas de temps journalier\n",
    "## selon 3 méthodes : contant, linéaire, spline\n",
    "kcinterp <- function(kc.doi=kc.doi,kc.os=NULL,doi=doi){\n",
    "    kc.capprox <- approx(x=kc.doi,y=kc.os,xout=doi,method='constant')$y\n",
    "    kc.lapprox <- approx(x=kc.doi,y=kc.os,xout=doi,method='linear')$y\n",
    "    kc.spline <- spline(x=kc.doi,y=kc.os,xout=doi)$y\n",
    "    return(cbind(doi,kc.capprox,kc.lapprox,kc.spline))\n",
    "}\n",
    "\n",
    "## Interpolation de Kc journalière\n",
    "mykc <- kcinterp(kc.doi=kc.doi,kc.os=kc.mais,doi=doi)\n",
    "head(mykc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>doi</th><th scope=col>kcc</th><th scope=col>kcl</th><th scope=col>kcs</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>0.2</td><td>0.2</td><td>0.1999332</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2</td><td>0.2</td><td>0.2</td><td>0.1998767</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>0.2</td><td>0.2</td><td>0.1998305</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>0.2</td><td>0.2</td><td>0.1997945</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>0.2</td><td>0.2</td><td>0.1997688</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6</td><td>0.2</td><td>0.2</td><td>0.1997534</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & doi & kcc & kcl & kcs\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 0.2 & 0.2 & 0.1999332\\\\\n",
       "\t2 & 2 & 0.2 & 0.2 & 0.1998767\\\\\n",
       "\t3 & 3 & 0.2 & 0.2 & 0.1998305\\\\\n",
       "\t4 & 4 & 0.2 & 0.2 & 0.1997945\\\\\n",
       "\t5 & 5 & 0.2 & 0.2 & 0.1997688\\\\\n",
       "\t6 & 6 & 0.2 & 0.2 & 0.1997534\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | doi &lt;dbl&gt; | kcc &lt;dbl&gt; | kcl &lt;dbl&gt; | kcs &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 1 | 0.2 | 0.2 | 0.1999332 |\n",
       "| 2 | 2 | 0.2 | 0.2 | 0.1998767 |\n",
       "| 3 | 3 | 0.2 | 0.2 | 0.1998305 |\n",
       "| 4 | 4 | 0.2 | 0.2 | 0.1997945 |\n",
       "| 5 | 5 | 0.2 | 0.2 | 0.1997688 |\n",
       "| 6 | 6 | 0.2 | 0.2 | 0.1997534 |\n",
       "\n"
      ],
      "text/plain": [
       "  doi kcc kcl kcs      \n",
       "1 1   0.2 0.2 0.1999332\n",
       "2 2   0.2 0.2 0.1998767\n",
       "3 3   0.2 0.2 0.1998305\n",
       "4 4   0.2 0.2 0.1997945\n",
       "5 5   0.2 0.2 0.1997688\n",
       "6 6   0.2 0.2 0.1997534"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## création d'un dataframe avec jj et valeur de kc interpolée\n",
    "Kc <- as.data.frame(mykc)\n",
    "##remplace les NA\n",
    "ind <- which(is.na(Kc),arr.ind=TRUE)\n",
    "for(i in 1:nrow(ind)){Kc[ind[i,1],ind[i,2]] <- Kc[ind[i,1]-1,ind[i,2]]}\n",
    "colnames(Kc) <- c(\"doi\",\"kcc\",\"kcl\",\"kcs\")\n",
    "head(Kc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sauvegarde du Kc au pas de journalier\n",
    "## dans le cas présente c'est le Kc du Maïs\n",
    "save(Kc, file=\"Kc.RData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bJbt24JCQn6+vpEdOjQoZEjR27fvj0k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\nAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdUCnOnj27dOlSa1cBAKh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      "text/plain": [
       "Plot with title “Kc Mais”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## trace evolution annuelle de Kc\n",
    "#pdf(\"figKc.pdf\", width = 6, height = 6)\n",
    "plot(kcc~doi,data=Kc,type=\"l\",col=\"red\", xlab = \"Jours\", main=\"Kc Mais\",\n",
    "     ylab=list(\"Coefficient cultural\",cex=1.2), ylim = c(min(Kc$kcs),max(Kc$kcs)))\n",
    "lines(kcl~doi,data=Kc,type=\"l\",col=\"darkgreen\")\n",
    "lines(kcs~doi,data=Kc,type=\"l\",col=\"blue\")\n",
    "legend(\"topright\", c(\"constant\", \"linéaire\",\"spline\"),col=c(\"red\",\"darkgreen\",\"blue\"),lwd = 1)\n",
    "#dev.off()"
   ]
  },
  {
   "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": "4.0.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}